Cadmium, an environmental toxin, is associated with a range of adverse health effects due to increased Reactive Oxygen Species production, including decreased bone mineral density and osteoporosis. Notably, cadmium is found at concentrations 4-5x higher in the blood of smokers versus non-smokers. Experiments performed in human cancer cells indicate that melatonin may directly protect against cadmium-induced tissue damage via regulation of mitochondrial activity. Further, recent evidence has demonstrated that melatonin can improve bone health for individuals with osteoporosis and partially protect against cadmium-associated inhibition of bone repair. Here we review this data and propose supplementation with melatonin as a strategy to protect against the negative impacts of cadmium exposure on bone mineral density within individuals regularly exposed to cadmium via cigarette smoking.

Autism spectrum disorder (ASD) is characterized by diverse patterns of social interaction, communication, and behavior. This study explores the application of clustering and stochastic migration models to visualize and understand the social and neural connectivity patterns associated with autism. The clustering model, with its strong internal connections and limited external connections, mirrors the tight-knit social groups and communication barriers often observed in individuals with autism. In contrast, the stochastic model, characterized by more dispersed connections and greater adaptability, represents the variability and flexibility seen in social interactions across typical individuals. By comparing these models, we can gain deeper insights into the unique challenges and strengths of individuals with autism, highlighting the importance of personalized interventions. Additionally, these models provide a visual framework for understanding the differences in brain connectivity patterns observed in autism, with implications for both behavioral and neural correlates. This study underscores the value of computational models in advancing our understanding of autism and guiding effective support strategies.

Glioblastoma is a highly aggressive brain tumor characterized by rapid growth, extensive infiltration, and poor prognosis. Understanding the complex interplay of biological processes driving glioblastoma progression is crucial for developing effective therapeutic strategies. In this study, we present a comprehensive mathematical model that incorporates key aspects of glioblastoma growth and dissemination, including tumor cell diffusion, angiogenesis, nutrient availability, molecular signaling pathways, and blood-brain barrier (BBB) interactions. The model is based on a system of coupled partial differential equations (PDEs) and ordinary differential equations (ODEs) that capture the spatiotemporal dynamics of tumor cell density, nutrient concentration, signaling molecule concentrations, and BBB integrity. We employ numerical simulation techniques, such as the finite difference method and the Runge-Kutta method, to solve the model equations and visualize the evolution of tumor growth and its associated biological processes. The model parameters are estimated using experimental data from the literature, and the model is validated against clinical observations. Furthermore, we perform a comprehensive sensitivity analysis to identify the key parameters that significantly influence tumor growth, diffusion, and dissemination. The sensitivity analysis reveals the relative importance of each parameter in driving glioblastoma progression and highlights potential targets for therapeutic interventions. Our computational framework provides a powerful tool for understanding the complex dynamics of glioblastoma growth and offers valuable insights into the underlying biological mechanisms. The model can be used to test hypotheses, predict tumor response to various treatment strategies, and guide the design of targeted therapies. The findings from this study contribute to the ongoing efforts in developing personalized treatment approaches for glioblastoma patients and improving their clinical outcomes.

Frailty syndrome is a complex and multifaceted condition that significantly impacts the health and well-being of older adults. This article provides a comprehensive overview of frailty syndrome, including its definitions, evolution, and the rising role of biomarkers and electroencephalography (EEG) in its diagnosis. The discussion explores the reasons behind the rise of frailty syndrome, the difficulties in managing it, and the global research efforts aimed at understanding and addressing this condition. The economic costs associated with frailty syndrome are highlighted, along with the countries that are currently and will be more affected in the near future. Strategies to postpone or avoid frailty syndrome are discussed, with an emphasis on the importance of muscle strength exercises over aerobic exercises to combat sarcopenia. The article concludes by emphasizing the need for a multidisciplinary approach to frailty management and the importance of social support and engagement in preventing and managing frailty.

Myelination, the process of forming a myelin sheath around nerve fibers, is critical not only during early development but also throughout adulthood. This article explores the multifaceted impacts of myelination on the adult brain, highlighting its importance in cognitive functions, neurological health, and overall brain efficiency. In adults, efficient myelination enhances information processing speed, supports memory consolidation, and contributes to cognitive reserve, potentially delaying the onset of neurodegenerative diseases. Myelination ensures synchronized neural activity and energy-efficient brain function, playing a key role in complex cognitive tasks and coordination. Despite a slower rate of plasticity compared to early development, the adult brain retains the ability to adapt and learn, with myelination facilitating these processes. This article also draws parallels between myelination in the brain and learning curves in deep learning Perceptrons, emphasizing how reduced stimulation and experience can significantly slow down cognitive and learning processes. Additionally, the clinical implications of myelination are discussed, including its role in multiple sclerosis and age-related cognitive decline, along with strategies to maintain and enhance myelin health through lifestyle choices and emerging pharmacological interventions. Understanding the ongoing significance of myelination in the adult brain, and its comparison to artificial neural networks, is crucial for developing interventions to support cognitive health and mitigate neurological disorders.

This small article examines the pivotal roles of redundant and degenerate neural networks in the brain across three critical dimensions: evolutionary development, early childhood neural formation, and their implications in neurological diseases. Redundant networks, characterized by parallel neural pathways with similar functions, provide a foundation for cognitive resilience, ensuring continuity of brain functions despite damage or developmental anomalies. Degenerate networks, comprising structurally varied but functionally equivalent pathways, contribute to cognitive flexibility and adaptability, essential for innovative problem-solving and learning. From an evolutionary perspective, these networks have been instrumental in the brain's adaptation to diverse environmental challenges, facilitating cognitive maximization and the development of complex behaviors. The genetic underpinnings and environmental influences that shaped these networks are explored, highlighting their contribution to the evolutionary success of the human species. In the realm of early childhood development, the formation and reinforcement of these networks are crucial. The brain's plasticity during this period leads to the establishment of robust redundant networks and versatile degenerate networks, shaped significantly by environmental stimuli and learning experiences. This developmental phase lays the groundwork for cognitive abilities and resilience to neurological challenges later in life. Furthermore, the paper delves into the implications of these networks in the context of brain diseases. The disruption of the equilibrium between redundancy and degeneracy is linked to various neurological conditions, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke and Huntington's disease. Understanding the roles and balances of these networks offers insights into the pathophysiology of these diseases and potential therapeutic approaches. This review underscores the significance of redundant and degenerate neural networks in the evolutionary context, their critical formation during early childhood, and their profound impact on brain health and disease. It highlights the need for further research into these networks to enhance our understanding of brain function and to develop targeted interventions for neurological disorders.

This study aims to refine our understanding of evolutionary processes, particularly as they apply to complex organisms, using an innovative graph-based simulation approach. We model the topological evolution of simple (5-node ring graph) and complex (20-node dense random graph) organisms over 10 generations, applying different mutation rates to reflect biological realities. Our results provide intriguing insights into the potentially different evolutionary dynamics at play in simple versus complex organisms. While simple organisms demonstrate high topological flexibility and rapid adaptability, aligning with classical evolutionary models, complex organisms exhibit a surprising degree of structural stability. This stability persists even under mutation rates that would typically drive significant changes in simpler systems. The simulation highlights an important consideration: the intricate, interdependent networks characteristic of complex organisms may create a form of evolutionary buffering. This buffering could modulate the impact of random mutations and natural selection – processes that are cornerstones of evolutionary theory. Our findings suggest that the high degree of complexity in advanced life forms may necessitate a more nuanced view of how evolutionary processes operate across different levels of biological organization. Furthermore, the emergence of new structural motifs in simple organisms, contrasted with the relative conservation in complex topologies, indicates that evolutionary mechanisms may manifest differently across the spectrum of biological complexity. This observation invites a more detailed exploration of how evolutionary principles apply to diverse life forms.These results contribute to the ongoing refinement of evolutionary theory, especially in its application to complex organisms. They suggest that additional mechanisms, such as constrained mutations, epigenetic changes, or higher-order organizational principles, may play significant roles alongside random mutations and natural selection in the evolution of complex life forms.

This article explores the intricate dynamics of complexity and emergence through the lens of a graphical model representing hippocampal functions. Utilizing a series of overlapping curves that intersect at varying degrees and frequencies, we create a visual metaphor for the sophisticated processes to store information observed in the hippocampus. This model not only depicts the spatial and frequency-related interactions of these curves but also illustrates the emergent patterns that arise from these interactions. These patterns exemplify the principles of complexity and emergence, where the collective behavior of simple elements results in intricate and unexpected outcomes. This graphical representation serves as a bridge between the abstract mathematical concepts of complexity theory and the tangible biological processes observed in neuroscience. By drawing parallels to the hippocampus, known for its role in memory formation and spatial navigation, the article sheds light on how complex systems, such as neural networks, exhibit properties that are more than just the sum of their parts. The model thus offers a unique perspective on understanding complex systems, not only in neuroscience but in broader scientific inquiries where complexity and emergent phenomena are pivotal.

Multiple Sclerosis (MS) is a chronic autoimmune disorder characterized by the demyelination of neural fibers in the central nervous system. Understanding the impact of demyelination on neural network efficiency is crucial for elucidating the pathophysiological mechanisms underlying MS. This study aims to model the effects of demyelination on neural network efficiency, specifically focusing on how increased resistance and path lengths due to demyelination correlate with the clinical symptoms of MS. We employed a graph-theoretical approach to simulate neural networks, introducing varying degrees of demyelination. The demyelination was represented by increasing the resistance (weights) of selected edges within the network, affecting both random and clustered distributions of network connections. The primary metric analyzed was the average shortest path length, indicative of network efficiency. The models demonstrated a significant increase in the average shortest path length in demyelinated networks compared to intact ones. Specifically, networks with clustered demyelination exhibited localized inefficiencies, correlating with the focal nature of MS lesions. The increase in path length was associated with reduced signal transmission efficiency, providing insights into a range of MS symptoms including motor and sensory impairments, cognitive dysfunction, visual disturbances, and fatigue. The graph-theoretical model of neural network demyelination offers a novel perspective on how structural changes in the brain's connectivity due to MS can lead to diverse clinical manifestations. While the model simplifies the complexity of neural networks, it underscores the potential of computational approaches in understanding and predicting the course of neurological disorders like MS.

This study explores the intriguing phenomenon of action potentials reverberating and propagating in high-resistance tissues, such as gliosis, leading to the emergence of short circuits within the brain. Gliosis, characterized by increased tissue resistance, presents a unique environment for electrical signals. My research investigates the possible mechanisms behind these reverberations and their role in the creation of self-empowering short circuits that can potentially affect the entire brain's functionality. Understanding these processes may have significant implications for the treatment and management of neurological disorders associated with gliosis and abnormal electrical activity in the brain.

This project aims to simulate and analyze population dynamics in evolutionary systems, using stochastic differential equations and probabilistic rules for crossbreeding and benefits. The study employs advanced computational techniques to model the evolution of populations over an extended period. Specifically, we explore a scenario with ten distinct groups, each starting with an equal population and evolving under defined genetic and cooperative behaviors. The simulation spans 1000 years, incorporating individual lifespans, stochastic updates, and probabilistic interactions. To ensure robustness and reliability, the study includes detailed statistical analysis, encompassing descriptive statistics, confidence intervals, and ANOVA tests. We further visualize the temporal behavior of populations and subpopulations, providing insights into the evolutionary dynamics and stability of the system. The results demonstrate the effectiveness of balanced cooperation and genetic diversity in maintaining stable population distributions. The project showcases the integration of mathematical modeling, statistical analysis, and computational simulations, contributing valuable insights into evolutionary biology and complex systems. It highlights the importance of interdisciplinary approaches in understanding and predicting population behaviors, with implications for both theoretical research and practical applications in fields such as ecology, genetics, probability and social sciences.

Psychiatric disorders, characterized by complex and fluctuating patterns of thoughts, emotions, and behaviors, can be effectively modeled and visualized using mathematical wave equations. This study explores the representation of three major psychiatric conditions— Psychosis, Bipolar Disorder and Obsessive Compulsive Disorder (OCD)—through the application of differential equations, damping factors, and noise perturbations. Psychosis is modeled using Lissajous figures with high-frequency components, damping, and noise, symbolizing the irregular and erratic thought patterns characteristic of this condition. The inclusion of random perturbations and varying frequencies illustrates the chaotic and unpredictable nature of psychotic episodes, while damping effects reflect the temporary reductions in symptom intensity. For bipolar disorder, a system of partial differential equations simulates the cyclical nature of manic and depressive episodes. The model captures high-energy manic phases with periodic disturbances and the subsequent transition to lower-energy depressive states through damping effects. This representation highlights the oscillatory behavior and recurring episodes inherent to bipolar disorder. OCD is represented with Circumferential Complex Equations in Mult directions. The resulting graphical representations provide intuitive visual insights into the dynamics of these psychiatric disorders. By leveraging wave equations and mathematical modeling, this approach offers a novel perspective on understanding and analyzing the complex behaviors associated with OCD, bipolar disorder and psychosis. This method not only aids in the conceptualization of psychiatric phenomena but also underscores the potential of mathematical tools in the study of mental health conditions.

