One of the major problem of photovoltaic grid integration is limiting the solar-induced imbalances since these can undermine the security and stability of the electrical system. Improving the forecast accuracy of photovoltaic generation is becoming essential to allow a massive solar penetration. In particular, improving the forecast accuracy of large solar farms generation is important both for the producers/traders to minimize the imbalance costs and for the Transmission System Operators to insure stability. In this article, we provide a benchmark for the day-ahead forecast accuracy of utility scale PV plants in 1325 locations spanning the country of Italy. We then use these benchmarked forecasts and real energy prices to compute the economic value of forecast accuracy and accuracy improvement in the context of the Italian energy market regulatory framework. Through this study, we further point out some several important criticisms of the Italian “single pricing” system that brings to paradoxical and counterproductive effects regarding the need to reduce the imbalance volumes. Finally, we propose a new market-pricing rule and innovative actions to overcome these undesired effects of the current dispatching regulations.

We introduce firm solar forecasts as a strategy to operate optimally overbuilt solar power plants in conjunction with optimally sized storage systems so as to make up for any power prediction errors, hence entirely remove load balancing uncertainty emanating from grid-connected solar fleets. A central part of this strategy is plant overbuilding that we term implicit storage. We show that strategy, while economically justifiable on its own account, is an effective entry step to least-cost ultra-high solar penetration where firm power generation will be a prerequisite. We demonstrate that in absence of an implicit storage strategy, ultra-high solar penetration would be vastly more expensive. Using the New York Independent System Operator (NYISO) as a case study, we determine current and future cost of firm forecasts for a comprehensive set of scenarios in each ISO electrical region, comparing centralized vs. decentralized production and assessing load flexibility’s impact. We simulate the growth of the strategy from firm forecast to firm power generation. We conclude that ultra-high solar penetration enabled by the present strategy, whereby solar would firmly supply the entire NYISO load, could be achieved locally at electricity production costs comparable to current NYISO wholesale market prices.

The French-Swiss Professor in inductive philosophy Richard Avenarius (1843-1896), the father of empirio-criticism together with Ernst Mach, is one of the most underrated and misunderstood philosophers ever. It mostly depends upon his terminology, which displays an insurmountable difficulty. However, influenced by the most innovative proposals of his times of transition between the idealistic/rationalist legacies and the new Materialism/scientific interpretation of reality – i.e, by cultural evolutionism, linguistics, biomechanics, entropy/energy and, above all, by the newborn experimental psychology-, he produced a complete system of philosophy and innovative methods of investigation of the laws of knowledge. Hints (chunk, scrap, fragments) of his original ideas can be found not only in philosophers of mind after him - such as Gestalt, phenomenalism, behaviourism, functionalism and cybernetics, autopoiesis, dynamical systems theory, embedded/embodied mind, free-energy principle of the brain – but also in recent neuroscientific theories – nervous transduction, electric spikes, cracking of neural code, multisensory integration -. Our aim is to provide the first chronological English summary of his masterpiece, the “Kritik”, to give the possibility to the (almost) totally unaware English speakers to appreciate such a neglected and innovative thinker.

In this research article we solve the problem of synchronization and anti-synchronization of chaotic systems described by discrete and time-delayed variable fractional order differential equations. To guarantee the synchronization and anti-synchronization of these systems, we use the well-known PID control theory and the Lyapunov-Krasovskii stability theory for discrete systems of variable fractional order.We illustrate the results obtained through simulation with examples, in which it can be seen that our results are satisfactory, thus achieving synchronization and anti-synchronization of chaotic systems of variable fractional order with discrete time delay.

The distribution of tiny sensors over a specific area was first proposed in the late 1990s as a concept known as Smart Dust. Several efforts focused primarily on computing and networking capabilities but quickly ran into problems related to power supply, cost, data transmission, and environmental pollution. To overcome these limitations, we propose using paper-based (confetti-like) chemosensors that exploit the inherent selectivity of chemical reagents, such as colorimetric indicators. In this work, cheap and biodegradable passive sensors made from cellulose could successfully indicate the presence of hazardous chemicals, e.g., strong acids, by a significant color change. A conventional color digital camera attached to a drone could easily detect this from a safe distance. The collected data was processed to define the hazardous area. Our work presents a combination of the smart dust concept, chemosensing, paper-based sensor technology, and low-cost drones for flexible, sensitive, economical, and rapid detection of hazardous chemicals in high-risk scenarios.

