Dynamic sparse training is an effective strategy to alleviate the training and inference demands of artificial neural networks. However, current sparse training methods face a challenge in achieving high levels of sparsity while maintaining performance comparable to that of their fully connected counterparts. The Cannistraci-Hebb training (CHT) method produces an ultra-sparse advantage compared to fully connected training in various tasks by using a gradient-free link regrowth method, which relies solely on the network topology. However, its rigid selection based on link prediction scores may lead to epitopological local minima, especially at the beginning of the training process when the network topology might be noisy and unreliable. In this article, we introduce the Cannistraci-Hebb training soft rule (CHTs), which applies a flexible approach to both the removal and regrowth of links during training, fostering a balance between exploring and exploiting network topology. Additionally, we investigate the network topology initialization using several approaches, including the bipartite scale-free and bipartite small-world network models. Empirical results show that CHTs can surpass the performance of fully connected networks with MLP architecture by using only 1% of the connections (99% sparsity) on the MNIST, EMNIST, and Fashion MNIST datasets and can provide remarkable results with only 0.1% of the links (99.9% sparsity). In some MLPs for image classification tasks, CHTs can reduce the active neuron network size to 20% of the original nodes (neurons), demonstrating a remarkable ability to generalize better than fully connected architectures, reducing the entire model size. This represents a relevant result for dynamic sparse training. Finally, we present evidence from larger network models such as Transformers, with 10% of the connections (90% sparsity), where CHTs outperform other prevalent dynamic sparse training methods in machine translation tasks.

We prove an analog of the removable singularity theorem for bounded harmonic functions on stratified sets. The harmonic functions are understood in the sense of the soft Laplacian. The result can become one of the main technical components for extending the well-known Poincaré–Perron’s method of proving the solvability of the Dirichlet problem for the soft Laplacian.

Telomeres, located at the ends of each chromosome play a vital role in determining cellular fate and aging. Accelerated shortening of telomeres may also result in accelerated aging and occurrence of age-related degenerative diseases. Anti-aging treatments may help in increasing the telomere length along with improving quality of life, reducing dependency for daily life activities, increasing life expectancy and reducing the risks associated with aging. In this study, we evaluated the effect of anti-aging integrative therapy in 36 individuals. The treatment included a combination of HBOT, ozone therapy, colon hydrotherapy, etc. Telomere length was used as a biomarker for objectively quantifying the outcome of the treatment. The effect of treatment was also assessed on various aging symptoms. At the end of the treatment, it was found that that 97.22% individuals showed an increase in telomere length with an average increase of 0.43 Kbp and 94.44% individuals showed reduced biological age with an average decrease of 3.8 years. These improvements were statistically significant. Improvements were also recorded in the majority of the symptoms. No major adverse events were observed. These results indicate that antiaging integrative therapy has the potential to reverse aging symptoms, cellular aging and improve quality of life.

A central hypothesis on brain functioning is that plasticity regulates the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granular layer has been shown to control the gain of signals transmitted through the mossy fiber pathway. Until now, the impact of plasticity on incoming activity patterns was analyzed by combining electrophysiological recordings in acute cerebellar slices and computational modeling, unraveling a broad spectrum of different forms of synaptic plasticity in the granular layer, often along with forms of intrinsic excitability changes. Here, we attempt to provide a brief overview of the most prominent forms of plasticity at excitatory synapses formed by mossy fibers onto principal neurons (granule cells, Golgi cells and unipolar brush cells) in the granular layer. Specifically, we will highlight current understanding of the mechanisms and their functional implications of the synaptic and intrinsic plasticity, providing valuable insights into how inputs are processed and reconfigured at the cerebellar input stage.

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

Short video addiction refers to a psychological dependency on short video applications, characterized by an intense attachment and an inability to control usage, leading to typical addiction symptoms. While its definition bears similarities to substance addiction, significant differences also exist. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), published in 2013, short video addiction has not yet been officially classified as an addiction disorder. To facilitate more in-depth and standardized research on short video addiction, it is essential to synthesize and organize existing studies on its theoretical foundations and mechanisms.

The AUC-ROC is a widespread measure to assess the performance of a binary classification model across various discrimination thresholds. Yet, it is not apt for unbalanced class problems and is misleading for early retrieval. For instance, link prediction in complex networks is an unbalanced early retrieval problem, whose goal is to offer a ranking of the nonobserved network links, prioritizing a small cohort of positive links (for which we are sure the labels are reliable) on top of a list largely populated by unlabeled links (for which the labels are uncertain). Differently from binary classification, here the evaluation focuses on how the predictor prioritizes the positive class, because the negative class does not exist and is replaced by an unlabeled class. For decades, scholars have been investigating the necessity to correct and to generalize the AUC-ROC to work for both unbalanced classification and early retrieval evaluation, this is requested in many applied domains of computational science. Here we propose the area under the magnified ROC (AUC-mROC), a new measure that adjusts the standard AUC-ROC to work for unbalanced early retrieval problems such as link prediction. Finally, we introduce the area under the generalized ROC (AUC-gROC), which unifies both AUC-mROC and AUC-ROC to provide a universal and reliable evaluation measure that can work for both classification and early retrieval problems, regardless of class proportion.This discovery represents a critical achievement, culminating in success after decades of contentious research. Its solution promises to significantly impact various domains within applied computational science.

Chip pad alignment inspection is of great importance in the industrial field. However, due to the fact that chip pads are usually small, problems such as misdetection and missed detection often occur. When applying deep learning methods for chip pad detection, it is necessary to ensure accurate detection of small target chips while meeting the requirements of lightweight detection models for industrial needs. To solve the above problems, this paper proposes a lightweight model based on improved YOLOv5s. Firstly, the feature extraction part is improved to increase the network's focus on the target. Secondly, the feature fusion layer is improved to double the resolution of the prediction head, and the context-aware network is designed to enhance the context-capture ability of key features of small targets. Finally, SIoU is adopted as the loss function to improve the speed and accuracy of the regression frame. The experimental results show that the improved YOLOv5s algorithm improves the detection accuracy by 2.3% and reduces the network parameters by 81.8% compared to the original algorithm. The improved algorithm is combined with image processing techniques to design correction methods for alignment anomalies and realize real-time alignment anomaly correction in industry.

Rubella infection is typically mild or asymptomatic except when infection occurs during pregnancy. Infection in early pregnancy can cause miscarriage, stillbirth, or congenital rubella syndrome. Only individuals that are still susceptible to rubella infection during child-bearing age are vulnerable to this burden. Rubella containing vaccine (RCV) is safe and effective, providing life-long immunity. However, average age-at-infection increases with increasing vaccination coverage, which could potentially lead to increased disease burden as well, if the absolute risk of infection during child-bearing age increases. Dynamics of rubella transmission were explored using EMOD, a software tool for building stochastic, agent-based infection models. Simulations of pre-vaccine, endemic transmission of rubella virus introduced RCV at varying levels of coverage to determine the expected future trajectories of disease burden. Introducing RCV reduces both rubella virus transmission and disease burden for a period of around 15 years. Increased disease burden is only possible more than a decade post-introduction, and only for contexts with low current burden and high transmission intensity. Low or declining rubella virus transmission intensity is concurrently associated with both greater disease burden and greater vaccine effectiveness. The risk of increasing burden through vaccination only exists for locations with persistently high infectivity, high connectivity, and high fertility. A trade-off between the risk of a small, future burden increase and a large, immediate burden decrease strongly favors RCV introduction.

The physics of the human brain has two components – basic physics common to all mammals and the physics of thinking inherent only in man. The development of the mental component of the structural and functional organization of the brain in phylogeny was associated with the chiral factor of the external environment, and in ontogenesis - with the social factor. The sensitivity of the brain to these factors was based on the single-connected nature of its aqueous basis, the mechanism of electromagnetic induction, and the features of the thermodynamics of the brain in a state of night sleep. In order to unify the description of the mechanism of electromagnetic processes in the brain, the concept of a quasiphoton has been introduced, combining all forms of excitation of electronic and molecular-cellular structures of the brain. Equivalent schemes of vibrational contours of neural network elements and macrostructures of the brain are proposed. Estimates of the kinetic parameters (activation energy, velocity) of the physical processes underlying the energy-information exchange of the brain with the external environment are made. Mechanisms of operative (physical) and permanent (chemical) memory of the brain, including a model of nonlocal quantum correlations, are discussed.

Sparse training (ST) aims to ameliorate deep learning by replacing fully connected artificial neural networks (ANNs) with sparse or ultra-sparse ones, such as brain networks are, therefore it might benefit to borrow brain-inspired learning paradigms from complex network intelligence theory. Here, we launch the ultra-sparse advantage challenge, whose goal is to offer evidence on the extent to which ultra-sparse (around 1% connection retained) topologies can achieve any leaning advantage against fully connected. Epitopological learning is a field of network science and complex network intelligence that studies how to implement learning on complex networks by changing the shape of their connectivity structure (epitopological plasticity). One way to implement Epitopological (epi- means new) Learning is via link prediction: predicting the likelihood of non-observed links to appear in the network. Cannistraci-Hebb learning theory inspired the CH3-L3 network automata rule for link prediction which is effective for general-purpose link prediction. Here, starting from CH3-L3 we propose Epitopological Sparse Meta-deep Learning (ESML) to apply Epitopological Learning to sparse training. In empirical experiments, we find that ESML learns ANNs with ultra-sparse hyperbolic (epi-)topology in which emerges a community layer organization that is meta-deep (meaning that each layer also has an internal depth due to power-law node hierarchy). Furthermore, we discover that ESML can in many cases automatically sparse the neurons during training (arriving even to 30% neurons left in hidden layers), this process of node dynamic removal is called percolation. Starting from this network science evidence, we design Cannistraci-Hebb training (CHT), a 4-step training methodology that puts ESML at its heart. We conduct experiments on 7 datasets and 5 network structures comparing CHT to dynamic sparse training SOTA algorithms and the fully connected counterparts. The results indicate that, with a mere 1% of links retained during training, CHT surpasses fully connected networks on VGG16, GoogLeNet, ResNet50, and ResNet152. This key finding is an evidence for ultra-sparse advantage and signs a milestone in deep learning. CHT acts akin to a gradient-free oracle that adopts CH3-L3-based epitopological learning to guide the placement of new links in the ultra-sparse network topology to facilitate sparse-weight gradient learning, and this in turn reduces the convergence time of ultra-sparse training. Finally, CHT offers the first examples of parsimony dynamic sparse training because, in many datasets, it can retain network performance by percolating and significantly reducing the node network size. Our code is available at: https://github.com/biomedical-cybernetics/Cannistraci-Hebb-training.

The origin of neurodegenerative diseases is linked to the abnormal folding of disease-associated proteins. The traditional amyloid hypothesis has been a focal point of research, but recent findings suggest a more complex picture. This review explores the role of physiological conformation of aggregation-prone domain (also known as prion-like low complexity domain) and multiple co-misfolded proteins in neurodegenerative diseases. We highlight the importance of targeting multi-heterotypic misfolded proteins for effective treatments and emphasize the irreversible neurodegeneration stemming from the loss of physiological folding in prion-like disease-causing proteins underscores the criticality of refolding. Additionally, we introduce the concept of ReproFold Therapeutics, a novel approach aimed at restoring the cellular prion-like folding of disease-causing proteins to rebuild normal cross-β interactomes and mitigate neurodegeneration. Baicalein, a potential drug candidate for this approach, shows promise in vitro and in animal models. Moreover, prion-like low complexity domains implicated in neurodegeneration are also associated with other disorders such as diabetes, cancers, and mental illnesses. Understanding the core pathology of neurodegenerative diseases and the broader implications of prion-like LC domains could revolutionize healthcare approaches and policies worldwide.