Attention-Deficit/Hyperactivity Disorder (ADHD) exhibits a notable prevalence across diverse populations, with estimates ranging from 4% to 6%. This wide range prompts an investigation into the evolutionary and genetic underpinnings of ADHD, especially considering the markedly different environments faced by ancestral human populations compared to today's structured societies. This study examines the potential adaptive advantages of ADHD-associated traits in hunter-gatherer contexts, where hyperactivity, impulsivity, and novelty-seeking behaviors may have conferred survival benefits. We employ the principles of population genetics, including Hardy-Weinberg equilibrium, selection coefficients, and genetic drift, to model the dynamics of ADHD allele frequencies over time. Additionally, we discuss balancing selection as a mechanism for maintaining genetic diversity and how gene-environment interactions (GxE) contribute to the expression of ADHD in contemporary settings. Our analysis suggests that traits associated with ADHD, while potentially maladaptive in modern structured environments, could have been preserved through evolutionary processes due to their adaptive value in ancestral contexts. Understanding these dynamics provides a comprehensive view of ADHD's prevalence and highlights the significance of genetic and environmental interplay in shaping human behavior.

This study presents a novel approach to understanding evolutionary dynamics through topological analysis of genotypic structures. We model the genome of a simple organism as a topological torus undergoing mutations at a constant rate of 1% per generation. Employing persistent homology and other topological data analysis techniques, we investigate how these mutations influence the overall topology of the genomic structure over time. Our simulations reveal that specific critical mutations lead to significant changes in the homotopic and homologic properties of the torus. We interpret these topological shifts as potential markers for the formation of new subspecies. By identifying the precise points of topological "breaks" within the toroidal genomic representation, our model provides a quantitative framework for observing the incremental transformations that underpin major evolutionary developments. The results offer new insights into the thresholds of speciation and adaptation, demonstrating how gradual genetic changes can accumulate to produce qualitative shifts in organismal traits. This approach bridges the gap between microscopic genetic mutations and macroscopic evolutionary outcomes, potentially revolutionizing our understanding of evolutionary processes. Our findings have implications for various fields, including theoretical biology, genetic engineering, and conservation biology. The model provides a novel perspective on genetic resilience and adaptability, offering a foundation for future research on the relationship between genomic topology and evolutionary trajectories. It also emphasizes the innovative nature of the research and its potential impact on multiple fields of study.

This study presents a computational simulation to explore the distinctive patterns of neural connectivity in Autism Spectrum Disorder (ASD), focusing on the concepts of spatial and topological proximities. ASD is characterized by unique cognitive and behavioral profiles, often attributed to atypical neural development and connectivity. Our simulation employs a two-pronged approach: first, by modeling spatial proximity through the generation of clustered 3D node positions, we mimic the observed over-connectivity in localized brain regions in ASD. Second, we construct a topological network to represent the complex interplay of under-connectivity and over-connectivity in functional brain networks, a hallmark of ASD's neural landscape. The spatial model demonstrates pronounced clustering, reflecting the structural brain differences and atypical growth patterns reported in neuroimaging studies of individuals with ASD. In contrast, the topological model reveals a network with a mix of sparse and dense connections, simulating the diverse and often contradictory findings of functional connectivity in ASD, such as reduced long-range connections and increased local connections. Our simulation provides a visual and conceptual framework for understanding the altered neural connectivity in ASD. It underscores the importance of considering both physical (spatial) and functional (topological) aspects of brain connectivity to grasp the full extent of neurodevelopmental deviations in ASD. This approach not only aids in elucidating the neural underpinnings of ASD's core symptoms but also offers a foundation for developing targeted interventions. Future research, integrating more complex and individual-specific data, could further refine this model, enhancing our understanding of ASD's neural dynamics and contributing to personalized therapeutic strategies.

Attention-Deficit/Hyperactivity Disorder (ADHD) is traditionally conceptualized within a framework emphasizing deficits in attention regulation and executive function. However, emerging evidence from the fields of predictive coding and active inference offers a novel lens through which to understand ADHD not merely as a disorder of attention but as a distinctive mode of neurodevelopmental variation. This article synthesizes current research and theoretical models to propose a redefined understanding of ADHD. By integrating insights from Ryan Smith, Paul B. Badcock, and Karl J. Friston (2020), we explore the premise that individuals with ADHD exhibit unique differences in how their brains generate and update predictions about the environment, leading to the characteristic behavioral patterns observed in ADHD. We argue that ADHD involves a divergence in the brain's predictive models and its ability to minimize prediction error, impacting sensory processing, attention allocation, and motor control. This perspective illuminates the adaptive aspects of ADHD, suggesting that what are often labeled as deficits could also reflect an adaptive fit to certain environmental contexts, underscoring the role of neurodiversity in human evolution. Furthermore, we discuss the implications of this reconceptualization for clinical practice, including diagnosis, treatment, and support for individuals with ADHD, emphasizing approaches that align with their unique neurocognitive profiles. This article calls for a shift in the narrative surrounding ADHD, advocating for a model that appreciates the complexity and adaptiveness of neurodevelopmental diversity.

Computational neuroscience has undergone a significant transformation in recent years, driven by the exponential growth of neural data and advancements in data science and machine learning. While traditional theoretical models remain valuable, there is a growing emphasis on integrating data-driven approaches to develop more accurate, biologically plausible, and predictive models of neural function. This shift has led to a new equilibrium in the field, where data-driven model development, machine learning techniques, and hybrid approaches that combine data and theory are increasingly prevalent. In this article, we discuss the evolving landscape of computational neuroscience, highlighting the importance of data-driven methods and the emergence of a more integrative approach that leverages both data and theory to unlock the mysteries of the brain. We also explore the implications of this shift for future research and clinical applications, emphasizing the potential for data-driven computational neuroscience to revolutionize our understanding of neural function and pave the way for new treatments for neurological disorders.

The complex dynamics of the neocortex, the outermost layer of the brain, have been a subject of intense research in neuroscience. Recent studies have suggested that magnetic fields generated by neuronal activity may play a significant role in neocortical dynamics. This paper presents a simplified mathematical model that explores the hypothesized interactions between magnetic dynamos, nondissipative systems, Hamiltonian equations, and the Lorenz force in the context of neocortical dynamics. The proposed model is based on a set of coupled Hamiltonian equations inspired by the Lorenz system, a well-known chaotic dynamical system. The generalized coordinates in the model represent the magnetic field components, while the generalized momenta are related to the electric field components or other relevant quantities. The Hamiltonian equations describe the conservative dynamics of the system, and the coupling terms in the equations are interpreted as representing the interactions between the magnetic and electric field components, analogous to the Lorenz force in electromagnetism. The model is implemented using Python, and the Hamiltonian equations are numerically solved using the odeint function from the scipy.integrate module. The resulting dynamics of the magnetic field components are visualized using matplotlib. While the model is highly simplified and does not capture the full complexity of neocortical dynamics, it serves as a starting point for investigating the potential role of magnetic dynamos and Lorenz force interactions in the brain. The paper discusses the limitations of the current model and highlights the need for further refinements, such as incorporating more realistic biological constraints, considering energy dissipation and compensation mechanisms, and validating the model against experimental data. Future work should focus on developing a more comprehensive and biologically plausible model that integrates the latest findings from neuroscience, physics, and computational modeling. By presenting this simplified Hamiltonian model, the paper aims to stimulate further research and discussion on the potential significance of magnetic dynamos and Lorenz force interactions in neocortical dynamics. The model provides a framework for exploring these hypothesized mechanisms and may guide future experimental and theoretical investigations in this emerging field of neuroscience

The intricate architecture of dendritic arborization is fundamental to the formation and functionality of neural networks, serving as the primary site for synaptic integration and signal propagation. This study presents a pioneering computational model that simulates dendritic growth dynamics, employing exponential and logarithmic scaling over an extended developmental period of 720 days. This model offers a valuable tool for investigating the implications of diverse dendritic growth patterns on neural development, synaptic connectivity, and ultimately, cognitive functions. The computational framework incorporates biologically plausible parameters, allowing for the systematic exploration of dendritic branching patterns and their potential impact on neuronal information processing. By simulating exponential and logarithmic growth scales, the model captures the inherent complexity and diversity of dendritic morphologies observed in various neuronal populations across different brain regions. The findings from this study hold significant implications for our understanding of neural circuit assembly, synaptic integration, and the potential functional consequences of aberrant dendritic growth patterns observed in neurodevelopmental disorders. Furthermore, the model's ability to simulate extended developmental trajectories over a prolonged period of 720 days offers insights into the dynamic interplay between dendritic growth and synaptic pruning, which is crucial for the refinement and optimization of neural networks.

Bipolar disorder (BD) is a complex mental health condition characterized by alternating periods of mania and depression. This review provides a comprehensive overview of BD, focusing on its genetics, causal hypotheses, clinical signs and evolution, potential biomarkers with an emphasis on electroencephalography (EEG), and treatment options. Genetic studies have identified several susceptibility genes and polygenic risk scores, highlighting the heritable nature of BD. Various causal hypotheses, including monoamine dysregulation, kindling, circadian rhythm disruptions, and neuroinflammation, have been proposed to explain its pathophysiology. The clinical course of BD is characterized by recurrent episodes of mania/hypomania and depression, with significant heterogeneity among individuals. EEG and other biomarkers hold promise for improving diagnosis, predicting treatment response, and elucidating the neurobiological underpinnings of BD. Current treatment options include pharmacotherapy, psychotherapy, and neuromodulation techniques, which can be tailored to individual needs based on clinical presentation and response to treatment. Future research should focus on identifying more specific biomarkers, developing novel treatments, and optimizing existing therapies to improve outcomes for individuals with BD.

The relationship between Portugal and England spans centuries, encompassing political alliances, economic cooperation, cultural exchanges, and significant periods of tension and exploitation. This article provides a comprehensive and critical overview of the historical relations between Portugal and England from medieval times to the present, highlighting both the cooperation and the conflicts that have shaped this complex alliance. It explores key events, treaties, and figures that have influenced the trajectory of the relationship, including the heavy indebtedness Portugal accumulated with England and how this debt was transferred to Brazil in exchange for its independence—a factor that perpetuated social distortions such as slavery and inequality in Brazil. The article also discusses the contemporary context of Portugal-England relations, emphasizing the continued importance and challenges of this partnership in the modern world.

The endocannabinoid system (ECS) is a complex neuromodulatory network in the human body that have enormous impact in developing the central nervous system, as well as modulates various physiological processes. The ECS is made up of anandamide, 2-arachidonoylglycerol (2-AG), which are responsible for the synthesis and degradation of endocannabinoids, and enzymes such as FAAH and MAGL ensures the precise control over endocannabinoid levels. The ECS relies on CB1 receptors found in the central nervous system (CNS), and CB2 receptors found in the peripheral nervous system and immune cells. Dysregulation of the ECS has been associated with several health conditions, prompting investigations into its association with phytocannabinoids to harness their therapeutic potential. The ECS is intricately linked with the nervous system, and it is known to modulate neuronal activities in the CNS, influencing pain and mood modulation. The ECS is also associated with neuropathies-peripheral nerve damage leading to symptoms such as weakness, numbness and pain as evidenced in diabetic neuropathy, Guillain-Barré Syndrome, carpal tunnel syndrome, peripheral neuropathy, and postherpetic neuralgia. The focus of this review is on the interplay between 2-arachidonoylglycerol and anandamide amidohydrolase in neuropathy, elucidating their roles in pain regulation, inflammation, and specific neuropathies. The knowledge generated is expected to have broad implications, providing comprehensive insights into neuropathic conditions and how to improve on them. Ultimately, this understanding may lead to more effective treatments and improved quality of life for individuals affected by these conditions.

Salmonella Typhimurium is a typical Gram-Negative bacterium associated with food-borne illness threatening public health. This pathogen is reported to have caused millions of infections and thousands of hospitalizations yearly in the United States alone. Salmonella Typhimurium has a sophisticated array of virulent factors, including its flagella, which contributes significantly to its pathogenicity. Cannabidiol (CBD), the non-psychoactive component of the cannabis plant, has attracted the interest of many researchers recently because of its antimicrobial properties. With the growing interest in exploring CBD’s antimicrobial properties, we, in this study, have delved into unravelling how prolonged exposure of Salmonella Typhimurium to CBD influences the expression of the flgA gene. We hypothesized that the exposure of Salmonella Typhimurium to CBD may result in flgA upregulation, causing the bacteria to withstand various stresses and potentially enhance its survival rates and pathogenicity. We have employed several assays, such as mRNA expression, motility assays, pH induction assays, nutrient starvation interventions, and biofilm analysis, to explore the influence of CBD on the pathophysiology of Salmonella Typhimurium. Our findings reveal that CBD-resistant strains exhibit enhanced resilience to extreme pH levels and motility and survive poorly under nutrient starvation, with more robust biofilm formation compared to the susceptible strains. The results contribute to the broader comprehension of CBD resistance mechanisms in bacteria. This work provides insight into the potential therapeutic interventions for controlling CBD-resistant bacterial infections. This study also underscores the essence of exploring novel alternative antimicrobial agents to tackle the ever-persistence of bacterial antibiotic resistance.

In this study, we explore the impact of quantum effects on synaptic activity and their implications for the deterministic model of neural processes. By simulating synaptic events in a large-scale neural network, we demonstrate how small probabilistic quantum events can accumulate and introduce variability in neural behavior. Our model incorporates Dirac delta functions to simulate sudden changes in synaptic probabilities, creating a non-linear, seesaw effect in the cumulative synaptic activity curve. The results reveal that even minimal quantum effects, when scaled to the vast number of synapses in the human brain, can lead to significant dissociations in neural activity. These findings challenge the traditional deterministic view, suggesting that neural processes—and by extension, decision-making and movements—may inherently incorporate elements of randomness. This probabilistic perspective aligns with theories of free will and underscores the need for further empirical research to validate the role of quantum mechanics in neuroscience. Our study provides a theoretical framework for understanding the complex interplay between quantum effects and neural dynamics, opening new avenues for exploring the nature of consciousness and the fundamental principles governing reality.