The affinity constant, also known as the equilibrium constant, binding constant, equilibrium association constant, or the reciprocal value, the equilibrium dissociation constant (K_d), can be considered as one of the most important characteristics for any antibody-antigen pair. Many methods based on different technologies have been proposed and used to determine this value. However, since a very large number of publications and commercial datasheets do not include this information, significant obstacles in performing such measurements seem to exist. In other cases where such data are reported, the results have often proved to be unreliable. This situation may indicate that most of the technologies available today require a high level of expertise that does not seem to be available in many laboratories. In this paper, we present a simple approach based on standard immunoassay technology that is easy and quick to perform. It relies on the effect that the molar IC₅₀ approaches the K_d value in the case of infinitely small concentrations of the reagent concentrations. A two-dimensional dilution of the reagents leads to an asymptotic convergence to K_d. The approach has some similarity to the well-known checkerboard titration used for the optimization of immunoassays. A well-known antibody against the FLAG peptide, clone M2, was used as a model system and the results were compared with other methods. This approach could be used in any case where a competitive assay is available or can be developed. The determination of an affinity constant should belong to the crucial parameters in any quality control of antibody-related products and assays and should be mandatory in papers using immunochemical protocols.

The Benford method can be used to detect manipulation of epidemiological or trial data during the validation of new drugs. We extend here the Benford method after having detected particular properties for the Fibonacci values 1, 2, 3, 5 and 8 of the first decimal of 10 runs of official epidemiological data published in France and Italy (positive cases, intensive care, and deaths) for the periods of March 1 to May 30, 2020 and 2021, each with 91 raw data. This new method – called “BFP” for Benford-Fibonacci-Perez - is positive in all 10 cases (i.e. 910 values) with an average of favorable cases close to 80%, which, in our opinion, would validate the reliability of these basic data.

The pathophysiology of Nonketotic Hyperglycinemia (NKH), a rare neuro-metabolic disorder associated with severe brain malformations and life-threatening neurological manifestations, remains incompletely understood. Therefore, a valid human neuronal model is essential. We aimed to investigate the impact of GLDC gene variants, which cause NKH, on cellular fitness during the differentiation process of human induced pluripotent stem cells (iPSCs) into iPSC-derived astrocytes and to identify sustainable mechanisms capable of overcoming GLDC deficiency. We developed the GLDC27-FiPS4F-1 line and performed metabolomic, mRNA abundance and protein analyses. The study showed that although GLDC27-FiPS4F-1 maintained the parental genetic profile, it underwent a metabolic switch to an altered serine-glycine-one-carbon metabolism with a coordinated cell growth and cell cycle proliferation response. We then differentiated the iPSCs into neural progenitor cells (NPCs) and astrocyte-lineage cells. Analysis showed that GLDC-deficient NPCs had shifted towards a more heterogeneous astrocyte lineage with increased expression of the radial glial markers GFAP and GLAST and the neuronal markers MAP2 and NeuN. In addition, we detected changes in other genes related to serine and glycine metabolism and transport, all consistent with the need to maintain glycine at physiological levels. These findings improve our understanding of the pathology and offer new perspectives for therapeutic options.

In recent years, many libraries and archives have started digitizing their collections thus making maps by Indigenous peoples more easily available for scholars to study. While a number of these maps were discussed by G. Malcolm Lewis in the History of Cartography series (volume 2, book 3, Chapter 4: 1984), more have since been found and disseminated. These maps are critical in understanding the historic and current land tenure of Indigenous groups. Further, Indigenous claims to land can be seen in their connections via toponymy. European concepts of territory and political boundaries did not coincide with First Nation/American Indian views resulting in the mistaken view that Natives did not have formal concepts of their territories. Further, Tribes/First Nations with cross-border territory have special jurisdictional problems. This paper will illustrate how many Native residents were very spatially cognizant of their own lands, as well as neighboring nations’ lands, overlaps between groups, hunting territories, populations, and trade networks. Currently, the Sinixt First Nation provides a perfect example of how an Aboriginal people are inputting and using a GIS representation of their territory with proper toponymy and use areas.