Molecular analysis of growing teratoma syndrome has not been extensively studied. Here, we report a 14-year-old boy with a growing mass during treatment for a mixed germ cell tumor of the pineal region. Tumor markers were negative; thus, growing teratoma syndrome was suspected. A radical resection via the occipital transtentorial approach was performed, and histopathological examination revealed a teratoma with malignant features. Methylation classifier analysis confirmed the diagnosis of teratoma, and DMRT1 loss and 12p gain were identified by copy number variation analysis, potentially elucidating the cause of growth and malignant transformation of the teratoma. The patient remains in remission after intense chemoradiation treatment as a high-risk germ cell tumor.

Regardless of the SARS-CoV-2 pandemic containment, it remains paramount to comprehensively understand its underlying mechanisms to mitigate potential future health and economic impacts, comparable to those experienced throughout the course of the pandemic. The angiotensin-converting enzyme 2 (ACE2) provide anchorage for SARS-CoV-2 binding, thus implicating that ACE and ACE2 might contribute to the variability in infection severity. This study aimed to elucidate predisposing factors influencing the disease course among people infected by SARS-CoV-2, focusing on angiotensin-converting enzyme (ACE) and ACE2 polymorphisms. Notably, despite similar demographics and comorbidities, COVID-19 patients exhibit substantial differences in prognosis. Genetic polymorphisms in ACE and ACE2 have been implicated in disease progression, prompting our investigation into their role in COVID-19 evolution. Using next-generation sequencing (NGS), we analyzed ACE and ACE2 genes in a sample group comprising six subjects infected by SARS-CoV-2. Our findings revealed a correlation between specific polymorphisms and COVID-19 outcomes. Specifically, ACE and ACE2 intronic deletions were observed in all deceased patients, suggesting a potential association with mortality. These results highlight the significance of genetic factors in shaping the clinical course of COVID-19, emphasizing the importance of further research into the impact of genetic variations on COVID-19 severity.

This study explores innovative strategies to enhance hands-on learning experiences in a Database Management Systems (DBMS) course while addressing challenges related to software installation complexities and financial barriers. The investigation focuses on an online course format catering to students majoring in information systems, healthcare administration, business administration, and marketing. Traditional software installation hurdles, especially for complex systems like Microsoft Access and MySQL, can impede students' engagement and access. To overcome these challenges, the study proposes leveraging cloud-based solutions and clientless remote desktop gateways like Apache Guacamole. Results indicate that these approaches effectively eliminate software installation obstacles, ensuring seamless access to essential tools for all students. The study underscores the importance of innovative technological solutions in promoting inclusive and impactful learning environments in higher education.

The representation of intelligence is achieved by patterns of connections among neurons in brains and machines. Brains grow continuously, such that their patterns of connections develop through activity-dependent specification with the continuing ontogenesis of individual experience. The theory of active inference proposes that the developmental organization of sentient systems reflects general processes of informatic self-evidencing, through the minimization of free energy, that may be described in information terms that are not dependent on a specific physical substrate. At a certain level of complexity, self-evidencing of living (self-organizing) information systems becomes hierarchic and reentrant, such that effective consciousness emerges as the consequence of a good regulator. These principles imply that an adequate reconstruction of the computational dynamics of an individual human brain is possible with a sufficient neuromorphic emulation.

The landscape of business intelligence (BI) is marked by rapid growth, extensive research, and practical implementations, reflecting its sustained relevance in modern business strategies. This paper delves into the dynamics of BI, particularly focusing on the interplay between maturity and agility. While BI maturity emphasizes optimization and predefined goals, BI agility prioritizes adaptability and response to change. The paper highlights the contradictions between these concepts, emphasizing the need for a balanced approach in BI strategy formulation.The role of organizational culture in fostering BI agility is explored through literature review and interviews with BI professionals. Key insights include the necessity of an open culture for effective BI utilization, clear strategic vision, decision delegation, information sharing, business-IT cooperation, and a learning-oriented mindset. These cultural factors contribute significantly to BI effectiveness and the ability to navigate dynamic business landscapes. The paper proposes pillars of agile maturity supported by an open organizational culture, including efficient communication, information integration, creative problem-solving, and close collaboration between business and IT. These pillars underscore the importance of a flexible and open mindset in navigating BI challenges and ensuring sustained value. Empirical validation and further research are suggested to deepen our understanding of the impact of organizational culture on BI agility and effectiveness.

Diabetes mellitus (DM) is the most common metabolic disease in humans, and its prevalence is increasing worldwide in parallel with the obesity pandemic. A lack of insulin or insulin resistance, and consequently hyperglycemia, leads to many systemic disorders, among which diabetic encephalopathy (DE) is a long-term complication of the central nervous system (CNS), characterized by cognitive impairment and motor dysfunctions. The role of oxidative stress and neuroinflammation in the pathomechanism of DE has been proven. Fractalkine (CX3CL1) has unique properties as an adhesion molecule and chemoattractant, and by acting on its only receptor, CX3CR1, it regulates the activity of microglia in physiological states and neuroinflammation. Depending on the clinical context, CX3CL1-CX3CR1 signaling may have neuroprotective effects by inhibiting the inflammatory process in microglia or, conversely, maintaining/intensifying inflammation and neurotoxicity. This review discusses the evidence supporting that the CX3CL1-CX3CR1 pair is neuroprotective and other evidence that it is neurotoxic. Interrupting the vicious cycle within neuron-microglia interactions may be a therapeutic goal in DE by limiting the inflammatory response.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

With the rapid growth of large language models (LLMs), there is increasing demand for memory and computation in LLMs. Recent efforts on post-training pruning of LLMs aim to reduce the model size and computation requirements, yet the performance is still sub-optimal. In this paper, we present a plug-and-play solution for post-training pruning of LLMs. The proposed solution has two innovative components: 1) Relative Importance and Activations (RIA), a new pruning metric that jointly considers the weight and activations efficiently on LLMs; and 2) Channel Permutation, a new approach to maximally preserve important weights under N:M sparsity. The two proposed components can be readily combined to further enhance the N:M semi-structured pruning of LLMs. Our empirical experiments show that RIA alone can already surpass all existing post-training pruning methods on prevalent LLMs, e.g., LLaMA ranging from 7B to 65B. Furthermore, N:M semi-structured pruning with channel permutation can even outperform the original LLaMA2-70B on zero-shot tasks, together with practical speed-up on specific hardware. Our code is available at: https://github.com/biomedical-cybernetics/Relative-importance-and-activation-pruning

As the most serious of the many worse new pathological changes caused by diabetes, there are many risk factors for the occurrence and development of DR. It mainly includes hyperglycemia, hypertension, hyperlipidemia and so on. Among them, hyperglycemia is the most critical cause, which plays a key role in the pathological changes of DR.High-sucrose diets(HSDs) lead to elevated blood glucose levels in vivo, which, through oxidative stress, inflammation, the production of AGEs and VEGF, causes plenty of pathological damages to the retina and ultimately bring about loss of vision. The existing therapies for DR primarily target the terminal stage of the disease, when irreversible visual impairment has appeared. Therefore, early prevention is particularly critical. Early prevention of DR-related vision loss requires adjustments to dietary habits, mainly by reducing sugar intake. This article primarily discusses the risk factors, pathophysiological processes, and molecular mechanisms associated with the development of DR caused by HSDs. It aims to raise awareness of the crucial role of diet in the occurrence and progression of DR, promote timely changes in dietary habits, prevent vision loss, and improve the quality of life. The aim is to make people aware of the importance of diet in the occurrence and progression of DR. According to the dietary modification strategies we give, patients can change their poor eating habits timely, avoid the retinopathy that can be avoided theoretically, and obtain a excellent outcome.

The fractional advection-dispersion equation FADE plays an important role, especially in explaining the process of solute-transport in natural porous media with different degrees of heterogeneity. However, studies of the flow and transport of contaminants in the underground environment are based on the knowledge of many functional parameters of the subsoil. The delay factor caused by nonlinear absorption is one of these parameters inheriting significant ambiguities in its estimation in heterogeneous media. In the study, a fractional advection-dispersion FADE model was developed by the implicit Euler method, based on a Grunwald and Caputo-Liousville approximation to explicitly evaluate and simulate the transport mechanism of contaminants in media with unsaturated porous materials subjected to a nonlinear sorption model. Better solute mass retention was observed for fractional advection-dispersion FADE model. Moreover, the absorption parameter of the retardation coefficient has a better impact on the motion of the solute in a saturated porous medium. In other words, it has a significant effect on the dispersion of pollutants in aquifers. This study highlights the need to characterize the aquifer to predict solute transport in porous media.

This study examines the contribution of early executive functions (EFs) in the association between fluency in reading and arithmetic. Kindergarten children (N = 1,185) were assessed on Executive Functions skills and on reading and arithmetic fluency in Grade 1 and Grade 3. The analysis revealed that beyond the connection within each domain there is a unidirectional effect between fluency measures, with Grade 1 reading fluency significantly influencing the development of arithmetic fluency in Grade 3. Furthermore, the findings indicate that kindergarten EFs significantly contribute to arithmetic fluency at both time points and to reading fluency in the first grade. Early EF skills also emerged as a mediator of associations between fluency performance in reading and arithmetic, suggesting that the influence of EFs extends beyond individual academic domains. These findings have implications for understanding the cognitive mechanisms that underlie the relations between these academic skills.

This review is focused on the current status of quantitative MR (qMR) of skeletal muscle. The first section covers the techniques of qMR in muscle with the focus on each quantitative parameter, the corresponding imaging sequence, discussion of the relation of the measured parameter to underlying physiology/ pathophysiology, the image processing and analysis approaches, and studies on normal subjects. We cover the more established parametric mapping from T1 weighted imaging for morphometrics including image segmentation, proton density fat fraction, T2 mapping, diffusion tensor imaging to emerging qMR features such as magnetization transfer including ultralow TE imaging for macromolecular fraction, and strain mapping. The second section is a summary of current clinical applications of qMR of muscle; the intent is to demonstrate the utility qMR in different disease states of the muscle rather than a complete comprehensive survey.

This paper is devoted to the study of multi-dimensional integral transform with Fox H-function in the kernel in weighted spaces integrable functions in the domain R+n with positive coordinates. Mapping properties such as the boundedness, the range, the representations of the considered transformation are established.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

: Background: Brain-derived neurotrophic factor (BDNF) is a crucial mediator of neuronal plasticity. Here, we investigated the effects of controlled normobaric hypoxia (NH) combined with physical inactivity on BDNF blood levels and executive functions. Methods: Twenty-five healthy adults (25.8±3.3 years, 15 female) were analyzed in a randomized, controlled cross-over study. Each intervention began with a 30 min resting phase under normoxia (NOR), followed by a 90 min continuation of NOR or NH (peripheral oxygen saturation [SpO₂] 85-80%). Serum and plasma samples were collected every 15 min. Heart rate and SpO₂ were continuously measured. Before and after each exposure, cognitive tests were performed and after 24 h another follow-up blood sample was taken. Results: NH decreased SpO₂ (p<.001, η_p²=.747) and increased heart rate (p=.006, η_p²=.116) significantly. The 30-min resting phase under NOR led to a significant BDNF reduction in serum (p <.001, η_p²=.581) and plasma (p <.001, η_p²=.362). Continuation of NOR further significantly reduced BDNF after another 45 min (p=.018) in serum and after 30 min (p=.040) and 90 min (p=.005) in plasma. There was no significant BDNF decline under NH. 24 h Follow-up examination showed a significant decline of serum BDNF, both after NH and NOR. Conclusion: Our results show that NH has the potential to counteract physical inactivity-induced BDNF decline. Therefore, our study emphasizes the need for a physically active lifestyle and its positive effects on BDNF. This study also demonstrates the need for a standardized protocol for future studies to determine BDNF in serum and plasma.