This article presents a novel computational approach to visualizing memristor dynamics within a simulated neural network. Memristors, known for their ability to emulate synaptic plasticity due to their variable resistance characteristics, are key components in neuromorphic computing and hold potential for advancing our understanding of neural processes. Our study introduces a sophisticated memristor model that incorporates non-linear resistance changes, simulating the complex behavior of synaptic connections in a neural network.The neural network, consisting of multiple interconnected neurons with memristor-based synapses, is subjected to a series of electrical stimuli. Each memristor's resistance is modulated in response to the applied voltage, mimicking the synaptic weight adjustments that occur during learning and memory formation in biological neural networks. To effectively illustrate these changes, we employ a high-contrast color mapping scheme, where the varying resistance of each memristor is represented by distinct colors, providing a clear and intuitive visualization of synaptic modifications over time.Our simulation runs through multiple iterations, demonstrating how synaptic weights evolve in response to different input patterns. The use of an extended voltage range and increased scaling factors ensures pronounced changes in memristance, enhancing the visibility of synaptic adaptations. The resulting visualizations offer a compelling representation of how memristors can mimic the dynamic nature of biological synapses, contributing to the field of neuromorphic engineering and deepening our comprehension of neural mechanisms underlying learning and memory.This work not only showcases the potential of memristors in simulating neural behavior but also provides a valuable educational tool for illustrating complex concepts in neuroscience and neuromorphic computing. The insights gained from this study pave the way for further exploration into the development of advanced neural network models and the design of memristor-based computing systems.

This article explores the intricate journey of neural progenitor cells from the ventricular zones of the embryonic brain, focusing on their pivotal role in the formation of rich club networks, which are integral to the brain's functional architecture. Initially, the ventricular zones, particularly the cerebral ventricular zone, serve as the primary sites for the proliferation and differentiation of neural progenitors, giving rise to various types of neurons and glial cells. We delve into the sequential migration patterns of these cells, emphasizing how early-migrating neurons, destined to form deeper cortical layers, establish extensive and robust connections, potentially evolving into rich club nodes. These nodes are characterized by their high-degree connectivity and serve as crucial hubs for integrating and disseminating information across disparate brain regions.The article further discusses the implications of this developmental pathway in the context of brain network efficiency, resilience, and cognitive function. We highlight how disruptions in early cell migration can lead to anomalies in rich club formation, drawing connections to various neurodevelopmental disorders such as autism, ADHD and schizophrenia. Additionally, the significance of rich club nodes in maintaining efficient information transfer and network resilience is elucidated, underscoring their role in supporting complex cognitive processes and overall brain dynamics.In summary, this article offers a comprehensive overview of the developmental trajectory from the ventricular zones to the establishment of rich club networks, providing insights into the fundamental processes that shape the brain's intricate connectivity and functional capabilities. Ric

Attention Deficit Hyperactivity Disorder (ADHD) diagnosis poses significant challenges due to its heterogeneous nature and reliance on subjective assessments. This study leverages deep learning to develop a robust diagnostic model by integrating a comprehensive set of EEG biomarkers. We incorporate parameters such as theta/beta ratio, coherency measures, delta power, event-related potentials (ERPs), power spectral density (PSD), microstates, entropy measures, fractal dimension, and source localization. Our convolutional neural network (CNN) model, designed to process these diverse features, demonstrates high accuracy and stability in distinguishing ADHD patients from controls, from all ages. The model's architecture includes convolutional layers for spatial feature extraction, followed by dense layers that integrate additional EEG parameters. The results indicate that deep learning, coupled with a rich feature set, can significantly enhance the predictive accuracy of ADHD diagnosis, offering a promising tool for clinical application.

This study explores the efficacy of using EEG-based parameters to diagnose bipolar disorder. A synthetic dataset was generated, including both correctly diagnosed and misdiagnosed cases, simulating realistic clinical conditions. EEG features such as theta-alpha mean, beta band mean, and coherence measures were used to train a multi-layer perceptron (MLP) model. The model achieved a validation accuracy of 92%, demonstrating strong potential for EEG-based diagnostics. However, challenges such as standardization of electrode configurations and addressing equipment differences are crucial for broader applicability and validity of the findings in diverse clinical settings.

This study investigates the ergodic behavior of brain waves through an innovative multidisciplinary approach, integrating insights from neuroscience, physics, economics, and biology. By examining the statistical properties and dynamic characteristics of alpha (8-13 Hz), beta (13-30 Hz), theta (4-8 Hz), and delta (0.5-4 Hz) brain waves, we argue that the ergodic nature of these neural oscillations can be comprehensively understood and quantified using analytical tools from diverse scientific disciplines. Our research begins with a thorough review of current neurophysiological understanding of brain wave generation and propagation. We then introduce the concept of ergodicity, originating from statistical physics, and explore its applicability to neural systems. The study employs advanced time series analysis techniques and stochastic process models, borrowed from economics and physics, to analyze large-scale electroencephalographic (EEG) datasets. Key findings suggest that brain waves exhibit ergodic properties under specific physiological conditions, with implications for understanding neural information processing and brain state transitions. We propose a novel framework that characterizes the ergodic behavior of brain waves across different frequency bands and cognitive states. This framework provides new insights into the relationship between local neural dynamics and global brain function.Furthermore, we discuss the biological implications of ergodic brain waves, considering evolutionary perspectives and potential adaptive advantages. Our analysis reveals how the ergodic properties of neural oscillations may contribute to the brain's ability to balance stability and flexibility, a crucial aspect of adaptive behavior and cognition. The study's findings have significant implications for multiple fields. In neuroscience, our framework offers new tools for analyzing brain function and may inform the development of innovative therapeutic interventions for neurological disorders. In physics and complex systems theory, it provides a concrete biological example of ergodic behavior in a highly complex, non-linear system. For the field of economics, it demonstrates the successful application of financial modeling techniques to biological time series data. This research contributes to a more holistic understanding of the brain as a complex, adaptive system operating at the intersection of physics, biology, and information processing. It also opens new avenues for future research, including investigations into the relationship between ergodic properties of brain waves and consciousness, cognitive flexibility, and the emergence of complex behavior from neural ensembles.

The phenomenon of emergence in complex systems is vividly exemplified by the coordinated behaviors of ant colonies, where apparent chaos evolves into order. This article delves into the essence of complexity and emergence through interdisciplinary research spanning biology, computer science, and information theory. It begins by examining the foundational principles of swarm intelligence, revealing how simple ant behaviors culminate in sophisticated collective intelligence. The discussion then transitions to the philosophical dimensions of emergent phenomena and the principles of self-organization that underpin systemic order. Key concepts such as ant colony optimization algorithms and stigmergy are synthesized, highlighting their relevance in both natural and artificial systems. The broader societal implications are considered through the lens of collective intelligence, drawing parallels between ant colonies and human social structures. Complementary online resources and multimedia content enrich the narrative, providing accessible insights and dynamic representations of ant intelligence. This convergence of diverse perspectives underscores the remarkable capacity for simplicity to generate complexity, culminating in a profound understanding of collective wisdom.

This article explores the pressing need for a paradigm shift in economics, challenging the dominance of classical economic theories and models that have long held sway. We delve into the limitations of traditional economic paradigms, which often rely on unrealistic assumptions and fail to capture the complexities of human behavior and the modern global economy. Drawing from insights in behavioral economics, environmental economics, and complexity theory, we argue for a more nuanced and multidisciplinary approach to economic analysis. Our discussion addresses the challenges of breaking away from deeply entrenched academic norms and the resistance to change within the economics profession. We highlight strategies for individuals and groups to advocate for the incorporation of modern economic theories in education and research. By fostering interdisciplinary collaboration, encouraging critical thinking, and engaging with policymakers and the public, we aim to catalyze a shift toward economics that is better equipped to address the intricate challenges of the 21st century. This article serves as a call to action for economists and educators alike to embrace a more dynamic and responsive approach to understanding human behavior and decision-making in the modern world.

This study explores the potential trajectories of human civilization influenced by the development and competition of advanced artificial intelligence (AI) systems. Using a system of stochastic partial differential equations (SPDEs), we model the probability density function representing the state of civilization in a phase space defined by prosperity and knowledge. The model incorporates diffusion, growth, saturation, and drift terms, alongside stochastic noise to reflect the uncertainties and random fluctuations in AI impacts.The simulation results are visualized in a probability density graph, revealing the likelihood of various outcomes ranging from extinction and regression, to prosperity and technological advancement. The analysis highlights the balanced prospects of human future, with significant probabilities in both positive and negative directions. Positive scenarios suggest potential for increased prosperity and knowledge, emphasizing the importance of effective AI management and international cooperation. Conversely, the notable risks of regression and extinction underline the need for strategic interventions to mitigate adverse impacts. Our findings stress the stochastic nature of future developments and the critical role of adaptive and flexible policies in steering human civilization towards favorable outcomes. This study provides a simple yet comprehensive framework for understanding the complex dynamics at play and underscores the importance of proactive strategies in the age of AI.

The endeavor to decode the brain's intricate architecture and its multifaceted functions has propelled scientists into the domain of complex differential equations, aiming to construct a computational framework capable of representing its uncountable intricacies. This article delves into the sophisticated and daunting task of modeling the brain, a system of seemingly uncountable coupled differential equations, each symbolizing the vast array of interconnected neural pathways and processes. We discuss the current limitations of computational resources and the significant challenges faced in accurately capturing the non-linear, high-dimensional, and plastic nature of neural dynamics. Despite these challenges, we explore the progressive strides in computational neuroscience that lay the groundwork for partial models, offering insights into the brain's localized circuits and broader network interactions. These models serve as pivotal stepping stones towards a more comprehensive understanding, which is incrementally built upon by interdisciplinary approaches, leveraging advancements in neuroimaging, data analysis, and theoretical biology. Looking ahead, the article speculates on the potential paradigm shifts that could arise from breakthroughs in computational technology, such as quantum computing, that may one day enable the simulation of the brain in its entirety. The article ultimately paints a picture of cautious optimism, where each incremental discovery is a valuable piece of the grand puzzle, bringing us closer to the epochal goal of comprehensively modeling the human brain.

In this study, we develop a stochastic partial differential equation (SPDE) model to assess the regional severity of temperature increases due to global warming. The model incorporates various stochastic processes to account for uncertainties such as solar explosions, economic upheavals, rising emissions, and irreversible changes in the climate system. Our base temperature model, influenced by latitude, is enhanced by these random factors to simulate realistic and diverse outcomes of climate change. Numerical simulations on a global grid provide a heatmap representing potential temperature increase severity across different regions. The results highlight the significant impact on tropical and polar regions, demonstrating the utility of SPDE models in understanding and predicting the complex dynamics of climate change. This approach offers valuable insights for policymakers and researchers aiming to develop adaptive strategies for mitigating the adverse effects of global warming.

Repetitive elements, often referred to as selfish genes, pose significant challenges to genome assembly and read mapping in bioinformatics. These elements can replicate within the genome without providing functional benefits to the host organism, leading to complexities in accurate genome analysis. This study presents a simulation-based approach to illustrate the impact of repetitive elements on read mapping accuracy. By comparing genome sequences with and without repetitive elements, we demonstrate how these selfish genes create ambiguities and errors in read alignment. The findings underscore the importance of developing advanced bioinformatics tools aligned with artificial intelligence to mitigate the effects of repetitive sequences and improve the reliability of genomic analyses.

The interaction between electromagnetic fields (EMFs) and biological systems has been a subject of intense research and debate. This study explores the potential influence of magnetic fields on neural circuits, which are the building blocks of the human brain. By developing a mathematical model and conducting computational simulations, we investigate how magnetic fields can affect the transmission of electrical signals within neural circuits. The results suggest that magnetic fields can alter the speed and direction of signal propagation, offering potential applications in medical treatments and brain-machine interfaces. However, the study also highlights the need for more comprehensive models that consider the complexity of real-world neural circuits and the multifaceted nature of magnetic fields. The interaction between EMFs and neural circuits is a rich and complex subject that requires a multidisciplinary approach, combining physics, biology, mathematics, and computer science. While this study provides valuable insights, further research is needed to fully understand the implications of this interaction and to realize its potential applications in medicine, neuroscience, and technology. The findings contribute to a growing body of knowledge that bridges the gap between physics and neuroscience, opening new horizons for understanding the human mind and its connection to the physical world.

This article encapsulates an exploration of seizure dynamics through the lens of computational modeling in epilepsy. Starting with a foundational understanding of the FitzHugh-Nagumo model, asimplified representation of neuronal activity, we try to understand the intricacies of epileptic seizures. We methodically demonstrated the transition from normal neuronal activity to a seizure-like state by altering key parameters in the model, such as external current and coupling strength. This was followed by an extension of the model to a network of neurons, simulating the complex interactions and synchronization patterns indicative of seizure propagation. Numerical simulations were conducted to visualize the impact of varying coupling strengths on network dynamics, offering insights into the mechanisms of seizure initiation and spread. The study was complemented by a discussion on the implications of these findings for understanding epilepsy, highlighting the bridging of theoretical models with clinical understanding. Our approach not only illuminates the potential of computational models in epilepsy research but also underscores the significance of interdisciplinary collaboration in advancing our comprehension of neurological disorders. Through this article, we aim to provide a nuanced perspective on the modeling of epileptic seizures, offering a valuable resource for researchers and clinicians in the field.

Chronic stress is a pervasive issue that significantly impacts both physical and mental health. Unlike acute stress, which is short-lived and can be beneficial, chronic stress persists over extended periods, leading to a range of adverse health outcomes. This paper explores the myriad physical, mental, and behavioral consequences of chronic stress, emphasizing the underlying physiological mechanisms and potential long-term health implications. Through a comprehensive literature review and data analysis utilizing Python, we aim to provide an in-depth understanding of chronic stress and its effects. Key findings include the critical roles of cortisol and other stress-related chemicals in exacerbating health issues, and the potential for the normalization of high stress levels as an adaptive yet harmful response.