ABSTRACT. In this paper, we run for all INDIA mutations and variants a biomathematical numerical method for analysing mRNA nucleotides sequences based on UA/CG Fibonacci numbers proportions (Perez, 2021). In this study, we limit ourselves to the analysis of whole genomes, all coming from the mutations and variants of SARS-CoV2 sequenced in India in 2020 and 2021. We then demonstrate - both on actual genomes of patients and on variants combining the most frequent mutations to the SARS-CoV2 Wuhan genomes and then to the B.1.617 variant - that the numerical Fibonacci AU / CG metastructures increase considerably in all cases analyzed in ratios of up to 8 times. We can affirm that this property contributes to a greater stability and lifespan of messenger RNAs, therefore, possibly also to a greater INFECTUOSITY of these variant genomes. Out of a total of 108 genomes analyzed: - None ("NONE") of them contained a number of metastructures LOWER than those of the reference SARS-CoV2 Wuhan genome. - Eleven (11) among them contained the same number of metastructures as the reference genome. - 97 of them contained a GREATER number of metastructures than the reference genome, ie 89.81% of cases. The average increase in the number of metastructures for the 97 cases studied is 4.35 times the number of SARS-CoV2 UA/CG 17711 Fibonacci metastructures. Finally, we put a focus on B.1.617.2 crucial exponential growth Indian variant. Then, we demonstrate, by analyzing the main worldwide 19 variants, both at the level of spikes and of whole genomes, how and why these UA / CG metastuctures increase overall in the variants compared to the 2 reference strains SARS-CoV2 Wuhan and D614G.

In the past years, consumers have increased their interest to buy healthier food products, rejecting those products with more additives and giving preference to the fresh ones. Moreover, the current environmental situation has made society more aware of the importance of reducing the production of plastic and food waste. In this way and considering the Food Industry need of reducing the food spoilage along the food chain, edible coating has been considered as an eco-friendly food packaging that can replace traditional plastic packaging providing an improvement in the product’s shelf-life. Edible coating are thin layers applied straightaway on the food materials’ surface, that are made of biopolymers that usually incorporate other elements, such as nanoparticles or essential oils, to improve their physicochemical properties. These materials must provide a barrier that can prevent the pass of water vapor and other gasses, the microbial growth, the moisture loss, and the oxidation, so the shelf-life could be extended. The aim of this review was to compile the current data available to give a global vision of the formulation process and the different ways to improve the characteristics of the coats applied in both fruits and vegetables. In this way, the suitability of compounds in by-products produced during the Food Industry chain were also considered for the edible coating production.