To elucidate mesenchymal stromal cells (MSC)-tumor intricate and ambiguous interactions, the reliable cell labeling and tracking techniques are imperative. Traditional methods of cell labelling have limitations mostly relevant to need for genetic modification what demands the exploration for alternative strategies. Here, we propose using fluorescent photoconvertible microcapsules containing Rhodamine B (RhB) to study mMSC migration in brain tumors. These 3 μm sized microcapsules are readily internalised by cells and undergo photoconversion under 561 nm laser exposure with fluorescence blue shift on demand. Optimal photoconversion was achieved with a laser irradiation duration of 0.4 ms, which provide a maximal brightness of photoconverted label without an excessive laser exposure on cells. Capsules modified with polyallylamine hydrochloride demonstrated the best potential for intracellular uptake without significantly affecting cell viability, motility, or proliferation. The optimal ratio of 20 capsules per mMSC was determined for labeling. Moreover, migration of individual mMSCs within 2D and 3D glioblastoma cell (EPNT-5) colonies over 2 days and in vivo tumor settings over 7 days was tracked. Our study unveils a robust platform for investigating MSC-tumor dynamics, offering insights into therapeutic strategies. Photoconvertible microcapsules could also become an indispensable tool for studying complex fundamental processes of cell-cell interactions for a wide range of problems in biology and medicine.

In this paper we suggest a first ever construction of fractional integral and differential operators based on signed measures including a vector-valued case. The study focuses on constructing the fractional power of the Riemann-Stieltjes integral with a signed measure, using semigroup theory. The main result is a theorem that provides the exact form of a semigroup for the Riemann-Stieltjes integral with a measure having a countable number of extrema. The article provides examples of semigroups based on integral operators with signed measures and discusses the fractional powers of differential operators with partial derivatives.

Arguably the most important parameter in treating cognitive decline associated with Alzheimer’s disease is the length of time in which improvement, if achieved at all, is sustained. However, monotherapies such as donepezil and memantine are associated with a more rapid decline than no treatment in patients over multi-year follow-ups {Kennedy, 2018 #535}. Furthermore, anti-amyloid antibody treatment, which at best simply slows decline, is associated with accelerated cerebral atrophy [2], resulting in earlier dementia-associated brain volumes for those treated at the MCI stage than untreated patients. In contrast, a precision medicine approach, in which the multiple potential drivers of cognitive decline are identified for each patient and then targeted with a personalized protocol (such as ReCODE), has led to documented improvement in patients with cognitive decline [3]; [4]; [5], but long-term follow-up (>5 years) has not been reported previously. Therefore, we report here sustained cognitive improvement, in some cases for over a decade, in patients treated with a precision medicine protocol—something that has not been reported in patients treated with anti-cholinesterase, glutamate receptor inhibitory, anti-amyloid, or other therapeutic methods. These case studies warrant long-term cohort studies to determine how frequently such sustained cognitive improvements occur in patients treated with precision medicine protocols.

(1) Background The SKT is a short cognitive performance test designed to assess impairments in memory and cognitive abilities such as attention and speed of information processing. In 2019, new regression-based norms for the English version of the SKT were calculated applying data from older cognitively healthy adults living in the US, Ireland and Australia (the norming sample) The study has two aims: to establish valid cut-offs for distinguishing between no cognitive impairment, mild cognitive impairment (MCI) and dementia (1), and to cross-validate the new norms for detecting MCI and dementia (2) in a community sample of older adults with clinical diagnoses; (2)Methods: The validation sample included 142 older adults (mean age=87.7, SD=3.55) from the Sydney Memory and Aging Study (MAS Study). Participants were classified as having normal cognition, MCI, or dementia through a consensus diagnosis as well as by the SKT; (3)Results: The sensitivity of the SKT for the differentiation of cognitive impairment (MCI or dementia) from normal cognition was 80.6%. The convergence between the SKT and the consensus diagnoses was 60.1% for all three diagnostic groups. The correlation between the SKT and the Addenbrooke’s Cognitive Examination III (ACE-III), a well validated measure of cognitive ability in older adults, was r = 0.3;. (4) Conclusions: In sum, it can be stated that the SKT is a valid tool for detecting early stages of cognitive impairment, performing very well in discriminating between no cognitive impairment and cognitive impairment (MCI or dementia).

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

If the development of machine learning and artificial intelligence plays a role in many fields of research and technology today, it has a special relationship with neurosciences. Indeed, historically inspired by our knowledge of the brain, deep learning shares some vocabularies with neurosciences and can sometimes be considered a brain’s model. Taking the particular example of epileptic seizure, which can develop in any biological neural tissue, we raise the question if and how the models used for deep learning can capture or model these pathological events. This particular example is a starting point to discuss the nature, limits, and functions of these models, and what we expect from a model of the brain. Finally, we argue that a pluralistic approach leading to the integrated coexistence of different models is necessary to study the brain in all its complexity.

The main purpose of our research is the evaluation of the efficacity of solvent polarity on phenolic composition, as well as the antibacterial, and antioxidant activities of extract of Moroccan Mentha aquatica L., leaves. To attempt these objectives, the HPLC-DAD was used to identify the biomolecules. The total amount of phenolic and flavonoid content was determined using the Folin-Ciocalteu and aluminum trichloride processes as well as the DPPH, RP, and TAC tests were used to evaluate the antioxidant capabilities. In addition, by employing both in vitro and in silico analysis through the microdilution method, the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the extracts were assessed against multidrug-resistant bacteria. Furthermore, the High-Performance Liquid Chromatography (HPLC) analysis indicated that all extracts of M. aquatica were abundant in noteworthy phytochemical compounds, especially gallic acid, vanillic acid, chlorobenzoic acid, quercetin, and kaempferol. Furthermore, the results indicated that the total phenolic content was higher in hydroethanolic extract (62.19 ± 1.19 mg GAE/g DW) in contrast to the acetonic and aqueous extracts (22.22 ± 0.64, and 22.44± 0.57 mg GAE/g DW) respectively. Similarly, the aqueous ethanol extract contained the greatest flavonoid concentration, with values of 29.15± 0.09 mg QE/g DW when compared with the other extracts. The seam extract poses excellent antioxidant power with an IC50 of 0.06 ± 0.00 mg/mL for the DPPH test, and lower RP with EC50 of 80± 0.00 µg/ml when compared with acetonic and aqueous extract (370± 0.00 and 460± 0.01 µg/mL) respectively. Moreover, the hydroethanolic extract has a high level of TAC 75.09± 2.00 mg EAA/g DW, followed by aqueous extract carrying a value of 28.53 ± 1.36 mg EAA/g DW. The antibacterial capacity is ranged between 0.78 ± 0.05 mg/mL and 12.60 ± 0.00 mg/L. The results of the in-silico study demonstrated that quercetin, vanillic acid, and naringenin components contribute to the antioxidant capability. While gallic acid, kaempferol, and quercetin components are attributed to the antibacterial capacity.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.

(1) Background: Alzheimer’s disease (AD) presents a significant challenge in terms of discovering novel diagnostic methods and therapies. Previous research has shed light on the neuroprotective properties of Apelin-13 in neurodegenerative disorders. However, elucidating the mechanism underlying its efficacy in combating AD-related nerve injury is imperative. In this study, we aimed to investigate the mechanism of action of Apelin-13 in an in vivo model of AD induced by streptozocin (STZ); (2) Methods: We utilized an STZ-induced nerve injury model of AD in mice to investigate the effects of Apelin-13 administration. The Apelin-13 was administered intranasally, and cognitive impairment was assessed using standardized behavioral tests. The main methods included behavioral assessment, histological analysis, and biochemical assays to evaluate synaptic plasticity and oxidative stress signaling pathways; (3) Results: Our findings indicate that intranasal administration of Apelin-13 ameliorated cognitive impairment in the STZ-induced AD model. Furthermore, we observed that this effect was potentially mediated through the enhancement of synaptic plasticity and attenuation of oxidative stress signaling pathways; (4) Conclusions: The results of this study suggest that intranasal administration of Apelin-13 holds promise as a therapeutic strategy for preventing neurodegenerative diseases such as AD. By improving synaptic plasticity and mitigating oxidative stress, Apelin-13 may offer a novel approach to neuroprotection in AD and related conditions.

This study aimed to investigate the relationship between sleep duration, dementia severity, age, and ANS regulation. This cross-sectional pilot study included 27 older patients with probable Alzheimer’s disease, hospitalized at a psychiatric center. We measured heart rate variability to assess ANS regulation at night, evaluated dementia severity using the Clinical Dementia Rating scale, and obtained sleep duration data from sleep diaries maintained by psychiatric nurses. The data were analyzed using repeated-measures generalized linear models with age, sex, dementia severity, and medication (antipsychotics) as covariates. A sleep duration of 7–8 h per night was associated with better ANS regulation than other sleep durations, as indicated by sympathovagal balance (p = 0.005). ANS dysregulation was characterized by a 20.7% increase in sympathetic nervous system activity and a 14.8% decrease in parasympathetic nervous system (PSNS) activity. We found a significant correlation between dementia severity and PSNS activity (p = 0.04) and observed an inverted U-shaped association between sleep duration and ANS regulation. This pilot study demonstrated that a sleep duration of 7–8 h per night may be critical in maintaining optimal ANS regulation. Bidirectional associations between sleep duration, inadequate sleep duration, aging, and ANS regulation warrant further investigation.

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, since these serve as a key element in the energy performance of an EV powertrain system. The Equivalent Circuit Model (ECM) technique at the cell level is commonly employed for this purpose, combining accuracy and efficiency in the representation of battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell is being developed using Modelica, an equation-based modeling language. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from Li-ion battery cells with 4 different commonly used cathode materials, have been utilized. This approach allows for a representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed and simulation results of the FTP75 driving cycle have been compared against an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both software across different system points. Specifically, the maximum deviation of SoC is limited to 0.08% and the maximum value of relative voltage deviation is 1.4%. The verified model enables the exploration of multiple possible architecture configurations of EV powertrains with Modelica.

This review explores the nuanced decision-making surrounding postoperative radiotherapy for diffuse low-grade glioma (DLGG) patients, emphasizing the need for personalized treatment to balance the benefits of tumor control with the risks of long-term side effects. Radiotherapy is a cornerstone of DLGG treatment, offering significant improvements in progression-free survival, but its potential for cognitive decline and vascular lesions necessitates careful consideration of individual patient factors. The review delves into the complexities of radiotherapy timing, revealing that while early radiotherapy enhances progression-free survival, it does not significantly impact overall survival and may lead to earlier onset of cognitive decline. Conversely, delayed radiotherapy may mitigate cognitive decline but may result in shorter progression-free survival and the onset of neurological symptoms. The review advocates for a patient-centered approach, emphasizing the importance of weighing individual risks and benefits, and highlights the potential of artificial intelligence in predicting cognitive decline and the need to revisit previous research findings in light of the 2021 WHO (World Health Organization) classification update, underscoring the evolving nature of DLGG treatment and the ongoing quest for optimal therapeutic strategies.

A facilitative transport system exists on the blood brain barrier (BBB) that has been tacitly assumed to be a path for glutamate entry to brain. But glutamate is a non-essential amino acid whose brain content is much greater than plasma, and studies in vivo show that glutamate does not enter brain in material quantities except in those small regions with fenestrated capillaries (circumventricular organs). The situation became understandable when luminal (blood facing) and abluminal (brain facing) membranes were isolated and studied separately. Facilitative transport of glutamate and glutamine exist only on the luminal membranes whereas Na+-dependent transport systems for glutamate, glutamine and some other amino acids are present only on the abluminal membrane. The Na+-dependent cotransporters of the abluminal membrane are in a position to actively transport amino acids from the extracellular fluid (ECF) into the endothelial cells of the BBB. These powerful secondary active transporters couple the energy of the Na+-gradient to move glutamate and glutamine into the ECF whereupon glutamate can exit to blood on the luminal facilitative glutamate transporter. Glutamine may also exit brain on a separate facilitative transport system that exists on the luminal membranes or glutamine can be hydrolyzed to glutamate within the BBB thereby releasing ammonia that is freely diffusible. The γ-glutamyl participate cycle participates indirectly by producing oxoproline (pyroglutamate) that stimulates almost all secondary active transporters yet discovered in the abluminal membranes of the BBB.