Solar pasteurization, as a means of treating milk in remote rural areas without electrical power, is based on using solar energy to thermally inactivate pathogenic microorganisms at temperatures below the boiling point to maintain or improve milk quality. A solar milk pasteurizer of flat‐plate water‐heating glass acting as the solar collector, connected to a stainless-steel cylindrical milk vat, was constructed and evaluated in Navrongo, in the tropical savanna zone of Ghana, West Africa. The novel approach taken in this project is to integrate the solar collection and heat exchanger into a single unit using materials and fabrication techniques readily available in the developing world. The vat comprises a 1.5 mm thick stainless steel cylindrical tank, a 2.2 cm wide hot water jacket, and an outer layer of 5.0 mm thick aluminium foil insulation. Hot water produced by the collector was used for pasteurizing milk. The optimum quantity of milk that this device could pasteurize under the study conditions was 8 L. The device could pasteurize raw milk to a maximum temperature of 74 °C at 14:00 h GMT. The ambient temperature during the pasteurization ranged from 30 °C to 43 °C. The microbial analysis before and after the pasteurization was done, and the result shows that the total bacteria count before pasteurization was 6.6x10⁶ log CFU/mL per unit volume, and the total bacteria count after pasteurization was 1.0x10² log CFU/mL. By acceptable standards, the total bacteria count after pasteurization is within a safe range for consumption, and coliform counts were negative. The solar milk pasteurization system is cost-effective. Hence, it is appropriate for milk producers in arid pastoral areas without electricity to adopt the solar pasteurizer as an alternative to firewood as a fuel source for milk pasteurization. This approach can potentially create a cottage industry that may also play a crucial role as a water treatment technology that will improve the health of rural populations.

MFGE8 is a major exosome (EV) protein known to mediate inflammation and atherosclerosis in type 2 diabetes mellitus (T2DM) in animal studies. However, its role in the pathophysiology of human T2DM, obesity, and cardiovascular disease has been controversial and less explored. Earlier, we reported a rare Asian Indian population-specific missense variant (rs371227978; Arg148His) in the MFGE8 associated with increased circulating Mfge8 and T2DM. Here, we have further investigated the role of Mfge8 with T2DM risk in additional Asian Indians (n=4917) and Europeans and other multiethnic cohorts from UK Biobank (UKBB) (n= 455,808) and the US (n=1150). We also evaluated the exposure of Mfge8-enriched human EVs (with and without mutation) in zebrafish (ZF) for their impact on the cardiometabolic organ system. Most individual carriers of Arg148His variant not only had high circulating Mfge8 but also showed a positive significant correlation with glucose (r= 0.42; p = 4.9x10-04), while the non-carriers showed a negative correlation of Mfge8 with glucose (r = -0.38; p = 0.001) in Asian Indians. However, the same variant was monomorphic in Europeans and other ethnic groups of UKBB and US cohorts. Even in the absence of Arg148His variant, serum Mfge8 correlated significantly with blood glucose both in Caucasians (r = 0.56; p = 8.4x10-10) and individuals from other ethnicities (African Americans, Hispanics, and others) (r = 0.31; p = 5.8x10-4). Since Mfge8 is an EV marker, the exposure of Mfge8-enriched human EVs to ZF larvae rapidly developed fatty liver disease and heart hypertrophy and exhibited redundant growth with poor muscular architecture with and without the high-fat diet (HFD), while the control group fishes developed fatty liver disease and heart hypertrophy only after the HFD feeding. With strong evidence from animal studies supporting the role of Mfge8 in obesity, insulin resistance, and the development of atherosclerosis in T2DM, the current research suggests that circulating Mfge8 may be a potential marker for preventing or reducing the risk of T2DM and cardiovascular disease.

Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are prevalent neurodevelopmental disorders with distinct diagnostic criteria but significant overlap in symptoms and comorbidity. This article explores the relationship between ADHD and ASD, examining their common neurodevelopmental origins, shared features, and distinct trajectories. Both disorders are highly heritable, with genetic studies identifying shared genetic variants and neurobiological abnormalities. Environmental factors, including prenatal and perinatal exposures, also contribute to their etiology. Despite these commonalities, ADHD and ASD exhibit distinct developmental trajectories, cognitive profiles, and long-term outcomes. Understanding the interplay between these disorders is crucial for developing effective diagnostic and treatment strategies. Future research should focus on elucidating the underlying mechanisms and developing targeted interventions for individuals with comorbid ADHD and ASD.

This study presents two interconnected mathematical models: one describing the stability and bifurcation dynamics of ecological biomes, and another simulating the progressive deformation and structural failure of a toroidal object. Both models explore topological changes in complex systems, bridging concepts from ecology, materials science, and mathematical physics. The first part introduces a dynamic model of ecological biomes, incorporating seasonal oscillations and external pressures. We define equations for topology updates, seasonal effects, and pressure impacts, culminating in a bifurcation condition that represents a critical shift in the biome's equilibrium state. This model provides insights into the resilience and tipping points of ecological systems under varying environmental stresses.The second part extends these concepts to a three-dimensional toroidal structure. We present parametric equations describing the torus geometry and develop a time-dependent deformation model that includes both stochastic and deterministic elements. A novel twisting deformation component enhances the realism of the structural changes. We define a critical threshold for maximum displacement, beyond which the torus undergoes structural failure, analogous to the bifurcation point in the biome model. Both models are implemented computationally, providing visual representations of the systems' evolution over time. The biome model illustrates the path to and consequences of bifurcation, while the torus simulation depicts the stages of deformation leading to structural break. This work contributes to the understanding of topological changes and critical transitions in both ecological and physical systems. By presenting these models in parallel, we highlight the universal nature of stability, bifurcation, and failure across different domains. The methodologies presented here have potential applications in ecology, materials science, structural engineering, and theoretical physics, offering a framework for studying the limits of system integrity under stress.

This paper explores a novel approach to establishing the topological equivalence of manifolds embedded in ℝ

This review article provides a comprehensive overview of the anatomical structure of the human brain, highlighting its key components and their functional significance. The brain is the central organ of the nervous system, responsible for controlling and coordinating various bodily functions. The article begins with an introduction to the brain's gross anatomy, including the cerebrum, cerebellum, brainstem, and diencephalon. It then delves into the detailed microstructure of the brain, discussing neurons, glial cells, and the complex network of neural connections. The review also explores the functional organization of the brain, emphasizing the roles of different regions such as the frontal, parietal, temporal, and occipital lobes. It discusses the importance of the limbic system in emotional processing and the basal ganglia in motor control. Additionally, the article covers the brain's vascular system and the blood-brain barrier, which are crucial for maintaining the brain's homeostasis. Recent advancements in neuroimaging techniques, such as magnetic resonance imaging (MRI) and functional MRI (fMRI), have significantly enhanced our understanding of brain anatomy and function. These techniques have enabled researchers to map brain activity in real-time, providing insights into cognitive processes and neurological disorders. The article concludes by discussing the implications of understanding brain anatomy for clinical practice, including the diagnosis and treatment of neurological conditions. It also highlights areas for future research, emphasizing the need for further exploration of the brain's structural and functional complexity.

It is widely accepted that plastic waste is one of our most urgent environmental concerns the world is currently facing. Plastic has contributed greatly to innovation in all fields, but the raw material for its production (fossil fuels), as well as the linear economy in which it is currently produced and used, makes the material problematic from a sustainability and human health perspectives. The emergence of bio-based plastics provides an opportunity to reduce dependency on fossil fuels and transition to a more circular plastics economy. For polyethylene terephthalate (PET), one of the most prevalent plastics in packaging and textiles, there exist two bio-based alternatives that are similar or superior in material property and recyclability. These are polyethylene furanoate (PEF) and polytrimethylene terephthalate (PTT). The overarching aim of this study was to examine the transition from fossil-based to renewable plastics, through the lens of PET upcycling into PEF and PTT. The process for the production of PEF and PTT from three feed streams (post-consumer PET waste, lignocellulosic biomass-derived cellulose and biodiesel-derived crude glycerol) was developed in the software SuperPro Designer and the economic viability assessed via a discounted cumulative cash flow (DCCF) analysis. This study represents a conceptual case study of the valorisation of existing plastic waste into new, recyclable bio-based plastics, for a more sustainable plastics production. A techno-economic analysis of the designed process revealed that the minimum selling price (MSP) of second generation-derived PEF and PTT is 3.13 USD/kg, and that utilities and the feedstock used for the production of 2,5-furandicarboxylic acid (FDCA) needed in PEF synthesis contributed the most to the process operating costs. The effect of recycling PEF and PTT through the process at three recycling rates (42%, 50% and 55%) was investigated and revealed that increased recycling could reduce the MSP of the 2G bio-plastics (by 48.5%) to 1.61 USD/kg. This demonstrates that increasing recycling rates would have a beneficial effect on the economic viability of the plastic biorefinery.

Comparative analysis of mortality rates between public and private healthcare systems remains a critical yet complex area of study. This article synthesizes findings from extensive academic literature and empirical studies to examine the relationship between healthcare system type and patient outcomes across various countries. By focusing on mortality rates as a key indicator, we elucidate the nuanced impacts of public versus private healthcare delivery. Our analysis reveals the multifaceted challenges inherent in both systems and their implications for patient survival. We conclude by identifying crucial gaps in current research and proposing policy recommendations to optimize healthcare outcomes in diverse global contexts.This revised abstract:Starts with a strong opening statement that immediately captures the topic's importance. Clarifies the article's methodology (synthesizing existing literature and studies).Specifies the focus on mortality rates as the primary outcome measure.Hints at the complexity of the findings, avoiding oversimplification.Ends with a clear statement about the article's contributions to both research and policy.

The integration of genetic technologies into neurological diagnostics has significantly enhanced our ability to understand and manage neurological disorders. This article discusses the functional analysis of key genetic tests, including microarray analysis, variant sequencing, exome sequencing, RNA sequencing, and whole genome sequencing. Each of these technologies offers unique advantages and challenges, providing a comprehensive toolkit for diagnosing and understanding the genetic basis of neurological conditions. The functional insights gained from these tests not only detect genetic abnormalities but also elucidate their functional consequences, aiding in the diagnosis, prognosis, and management of neurological disorders. As these technologies continue to evolve, they are expected to play an increasingly important role in the field of neurological diagnostics, paving the way for personalized medicine and improved patient outcomes.

Epigenetics, the study of heritable changes in gene expression that do not involve alterations to the DNA sequence, is revolutionizing our understanding of psychiatric disorders. This article provides a comprehensive overview of the role of epigenetics in psychiatry, discussing its implications for genomics, evolution, inheritance patterns, and the development of biomarkers. Epigenetic mechanisms, including DNA methylation, histone modifications, and non-coding RNAs, are explored in the context of psychiatric conditions such as depression, schizophrenia, and post-traumatic stress disorder (PTSD). The discussion highlights how epigenetic modifications can influence brain dynamics, as measured by electroencephalography (EEG), and how artificial intelligence (AI) can accelerate the development of treatment options. The potential for epigenetic biomarkers to improve diagnosis, prognosis, and treatment is emphasized, along with the role of behavioral interventions in inducing beneficial epigenetic modifications. Future directions in epigenetic research, including the development of epigenetic therapies and the elucidation of molecular mechanisms underlying epigenetic inheritance, are also discussed. This article underscores the transformative potential of epigenetics in advancing our understanding of mental health and disease, paving the way for novel therapeutic interventions.

The global landscape of health and disease has undergone significant transformations over the past several decades. While infectious diseases once dominated the morbidity and mortality statistics, non-communicable diseases (NCDs) have emerged as the leading cause of death and disability worldwide. This shift has profound implications for public health systems, healthcare delivery, and global health policies. This introduction aims to explore the main tendencies of disease burden, morbidity, and mortality in the world, with a special emphasis on NCDs and the Implications of Emerging AI Technologies. By examining the current trends and underlying factors, we can better understand the challenges and opportunities for improving global health outcomes.

This paper presents a novel game-theoretic model simulating the global artificial intelligence (AI) development race among nations. We introduce a dynamic system of equations that captures the complex interplay between a country's development level, investment strategies, market share, and the possibility of technological domination. The model incorporates key features of technological races, including returns subject to diminishing marginal utility, exponentially increasing costs, market dynamics, and strategic investment decisions based on relative market positions. Through Monte Carlo simulations, we analyze various scenarios of the AI race, focusing on the emergence of dominant players and the challenges faced by countries attempting to catch up. Our results indicate a tendency towards oligopolistic market structures, with a few countries achieving technological domination while others struggle to close the gap. The model provides insights into the potential trajectories of global AI development and the conditions under which technological leapfrogging might occur. These findings have implications for policymakers and researchers in understanding the dynamics of technological races and formulating strategies for national AI development.