ABSTRACT. In this paper, we suggest a biomathematical numerical method for analysing mRNA nucleotides sequences based on UA/CG Fibonacci numbers proportions. This method is used to evaluate then compare the spike genes related to the main SARS-CoV2 VARIANTS currently circulating within the world population. The 10 main results proposed to be reproduced by peers are: 1/ SARS-CoV2 genome and spike evolution in one year 2020-2021. 2/ SARS-CoV2 Origins. 3/ Comparing 11 reference variants spikes. 4/ analysing 32 CAL.20C California variant patients spikes. 5/ Toward a meta mRNA Fibonacci gene end message code. 6/ Analysing S501 UK, S484 South Africa and « 2 mutations » INDIA variants. 7/ Suggesting a possible variants spike mRNA palindrome symmetry metastructure improving mRNA stability then infectiousness. 8/ Analysing Fibonacci Metastructures in the mRNA coding for the vaccines PFIZER and MODERNA. 9/ Does the CG-rich modification of the synonymous codons of the spikes of the 2 mRNA vaccines affect the expression and quantity of SARS-CoV2 antibodies? 10/ The exceptional case of the Brazilian variant P.1. Particularly, we suggest the following conjecture at mRNA folding level : CONJECTURE of SARS-CoV2 VARIANTS: The growth of long Fibonacci structures in the shape of "podiums" for almost all of the variants studied (UK, California, South Africa, India, etc.) suggests the probable folding of the Spike mRNA in the form of a "hairpin", which can strengthen the cohesion and the lifespan of this mRNA. Finally, we show that these kinds of Fibonacci matastructures disapear TOTALLY by analysing the published mRNA sequences of PFIZER and MODERNA vaccines. One fact is certain, the two mRNAs of the Moderna and Pfizer vaccines will result in a low functionality of the spike vaccine. This is because their designers by seeking greater stability, have doped to build CG rich sequences which, as soon as they are inserted into the human host, will, paradoxically, seek to mutate, like SARS-CoV2 variants, towards CG ==> UA forms in order to improve their STABILITY and LIFETIME. We conclude using new biomathematics theoretical methods (Master code and numerical standing waves), and comparing the Spikes of the two vaccines Moderna and Pfizer, that there will be very probable differences in stability and shelf life of the two respective mRNAs vaccines. However, “State of the Art” analyzes will disclose that their two protein sequences are strictly identical. By modified their synonymous codons using different strategies, no one can guarantee that the quantity of antibodies generated will be identical in the two cases. We wish to draw attention to the great ADAPTATION power - at the global scale of their genomes - of the most infectious VARIANTS, such as the BRAZIL 20J / 501Y.V3 variant (P.1). This is very worrying for the VACCINES <==> VARIANTS run: We demonstrate how the Brazilian variant P.1 which becomes uncontrollable in Brazil in April 2021 has a level of organization of long metastructures of 17,711 bases covering the genome which is 3.6 more important than that of the 2 reference genomes SARS-CoV2 and worldwide D614G. We suggest that this high level of overall structure of this variant contributes to the stability of this genome and, might explain its greater contagiousness.

In (Venetz et al., 2019), authors rebuilt the essential genome of Caulobacter crescentus through the process of chemical synthesis rewriting and studied the genetic information content at the level of its essential genes. Then, they reduced the native Caulobacter crescentus native Caulobacter NA1000 genome sequence real genome ( 4042929 bp ) to the 785,701-bp reduced synthetic genome. Here we demonstrate the existence of a palindromic-like mirror structure that exists in real genomes and disappears totally in the synthetic genome. This biomathematic meta-organization is based on characteristic proportions of Fibonacci numbers between DNA single strand nucleotides proportions TC / AG on the one hand and TG / AC on the other hand. In both cases, we suggest that this meta-structure enhances the three-dimensional cohesion of the two DNA strands of the genome. We then generalize this study to the different synthetic genomes and synthetic cells published by the Craig Venter Institute on Mycoplasma Mycoides JCVI-syn1.0 (in 2010), JCVI-syn3.0 (in 2016) and JCVI-syn3A (in 2019). Finally, in the discussion section, we extend this study to synthetic genomes of E-Coli and Yeast chromosome XII.

The discovery of a simple numerical formula for the projection of all the atomic mass of life-sustaining CONHSP bioatoms leads to the emergence of a set of Nested CODES unifying all the biological, genetic and genomic components by unifying them from bioatoms up to 'to whole genomes. In particular, we demonstrate the existence of a digital meta-code common to the three languages ​​of biology that are RNA, DNA and amino acid sequences. Through this meta-code, genomic and proteomic images appear almost analogous and correlated. The analysis of the textures of these images then reveals a binary code as well as an undulatory code whose analysis on the human genome makes it possible to predict the alternating bands constituting the cariotypes of the chromosomes. The application of these codes to perspectives in astrobiology, in Cancers basic research and the emergence of binary codes and regions of local stability (voting process), whose fractal nature we demonstrate, is illustrated.

In this work we present a new algorithm that achieves the perfect Shannon secret by means of the XOR function and a method that we call multiple key reuse. The algorithm has two execution modes: message authentication and data encryption. The XOR encryption scheme allows for batch encryption and exhibits Perfect Forward Secrecy (PFS). Furthermore, based on our fundamental algorithm, we have developed a new strategy for blockchain implementation that does not require Proof of Work (PoW), but defines a fair mechanism for miner selection and secure addition of blocks to the chain. Since our method is mainly based on the Boolean XOR function, the strength of the cryptosystem can be directly established thanks to its mathematical properties. Due to the risk that quantum computers represent for current cryptosystems based on prime factorization or discrete logarithm, we postulate that our method represents a promising alternative in the quantum era for the security of communications between Internet of Things devices as well as Blockchain technology.