The main contribution of this paper is to develop a graphical tool to evaluate the suitability of a candidate parametric model to fit a data set. The practical motivation is to check the adequacy of the so called SAINT model proposed in Jarner and Kryger (2011). This model has been implemented in practice by an important European pension fund concerned with setting annuity reserves for all current or former employees of Denmark. So, one particular relevant question is whether this mortality model is actually fitting old-age. Our graphical test can be described as follows. First we transform the data by means of the parametric model which is being evaluated. Should the model be correct, the transformed data will be in accordance with an Exponential distribution with rate 1. Then we construct a family of local linear hazard estimates based on the data on the transformed scale. Finally we use the statistical tool SiZer to assess the goodness-of-fit of the Exponential distribution to the data on the transformed scale. If the model is correct the SiZer map should not reveal any significant feature in the family of kernel estimates. Our method allow us to establish a diagnostic regarding the validity of the SAINT model when describing mortality patterns in four different countries.

Intraprocedural rupture (IPR) is a severe complication of coil embolization (CE) for intracranial aneurysms, yet the clinical and hemodynamic features of rupture risk areas remain unclear. From January 2013 to December 2023, 435 saccular cerebral aneurysms (316 unruptured, 119 ruptured) underwent CE at our institution. Included were cases where extravasation or coil protrusion occurred during CE. The rupture point was determined from postoperative data, and computational fluid dynamics (CFD) analysis was conducted to assess hemodynamic features. IPR occurred in six aneurysms (1.3%; three ruptured, three unruptured; dome size: 4.7±1.8 mm, D/N: 1.5±0.5, locations: four internal carotid artery [ICA], one anterior cerebral artery, one middle cerebral artery). ICA aneurysms were treated using adjunctive techniques (three balloon-assisted, one stent-assisted). Two aneurysms (M1M2 and A1) were treated simply, yet had relatively small and misaligned domes. CFD analysis identified the rupture point as a flow impingement zone with maximum pressure (Pmax) in five aneurysms (83.3%). Time-averaged wall shear stress (WSS) was locally reduced around this area (1.3±0.7 [Pa]). Hemodynamically unstable areas indicated fragile, thin walls with rupture risk. Microcatheter insertion along the inflow zone, directed towards the caution area, was practiced. The presence of a flow impingement zone with Pmax and low WSS suggests potential aneurysm rupture in CE, particularly with adjunctive techniques. Small aneurysms, especially those with axial misalignment, warrant careful consideration due to the risk of excessive flow impact stress on the aneurysm wall.

Background: Previous neuroimaging studies have identified brain regions related to respiratory motor control and perception. However, little is known about the resting-state functional connectivity (FC) associated with respiratory impairment. We aimed to determine the FC involved in mild respiratory impairment without changing the saturation of percutaneous oxygen. Methods: We obtained resting-state functional magnetic resonance imaging data from 36 healthy volunteers during normal respiration and mild respiratory impairment induced by resistive load (effort breathing). ROI-to-ROI and seed-to-voxel analyses were performed using Statistical Parametric Mapping 12 and the CONN toolbox. Results: Compared to normal respiration, effort breathing activated FCs within and between the sensory perceptive area of respiration (the postcentral gyrus, anterior insular cortex (AInsula), and anterior cingulate cortex) and visual cortex (the visual occipital, occipital pole (OP), and occipital fusiform gyrus). Graph theoretical analysis showed strong centrality in the visual cortex. A significant positive correlation was observed between the dyspnoea score (modified Borg scale) and FC between the left AInsula and right OP. Conclusions: These results suggested that the FCs within the respiratory sensory area via the network hub may be neural mechanisms underlying effort breathing and modified Borg scale scores.

The nature of the interaction between Th17 cells and the blood-brain barrier (BBB) is critical for the development of autoimmune inflammation in the central nervous system (CNS). TNF-a or IL-17 stimulation is known to enhance the adherence of Th17 cells to the brain endothelium. The brain endothelial cells (bEnd.3) express VCAM-1, the receptor responsible for inflammatory cell adhesion, which binds VLA-4 on migrating effector lymphocytes at the early stage of brain inflammation. The present study examines the effect of the pro-inflammatory cytokines TNF-a and IL-17 on the adherence of Th17 cells to bEnd.3 The bEnd.3 cells were found to increase production of CCL2 and CXCL1 after stimulation by pro-inflammatory cytokines, while CCL2, CCL5, CCL20 and IL17 induced Th17 cell migration through a bEnd.3 monolayer. This interaction between Th17 cells and the brain endothelium appears to be mediated by VCAM-1 and some chemotactic cytokines. This observation may suggest potential therapeutic targets for the prevention of autoimmune neuroinflammation development in the CNS.

The escalating levels of atmospheric carbon dioxide (CO₂) due to anthropogenic activities have raised significant concerns regarding their far-reaching consequences, including potential impacts on human brain function. This study employs Agent-Based Modeling (ABM) and a comprehensive literature review to comprehensively examine the effects of varying atmospheric CO₂ concentrations on human brain activity. Historical and projected CO₂concentration data, along with neuroscientific insights, were integrated into the ABM framework to simulate brain activity responses under different CO₂ scenarios. The simulations revealed that elevated CO₂ levels disrupt neural activity and alter neurotransmitter profiles, indicating potential cognitive consequences. Sensitivity analysis emphasized the importance of accurate parameterization for robust simulations. The findings underscore the urgent need to address global CO₂ emissions to preserve cognitive health. Further research is essential to validate and refine the model, incorporating additional factors for a comprehensive understanding of the cognitive repercussions of rising CO₂ levels, crucial for informed policy-making and public awareness campaigns.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

In the brain, astrocytes regulate shape and functions of the synaptic and vascular compartments through a variety of released factors and membrane-bound proteins. An imbalanced astrocyte activity can therefore have drastic negative impacts on brain development, leading to the onset of severe pathologies. Clinical and pre-clinical studies show alterations in astrocyte cell number, morphology, molecular makeup and astrocyte-dependent processes in different affected brain regions in neurodevelopmental (ND) and neuropsychiatric (NP) disorders. Astrocytes proliferate, differentiate and mature during the critical period of early postnatal brain development, a time window of elevated glia-dependent regulation of a proper balance between synapse formation/elimination, which is pivotal in refining synaptic connectivity. Therefore, any intrinsic and/or extrinsic factors altering these processes during the critical period may result in an aberrant synaptic remodelling and onset of mental disorders. The peculiar bridging position of astrocytes between synaptic and vascular compartments further allows them to “compute” the brain state and consequently secrete factors in the bloodstream, which may serve as diagnostic biomarkers of distinct healthy or disease conditions.Here, we collect recent advancements regarding astrogenesis and astrocyte-mediated regulation of neuronal network remodelling during early postnatal critical periods of brain development, focusing on synapse elimination. We then propose alternative hypotheses for an involvement of aberrancies in these processes in the onset of ND and NP disorders. In light of the well-known differential prevalence of certain brain disorders between males and females, we also discuss putative sex-dependent influences on these neurodevelopmental events.From a translational perspective, understanding age- and sex-dependent astrocyte-specific molecular and functional changes may help to identify biomarkers of distinct cellular (dys)functions in health and disease, favouring the development of diagnostic tools or the selection of tailored treatment options for male/female patients.

Until recently, no disease-specific health-related quality of life (HRQoL) instrument existed for pediatric traumatic brain injury (TBI). In this revalidation study, the psychometric properties and the validity of the final German version of the 35-item QOLIBRI-KID/ADO questionnaire were examined in 300 children and adolescents. It is the first self-reported TBI-specific instrument for measuring pediatric HRQoL in individuals aged 8–17 years. The six-factor model fits the data adequately. Internal consistencies were excellent for the total score and satisfactory to excellent for the scale scores. Intraclass correlations indicated good test-retest reliability. Construct validity was supported by overlaps between the QOLBRI-KID/ADO and the PedsQL, which measures generic HRQoL. Discriminant validity indicated that HRQoL was significantly lower in older children and girls, and in those with anxiety, depression, and post-concussion symptoms than in comparison groups, replicating the results of the questionnaire’s first development study. Our results suggest that the QOLIBRI-KID/ADO is a valid and reliable multidimensional instrument for assessing disease-specific HRQoL after pediatric TBI in clinical and research settings, which can additionally be used across the lifespan in combination with the adult version. This may help to improve care, treatment, daily functioning and HRQoL after TBI.

The combined use of lasmiditan and triptan is unexplored in medical literature. This study aimed to investigate whether the intake of lasmiditan following triptan improves migraine pain. Following triptan intake, if headache relief was less than 50% at 1 hour, patients took 50 mg lasmiditan within 2 hours of migraine onset. Patients recorded headache intensity and adverse events(AEs) caused by lasmiditan at 1, 2, and 4 hours after intake of additional 50 mg of lasmiditan. A significant reduction in pain scale was observed post 50 mg lasmiditan intake (p &lt; 0.001, t-test). Pain relief was reported for 32 migraine attacks (80%) at 1 hour after additional lasmiditan intake. Although AEs were observed in 63% of the patients who took an additional lasmiditan, most were mild and resolved 1 hour after lasmiditan intake. Our study revealed the significant headache reliefs provided by an additional lasmiditan for patients who did not achieve satisfactory results following initial triptan intake for treating migraine. The AEs associated with this treatment strategy were mild and lasted for a short term. This study suggested that the combination of triptan and lasmiditan is promising for the treatment of migraine and should be studied in a randomized placebo-controlled trial.

Multiple sclerosis (MS) is an immune-mediated disease characterized by inflammation, demyelination, and neurodegeneration within the central nervous system. Brain plasticity, the brain's ability to adapt its structure and function, plays a crucial role in mitigating MS's impact. This paper explores the potential benefits of lifestyle changes and nutraceuticals on brain plasticity in MS patients. Lifestyle modifications, including physical activity and dietary adjustments, can enhance brain plasticity by upregulating neurotrophic factors, promoting synaptogenesis, and reducing oxidative stress. Nutraceuticals, such as vitamin D, omega-3 fatty acids, and antioxidants like alpha-lipoic acid, have shown promise in supporting brain health through anti-inflammatory and neuroprotective mechanisms. Regular physical activity has been linked to increased levels of brain-derived neurotrophic factor (BDNF) and improved cognitive function. Dietary interventions, including caloric restriction and the intake of polyphenols, can also positively influence brain plasticity. Integrating these lifestyle changes and nutraceuticals into the management of MS could provide a complementary approach to traditional therapies, potentially improving neurological outcomes and enhancing the quality of life for MS patients.

Health-Related Quality of Life (HRQoL) encompasses the comprehensive evaluation of an indi-vidual's health and well-being across multiple dimensions. Mindfulness, recognized as a scientifi-cally supported strategy for anxiety management, plays a role in enhancing both physical and mental health. The Gut-Brain Axis (GBA) serves as the bidirectional communication link between the gastrointestinal system and the central nervous system. This study aims to explore the associa-tion between Mindfulness and factors influencing the GBA in both healthy individuals and those with Inflammatory Bowel Diseases (IBD). A cross-sectional investigation involved 338 adults, in-cluding 50 IBD patients, utilizing validated Greek scales for Mindfulness (MAAS-15), Mediterra-nean Diet (14-MEDAS), and HRQoL (EQ-5D-5L). The questionnaire gathered demographic, an-thropometric, and lifestyle data. Among healthy participants, EQ-5D-5L showed a moderate cor-relation with the MAAS-15 scale (r=0.389, p

Fever control is essential in patients with severe traumatic brain injury (TBI). The efficacy of therapeutic hypothermia (TH) in severe TBI has been investigated over the last few decades; however, in contrast to experimental studies showing benefits, no evidence of efficacy has been demonstrated in clinical practice. In this review, the mechanisms and history of hypothermia were briefly outlined, while the results of major randomized controlled trials (RCTs) and meta-analyses investigating TH for adult TBI are introduced and discussed. The retrieved meta-analyses showed conflicting results, with a limited number of studies indicating benefits of TH. Some studies have shown benefits of long-term TH compared with short-term TH. Although TH is effective at lowering elevated intracranial pressure (ICP), reduced ICP does not lead to favorable outcomes. Low-quality RCTs overestimated the benefits of TH, while high-quality RCTs showed no difference or worse outcomes with TH. RCTs assessing standardized TH quality demonstrated the benefits of TH. As TBI has heterogeneous and complicated pathologies, applying a uniform treatment may not be ideal. A meta-analysis of young patients who underwent early cooling and hematoma removal showed better TH results. TH should not be abandoned, and its optimal should be advocated on an individual basis.