The intricate relationship between genomic mutations and proteomic network topology represents a frontier in understanding cellular resilience and disease mechanisms. This study presents a novel approach to analyzing how proteomic topological structures evolve and potentially break down in response to accumulating genomic mutations, offering insights into the complex interplay between genetic alterations and protein interaction networks. We developed a computational model simulating a network of ten key proteins (A-J) over 20 discrete time points, each representing a state of increasing genomic mutation load. Our model incorporates dynamic protein interaction strengths that fluctuate in response to simulated genomic alterations and a topological representation of the protein network, allowing for visualization of structural changes. Not forgetting a quantitative measure of network integrity, correlating with the system's ability to maintain function under mutational stress. Our Key findings include: visualization of proteomic network evolution through a series of topological graphs, revealing how genomic mutations progressively alter protein interaction patterns and identification of a critical "breakage point" in the network topology, signifying a threshold where genomic mutations overwhelm the protein network's ability to maintain its functional structure. And also quantification of network properties, including interaction strengths and overall network density, demonstrating the non-linear relationship between genomic mutations and proteomic topological integrity.The observed breakage point may represent a critical threshold in disease progression, where the accumulation of genomic mutations leads to a collapse in the proteomic interaction network, potentially triggering pathological states. This methodology offers a new perspective on the genotype-phenotype relationship, viewing it through the lens of protein network topology. Our approach provides a framework for future studies to analyze how specific genomic mutations might impact proteomic structures, potentially leading to new insights in cancer research, neurodegenerative disorders, and other mutation-driven diseases. It also suggests that maintaining proteomic topological integrity could be a key factor in cellular resilience against genomic instability.

The concept of a ‘just transition’ encompasses political and policy imperatives to minimise the harmful impacts of industrial and economic transitions on workers, communities and society more generally, and to maximise their potential benefits. This imperative has gained heightened importance as governments commit to reducing greenhouse gas emissions. A wide range of policies strategies and initiatives have been adopted by national and regional governments to facilitate and help manage a just transition. It is a concept that is increasingly being put into practice. This scoping study identifies and compares strategies, policies and practices that are presently being implemented in order to manage a just transition across 25 countries and 74 regions alongside European Union-level policies. This work develops a typology of policy instruments to manage just transitions and identifies implementation gaps and leading practices.

Frailty syndrome, a significant contributor to morbidity and mortality in older adults, has traditionally been addressed through aerobic exercises. However, recent research has highlighted the importance of anaerobic and strength training activities in mitigating frailty. This article explores the shift from aerobic to anaerobic/strength training exercises, emphasizing the benefits of the latter in improving muscle strength, balance, and overall physical function. The discussion includes the physiological mechanisms underlying this shift, the evidence supporting strength training, and practical recommendations for implementation.

In this paper, we present a rigorous proof demonstrating the stabilization of percolation probability in supercritical regimes for percolation models. We analyze a sequence of expanding compact balls in ℝ

Anticonvulsant drugs are widely used in the management of epilepsy and other neurological disorders. However, their concomitant use with other medications can lead to significant drug interactions, impacting both efficacy and safety. This article aims to provide a comprehensive overview of common anticonvulsant interactions with other frequently used drugs. It discusses the mechanisms of these interactions, their clinical implications, and strategies for managing them. The article also highlights the importance of monitoring and patient education in minimizing adverse outcomes.

Metabolomics studies require a deep understanding of enzyme dynamics and their responses to environmental disturbances within a complex metabolic network. This paper presents a novel approach to modeling enzyme activity as a dynamic topological manifold, where each enzyme is represented as a node, and their interactions are described by differential equations. These equations account for both compensatory interactions and external perturbations, simulating how enzymes dynamically stabilize their activities in response to varying conditions. We explore the application of this model to visualize enzyme interactions, using graph theory to represent the network structure and color gradients to illustrate interaction intensities. Multiple disturbances are introduced to analyze the network's resilience and adaptability over time. The results provide insights into the compensatory mechanisms within enzymatic networks, offering a comprehensive visualization through both static and dynamic representations. Our approach allows for a better understanding of how enzymatic systems buffer against mutations and environmental stressors, contributing to the broader field of systems biology and metabolomics.

This article delves into the historical development and ramifications of divergent theories that have emerged since the publication of Darwin's seminal work, "On the Origin of Species" in 1856. It traces the evolution of thought from Darwin's revolutionary ideas through the controversial era of eugenics, the groundbreaking discovery of DNA, the elucidation of Mendelian genetics, and the subsequent development of modern genetic theories. The article also explores the critical role of mathematical models, statistics, and data science in advancing genomics research over the last decade. This comprehensive exploration highlights how these theories and methods have fundamentally shaped our understanding of biology, evolution, human genetics, and complex biological systems.

The COVID-19 pandemic has significantly reshaped global power dynamics, impacting economies, political landscapes, international relations, and technological advancements. Historically, pandemics have acted as catalysts for social and economic transformations, and COVID-19 has followed this pattern by intensifying existing trends and creating new opportunities for power shifts. Economically, the pandemic led to unprecedented disruptions, with East Asian countries demonstrating greater resilience and adaptability compared to Western economies. This has resulted in notable economic power shifts, particularly benefiting China, which experienced a swift recovery. Politically, the pandemic exacerbated geopolitical tensions, notably between the United States and China, while also fostering new forms of international cooperation, such as vaccine diplomacy and multilateral health initiatives. Technologically, the pandemic accelerated digital transformation and innovation in biotechnology, with significant advancements in healthcare systems. Socially, it exposed and exacerbated inequalities, prompting discussions on social justice and healthcare reforms. Environmentally, short-term improvements were observed, but the need for sustainable development and green recovery strategies became evident. This article examines these shifts, highlighting the interconnectedness of global challenges and the necessity for coordinated, multilateral responses to build a more equitable and sustainable future.

The field of neurology has undergone a profound transformation over the past two decades, shifting from traditional clinical and pathological approaches to a more genetically informed perspective. This essay explores the drastic turn in neurology from classical disciplines to neurogenetics, examining the factors that have contributed to this shift and the implications for clinical practice and research. The traditional approach to neurology, grounded in clinical examination, neuroanatomy, and neuropathology, has been effective in diagnosing and treating many conditions but has had limitations in understanding the etiology of complex and rare neurological disorders. The emergence of neurogenetics has been driven by advancements in genetic technologies, such as next-generation sequencing (NGS) and genome-wide association studies (GWAS), as well as the recognition of the genetic basis of many neurological disorders. This shift has led to more accurate diagnoses, targeted treatments, and a deeper understanding of the molecular mechanisms underlying neurological disorders. However, challenges remain, including the complexity of genetic architecture and the ethical implications of genetic testing. The future of neurogenetics holds great promise for improving the diagnosis, treatment, and prevention of neurological conditions, with new technologies and interdisciplinary approaches poised to further advance the field.

This article conducts a bibliometric analysis focusing on the influence of globalization and technology in the academic context. Previous studies have focused on analyzing the relationship between the number of research citations and international collaboration, but no study has explored it in the scope of consumer behavior and retail. Leveraging the Scopus database, the research analyzes a significant corpus of 2,111 documents from 1971 to 2022. The findings reveal a distinct surge in academic output related to the topic of technology, reflecting a growing interest in understanding the interaction between technological factors and consumer behavior. The study also explores trends in international collaboration in publications as well as its impact on citation counts.

Streptococcus pneumoniae is the major cause of invasive pneumococcal disease (IPD). Since 1998, multilocus sequence typing (MLST) has been used for identifying the genotypes of strains of S. pneumoniae and helped reveal a diversity of local and regional epidemiological patterns for IPD, resulting in an archived MLST dataset of over 74,000 isolates. However, the global patterns of MLST sequence type (ST) and allele type (AT) distributions remain largely unexplored. In this study, we investigated the spatial and temporal patterns of AT and ST distributions of S. pneumoniae. We extracted S. pneumoniae MLST data from PubMLST.org and conducted various population genetic and phylogenetic analyses. Our analyses demonstrated both shared and unique distributions of STs and ATs among continental and national/regional populations. Among the 17915 STs in the dataset, 36 STs representing 15263 isolates were broadly shared among all six continents, consistent with recent gene flow and clonal dispersal of this pathogen. The analysis of molecular variance revealed that >96% genetic variations were found within individual continental and national/regional populations. However, though low (<4%), statistically significant genetic differentiation among continental and national populations were observed. Comparisons between non-clone-corrected and clone-corrected datasets showed that localized clonal expansion contributed significantly to the observed genetic differentiations among continents and countries/regions. Temporal analyses of the isolates showed that implementation of pneumococcal conjugate vaccine impacted the distributions of STs. Linkage disequilibrium analyses identified evidence for non-random recombination in all continental populations of this species. We discussed the implications of our analyses to the global epidemiology and future vaccine developments for S. pneumoniae.

Heated intraperitoneal chemotherapy (HIPEC) has several potential benefits. Higher doses of chemotherapy can be used with HIPEC because the plasma-peritoneal barrier results in little absorption into the blood stream. HIPEC offers higher peritoneal penetration in comparison to an intravenous (IV) regimen and does not have the traditional normothermic intraperitoneal (IP) regimen limitation of post-operative adhesions. Hyperthermia itself has cytotoxic effects and can potentiate antineoplastic effects of chemotherapy in part by increasing the depth of tumor penetration by up to 3 mm. For the treatment of ovarian cancer, HIPEC has been evaluated in the recurrent setting with secondary cytoreduction. Recent studies, including a prospective trial, have evaluated its role in primary management of ovarian cancer. This review summarizes previous and ongoing studies regarding the use of HIPEC in the management of ovarian cancer.

This study assessed the literature about the use of high tunnels (HTs) in agricultural systems in the United States since the 2009 launch of the Natural Resources Conservation Service (NRCS) High Tunnel Initiative. This NRCS program led to an increase in HT adoption nationwide. The literature searches were conducted using the Web of Science (WoS) database. The final sample was 133 peer-reviewed articles published between 2009 and February 2023. We used CiteSpace 6.2.R1 and Gephi 0.9.2 to conduct co-citation, co-author, co-institution, and clustering techniques. The findings showed that the peer-reviewed literature about HT use has increased since 2009, substantially rising between 2017 and 2021. Horticulture was the top subject category in the literature, and most articles were published in peer-reviewed journals of the American Society for Horticultural Science (i.e., HortTechnology and HortScience). The research field evolved from general HT practices, nutrient management, and plant pathology to focus on trials of specific crops and integrated pest management. The institutions with the most contributions to the HT literature were Kansas State University, the University of Florida, Michigan State University, Purdue University, and the University of Minnesota. The patterns of HT research revealed in this study offer a greater understanding of the current state of knowledge to inform the focus of future research.

In 2011 over 1.7 million people were hospitalized because of a fragility fracture, and direct costs associated with osteoporosis treatment exceeded 70 billion dollars in the United States. Failure to reach and maintain optimal peak bone mass during adulthood is a critical factor in determining fragility fracture risk later in life. Physical activity is a widely accessible, low cost, and highly modifiable contributor to bone health. Here, we will review the evidence linking exercise and physical activity to bone health in women. Bone structure and quality will be discussed, especially in the context of clinical diagnosis of osteoporosis. We will review the mechanisms governing bone metabolism in the context of physical activity and exercise. Questions such as, when during life is exercise most effective, and what specific types of exercises improve bone health, will be addressed. Finally, we will discuss some emerging areas of research on this topic, and will summarize areas of need and opportunity.

In tropical developing regions, typhoid fever and malaria often co-occur, posing serious health risks. The two illnesses have similar symptoms, which makes diagnosis and treatment more difficult. Through a thorough analysis of existing literature on typhoid fever, malaria, and co-infection, including research articles, case studies, and review papers, this review seeks to understand the dynamic interactions between typhoid fever and malaria co-infection. The review highlights important problems with co-infection between typhoid and malaria, such as higher morbidity and death, diagnostics-related challenges, and antibiotic resistance. Overlapping symptoms and limitations in current diagnostic tests impede accuracy. Treatment plans are made more difficult by the advent of typhoid strains that are resistant to drugs. The analysis concludes by highlighting the necessity of developing more sensitive and specific diagnostic methods in order to accurately diagnose co-infections. Going forward, the review emphasizes the need for more investigation into the immunological connections between malaria and typhoid illnesses. Essentially, this paper offers a thorough summary of the difficulties and possibilities related to co-infection between typhoid and malaria. It provides a framework for upcoming studies and initiatives that seek to improve public health and patient outcomes in the face of these intricately linked health problems.

Background: Partially hydrolyzed guar gum (PHGG) is a water-soluble, prebiotic fiber that is used in foods and supplements. The effects of PHGG and its role in gut health are still being studied. The purpose of this study was to evaluate changes in the gut microbiome composition of healthy individuals in response to low-dose PHGG supplementation compared with a low fiber diet. Methods: A randomized, double-blind, placebo-controlled crossover study was performed on 33 healthy subjects (17 male, 16 female). Each subject completed three 14-day treatment periods with a 2-week washout between each period. Treatments included supplementation with 3 g PHGG, 6 g PHGG, or placebo. During all periods, the participants followed a low fiber diet (<14 g/day). Stools were collected on days 0 and 14 of each period. Gut microbiome profiling was performed using 16S rRNA sequencing. Results: Supplementation of 3 g and 6 g PHGG significantly increased Verrucomicrobia on day 14 when compared to the placebo (p=0.0066 and p= 0.0068, respectively). On the genus level, Akkermansia was significantly increased on day 14 with both the 3 g and 6 g PHGG doses (p=0.0081 and p=0.0083). Faecalibacterium was significantly decreased on day 14 with 3 g PHGG (p=0.0054). Conclusion: Supplementing with low doses of PHGG has the potential to cause shifts in gut microbiome composition. By increasing beneficial microbes, PHGG can improve the microbiome composition of healthy individuals and may play a role in the treatment of inflammatory gastrointestinal diseases.