Abstract: Peptide pools composed of short amino acid sequences have proven to be versatile tools in various research areas and clinical applications. They are powerful tools for epitope mapping, immunotherapy, and vaccine development. Their importance lies in their ability to map complex protein structures, enabling a comprehensive understanding of immune responses and facilitating the identification of potential therapeutic agents. The application of peptide pools also extends to the field of personalized medicine, offering tailored solutions for diseases such as cancer and infectious diseases. Peptide pools are complex mixtures of immunostimulatory antigens primarily intended for T-cell stimulation. They are commercially available in many different compositions and variants. However, unlike other reagents that consist of only one or a few compounds, peptide pools are highly complex products with limited stability. This makes their quality control a major challenge. Quantitative peptide analysis usually requires sophisticated methods, in most cases isotope-labeled standards and reference materials. None of these are routinely available for these products. Synthesis and purification of all labeled peptides might be required. Usually, this approach would be prohibitively laborious and expensive. Therefore, an approach is needed to provide a practical and feasible method for quality control of peptide pools. With insufficient quality control, the use of such products could lead to incorrect experimental results, which would worsen the well-known reproducibility crisis in the biomedical sciences. Here we propose the use of ultra-high performance liquid chromatography (UHPLC) with two detectors, a standard UV detector (at 214 nm) for quantitative analysis and a high-resolution mass spectrometer (HRMS) for identity confirmation. To be cost-efficient and fast, quantification and identification are performed in one chromatographic run. An optimized protocol is shown, and different peak integration methods are compared and discussed. This work was performed using a peptide pool known as CEF, which consists of 32 peptides derived from cytomegalovirus (CMV), Epstein-Barr virus (EBV) and influenza virus, ranging from 8 to 12 amino acids in length.

In an analogous manner as occurred during the development of a connected metabolism that at some point reached characteristics associated with what is called ‘life’ ―due mainly to a catalytic closure phenomenon when chemicals started to autocatalyze themselves forming a closed web of chemical reactions― it is here proposed that cognition and consciousness (or features associated with them) arose as a consequence of another type of closure within the nervous system, the brain especially. Proper brain function requires an efficient web of connections and once certain complexity is attained due to the number and coordinated activities of the brain cell networks, the emergent properties of cognition and consciousness take place. Seeking to identify main features of the nervous system organization for optimal function, it is here proposed that while catalytic closure yielded life, neuroglial closure produced cognition/consciousness.

Matter grows and self-assembles to produce complex structures such as virus capsids, carbon fullerenes, proteins, glasses, etc. Due to its complexity, performing pen-and-paper calculations to explain and describe such assemblies is cumbersome. Many years ago, Richard Kerner presented a pen-and-paper path integral approach to understand self-organized matter. Although successfully addressed many important problems including the yield of fullerene formation, the glass transition temperature of doped chalcogenide glasses, the fraction of boroxol rings in B2O3 glasses, the first theoretical explanation for the empirical recipe of window and Pyrex glass and the understanding of virus capsid self-assembly, still is not the primary choice when tackling similar problems. The reason lies in the fact that it diverges from mainstream approaches based on the energy landscape paradigm and non-equilibrium thermodynamics. In this context, a critical review is presented, demonstrating that the Richard Kerner method is, in fact, a clever way to identify relevant configurations. Its equations are simplified, common physical sense versions to those found in the energy landscape kinetic equations. Subsequently, the utilization of equilibrium Boltzmann factors in the transition Markov chain probabilities is analyzed within the context of local two-level energy landscape models kinetics. This analysis demonstrates that their use remains valid when the local energy barrier between reaction coordinate states is small compared to the thermal energy. This finding places the Richard Kerner model on par with other more sophisticated methods and, hopefully, will promote its adoption as an initial and useful choice for describing the self-agglomeration of matter.