Systematic reviews represent a powerful instrument to summarize the existing evidence in medical literature. However, articles for a systematic review are hard to identify, and mostly require a structured search of the literature through a number of databases, using keyword-based search strategies, followed by the painstaking manual selection of pieces of evidence that are pertinent to the query. A.I. algorithms may offer solutions to reduce the workload on investigators. We applied BERTopic, a newer and much-recognized transformer-based topic-modeling algorithm, to two datasets of 6137 and 5309 articles of newly published systematic reviews in the area of peri-implantitis and bone regeneration in implant dentistry. In the two datasets, BERTopic identified 14 and 22 clusters, respectively, and it automatically created labels describing the nature of the topics for each individual cluster based on semantic interpretation of their titles. Most themes regarded the query theme, but in both conditions, BERTopic also uncovered articles related to off-themes, which composed around 30% of the dataset—with sensitivity up to 0.79 and specificities of at least 0.99. Our results suggest that adding a topic-modeling step to the screening process could potentially save working hours for researchers involved in systematic reviews of the literature.

Advanced glycation end products (AGEs) play a significant role in the aging processes, further regarding as a hallmark of aging. Despite the importance, a lack of monitoring tools has hampered the study of relationship between AGEs and aging. Here, we firstly report a novel AGEs-selective probe, AGO. AGO presented its ability to detect AGEs more sensitively than a conventional approach of measuring autofluorescence from AGEs. In addition, we confirmed that AGO can detect AGEs depending on kinetics, reflecting by a preference to ribose-derived AGEs. Finally, AGO revealed its competence to visualize glycation products in glycation mimicking model, made by collagen framework. We believe that this study will enrich molecular toolboxes to understand physiological processes of AGEs during aging processes.

Rabies remains a significant global threat, yet accurate estimations of its impact are hindered by the lack of confirmatory diagnoses. Postmortem confirmation of rabies traditionally involves invasive brain tissue testing, a process met with resistance from deceased patients' families, impeding consent. This paper presents and evaluates an innovative yet unpublished transnasal approach for postmortem brain tissue collection, offering a minimally invasive, easier, faster, and safer method. This method preserves the cadaver's integrity, potentially easing family reluctance towards autopsies. The limited testing of both human and animal rabies in Ghana highlights the challenges in diagnosing this fatal disease. Scarce diagnostic resources and the complexity of obtaining brain tissue samples exacerbate the issue. Cultural and religious beliefs surrounding autopsies contribute to familial hesitation, viewing these procedures as disruptive and disfiguring, further complicating consent. The transnasal technique involves approaching the brain tissue through the nostrils and cribriform plate without any superficial manipulation of the patient's head and face, thereby preserving aesthetics and natural features of the person. Technological advancements and seamless One Health collaboration among governmental, non-governmental, and research entities locally and globally culminated in Ghana's first confirmed rabies diagnosis using this method of brain tissue collection. This success emphasizes the efficiency and feasibility of transnasal brain collection approach and the invaluable role of the One Health approach and collaborative efforts in overcoming diagnostic challenges in rabies control.

Brain aging causes a wide variety of changes at molecular and cellular levels, leading to the decline of cognitive functions and increased vulnerability to neurodegenerative disorders. The research aimed at understanding the aging of the brain has made much progress in recent decades. Technological innovations such as single-cell RNA-sequencing (scRNA-seq), proteomic analyses, and spatial transcriptomic analyses have facilitated the research on the dynamic changes occurring within neurons, glia, and other cells along with their impacts on intercellular communication during aging. In this review, we introduce recent trends of how neurons and glia change during aging and discuss the impact of the brain microenvironment such as the blood-brain barrier (BBB).

The quest for youthful, healthy skin and full, vibrant hair has long been a driving force in the dermocosmetics field. However, traditional approaches often struggle to address the underlying causes of aging, damage, and hair loss. Regenerative cosmetics, powered by skin tissue engineering, offer a transformative alternative. This review explores the emerging field of using engineered skin tissues for cosmetic purposes, focusing specifically on their potential for anti-aging, repair, and hair restoration applications. We discuss how these technologies aim to rejuvenate aging skin by promoting collagen production, reducing wrinkles, and improving overall skin function. Additionally, the use of engineered skin for wound healing and scar reduction is examined, highlighting their potential to improve the appearance and functionality of damaged skin. Finally, we advance the exciting prospects of utilizing skin tissue engineering techniques to regenerate hair follicles, potentially offering solutions for hair loss and promoting denser hair growth.

Marmosets from the Callithrix genus (six species) are anthropoids distributed in many Brazilian biomes, but anthropogenic pressure is a risk for hybridization and virtually the extinction of some species. Despite this, only a few data are available on reproductive ecology, which is a gap in understanding the physiological modulation of the socio-sexual behavior of free-ranging marmosets. In this study, we characterized fecal concentrations of progesterone, estrogens, and glucocorticoids of six breeding and non-breeding females from two groups of free-ranging C. penicillata, which is the species with the largest geographical distribution and most important in hybridization studies. The fecal progesterone was significantly higher in females who gave birth than non-breeding females, which was linked to reproductive status. The levels of fecal estrogens and glucocorticoids did not differ between breeding and non-breeding females. The data are according to the few studies on steroid values of free-ranging and captive marmosets. This study shows the concentrations of progesterone and glucocorticoids in free-ranging C. penicillata for the first time. Overall, the high levels of progesterone associated with pregnancy in free-ranging C. penicillata as well as levels of estrogens and glucocorticoids close to those reported for other species, suggest a conserved pattern of hormonal secretion between Callithrix species that have been studied in captivity.

It’s very well known that the world's attention is currently focused on the energy transition. The energy security, in addition to the drive to reduce greenhouse gas emissions in order to limit global warming, and the generalization of the access to energy, have contributed to adoption of the Moroccan Energy Strategy with a strong focus on Renewable Energies (RE). Morocco is notoriously poor in conventional fossil primary energy resources with an energy dependence on the order of 90%. The energy crisis and that resulted from Covid-19 pandemic and the geopolitical conflicts, combined compounded with steady increase in demand have heavily affected the security and the stability of the country's energy situation. The transition to RE, by strongly engaging in the implementation of several solar, wind and hydro energy projects benefiting from the country's excellent solar and wind energy potential, has made the country the leader in RE in Africa. By 2030, the share of RE in the installed capacity is expected to reach 52%. In this work, an overview of the current situation of RE (particularly solar energy) in Morocco is provided, including, the potentials, obstacles and challenges, and future perspectives. Thanks to its high solar potential location, it’s predictable that Morocco effort will be focused on this field: the Erasmus Plus Innomed Project is a virtuous example of international cooperation, aiming at create a Solar Energy Network, in synergy with EU Partners.

Among the various types of gas sensors, the chemoresistive gas sensors based on metal oxides are considered the most attractive ones due to their simple operation, low costs and miniaturization. Thin films of V2O5 undoped and doped with TiO2 were studied as nitrogen dioxide, methane and hydrogen sensors. The experimental kinetics of the electrical conductivity changes due to introduction and removal of gas on metal oxide gas sensors were successfully explained in terms of the Langmuir adsorption theory.

The reasons for the circular sense of human color perception generated by two sorts of color opponent neurons and three cone types are not well understood. Here we use geometrical analysis to examine the hypothesis that opponency, the recursive nature of color perception, and trichromacy arise as the most efficient ways of distinguishing spectrally different points on a plane using a minimum of color classes and receptor types.

Abnormal connections in brain networks of healthy people always bring the problems of cognitive impairments and degeneration of specific brain circuits, which may finally result in Alzheimer’s disease (AD). Exploring the development of the brain from normal controls (NC) to AD is an essential part of human research. Although connections changes have been found in the development, the connection mechanism that drives these changes remain incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncover the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a model named MINM from the perspective of topology-based mutual information to achieve our aim. MINM concerns the question of estimating the connection probability between two cortical regions with the consideration of both the mutual information of their observed network topologies and their Euclidean distance in anatomical space. In addition, MINM considers establishing and deleting connections, simultaneously, during the networks modeling from the stage of NC to AD. Experiment results show that MINM is sufficient to capture an impressive range of topological properties of real brain networks such as characteristic path length, network efficiency, and transitivity, and it also provides an excellent fit to the real brain networks in degree distribution compared to experiential models. Thus, we anticipate that MINM may explain the connection mechanism for the formation of the brain network organization in AD patients.

Background: many studies, on for example taste-visual dissonance, have shown that the influence of the visual cortex on taste sensation is enormous. The aim of the presented work is to investigate, using fNIRS, whether a taste stimulus, in this case the taste of bitter, also causes stimulation of the visual cortex in fNIR study. Methods: 51 participants (204 examinations, 9 996 records) were examined with fNIRS, which collects signals from whole left hemisphere. Differences between the maximum and minimum changes in oxyHb concentrations (ΔoxyHb) for the areas of the brain cortex considered responsible for recording visual and gustatory signals were analyzed. Protocol I, II, III and IV – activation with distillate water, coffee with lower concentration, reference (no stimulation) and coffee with higher concentration respectively, were used. Results: We recorded high signals for teste activation on channels covered gustatory cortex, which confirms the correctness of the choice of research method. What seems important, and somewhat surprising, is the fact that we also received high signals for the channels 45-49, which cover the visual cortex. The statistical analysis does not show any statistical differences between the protocols I, II and IV (different taste activation – water, coffee A and coffee B) for specific channels. As a results of the analysis of the correlation between the subjective bitterness assessment solutions, and the signal ΔoxyHb height, it was observed that statically important correlation, although weak, occurs only for 14 and gustatory channels, only for coffee with a higher concentration. Additionally we recorded the only statistically significant difference between women and men for protocol I (water), where the ΔoxyHb signal was twice as high for women. Conclusions: In conclusion, we can clearly state that the senses of sight and taste work closely together. Moreover, this cooperation is not one-sided - not only the visual activation affects the perception of taste, but interestingly, the taste stimulus can generate a hemodynamic response - activity in the visual cortex.

Brain is the most complex organ in the human body and it is divided into two hemispheres - left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

The gut microbiota (GM) communicates with the brain via biochemical signaling constituting the gut-brain axis, which significantly regulates the body's physiological processes. The GM dysbiosis can impact the digestive system and the functioning of the central nervous system (CNS) linked to the onset of neurodegenerative diseases. In this review, the scientific data compiled from diverse sources primarily emphasizes the neuropathological characteristics linked to the accumulation of modified insoluble proteins (such as β-Amyloid peptides and hyperphosphorylated tau proteins) in Alzheimer’s Disease (AD) and the potential impact of gut microbiota (GM) on AD susceptibility or resilience. The specific GM profile of human beings may serve as an essential tool for preventing or progressing neurodegenerative diseases like AD. This review focuses mainly on the effect of gut microfauna on the gut-brain axis in the onset and progression of AD. The GM produces various bioactive molecules that may serve as proinflammatory or anti-inflammatory signaling, contributing directly or indirectly to the repression or progression of neurodegenerative disorders by modulating the response of the brain axis. Human studies must focus on further understanding the gut-brain axis and venture to clarify microbiota-based therapeutic strategies for AD.