Introduction: Decreasing overweight and obesity prevalence requires precise data at sub-national levels to monitor progress and initiate interventions. This study aimed to estimate baseline age-standardized overweight prevalence at the lowest administrative units among women, 18 years and older, in seven African countries. The study aims are synonymous with indicator 14 of the global non-communicable disease monitoring framework. Methods: We used the most recent Demographic and Health Survey and administrative boundaries data from the GADM. Three Bayesian hierarchical models were fitted and model selection tests implemented. The age-standardized prevalence of overweight among adult women at national, first and second administrative levels were individually reported in each country in the form of maps and tables. Results: Substantial variation in the age-standardized prevalence of adult female overweight was noted across several second-level administrative units. In numerous locations in Tanzania, Nigeria and Zimbabwe, more than half of the adult female population were overweight and in one location in Tanzania, over 72% of the adult female population were overweight. These estimates were roughly twice the national level overweight prevalence and, in some cases, roughly 10 – 20% greater than the overweight prevalence in first-level administrative units. Conclusion: The observed overweight burden in subnational administrative units suggests the presence of an epidemic tantamount to the situation in more affluent economies. African countries lack the resources to effectively handle the fallout from such epidemic, therefore motivating the need for increased urgency in adopting WHO obesity-related intervention guidelines and implementing more rigorous studies to validate the study findings.

Most pathogens are capable of infecting multiple host species and such cross-species transmission (CST) can have dramatic consequences, as highlighted by recent disease emergence events affecting human, animal and plant health. Understanding the ecological and evolutionary factors that constrain or facilitate the ability of disease agents to infect and establish in a novel host is therefore a timely and important research area. Previous work across different pathogens, including rabies virus (RABV), found that increased evolutionary distance between hosts reduces the frequency of cross-species transmission and of permanent host shifts. However, whether this effect of host relatedness still holds for transmission among recently diverged hosts, and the importance of this effect relative to other predictors, is not well understood. We addressed this question by quantifying the CST frequency of RABV between North American bat species within the genus Myotis, using a multi-decade data set containing 128 nucleoprotein (N) sequences from ten host species. For comparison, we also conducted an equivalent analysis of a RABV dataset from North American bat species comprising nine genera. We found that at the within genus scale, host relatedness failed to explain the frequency of CST events. However, CST frequency increased with overlap in species’ host range. Moreover, we found evidence of CST occurring among a higher proportion of species, and CST more frequently resulting in sustained transmission in the novel host in the Myotis dataset compared to the multi-genus dataset. Our results suggest that among recently diverged species, the ability to infect a novel host is no longer restricted by physiological barriers but instead is limited by physical contact. Our results improve predictions of where future CST events for RABV might occur and clarify the relationship between host divergence and pathogen emergence.

23-Hydroxy ursolic acid (23-OH UA) is a potent atheroprotective and anti-obesogenic phytochemical, with anti-inflammatory and inflammation resolving properties. In this study, we examined whether dietary 23-OH UA protects mice against the acute onset and progression of experimental autoimmune encephalomyelitis (EAE), a mouse model of multiple sclerosis (MS). Female C57BL/6 mice were fed either a defined low-calorie maintenance diet (MD) or an MD supplemented with 0.2 % wgt/wgt 23-OH UA for 5 weeks prior to actively inducing EAE and during the 30 days post-immunization. We observed no difference in the onset of EAE between both groups of mice, but ataxia and EAE disease severity were suppressed by 52% and 48%, respectively, and disease incidence was reduced by over 49% in mice that received 23-OH UA in their diet. Furthermore, disease-associated weight loss was strikingly ameliorated in 23-OH UA-fed mice. ELISPOT analysis showed no significant differences in frequencies of T cells producing IL-17 or IFN- between 23-OH UA fed mice and control mice, suggesting that 23-OH UA does not appear to regulate peripheral T-cell responses. In summary, our findings demonstrate that dietary 23-OH UA may represent an effective oral adjunct therapy for the prevention and treatment of relapsing-remitting MS.

Measuring the status and trends of biodiversity is critical for making informed decisions about the conservation, management or restoration of species, habitats and ecosystems. Defining the reference state against which status and change are measured is essential. Typically, reference states describe historical conditions, yet historical conditions are challenging to quantify, may be difficult to falsify, and may no longer be an attainable target in a contemporary ecosystem. We have constructed a conceptual framework to help inform thinking and discussion around the philosophical underpinnings of reference states and guide their application. We characterise currently recognised historical reference states and describe them as Pre-Human, Indigenous Cultural, Pre-Intensification and Hybrid-Historical. We extend the conceptual framework to include contemporary reference states as an alternative theoretical perspective. The contemporary reference state framework is a major conceptual shift that focuses on current ecological patterns and identifies areas with higher biodiversity values, regardless of the disturbance history. The specific context for which we design the contemporary conceptual frame is underpinned by an overarching goal—to maximise biodiversity conservation and restoration outcomes in existing ecosystems. The contemporary reference state framework can account for the inherent differences in the diversity of biodiversity values (e.g., species richness, habitat complexity) across spatial scales, communities and ecosystems. In contrast to historical reference states, contemporary references states are measurable and falsifiable. This ‘road map of reference states’ offers perspective needed to define and assess the status and trends in biodiversity and habitats. Our framework for contemporary reference states provides a tractable way for policy-makers and practitioners to navigate biodiversity assessments to maximise conservation and restoration outcomes in contemporary ecosystems. We illustrate how to define a contemporary reference state using an example from south-eastern Australia.

Condition-specific competition, when environmental conditions alter the outcome of competition, can foster the persistence of resident species after the invasion of a competitively superior invader. We test whether condition-specific competition can facilitate the areawide persistence of the resident and principal West Nile virus vector mosquito Culex pipiens with the competitively superior invasive, Aedes albopictus, in water from different urban container habitats. (2) Methods: We tested the effects of manipulated numbers of A. albopictus on C. pipiens survival and development in water collected from common functional and discarded containers in Baltimore, Maryland, USA. The experiment was conducted with typical numbers of larvae found in field surveys of C. pipiens and A. albopictus and container water quality. (3) Results: We found increased densities of A. albopictus negatively affected the survivorship and development of C. pipiens in water from discarded containers but had little effect in water from functional containers. This finding was driven by water from trash cans, which allowed consistently higher C. pipiens survival and development and had greater mean ammonia and nitrate concentrations that can promote microbial food than other container types. (4) Conclusions: These results suggest that the contents of different urban containers alter the effects of invasive A. albopictus competition on resident C. pipiens, that trash cans, in particular, facilitate the persistence of C. pipiens, and that there could be implications for West Nile virus risk as a result.

In urban areas like Chicago, daily life extends above ground level due to prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 levels were measured using PurpleAir sensors, installed between April 8 and May 7, 2023, on floors one, four, six, and nine of an office building in Chicago. Additionally, data were collected from a public outdoor PurpleAir sensor on the fourteenth floor of a condominium located 800 meters away. Results show that outdoor PM2.5 concentrations peak at 14 meters height, then decline by 0.11 μg/m3 per meter elevation, especially noticeable from midnight to 8 AM under stable atmospheric conditions. Indoor PM2.5 concentrations increase steadily by 0.02 μg/m3 per meter elevation, particularly during peak work hours, likely caused by greater infiltration rates at higher floors. Both outdoor and indoor concentrations peak around noon. We find that indoor and outdoor PM2.5 are positively correlated, with indoor levels consistently remaining lower than outside levels. These findings align with previous research suggesting decreasing outdoor air pollution concentrations with increasing height. The study informs decision-making by community members and policymakers regarding air pollution exposure in urban settings.

Background: Blood flow restriction training (BFRT) uses occlusion during low-intensity resistance training (&lt;50% of repetition maximum, 1RM) to reduce arterial blood flow and venous return, imposing greater metabolic stress but similar muscular hypertrophy and strength gains as high-intensity resistance training (HIRT). However, no study, to date, has incorporated BFRT in a collegiate strength and conditioning setting to assess ecological validity. We aimed to investigate the effects of adding 6-weeks of accessory BFRT or HIRT to NCAA Division III soccer players prescribed resistance training regimen on muscle strength and hypertrophy. Methods: Male and female (n=17) athletes were randomly assigned to complete biceps curls 2x/week under BFRT or control (HIRT), following the regularly scheduled strength training. Bicep strength (1RM) and circumference (BC) were assessed at weeks 0, 3, and 6. Results: In men, no significant interaction between time and condition was observed for BC (p=0.861), though condition (BFRT vs Control, p=0.025) and time (p=0.024) were significant. For 1RM, there was no significant interaction between time and condition (BFRT vs HIRT, p=0.067) or of condition (p=0.598), but there was a significant effect of time (p=0.004). In women, there was no significant interaction between time and condition (p=0.765) or of condition (p=0.971) on BC, but time was significant (p=0.045). For 1RM, there was no significant interaction between time and condition (p=0.227) or of condition (p=0.741), but time was (p=0.018). Conclusion: BFRT induced similar increases in muscle strength and hypertrophy as HIRT in soccer players, suggesting that BFRT could be incorporated into collegiate athlete training.

Introduction: Bystander CPR rates have increase from 15% to 30% in many countries over the last 20 years. However, these gains are at risk of being lost in the face of fear of transmission of the SARS-COV-2 virus given the global COVID-19 pandemic. This survey study was designed to examine the opinions and perceptions of the Canadian public on bystanderism as it relates to out-of-hospital cardiac arrest during the COVID-19 pandemic with a view to understanding the implications of those opinions on our expectations of bystanders during, and after the pandemic. Methods: In order to capture a pan-Canadian sample of the general public, we engaged with a well-established public opinion polling vendor, IPSOS (www.ipsos.com), to conduct a robust survey on public willingness to do bystander CPR in the context of the global COVID-19 pandemic. Results: A total of n=1,000 surveys were completed by Canadian adults using the IPSOS eNation Canadian Online Omnibus survey platform.In summary there were five key findings from this pan-Canadian survey: 1) the ability to recognize cardiac arrest remains a point of confusion for the lay public, 2) at the time of the survey only about half of respondents said they would respond to someone having cardiac arrest, 3) the majority of respondents said the pandemic has changed their willingness to respond to someone in cardiac arrest, 4) willingness to use an AED is low and the greatest barrier was reported as lack of experience, and lastly 5) most Canadians say it is important to be certified in CPR and are willing to take a CPR training course, however only half are willing to pay for it. Conclusion: Bystander CPR is an out-of-hospital cardiac arrest victim’s best chance of survival and so our understanding of how we encourage more citizens to respond given the current realities is more crucial than ever. Wider dissemination of the growing evidence on the safety of CPR in the context of COVID-19 and resuscitation training courses implemented with specific CPR maneuvers to reduce the risk of infection should be implemented to continued uncertainty and erosion in bystander CPR culture.

Atomic structure of N-electron atoms is often determined using the Hartree-Fock method, which is an integro-differential equation. The exchange term of the Hartree-Fock equations is usually treated as an inhomogeneous term of a differential equation, or with a local density approximation. This work uses matrix methods to solve for the Hartree-Fock equations, rather than the more commonly-used shooting method to integrate an inhomogeneous differential equation. It is well known that a derivative operator can be expressed as a matrix made of finite-difference coefficients; energy eigenvalues and eigenvectors can be obtained by using computer linear-algebra packages. We extend the same technique to integro-differential equations, where a discretized integral can be written as a sum in matrix form. This method is compared against experiment and standard atomic structure calculations. We also can use this method for free-electron wavefunctions. This technique is important for spectral line broadening in two ways: improving the atomic structure calculations, and improving the motion of the plasma electrons that collide with the atom.

Nature-based solutions (NbS) are increasingly recognised for their potential to address both the climate and biodiversity crises. These outcomes are interdependent, and both rely on the capacity of NbS to support and enhance the health of an ecosystem: its biodiversity, the condition of its abiotic and biotic elements, and its capacity to function normally despite environmental change. However, while understanding of ecosystem health outcomes of nature-based interventions for climate change mitigation is growing, the outcomes of those implemented for adaptation remain poorly understood with evidence scattered across multiple disciplines. To address this, we conducted a systematic review of the outcomes of 109 nature-based interventions for climate change adaptation using 33 indicators of ecosystem health across eight broad categories (e.g. diversity, biomass, ecosystem functioning and population dynamics). We showed that 88% of interventions with positive outcomes for climate change adaptation also reported measurable benefits for ecosystem health. We also showed that interventions were associated with a 67% average increase in local species richness. All eight studies that reported benefits in terms of both climate change mitigation and adaptation also supported ecosystem health, leading to a triple win. However, there were also trade-offs, mainly for forest management and creation of novel ecosystems such as monoculture plantations of non-native species. Our review highlights two major limitations of research to date. First, only a limited selection of metrics are used to assess ecosystem health and these rarely include key aspects such as functional diversity and habitat connectivity. Second, taxonomic coverage is poor: 67% of outcomes assessed only plants and 57% did not distinguish between native and non-native species. Future research addressing these issues will allow the design and adaptive management of NbS to support healthy and resilient ecosystems, and thereby enhance their effectiveness for meeting both climate and biodiversity targets.

Community-level wastewater monitoring for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA has demonstrated useful correlation with both coronavirus disease 2019 (COVID-19) case numbers and clinical testing positivity. Wastewater monitoring on college campuses has demonstrated promising predictive capacity for the presence and absence of COVID-19 cases. However, to date, such monitoring has largely relied upon composite or grab samples and reverse transcription quantitative PCR (RT-qPCR) techniques, which limits the accessibility and scalability of wastewater monitoring. In this study, we piloted a workflow that uses tampons as passive swabs for collection and reverse transcription loop-mediated isothermal amplification (RT-LAMP) to detect SARS-CoV-2 RNA in wastewater. Results for the developed workflow were available same day, with a time to result following tampon swab collection of approximately three hours. The RT-LAMP 95% limit of detection (76 gene copies reaction-1) was greater than RT-droplet digital PCR (ddPCR; 3.3 gene copies reaction-1). Nonetheless, during a building-level wastewater monitoring campaign conducted in the midst of weekly clinical testing of all students, the workflow demonstrated a same-day positive predictive value (PPV) of 33% and negative predictive value (NPV) of 80% for incident COVID-19 cases. The NPV is comparable to that reported by wastewater monitoring using RT-qPCR. These observations suggest that even with lower analytical sensitivity the tampon swab and RT-LAMP workflow offers a cost-effective and rapid approach that could be leveraged for scalable same-day building-level wastewater monitoring for COVID-19.