Background: Colorectal cancer (CRC) patients experienced multiple chemotoxicity affecting treatment compliance, survival, and quality of life (QOL). Prior research shows clinician-reported chemotoxicity (i.e., grading scales or diagnostic codes) predicts rehospitalization and cancer survival. However, a comprehensive synthesis of clinician-reported chemotoxicity is still lacking. Objectives: We conducted a systematic review and meta-analysis to determine the chemotoxicity's prevalence and risk factors in CRC. Methods: A systematic search from 2009 to 2024 yielded 30 studies for review, with 25 included in the meta-analysis. Results: Pooled prevalences of overall, non-hematological, and hematological moderate-to-severe toxicities were 45.7%, 39.2%, and 25.3%, respectively. The most common clinician-reported chemotoxicities were gastrointestinal (GI) toxicity (22.9%) and neuropathy or neutropenia (17.9%). Significant risk factors at baseline were malnutritional status, frailty, impaired immune or hepato-renal functions, short telomere lengths, low gut lactobacillus levels, age, female sex, aggressive chemotherapy, and low QOL. Age was associated with neutropenia (β: -1.44) and GI toxicity (β:1.85) (Ps 65 y.o.) had higher prevalences of overall (OR: 1.14) and GI (OR: 1.65) toxicities, but a lower prevalence of neutropenia (OR: 0.65) than younger adults (Ps

The management of urban water has evolved from single-function systems to more sustainable designs promoting society and nature as inputs to engineer novel infrastructure. In transdisciplinary research, co-design refers to a design thinking strategy in which people jointly frame a problem-solution. This article presents a conceptual framework to assess a case study focusing on the process of co-design and implementation of green infrastructure as a prototype for stormwater management. The evaluation is carried out from a self-reflective post-implementation perspective. Research activities are translated into the framework to evaluate conditions shaping the trajectory of the prototype. As a result, key aspects driving the research regarding levels of stakeholder participation and dimensions of power are identified. Planning resilient co-design strategies to retrofit urban spaces is necessary to avoid unintended consequences, especially at the initial experimental stages. This study aims to contribute to the continuous improvement of pilot strategies in urban spaces by providing a framework for a structured evaluation of research experiences.

: For a long time, atherosclerosis has been regarded as a mere lipid deposit in the blood vessels. However, in the recent years a growing body of experimental and clinical evidence have highlighted the role of inflammation and immunity as a central mechanism of disease. Moreover, in the last decade the coming of next-generation sequencing and its application to large human population has broken the barrier between inflammation and cancer. Indeed, acquired mutation in key genes related to the control of hematopoiesis and myeloproliferation have paved the way to establish the new concept of clonal hematopoiesis of indeterminate potential. This phenomenon is being considered not such “indeterminate”, but as an emerging cardiovascular risk factor. Thus, this may explain the mechanisms of myeloproliferation and inflammation in atherosclerosis. And in a bidirectional journey, it has helped to explain the extremely high cardiovascular risk in cancer survivors, in particular in myeloprolfierative neoplasm patients. A deeper understanding of these mechanisms may pave the way for the future early diagnosis and potential preemptive treatments of the leading worldwide cause of death.

To implement renewable energy resources, microgrid systems have been adopted and developed into the technology of choice to assure mass electrification in the next decade. Microgrid systems have a number of advantages over the conventional utility grid systems, however, it faces severe instability issues due to continually increasing constant power loads. To improve the stability of the entire system, load side compensation technique is chosen because of its robustness and cost effectiveness. In this particular occasion, a sliding mode controller is developed for microgrid system in the presence of CPL to assure certain control objective of keeping the output voltage constant at 480V. After that, the robustness analysis of the sliding mode controller against parametric uncertainties is presented. The sliding mode controller robustness against parametric uncertainties, frequency variations, and additive white Gaussian noise (AWGN) are illustrated in this paper. Later, the performance of the PID and sliding Mode controller is compared in case of nonlinearity, parameter uncertainties, and noise rejection to justify the selection of Sliding Mode controller over PID controller. All the necessary calculations are reckoned mathematically and results are verified in the virtual platform such as MATLAB/Simulink with the appreciable outcome.