The problems of modeling the human bladder and its stress-strain state under external static influence were considered. A method for identification of the anisotropic biomechanical characteristics of bladder tissue was proposed. A FEM model was created, which accepts into regard that the bladder is surrounded by fiber and affected by surrounding organs, and partially protected by pelvic bones. The model considered the presence of constant hydrostatic pressure on the walls of the bladder when it is full. It has been shown that isotropic mechanical characteristics of biological tissue can be used for studying the deformed state of a filled bladder if the filled bladder 300 ml is considered as initial non-deformed stage. This was shown by modeling and verification the effect of the external static force on the bladder. Numerical experiments were conducted based on the constructed model. To validate the results obtained, a series of natural experiments on the effect of external pressure on the bladder under ultrasound control was conducted. In the future, it is planned to use the constructed model to study rupture deformations of the bladder under the influence of static and dynamic loads.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

Electrolyte disturbances are common in patients with traumatic brain injury (TBI), particularly affecting sodium, potassium, chloride and calcium levels. This study aims to provide insights into these disturbances within the first 24 hours post-injury. We conducted a cross-sectional analysis of 50 TBI patients, excluding those with conditions affecting electrolyte balance. Electrolyte levels were measured, and correlations with demographic data, trauma mechanisms, imaging findings, and Glasgow Coma Scale (GCS) scores were analyzed. Results indicated that chloride levels inversely correlated with GCS scores (ρ = -0.515; p = 0.002), suggesting that elevated chloride may indicate severe neurological impairment. Potassium levels were significantly associated with subdural hematoma (p = 0.032) and subarachnoid hemorrhage (p = 0.043), highlighting their potential as markers for severe brain injuries. No significant associations were found between sodium or calcium levels and the studied variables. These findings underscore the importance of early monitoring of chloride and potassium levels in TBI patients to improve management and outcomes. Future research should focus on larger, multi-center studies to validate these findings and develop comprehensive guidelines for managing electrolyte imbalances in TBI patients.

We develop an action principle for producing a single fluid, two-constituent system with dissipation in general relativity. The two constituents in the model are particles and entropy. The particle flux creation rate is taken to be zero, while the entropy creation rate is non-zero. Building on previous work, it is demonstrated that a new term (the proper time derivative of the matter space ``metric'') is required in the Lagrangian in order to produce terms typically associated with bulk and shear viscosity. Equations of motion, entropy creation rate, and energy-momentum-stress tensor are derived. Using an Onsager approach of identifying thermodynamic ``forces'' and ``fluxes'' a model is produced which delivers the same entropy creation rate as the standard, relativistic Navier-Stokes equations. This result is then contrasted with a model generated in the spirit of the action principle, which takes as its starting point a specific Lagrangian and then produces the equations of motion, entropy creation rate, and energy-momentum-stress tensor. Unlike the equations derived from Onsager reasoning, where the analogs of the bulk and shear viscosity coefficients are prescribed ``externally'', we find that the form of the coefficients in the second example are a direct result of the specified Lagrangian. Furthermore, the coefficients are shown to satisfy evolution equations along the fluid worldline; also a product of the specific Lagrangian.

The development of computational acoustics allows simulation of sound generation and propagation in complex environment. In particular, meshfree methods are widely used to solve acoustics problems through arbitrarily distributed field points and approximation smoothness flexibility. As a Lagrangian meshfree method, smoothed particle hydrodynamics (SPH) method reduce the difficulty in solving problems with deformable boundaries, complex topologies, or multiphase medium. The traditional SPH method has been applied in acoustic simulation. This study presents the corrective smoothed particle method (CSPM), which is a combination of SPH kernel estimate and Taylor series expansion. The CSPM is introduced as a Lagrangian approach to improve accuracy in solving acoustic wave equations in the time domain. Moreover, a boundary treatment technique based on the hybrid meshfree and finite difference time domain (FDTD) method is proposed to represent different acoustic boundaries with particles. To model sound propagation in pipes with different boundaries, soft, rigid, and absorbing boundary conditions are built with this technique. Numerical results show that the CSPM algorithm is consistent and demonstrates convergence with exact solutions. Main computational parameters are discussed, and different boundary conditions are validated to be effective for benchmark problems in computational acoustics.

Soil and Water Assessment Tool (SWAT) was used to simulate five glacierized river basins that are global in coverage and vary in climate. The river basins included the Narayani (Nepal), Vakhsh (Central Asia), Rhone (Switzerland), Mendoza (Central Andes, Argentina), and Central Dry Andes (Chile) with a total area of 85,000 km2. A modified SWAT snow algorithm was applied in order to consider spatial variation of associated snow melt /accumulation by elevation band across each subbasin. In the previous studies, melt rates varied as a function of elevation resulting from an air temperature gradient while the snow parameters were constant throughout the entire basin. A major improvement of the new snow algorithm is separating the glaciers from seasonal snow based on their characteristics. Two SWAT snow algorithms were evaluated in simulation of monthly runoff from glaciered watershed: 1) the snow parameters are lumped (i.e. constant throughout the entire basin) and 2) the snow parameters are spatially variable based on elevation band-subbasin (i.e. modified snow algorithm). Applying the distributed SWAT snow algorithm improved the model performance in simulation of monthly runoff with snow-glacial regime, so that mean RSR decreased to 0.49 from 0.55 and NSE increased to 0.75 from 0.69. Improvement of model performance was negligible in simulation of monthly runoff from the basins with monsoon runoff regime.

This study analyses regional development in one of the poorest provinces in China, Guizhou Province, between 2000 and 2012 using a multiscale and multi-mechanism framework. In general, regional inequality has been declining since 2000. In addition, economic development in Guizhou Province presented spatial agglomeration and club convergence, which shows how the development pattern of one core area, two-wing areas and a contiguous area at the edge of the province have been developed between 2006 and 2012. Multilevel regression analysis revealed that industrialization and investment level were the primary driving forces of regional economic disparity in Guizhou Province. The influences of marketization and decentralization on regional economic disparity were relatively weak. Investment level reinforced regional economic disparity and the development of core-periphery structure in the province. However, investment level actually weakened the regional economic disparity in Guizhou Province when the variable of time was considered. In addition, both the topography and urban–rural differentiation were the two main reasons for forming a core-periphery structure in Guizhou Province.

Background: Clinical trails have revealed that patients with depression generally accompanied with learning and memory impairment, which critically impact on individual’s health and development. Danggui-Shaoyao-San (DSS), a famous Chinese complex prescription, significantly overcame depression and relieved cognitive disorder based on previous research and publication. However, its effectsand potential mechanism against chronic restraint stress (CRS) remained unknown. Methods: CRS animal model was established and mice were divided to six groups while they were oral administrated with Danggui-Shaoyao-San at doses of 1.25, 2.5 and 5 g/kg for 14 days. Emotional and cognitive performances were detected by behavior tests, meanwhile neural plasticity and its molecular mechanism were examined by Dil staining, western blot and immunofluorescence. Results: DSS treatment dose-dependently improved locomotion ability in open filed test, overcame depression behavior in forced swimming test and elevated plus maze test, enhanced learning and memory ability in Morris water maze test. CRS decreased number of total spines and mushroom spines, while DSS treatment dose-dependently restored these by Dil staining. Expression of BDNF and GluR1 were significantly down-regulated in CRS group, which were significantly normalized by DSS. Conclusions: DSS treatment dose-dependently reversed CRS-induced cognitive impairments by inducing structural remodeling of neurons.

A comprehensive modeling framework for the thermoforming of polymer matrix woven laminate composite was developed. Two numerical indicators, the slip path length and traction magnitude, have been identified to be positively correlated to matrix smearing and wrinkling defects. The material model has been calibrated with picture-frame experimental results, and the prediction accuracy for intra-ply shear and thickness distribution were examined with measurements on the physically formed parts. Finally, a parametric study was conducted to determine the relationship between various process parameters and the quality of the formed part. For the trapezoidal part, orienting the laminate at 45-degrees to the mold axis reduces the likelihood of matrix smear and wrinkling defects. Although this laminate orientation yielded a greater spatial variation of part thickness, the thickness deviation is lower than that for the 0-degree orientation case. Higher forming rates were found to result in more defects due to increased slip path lengths and traction magnitudes. Optimal forming speeds should balance rapid shaping within the operating temperature window while avoiding tear. It was discovered that shallower molds benefit from faster ramp rates, while deeper molds require slower rates to manage extensive shearing, stretching, and bending. Faster forming rates lead to smaller thickness increases at high intra-ply shear regions, indicating a shift from intra-ply shear to out-of-plane bending due to the visco-plastic effect of the molten laminate, and can negatively impact part quality. Lastly, it was shown that a well-conceived strategy using darts could improve the part quality by reducing the magnitude of the defect indicators.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

Modularity is arguably one of the most influential theses guiding research on brain and cognitive function since phrenology. This paper considers the following question: is modularity entailed by recent Bayesian models of brain and cognitive function, especially the predictive processing framework? It starts by considering three of the most well-articulated arguments for the view that modularity and predictive processing work well together. It argues that all three kinds of arguments for modularity come up short, albeit for different reasons. The analysis in this paper, although formulated in the context of predictive processing, speaks to broader issues with how to understand the relationship between functional segregation and integration and the reciprocal architecture of the predictive brain. These conclusions have implications for how to study brain and cognitive function. Specifically, when cognitive neuroscience works within an acyclic Markov decision scheme, adopted by most Bayesian models of brain and cognitive function, it may very well be methodologically misguided. This speaks to an increasing tendency within the cognitive neurosciences to emphasise recurrent and reciprocal neuronal processing captured within newly emerging dynamical causal modelling frameworks. The conclusions also suggest that functional integration is an organising principle of brain and cognitive function.

The success of deep learning (DL) in offline brain-computer interfaces (BCIs) has not yet translated into efficient online applications. This is due to two limiting factors: the need for large amounts of training data in DL and the fact that current DL solutions are primarily developed for offline decoding. To enable real-time decoding, even across subjects without calibration data, we first introduce a novel method real-time adaptive pooling (RAP) to tune existing offline DL models towards online decoding by modifying the pooling layers. To reduce the amount of training data required, our approach leverages different levels of domain adaptation. We show how different settings enable different adaptation solutions. Our results demonstrate that our approach is both powerful and can be calibration-free, providing a robust and practical solution for real-time BCI applications. These findings pave the way for the development of co-adaptive and highly efficient DL-based BCI systems.

Stress mechanisms have long been associated with neuronal loss and neurodegenerative diseases. The origin of cell stress and neuronal loss likely stems from multiple pathways. These include (but are not limited to) bioenergetic failure, neuroinflammation, and loss of proteostasis. Cells have adapted compensatory mechanisms to overcome stress and circumvent death. One mechanism is mitophagy. Recent studies have implicated mitophagy in several neurodegenerative diseases and clinical trials are underway which target mitophagy pathways. Here, we review mitophagy pathways, the role of mitophagy in neurodegeneration, potential therapeutics, and the need for further study.

Background. Transferrin receptor 1 (TfR1), glucose transporter 1 (GLUT1), and CD98hc are candidates for targeted therapy at the blood-brain barrier (BBB). Our objective was to challenge the expression of TfR1, GLUT1, and CD98hc in brain capillaries by the histone deacetylase inhibitor (HDACi) valproic acid (VPA). Methods. Primary mouse brain capillary endothelial cells (BCECs) and brain capillaries isolated from mice injected intraperitoneally with VPA were examined using RT-qPCR and ELISA. Targeting to the BBB was performed by injecting monoclonal anti-TfR1 (Ri7217)-conjugated gold nanoparticles measured using ICP-MS. Results. In BCECs co-cultured with glial cells, Tfrc mRNA expression was significantly higher after 6 h VPA returning to baseline after 24 h. In vivo Glut1 mRNA expression was significantly higher in males, but not females', receiving VPA, whereas Cd98hc mRNA expression was unaffected by VPA. TfR1 increased significantly in vivo after VPA, whereas GLUT1 and CD98hc were unchanged. Uptake of anti-TfR1-conjugated nanoparticles was unaltered by VPA in spite upregulated TfR expression. Conclusions. VPA upregulates TfR1 in brain endothelium in vivo and in vitro. VPA does not increase GLUT1 and CD98hc proteins. The increase in TfR1 does not result in higher anti-TfR1 antibody targetability, suggesting targeting sufficiently occurs with available transferrin receptors without further contribution from accessory VPA-induced TfR1.