Automated charging technologies are becoming increasingly important in the electrification of heavy road freight transport, especially in combination with autonomous driving. This study provides a comprehensive analysis of automated charging technologies for electric heavy-duty vehicles (HDVs). It encompasses the entire spectrum of feasible technologies, including static and dynamic approaches, with each charging technology evaluated for its advantages, potentials, challenges, and technology readiness level (TRL). Static conductive charging methods such as charging robots, underbody-couplers or pantographs show good potential, with pantographs being the most mature option. These technologies are progressing towards higher TRLs, with a focus on standardization and adaptability. While static wireless charging is operational for some prototype solutions, it encounters challenges related to implementation and efficiency. Dynamic conductive charging through an overhead contact line or contact rails holds promise for high-traffic HDV routes with the overhead contact line being the most developed option. Dynamic wireless charging, although facing efficiency challenges, offers potential for seamless integration into roads and minimal wear and tear. Battery swapping is emerging as a practical solution to reduce downtime for charging, with varying levels of readiness across different implementations. To facilitate large-scale deployment further standardization efforts are required. The study emphasizes the necessity for continued research and development to enhance efficiency, decrease costs, and ensure seamless integration into existing infrastructures. Technologies that achieve this best will have the highest potential to significantly contribute to the creation of an efficiently automated and environmentally friendly transport sector.

Objectives Assess rates of adverse events (AE) after COVID-19 vaccines experienced by women of reproductive age, focusing on pregnancy and menstruation, using data collected by the US Centers for Disease Control and Prevention (CDC) Vaccine Adverse Events Reporting System (VAERS) database. Design Population-based retrospective cohort study. Setting US and global entries in US Centers for Disease Control and Prevention (CDC) Vaccine Adverse Events Reporting System (VAERS). Participants CDC VAERS entries from January 1, 1998 to June 30, 2022. Interventions None. Main Outcome Measures A proportional reporting ratio analysis is performed using data in the VAERS system comparing adverse events (AE) reported post-COVID-19 vaccines with that of post-Influenza vaccines. Results COVID-19 vaccines, when compared to the Influenza vaccines, are associated with a significant increase in AE with all proportional reporting ratios of > 2.0: menstrual abnormalities, miscarriage, fetal chromosomal abnormalities, fetal malformation, fetal cystic hygroma, fetal cardiac disorders, fetal arrhythmias, fetal cardiac arrest, fetal vascular malperfusion, fetal growth abnormalities, fetal abnormal surveillance, fetal placental thrombosis, low amniotic fluid, preeclampsia, premature delivery, preterm premature rupture of membrane, fetal death/stillbirth, and premature baby death (all p values were much smaller than 0.05). When normalized by time-available, doses-given, or persons-received, all COVID-19 vaccine AE far exceed the safety signal on all recognized thresholds. Conclusions Pregnancy complications and menstrual abnormalities are significantly more frequent following COVID-19 vaccinations than Influenza vaccinations. A worldwide moratorium on the use of COVID-19 vaccines in pregnancy is advised until randomized prospective trials document safety in pregnancy and long-term follow-up in offspring.

. 1) Background: Intersectionality contests that individuals have multiple characteristics in their identity that cannot be siloed or deemed exclusive to each other. Understanding and utilising an intersectional lens in organisations can increase inclusion of individuals and organisational performance. An educational package known as the Intersectionality Walk (IW) was developed by the authors, piloted, and evaluated in order to break down the commonly held descriptors of diversity silos that fragments inclusion, and to understand how various identity characteristics compound disadvantage. The paper outlines the need to transition from siloed views of diversity to a more intrinsic view of identity to achieve inclusivity. 2) Methods: The IW was developed and trialled with a series of work-based scenarios and realistic multifaceted personas. Data collection occurred pre- and post- IW utilising a mixed methods approach. Responses to Likert scale surveys and open-ended questions were captured and analysed via inductive and ground theory perspectives. 3) Results: An improved awareness and understanding of individual knowledge, reflectivity and positionality relating to intersectionality and intersectional approaches was reported on completion of the IW. Further, responses reported how and why organisations can approach and improve inclusivity via using intersectional approaches. 4) Conclusions: The IW as an educational package has a positive impact and is a key linkage for all employers to build an inclusive culture and to harness the talent of all employees. Further research will occur to measure the implemented change in organisations following the IW.

Our prior studies have shown that B type Natriuretic Peptide (BNP) decreases absorption from the gastrointestinal (GI) tract. Since non-muscle myosin-type II (NMM-II) is known to have a role in epithelial tight junction regulation, we aimed to test whether BNP has an effect on the localization and expression of NMM-II in the intestinal villi. We treated wild type mice with a 10 ng/g intravenous bolus of BNP followed by infusion of BNP at 1ng/g/minute vs. vehicle control. The mice were then euthanized and intestinal tissue isolated and sectioned. The tissue was immune-stained for NMM-II and examined using fluorescence microscopy. The tissue section not treated with antibody to NMM-II was used as a methodological control to evaluate the non-specific binding. Scanning electron micrographic images were taken to compare structural differences between the two groups. Western blotting was performed to compare the regional protein expression of NMM-II and associated proteins including smooth muscle actin, kinesin, and E cadherin in small intestinal tissue between BNP vs. vehicle infused mice. Fluorescence microscopy revealed markedly increased localization of NMM-II at the crypts and core of intestinal villi of the jejunum, ileum and colon of BNP treated mice compared to vehicle. Transmission electron microscopy revealed that the microvilli of BNP treated mice assumed the distinctive appearance of ‘relaxed’ microvilli compared to vehicle. Such decrease in contractility was previously shown to decrease para-cellular permeability of epithelia. Our observation indicates that BNP alters the localization of NMM-II in intestinal villi and makes the intestinal villi structures assume a state of decreased permeability of the apical junctional complex. Further characterization of this process and understanding the specific mediators involved could lead to the identification of novel therapeutic targets for various disease states such as heart failure and malabsorption.

Personality changes have been reported to occur following organ transplantation, and most commonly among heart transplant recipients. We set out to examine whether personality changes do occur following transplantation, and specifically, what types of changes occur among heart transplant recipients compared to other organ recipients. A cross-sectional study was conducted in 2022, in which 47 participants (23 heart recipients and 24 other organ recipients) completed an online survey. In this study, 89% of all transplant recipients reported experiencing personality changes after receiving their organ transplant, which was similar for heart and other organ recipients. With the exception of physical attributes, the types of personality changes reported were similar between the two groups. These finding indicate that heart transplant recipients are not unique in their reported experience of personality changes following organ transplantation. Further studies are needed to deepen our understanding of what causes these personality changes following organ transplantation.

Previous studies have found that individuals with limited motor capabilities due to acquired neurological injury (e.g., spinal cord injury and stroke) can make accurate action possibility judgements for neurologically healthy individuals. Previous studies have shown that people with limited motor capabilities may rely on previous motor experience (i.e., pre-injury) when making action possibility judgments for others. In the present study, we examined whether having severely limited previous motor experience from birth, as a consequence of spinal muscle atrophy (SMA), alters the action possibility judgments made for neurologically healthy individuals. Participants with SMA and Neurologically Healthy (NH) sex- and age-matched controls performed a perceptual-motor judgment task using the Fitts’s law paradigm (see Fitts, 1954). Participants observed apparent motion videos of reciprocal aiming movements with varying indices of difficulty (ID, see: Manson et al., 2014). For each movement, participants predicted the shortest movement time (MT) at which a neurologically healthy young adult could perform the task while maintaining accuracy. Between-group comparisons revealed that participants with SMA predicted significantly longer MTs compared to controls. Regression analyses revealed that predicted MTs of both NH and SMA participants exhibited a Fitts’s law relationship (i.e., the predicted MTs significantly increased as movement difficulty increased). A supplementary analysis on the SMA group revealed no differences in predicted MTs between the participants with some and no motor function as assessed by the SMA health index. Overall, these results provide evidence that participants with SMA who have limited or no motor experience may make more conservative action possibility judgments for others. Critically, our finding that the pattern of action possibility judgements (e.g., the slopes of the regression lines) were not different between SMA and NH groups provides evidence that limited previous motor experience may not completely impair action possibility judgements.

Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC) but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis, however current models still fail to identify small (&lt;2cm) lesions. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. Additionally, the impact of integrating surrounding anatomy was investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a nnUnet for automatic lesion detection and segmentation (nnUnet_T). Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor (nnUnet_TP), (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures (nnUnet_MS). An external, publicly available test set was used to compare the performance of the three networks. The nnUnet_MS achieved the best performance, with an area under the receiver operating characteristic curve of 0.91 for the whole test set and 0.88 for tumors &lt;2cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information.

Artificial insemination (AI) is considered as the most efficient feasible tool to improve the dairy cows’ profitability and productivity. In bovines, AI helps to promote genetic improvement and can positively impact the rural economy. The achievement of education in rural populations was one of the Millennium Development Goals (MDGs), made by the United Nations. During the last World Summit on Sustainable Development (WSSD), it was concluded that most of the world’s poor, illiterate and undernourished population lives in rural areas; therefore, access to education is considered one of the greatest challenges for governments in countries with developing economies. To determine the effects of a theoretical-practical training program related to rural management and leadership, AI, and bovine genetic improvement, on the perception and level of knowledge in a rural Colombian population that has been affected by the armed conflict. Phase 1. Included the practical and theoretical training of undergraduate animal sciences students in three dimensions rural management; leadership (D-ML), AI in bovines (D-AI), and genetic improvement (D-GI) after training students were evaluated and selected according to their skills, characteristics, solidarity skills, and their social sensitivity was identified. Phase 2. Included the socioeconomic characterization and training of rural residents by students and field experts in three dimensions D-ML, D-AI, and D-GI. After training we evaluated the perception and level of knowledge before and after training in 63 rural residents using an evaluative instrument. The data before and after (post-test) of the theoretical-practical training, were analyzed to obtain a statistical significance. The perception of knowledge in rural residents was low for general knowledge (2.48±0.76, p ˂ 0.05), D-ML (2.89±1.18, p ˂ 0.05) and D-AI and D-GI (2.17±0.83, p ˂ 0.05). On the other hand, before the theorical-practical training the level of general knowledge was 45.9%, however, after the training increased at 77.6% (p<0.01), while the level of knowledge for the D-AI was increased from 38.5 % to 80.6 % (p ˂ 0.05) after training. Regarding the level of knowledge for the D-GI pre-training was 50.8 and increased to 73.0% (p<0.01) then after. Finally, the level of knowledge for the D-ML was increased (p<0.01) from 54.8 before training to 75% after training. Altogether, the rural extension programs contribute to the closing of knowledge gaps in relation to the use of reproductive biotechnologies and bovine management in rural areas affected by armed conflict.

Abstract Congenital rubella syndrome is a constellation of birth defects that can have devastating consequences, impacting approximately 100,000 births worldwide each year. The incidence is much lower in countries that routinely vaccinate their population. In the US, postnatal immunization of susceptible women is an important epidemiological strategy for the prevention of rubella. However, concerns that co-administration of Rubella vaccine with other immunoglobins (i.e., Rhogam) could comprise vaccine efficacy has produced warnings that can delay the administration of Rubella vaccination postpartum, leaving women susceptible in a subsequent pregnancy. We aimed to address whether co-administration of Rubella vaccine and Rhogam decreased antibody responses as compared to those receiving only Rubella vaccination. This retrospective cohort study utilized clinical data from 80 subjects who received Rubella vaccine and Rhogam after delivery and 43 subjects received Rubella vaccine alone. Maternal demographics, pregnancy complications and rubella status at the start of a subsequent pregnancy were recorded for analysis. Overall, the two cohorts had similar baseline characteristics; however, lower parity was noted in the participants having both Rubella vaccination and Rhogam. Based on maternal antibody IgG index for rubella during the next pregnancy, we observed that 88% of the Rhogam + Rubella vaccine group had positive titers, which was not significantly different than the 81% in the Rubella vaccine only cohort (p=0.36). In conclusion, no differences in the rubella immunity status in a subsequent pregnancy were observed in those mothers administered both Rubella vaccine and Rhogam concurrently. Given these findings, warnings against co-administration of Rubella vaccine in combination with other immunoglobulins should be discontinued.

The sustainability of agricultural commodities produced in the Southern U.S. under irrigation is increasingly at risk due to erratic rainfall patterns, inadequate water supplies and compromised water quality. This study assessed the needs of crop growers in the United States Department of Agriculture (USDA) Southern Region. The purpose was to identify growers’ critical water resource management concerns to identify research priorities from the perspective of growers in this region. To obtain this information, a questionnaire was developed and distributed to growers throughout the Southern Region in 2020. The final sample included 112 crop (row and specialty) production growers from the Southern Region. Overall, respondents named the water available to irrigate as the greatest water resource management priority. Within all the water availability priorities asked, respondents’ first choice was declining water supply in the future. Declining water supply and the cost of irrigating presently were the next most frequently named water availability priorities. Growers named both increasing the efficiency of irrigation and development of farming practices to improve soil moisture retention as the first and second focus of future irrigation research, respectively. These results will aid in strengthening existing and developing new initiatives for water research and Extension in this region.

Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing, data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai'i island, using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that, while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m x 2 m M. polymorpha presence dataset and a 30 m x 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy.

Urban settings are increasingly faced with challenges across natural and engineered environmental systems, threatening the sustainability of urban centers where >50% of the world's population resides. The pressures of aging infrastructure, water and air pollution, and environmental justice exemplify the growing need for urban professionals to employ complex scientific reasoning across disciplines where they can effectively address the multi-faceted issues of urban sustainability. Here we present an innovative model for preparing the next generation of public, private, and academic leaders to address complex problems in urban sustainability. Specifically, we outline the design and implementation of an integrated, adaptable graduate training program, with the goals of science leadership, curriculum relevancy, community impact, broader applicability, recruitment into science, technology, engineering and mathematics (STEM) programs and careers, and program sustainability. This program addresses human-ecosystem challenges using a transdisciplinary approach to produce scientific products in partnership with local communities, businesses, industries, scientists, and policy makers, while providing a mechanism to understand and overcome contemporary societal challenges. Students receive rigorous training in their home disciplines, coupled with training across disciplinary lines and developmental experiences, to prepare them to communicate, collaborate, and innovate in a variety of contexts. Training success is evaluated across measurable competency domains including problem definition, research methods, communication, collaboration, and problem-solving. After three years the program expanded relationships across fields and professions, successfully established 18 internship opportunities with community partners, created a new dual-title PhD program open to students in 5 academic departments, and facilitated the co-production of knowledge with external partners. This model bridges the gaps between research, education, and application, providing an integrated, rigorous graduate training program that fosters collaborative problem-solving between STEM graduate students and the broader community of professionals conducting sustainability work in a post-industrial urban setting.

According to the standard cold dark matter (CDM) cosmology, the structure of dark halos including those of galaxy clusters reflects their mass accretion history. Older clusters tend to be more concentrated than younger clusters. Their structure, represented by the characteristic radius $r_s$ and mass $M_s$ of the Navarro-Frenk-White (NFW) density profile, is related to their formation time. In this study, we show that $r_s$, $M_s$, and the X-ray temperature of the intracluster medium (ICM), $T_X$, form a thin plane in the space of $(\log r_s, \log M_s, \log T_X)$. This tight correlation indicates that the ICM temperature is also determined by the formation time of individual clusters. Numerical simulations show that clusters move along the fundamental plane as they evolve. The plane and the cluster evolution within the plane can be explained by a similarity solution of structure formation of the universe. The angle of the plane shows that clusters have not achieved “virial equilibrium” in the sense that mass/size growth and pressure at the boundaries cannot be ignored. The distribution of clusters on the plane is related to the intrinsic scatter in the halo concentration--mass relation, which is originated from the variety of cluster ages. The well-known mass--temperature relation of clusters ($M_\Delta\propto T_X^{3/2}$) can be explained by the fundamental plane and the mass dependence of the halo concentration without the assumption of virial equilibrium. The fundamental plane can also be used for calibration of cluster masses.

Gardasil 4, a human papilloma virus vaccine, has been shown to protect against various cancers, including cervical cancer. Common side effects include injection site pain, fever, headaches, and muscle aches. In some individuals, the severe side effect of postural orthostatic tachycardia syndrome (POTS) has been reported. POTS is characterized by the abnormal response of lightheadedness, blurry vision, and dizziness while transitioning to an upright posture. POTS predominately affects women, with more than eighty-five (85) percent of POTS patients being female. POTS on average, takes five years and eleven months to receive the diagnosis. Additionally, ​A strong association between POTS and Ehlers Danlos Syndrome Type III (EDS) exists. Eighty (80) percent of patients with EDS have POTS. This severe side effect dictates that providers need to be aware of this strong association of HPV vaccinations and POTS. ​In this report we will present a case of a young women with a past medical history significant for EDS type III who was diagnosed with POTS after receiving Gardasil 4 vaccination. This case demonstrates the need for physicians to be aware of the association of POTS with EDS type III and HPV vaccination. Physician awareness of the associations, signs and symptoms of POTS and earlier testing at the first presentation of signs and symptoms will limit the negative impact on patient’s quality of life.

Background: Poor patient flow can lead to adverse outcomes for patients and organisational inefficiency. Many hospitals have addressed suboptimal patient flow by increasing resourcing, such as bed stock and staffing, however, this is an unsustainable approach. In determining the nature of poor patient flow issues, it is important to collect data from healthcare professionals who manage patient flow on a daily basis. Doing so provides insights into the current state of patient flow management in its entirety, whilst also helping with the development of sustainable solutions.; Methods: Thirteen semi-structured interviews were conducted with clinicians who were directly involved with patient flow at a tertiary care centre in Tasmania, Australia. Results: Through a thematic analysis method, four major themes were developed: managing patient flow, communication for decisions, tools as enablers and barriers and increasing complexity. Conclusions: The findings of this study provide great insights into patient flow issues, with potential solutions identified to address them.

Purinergic receptors (P2X) are ATP-gated ion channels with an elusive role in the CNS. While the P2X2 and P2X4 subtypes are widely expressed in most neurons (i.e. at the edge of the postsynaptic densities of excitatory synapses), the direct contribution of P2X to synaptic transmission is uncertain. Several P2X have been shown to participate in receptor cross-talk: an interaction where one receptor (e.g. P2X2) influences the activity of another (e.g. GABA or AMPA receptors.) In this study, we tested for interactions between P2X2 or P2X4 and N-methyl-D-aspartate receptors (NMDARs). Using two-electrode voltage-clamp electrophysiology experiments in X. laevis oocytes, we demonstrate that both P2X2 and P2X4 interact with NMDARs in an inhibitory manner. When investigating the molecular domains responsible for this phenomenon, we found that the P2X2 c-terminus (CT) could interfere with P2X2 and P2X4 interactions with NMDARs. We also report that 11 distal CT residues on the P2X4 facilitate the P2X4-NMDAR interaction, and that a peptide consisting of these P2X4 CT residues (11C) can disrupt the interaction between NMDARs and P2X2 or P2X4. Collectively, these results provide new evidence for the modulatory nature of P2X2 and P2X4, suggesting they might play a more nuanced role in the CNS.

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based studies using these datasets require thoughtful modification to sample selection and analytic strategies, relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings. This paper offers some practical guidance to the full analytical process, demonstrates methods for estimating causal treatment effects on several types of outcomes common to such studies, such as binary, count, time to event and longitudinally varying repeated measures outcomes, and aims to increase transparency and reproducibility. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insurees from 2001-2016.

Background: There are ongoing workforce challenges with the delivery of long-term care (LTC), such as staffing decisions based on arbitrary standards. The Synergy tool, a resident-centered approach to staffing, pro-vides objective, real-time acuity and dependency scores (Synergy scores) for residents. The purpose of this study was to implement and evaluate the impact of the Synergy tool on LTC delivery. Methods: A longitudinal mixed methods study took place within two publicly-funded LTC homes in British Columbia, Canada. Quantitative data included weekly Synergy scores for residents (24 weeks), monthly aggregated resident falls data (18 months) and a six-month economic evaluation. Qualitative data were gathered from family caregivers and thematically analyzed. Results: Quantitative findings from Synergy scores revealed considerable variability for resident acuity/dependency needs within and across units; and falls decreased during implementation. The six-month economic evaluation demonstrated some cost savings by comparing Synergy tool training and implementation costs with savings from resident fall rates reductions. Qualitative analyses yielded three positive impacts themes (improved care delivery, better communication, and improved resident-family-staff relationships), and two negative structural themes (language barrier and staff shortages). Conclusions: The Synergy tool provides useful data for enhancing a ‘fit’ between resident needs and available staff.

Targeted radionuclide therapy (TRT) has recently seen a surge in popularity, with the use of radionuclides conjugated to small molecules and antibodies. Similarly, immunotherapy also has shown promising results – an example being chimeric antigen receptor (CAR) T-cells therapy in hematologic malignancies. Moreover, TRT and CAR T therapies possess unique features that require special consideration when determining how to dose, time, and sequence combination treatments, including the distribution of TRT dose in the body, the decay rate of the radionuclide, and the proliferation and persistence of the CAR-T cells. These characteristics complicate additive or synergistic effects of combination therapies and warrant a mathematical treatment which includes these dynamics in relation to the proliferation and clearance rates of the target tumor cells. Here we combine two previously published mathematical models in a multiple myeloma setting to explore the effects of dose, timing, and sequencing of TRT and CAR-T cell based therapies. We find that for a fixed TRT and CAR-T cell dose, the tumor proliferation rate is the most important parameter in determining the best timing of TRT and CAR T therapies.

The National Institute of Justice reported that current methods for processing sexual assault samples have a high failure rate, with 60 to 80 percent of tested kits unable to produce usable DNA profiles. Even when samples test positive for male DNA, 34 percent of sexual assault kits (SAK) do not yield recovered male DNA after differential extraction. Less than 30% recovery of available sperm DNA contributes to this low success rate. The SpermX™ method (SX) has been shown to recover 80 percent or more of sperm DNA from sexual assault samples. An interlaboratory evaluation compared SX to standard differential extraction (DE) protocols. Mock samples with known ratios of female epithelial cells and sperm cells were processed using both methods. Results revealed that SX consistently provided CODIS up-loadable DNA profiles, even with as few as 25 sperm cells, whereas DE failed to produce usable results. On average, SX yielded a seven-fold increase in the recovery of unshared male alleles compared to DE. In conclusion, SX outperformed DE in recovering higher quantities of male DNA with minimal female carryover in sexual assault-type samples. This improved success rate in obtaining usable DNA profiles can significantly aid in solving sexual assault cases. Keywords: Differential extraction, Sexual Assault Kit (SAK), Sperm DNA, STRmix™, Combined DNA Index System (CODIS), Forensic DNA analysis, Low Sperm Samples, DNA Mixtures, Autosomal STR Analysis, SpermX.

1) Background: This study focused on the validity of audible replies to voice queries in gyneco-logic oncology by Siri, Alexa, Google, and Cortana virtual assistants (VAs).; (2) Methods: 21 evaluators analyzed VA audible answers to voice queries related to gynecologic oncology. A 3-tier template was utilized for each voice query: “X?” (A), “What is X?” (B), and “Define X?” (C) in a 24-question panel, allowing questions to be posed in different formats to assess the VAs at a greater depth. Responses were scored using a rubric designed to assess the validity and quality of each answer; (3) Results: For general queries, Google provided the most correct audible replies (ncorrect = 20; 83.3% correct), followed by Alexa (ncorrect = 16; 66.7% correct), Siri (ncorrect = 11; 45.8% correct), and Cortana (ncorrect = 5; 20.8% correct.; For Gynecologic Oncology related queries, Google also provided the most correct audible replies (ncorrect=222; 18.14%), followed by Alexa (ncorrect=75; 6.51%), Siri (ncorrect=55; 5.46%), and Cortana (ncorrect=23; 2.28%). (4) Conclusions: The audible replies to oral queries on gynecologic oncology topics varied between Siri, Alexa, Google, and Cortana. Google’s audible reply accuracy was superior to audible replies by Siri, Alexa, and Cortana. Since there is room for considerable improvement in VA performance, the authors advise caution when using VAs for medical queries in gynecologic oncology.

Improving HIV testing efficiency saves financial and material resources for health. We conducted a retrospective secondary data analysis of routinely collected HIV risk screening program data in Uganda, from October to November 2019, to determine the performance characteristics of the adolescent and adult HIV risk screening tool in public health facility settings. A total of 19,854 clients had been screened for HIV testing eligibility and tested for HIV. The overall positivity rate (cluster weighted prevalence of HIV) among those screened was 4.5% (95% CI: 4.1%-4.8%) versus 3.71% (95% CI: 3.06-4.50) among those not screened. The sensitivity and specificity of the risk screening tool were 90.7% (95% CI: 88.4%, 92.7%) and 75.8%, (75.2-76.4) respectively. With screening, the number needed to test to identify one PLHIV was reduced from 27 to 22. Although risk screening would have led to a 24.5% (4,825/19,704) reduction in testing volume, 9.3% (68/732) of PLHIV would have been missed, being misclassified as not eligible for testing. The cost per PLHIV identified fell by 3% from $69 without screening to $66.9 with screening. We recommend the use of scientifically validated HIV risk screening tools, and a need to explore the use of HIV self-testing as a test for tirage.

Infections are a common post-operative ailment for patients who have received a dental implant or device and can be attributed to biofilm formation in tissue or on the implant. Many current solutions for oral hygiene have side effects and affect the natural oral microbiome. 2-heptylcyclopropane-1-carboxylic acid (2CP) is a medium-chain fatty acid and synthetic diffusible signaling factor that can prevent and disperse biofilm. The purpose of this work was to evaluate an immersion strategy for coating hydroxyapatite (HAp) with 2CP to prevent biofilm attachment on and around natural teeth and dental implants. The release profile of 2CP-loaded and 2CP+oral rinse-loaded HAp coupons (n=6) was assessed by a 3-day exposure to phosphate buffered saline (PBS). Antimicrobial properties against Porphyromonas gingivalis and cytocompatibility of 2CP-loaded HAp coupons (n=4) were also assessed alone and in combination with 0.12% chlorhexidine gluconate oral rinse. The majority of 2CP is released by 12 hours. 2CP, oral rinse, and 2CP+oral rinse significantly reduced P. gingivalis viability, though direct contact assay demonstrates a significant reduction in Saos-2 viability for oral rinse and 2CP+oral rinse coupons. Immersion or rinsing hydroxyapatite with 2CP could inhibit biofilm-associated dental infections and prevent further complications including caries, gingivitis, and peri-implantitis.