It has been widely established that the characterization of extracellular vesicles (EVs), particularly small EVs (sEVs), shed by different cell types into biofluids, helps identify biomarkers and therapeutic targets in neurological and neurodegenerative diseases. Recent studies are also exploring the efficacy of mesenchymal stem cell-derived extracellular vesicles naturally enriched with therapeutic microRNAs and proteins for treating various diseases. Besides, EVs released by various neural cells play a crucial function in the modulation of signal transmission in the brain in physiological conditions. However, in pathological conditions, such EVs can facilitate the spread of pathological proteins from one brain region to the other. On the other hand, analysis of EVs in biofluids can identify sensitive biomarkers for diagnosis, prognosis, and disease progression. This review discusses the potential therapeutic use of stem cell-derived EVs in several central nervous system diseases. It lists their differences and similarities and confers various studies exploring EVs as biomarkers. Further advances in EV research in the coming years will likely lead to the routine use of EVs in therapeutic settings.

Electrical contacts are important circuit components with diverse industrial applications, and their failure can lead to multiple unwanted effects. Hence, the behavior of electrical contacts is a widely studied topic in scientific literature based on various approaches, tools, and techniques. The present study proposes a numerical modeling and simulation approach based on the Holm contact theory to study the dependence between the electric potential and the temperature within an electrical contact. Structured in 5 sections, the research was conducted using COMSOL Multiphysics software and its solid-state mechanics, electric current, and heat transfer modules in order to highlight contact behavior from mechanical, electrical and thermal points of view: the von Mises stress, contact force, the electric field amplitude, the variation of the electrical potential along the current path, the temperature gradient, and dependence of temperature along the contact elements edges were obtained by simulation and graphically represented. The results are in line with the expectations and with past research that was studied in the literature review.

Chemical vapor deposition (CVD) is a vital process for depositing thin films of various materials with precise control over thickness, composition, and properties. Understanding the heat and mass transfer mechanisms during CVD is essential for optimizing process parameters and ensuring high-quality film deposition. This review provides an overview of recent advancements in the heat and mass transfer modeling for chemical vapor deposition processes. It explores innovative modeling techniques, recent research findings, emerging applications, and challenges in the field. Additionally, it discusses future directions and potential areas for further advancement in CVD modeling.

Advanced Finite Element (FE) modeling and simulations were performed on vehicular crashes into a Three-bar Metal Bridge Rail (TMBR). The FE models of a sedan, a pickup truck, and a TMBR section were adopted in the crash simulations subject to Manual for Assessing Safety Hardware (MASH) Test Level 2 (TL-2) and Test Level 3 (TL-3) requirements. The test vehicle models were first validated using full-scale physical crash tests conducted on a two-bar metal bridge using a sedan and a pickup truck with similar overall physical properties and sizes to their respective vehicles used in the simulations. The validated vehicular models were then used to evaluate the crash performance of the TMBR using MASH Evaluation Criteria for structural adequacy, occupant risk, and post-impact trajectory. The TMBR met all MASH TL-2 requirements but failed to meet the MASH TL-3 Criteria H and N requirements when impacted by the sedan. The TMBR was also evaluated under in-service conditions (behind a 1.52-m wide sidewalk) and impacted by the sedan under MASH TL-3 conditions. The simulation results showed that the TMBR behind a sidewalk met all safety requirements except for the occupant impact velocity in the longitudinal direction, which exceeded the MASH limit by 3.93%.

Memory and emotion are highly vulnerable to psychiatric disorders like post-traumatic stress disorder (PTSD), which has been linked to serotonin (5-HT) metabolism disruptions. In fact, over 90% of the 5-HT precursor tryptophan (Trp) is metabolized via the Trp-kynurenine (KYN) meta-bolic pathway, producing a variety of bioactive molecules. The aadat (kat2) gene encodes mito-chondrial kynurenine aminotransferase (KAT) isotype 2, responsible for kynurenic acid (KYNA) production. Little is known about its role in behavior. In CRISPR/Cas9-induced aadat knockout (kat2−/−) mice, we examined the effects on emotion, memory, motor function, Trp and its metabolite levels, enzyme activities in the plasma and the urine of 8-week-old males compared to wild-type mice. Transgenic mice showed more depressive-like behaviors in the forced swim test, but not in the tail suspension, anxiety, or memory tests. They also had fewer center field and corner entries, shorter walking distances, and fewer jumping counts in the open field test. Plasma metabolite levels are generally consistent with those of urine: KYN, antioxidant KYNs, 5-hydroxyindolacetic acid, and indole-3-acetic acid levels are lower; enzyme activities in KATs, kynureninase, and monoamine oxidase/aldehyde dehydrogenase are lower, but kynurenine 3-monooxygenase is higher; and ox-idative stress and excitotoxicity indices are higher. Transgenic mice show depression-like behavior in a learned helplessness model, emotional indifference, and motor deficits, coupled with a decrease in KYNA, a shift of Trp metabolism toward the KYN-3-HK pathway, and a partial decrease in the gut microbial Trp-indole pathway metabolite. This is the first evidence that deleting the aadat gene causes depression-like behaviors that are unique to despair experience, which appears to be linked to excitatory neurotoxic and oxidative stresses. This may lead to the development of a double-hit preclinical model in experience-based depression, better understanding of these complex condi-tions, and more effective therapeutic strategies by elucidating the relationship between Trp me-tabolism and PTSD pathogenesis.

Our recent analysis of empirical limit order flow data in financial markets reveals a power-law distribution in limit order cancellation times. These times are modeled using a discrete probability mass function derived from the Tsallis q-exponential distribution, closely aligned with the second form of the Pareto distribution. We elucidate this distinctive power-law statistical property through the lens of agent heterogeneity in trading activity and asset possession. Our study introduces a novel modeling approach that combines fractional Lévy stable motion for limit order inflow with this power-law distribution for cancellation times, significantly enhancing the prediction of order imbalances. This model not only addresses gaps in current financial market modeling but also extends to broader contexts such as opinion dynamics in social systems, capturing the finite lifespan of opinions. Characterized by stationary increments and a departure from self-similarity, our model provides a unique framework for exploring long-range dependencies in time series. This work paves the way for more precise financial market analyses and offers new insights into the dynamic nature of opinion formation in social systems.

Vascular impairment, muscle atrophy, and cognitive decline are important age-related conditions that have a significant impact on health. Vascular impairment directly disrupts the flow of blood, leading to the decline of muscle mass associated with aging (sarcopenia) and the decline of neuronal functions (neurodegeneration). Our inadequate understanding of the complex relationships between these conditions impedes precise diagnosis and effective treatment. This review analyzes the interrelated mechanisms that contribute to these conditions, with a specific focus on oxidative stress, chronic inflammation, and impaired nutrient delivery. The aim is to understand the common pathways involved and to suggest comprehensive therapeutic approaches. Vascular dysfunctions hinder the circulation of blood and the transportation of nutrients, resulting in sarcopenia characterized by muscle atrophy and weakness. Vascular dysfunction and sarcopenia have a negative impact on physical function and quality of life. Neurodegenerative diseases exhibit comparable pathophysiological mechanisms that affect cognitive and motor functions. Preventive and therapeutic approaches encompass lifestyle adjustments, addressing oxidative stress, inflammation, and integrated therapies that focus on improving vascular and muscular well-being. Better understanding of these links can enhance therapeutic strategies and lead to better outcomes for patients. Here, we highlight the intricate relationship among vascular dysfunction, degenerative muscle, and cognitive decline, underscoring the need for multidisciplinary approaches to treatment. Progress in this field can result in improved diagnosis, more potent therapeutic options, and enhanced preventative measures, all of which can contribute to an improved quality of life for the aging population.

A power factor correction (PFC) rectifier is compulsory for any electronic circuitry connected to the electrical grid. The majority of PFCs that are utilized are based on a step-up converter that supplies high DC voltage at its output terminal. Electric vehicle battery voltage varies from a few hundred to almost a thousand volts. Thus, an additional conversion step is obligatory when designing an all-brand universal charger. This paper presents a new type of front-end versatile step up / down three voltage level PFC. The rectifier can operate in continuous conduction mode or discontinuous conduction mode. First, the rectifier’s principle of operation is described, and then an innovative rectifier is analyzed for continuous and discontinuous conduction modes. Finally, the proposed theory is experimentally validated in a multiplier-less dual loop mode at discontinuous conduction modes. It was shown that although there is no multiplier use in the control circuitry, the power factor is near unity. It was revealed that the rectifier could swing the output voltage from 50 V to 900 V while the input voltage was 230 Vrms. The innovative topology suits any standard PFC rectifier, three-level voltage supplement, and dual-stage low-voltage power supply.

Few studies have examined the dietary protein intake and sources in combination with the longitudinal changes in brain structure markers. Our study aimed to examine the association between dietary protein intake and different sources of dietary protein with the longitudinal rate of change in brain structural markers. A total of 2723 and 2679 participants from the UK Biobank were included in the analysis. The relative and absolute amounts of dietary protein intake were calculated through a 24-hour dietary recall questionnaire. The longitudinal change rates of brain structural biomarkers were computed using two waves of brain imaging data. We utilized multiple linear regression to examine the association between dietary protein and different sources and the longitudinal changes in brain structural biomarkers. Restrictive cubic splines were used to explore nonlinear relationships, and stratified and sensitivity analyses were conducted. Increasing the proportion of animal protein in dietary protein intake is associated with a slower reduction in the total hippocampus volume (THV, β: -0.02524, P&lt;0.05), left hippocampus volume (LHV, β: -0.02435, P&lt;0.01) and right hippocampus volume (RHV, β: -0.02544, P&lt;0.05).Higher intake of animal protein relative to plant protein is linked to a lower atrophy rate in THV (β: -0.01249, P&lt;0.05) and LHV (β: -0.01173, P&lt;0.05) and RHV (β: -0.01193, P&lt;0.05). Individuals with higher intake of seafood exhibit lower longitudinal rate of change in HV compared to those with no consume (THV, β: -0.004514; P&lt;0.05; RHV, β: -0.005527, P&lt;0.05). In subgroup and sensitivity analyses, there was not a significantly alteration. A moderate increase in the intake and proportion of animal protein in the diet, especially from seafood, is associated with a lower longitudinal change rate in hippocampus volume.

This article discusses the application of thermal quality control methods for plant tissues. The purpose of the work is to determine the optimal parameters of thermal impact on a spherical-shaped plant control object, ensuring the detection of surface and subsurface defects. The authors of the work proposed mathematical models of the temperature field for a spherical body with defects and a flat sample when exposed to the thermal influence of a pulsed source. As a result of the use of mathematical models, the thermophysical characteristics of plant tissues of varying degrees of disease damage were obtained, which made it possible to simulate the temperature field of the control object and obtain an image of classified tissues. The developed recommendations made it possible to select the optimal parameters of the thermal effect on the test object.

Background: Alzheimer's disease (AD) is a leading cause of dementia, significantly influenced by the Apolipoprotein E4 (APOE4) gene and gender. This study aims to use machine learning (ML) algorithms to predict brain age and assess AD risk by considering the effects of APOE4 genotype and gender. Methods: We collected brain volumetric MRI data and medical records from 1100 cognitively unimpaired individuals and 602 AD patients. We applied three ML regression models—XGBoost, Random Forest (RF), and Linear Regression (LR)—to predict brain age. Additionally, we introduced two novel metrics, Brain Age Difference (BAD) and Integrated Difference (ID), to evaluate model performance and analyze the influence of APOE4 genotype and gender on brain aging. Results: AD patients displayed significantly older brain ages compared to their chronological ages, with BADs ranging from 6.5 to 10 years. The RF model outperformed both XGBoost and LR in terms of accuracy, delivering higher ID values and more precise predictions. Comparing APOE4 carriers with non-carriers, the models showed enhanced ID values and consistent brain age predictions, improving overall performance. Gender-specific analyses indicated slight enhancements, with models performing equally well on both genders. It indicates that APOE4 may be a more robust predictor of brain age than gender. Conclusion: Robust ML models for brain age prediction can be pivotal in the early detection of AD risk via MRI brain structural imaging, especially for APOE4 carriers. Such early identification may facilitate timely preventive interventions for AD.

Sensory integration is an essential function of human life, and its decline causes difficulties in the quality of life, especially in older adults. In this study, we proposed an arm-reaching task based on a virtual reality head-mounted display (VR-HMD) system to assess sensory integration in daily life, and examined whether reaching task performance was associated with age and resting-state functional connectivity (rsFC) between the brain regions of interest involved in sensory integration. We hypothesized that performance during a reaching task with multiple cognitive loadings would be affected by aging. The gap angle between the reaching hand and target positions was compared between older and middle-aged adults as a measure of sensory integration function. In addition, rsFC, which was significantly correlated with the gap angles of individual participants in the older adult group, was identified using resting-state functional magnetic resonance imaging data. The mean gap angle was significantly larger in the older adults than in the middle-aged group and was also significantly correlated with connections between the left primary motor area and the left inferior temporal gyrus, temporo-occipital part. Because the connectivity between the areas is known to be related to visuomotor integration, the results suggest the involvement of visuomotor integration in the decline of sensory integration function in old age and the validity of the gap angle during the proposed VR-HMD task as an index for the decline in function.

Urea cycle disorders (UCDs) are common inborn errors of metabolism, with an incidence of one in 30,000 births. They are caused by deficiencies in any of six enzymes and two carrier proteins, the most common being Ornithine Transcarbamylase Deficiency (OTCD). OTCD results in impairment to excrete nitrogen, causing toxic buildup of ammonia with resultant encephalopathy. Hyperammonemia (HA) induces the conversion of glutamate to glutamine in the brain. Excess glutamine in the brain causes osmotic changes cerebral edema, changes in astrocyte morphology, and cell death. Acute symptoms of HA include vomiting, hyperventilation, seizures, and irritability. Long-term neurological changes include deficits in working memory and executive function. To date, there are no predictors of prognosis of infants with neonatal onset OTCD outside of plasma ammonia level at presentation and duration of hyperammonemic coma. We provide a comprehensive analysis of a 16-year-old male with neonatal onset of OTCD as an example of how brain biomarkers may be useful to monitor disease course and outcome. This male presented at 8 days post natal with plasma ammonia and glutamine of 677 and 4024 micromol/L and had a missense mutation in Exon 4 (p.R129H). Treatment included protein restriction, sodium benzoate, and citrulline, arginine, and iron. He suffered recurrent acute hyperammonemic episodes despite compliance, triggered by infections or catabolic stressors. We discuss the long-term effects of the hyperammonemic episodes by following MRI based disease biomarkers.

Abstract: Endoglin (ENG) is a causative gene of type 1 hereditary hemorrhagic telangiectasia (HHT1). HHT1 patients have arteriovenous malformations (AVMs) in multiple organs, including the brain. In mice, Eng deletion induced by R26RCreER or SM22aCre leads to AVM development in the brain and other organs. We have reported that the severity of brain AVM in mice with activin receptor-like kinase 1 (Alk1, an HHT2 causative gene) mutation is correlated with the number of Alk1 negative endothelial cells (ECs). We hypothesized that increase of Eng negative ECs will enhance AVM severity. To increase EC Eng deletion, we used a codon improved cre (icre), which is more potent in recombination of floxed alleles than the wild type (WT) cre. R26RCreER;Eng^f/f mice that have Rosa promoter driving and tamoxifen (TM) inducible WT cre expression globally and PdgfbiCreER;Eng^f/f that have Pdgfb promoter driving and TM inducible icre expression in ECs were treated with 3 intra-peritoneal injections of TM (2.5 mg/25g of body weight) to delete Eng globally or in the ECs. AAV-VEGF was stereotactically injected into the brain to induce brain focal angiogenesis and brain AVM. We found that iCre caused more Eng deletion in the brain indicated by lower level of Eng proteins (p<0.001) and fewer Eng positive ECs (p=0.01) than mice with WT cre. Mice with iCre mediated Eng deletion have more abnormal vessels (p=0.02), CD68⁺ macrophages (p=0.002), and hemorrhage (p=0.04), and less vascular pericyte and smooth muscle coverage than mice with WT cre. In addition, arteriovenous shunts were detected in the intestines of icre mice, a phenotype that have not been detected in WT cre mice before. RNAseq analysis showed that 8 out of the 10 top upregulated pathways identified by Gene Ontology (GO) analysis are related to the inflammation. Therefore, increase of Eng deletion in ECs exacerbates AVM severity, which is associated with enhanced inflammation. Strategies that can reduce Eng negative ECs could be used to develop new therapies to reduce AVM severity for HHT1 patients.

Existing research indicates that mineralization of Carlin-type gold deposit is related to geological structures, but current studies mostly focus on the qualitative association, and lack quantitative analysis of the relationship between mineralization and structural features, which limits the potential for predictive mineral exploration. In this study, the Nibao Carlin-type gold deposit was selected as the research area to conduct three-dimensional geological modeling, spatial analysis, and three-dimensional prospectivity modeling. Spatial analysis revealed that the majority of gold ore bodies are situated near the main fault, with gold mineralization primarily occurring within a spatial range of approximately -80 meters to 20 meters from the main fault plane. This spatial distribution predominantly aligns in a SW-NE direction. Spatial characteristic extraction of fault slope indicators suggests that the slope angles are conducive to the migration and accumulation of ore-forming hydrothermal fluids, with ore body metal content rapidly increasing within an angle range of approximately 20° to 60°. The shape analysis of the mainfault indicates that the principal spaces of mineralization enrichment are within -10m to 20m of the fault plane undulations, where gold resources are concentrated in a relatively flat area. However, the degree of mineralization is more pronounced in the protruding sections than in the depressed areas. The areas with significant dip changes in faults display higher variations in orebody attitudes, with mineralization noticeably weaker in areas where the fault steepness changes markedly. Ore-bearing units exhibit significantly higher grades within the -17° to 3° interval of fault steepness transition. This interval also sees a rapid accumulation of gold resources. The Nibao gold mine's mineralization is derived from deep, ore-bearing hydrothermal fluids ascending along major fractures, precipitating, and accumulating in favorable ore-hosting spaces (faults, anticline cores) and lithological combinations (sedimentary tuffs, tuffs). Three deep potential gold targets were identified, highlighting the deep western edge of the F1 fault and the deep southeast-trending lower plate as prospective areas for future exploration. This study underscores the effectiveness of 3D prospective modeling as an indispensable tool for elucidating the spatial distribution of fault-controlled Carlin-type gold deposits and their tectonic determinants.

Three-dimensional (3D) geological modeling technology offers intricate geological information services and technical support for the research of urban geological conditions and their evolutionary mechanisms. It further facilitates the analysis of environmental geological problems, encompassing their distinguishing features and genetic mechanisms. The precision of such three-dimensional models is predominantly influenced by the caliber of modeling data, the chosen modeling methodologies, and the intricacy of the modeled geological objects. This research, exemplified by the three-dimensional geological structure modeling of the Wuhan metropolitan development area, aims to explore a comprehensive three-dimensional modeling framework for urban geology, particularly in scenarios with large-scale and complex geological conditions. This study holds significant technical value in enhancing the application of 3D geological modeling, as well as practical importance for urban geological investigations and research.

Background: Preserving health and physical fitness is critical to ensure independent living across the lifespan. Lower levels of physical fitness are associated with age-related cognitive decline and a higher prevalence of mild cognitive impairment (MCI). Thus, this study investigates the influence of a six-month dance intervention on selected measures of physical fitness in older adults with MCI. Methods: In this randomized controlled trial 55 patients with MCI were randomized into a sportive dance training (IG; n=26; Age: 70.7 +/- 5.6 years; 62% female) or an inactive control group (CG; n=24; Age: 69.1 +/- 6.8 years; 46% female).The dance group received two 90-minute dance training sessions per week over a duration of six-months, which focused on learning dance movement patterns. During the training sessions, heartrate was measured to control exercise intensity. Physical fitness was assessed using cardiopulmonary exercise testing (CPET), lower limb functional fitness via sit-to-stand test, handgrip strength and heart rate variability (HRV). Results: We observed that the dance intervention preserved the cardiorespiratory fitness as measured by maximal oxygen uptake (VO2max) during CPET, which decreased in the CG. Furthermore, participants in the IG demonstrated increases in leg and handgrip strength although not statistically significant. HRV displayed a nonsignificant decrease following the intervention. Conclusion: The results of this randomized controlled trial suggest that sportive dance training can preserve physical fitness in older adults with MCI. Although improvements in the other parameters were statistically non-significant, likely due to the small sample size, stabilizing muscular fitness and preventing age-related decline in older adults with MCI is important for maintaining functional independence.

In order to solve the problem of large eddy current loss and high temperature rise caused by large amount of permanent magnet, a new type of combined magnetic pole permanent magnet motor is proposed in this paper. The sinusoidal distribution subdomain model of non-uniform air gap rotor is established by using Laplace equation, and the analytical expression of eddy current loss of rotor in uniform air-gap and non-uniform air-gap is derived. The effect of rotor eccentricity on eddy current loss is obtained. According to the characteristics of distributed winding of non-uniform air gap combined pole permanent magnet motor, equivalent treatment is performed to obtain the equivalent thermal conductivity, so as to establish equivalent thermal network model of motor, calculate the temperature value of each component of the motor, and verify the correctness of the thermal network model through the method of magnetothermal bidirectional coupling. Finally, an experimental platform is set up to carry out temperature rise experiments on the two prototypes respectively. Experimental results show that the non-uniform air-gap rotor structure can effectively reduce the rotor eddy current loss and motor temperature rise, and also verify the accuracy of the analytical model calculation results.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

Preterm birth accounts for about 10% of births worldwide. Studying risk factors for perinatal brain damage is essential, as findings suggest that almost 20% of disabilities are linked to risks in the early stages of development. This research aimed to study longitudinal changes in intelligence from 6 to 8 years of age in a sample of 39 preterm children with a history of risk of brain damage and a control group of 35 children born at term. The Wechsler Intelligence Scale (WISC-IV) was used for cognitive measurement. The results showed that the preterm group obtained significantly lower scores than the control group. The working memory indicator significantly affected the interaction between age and prematurity. We consider that it is crucial to expand the knowledge we have about the neurocognitive development of premature infants both in specific cognitive domains and in age ranges so that the information obtained helps to predict the probability of presenting cognitive alterations from early stages and, therefore, to implement intervention strategies and programs based on scientific evidence and complemented in their design by clinical experience, empirical and theoretical knowledge of the different professionals involved in infant cognitive intervention.

Traumatic Brain Injury (TBI) is one of the leading causes of death and disability worldwide. Patient treatment and management pose a challenge for healthcare systems. The prognosis of these patients may be influenced by factors such as sex. This study aims to describe the clinical parameters of patients hospitalized in the Intensive Care Unit (ICU) of a Spanish hospital due to Trauma Brain Injury and to establish differences based on sex. Methodology: An observational, cross-sectional, and analytical study was conducted from March 2015 to December 2019. All patients admitted to the ICU during this period who presented isolated significant Traumatic Brain Injury, defined as an abbreviated injury scale (AIS) ≥ 3, were included. Data on baseline characteristics, mechanism of injury, substance use, and acute management in the prehospital and hospital stages (including the need for hospitalization) were recorded. A descriptive and in-ferential analysis was conducted using the chi-square test. Results: A total of 950 patients were ultimately included. The majority of the patients were men (723) with a mean age of 45.16 (± 17.55). There were statistically significant differences in terms of the mechanism of injury (p