In the Mediterranean area, vegetation dynamics and phenology analyzed over a long time can have an important role in highlighting changes in land use and cover as well as the effect of climate change. Over the last 30 years, remote sensing has played an essential role in bringing about these changes thanks to many types of observations and techniques. Satellite images are to be considered an important tool to grasp these dynamics and evaluate them in an inexpensive and multidisciplinary way thanks to Landsat and Sentinel satellite constellations. The integration of these tools holds a dual potential: on one hand, allowing to obtain longer historical series of reflectance data, while on the other hand, making data available with a higher frequency even within a specific timeframe. The study aims to conduct a comprehensive cross-comparison analysis of long-time series pixel values in the Mediterranean regions. For this scope comparisons between Landsat-7 (ETM+), Landsat-8 (OLI), and Sentinel-2 (MSI) satellite sensors were conducted based on surface reflectance products. We evaluated these differences using Ordinary Least Squares (OLS) and Major Axis linear regression (RMA) analysis on points extracted from over 15,000 images across the Mediterranean basin area from 2017 to 2020. Minor but consistent differences were noted, necessitating the formulation of suitable adjustment equations to better align Sentinel-2 reflectance values with those of Landsat-7 or Landsat-8. The results of the analysis are compared with the most used harmonization coefficients proposed in the literature, revealing significant differences. The root mean square deviation, the mean difference and the orthogonal distance regression (ODR) slope show an improvement of the parameters for both models used (OLS and RMA) in this study. The discrepancies in reflectance values lead to corresponding variations in the estimation of biophysical parameters, such as NDVI, showing an increase in the ODR slope of 0.3. Despite differences in spatial, spectral, and temporal characteristics, we demonstrate that integration of these datasets is feasible through the application of band-wise regression corrections for a sensitive and heterogeneous area like those of the Mediterranean basin area.

La minería es una industria intensiva en energía que requiere una fuente estable de electricidad. Con el aumento de la demanda de minerales y la disminución de las leyes, se espera que la demanda de energía aumente en un 36% para el 2035. Este aumento de la demanda depende del uso de combustibles fósiles ya que la electricidad producida y comprada en las empresas mineras se basan principalmente en el caso de los combustibles fósiles, los costos de los sistemas de almacenamiento de energía solar y eólica han caído a una escala sin precedentes, lo que alienta a las empresas mineras a probar estas tecnologías.Entre los factores que influyen en el uso de energía renovable, se encuentra el beneficio a la comunidad en la etapa de operación y cierre o post-cierre de un proyecto minero, el siguiente factor es el perfil de consumo eléctrico o demanda es importante para el cálculo de su almacenamiento o no, de las energías renovables a utilizar. Como tercer factor está el cálculo del consumo en los procesos. Los rangos de penetración determinarán si es posible su almacenamiento de energía, y si es posible utilizar control híbrido (solar - eólico), para ello es importante el uso de herramientas digitales como el programa Holmer, es una herramienta importante que técnica y económica.El gobierno debe tener una perspectiva que ayude en los temas regulatorios necesarios para impulsar proyectos de energía renovable con diferentes incentivos e intereses según el contexto minero.

Metal corrosion poses a significant challenge for industries by decreasing the lifespan of materials and escalat-ing maintenance and replacement costs. This study is critically important as it assesses the corrosion resistance properties of annealed steel wire electrodes coated with manganese, employing chronoamperometry and linear voltammetry techniques. The electrodes were immersed in an electrolyte solution and subjected to chronoam-perometry at various voltages (-0.55 V, -0.60 V, and -0.70 V) and durations (60 seconds and 1800 seconds). Sub-sequently, linear voltammetry was performed over a potential range from -0.8 V to 0.8 V to generate Tafel plots. The Butler-Volmer equation was applied to the data obtained to determine the corrosion current density. The re-sults indicate that the optimal conditions for forming a highly effective protective manganese layer occur at a potential of -0.70 V for 1800 seconds. Under these conditions, the electrodes exhibited superior corrosion re-sistance. The study also revealed that shorter durations and less negative potentials led to less effective manga-nese coatings, with higher corrosion rates and reduced stability. These findings are significant for developing ef-ficient corrosion protection methods in industrial and research applications, providing clear parameters for op-timizing the manganese electrodeposition process on annealed steel.

Autophagy is a multistep catabolic process through which misfolded, aggregated or mutated proteins and damaged organelles are internalized in membrane vesicles called autophagosomes and ultimately fused to lysosomes for degradation of sequestered components. The multi-step nature of the process offers multiple regulation points prone to be deregulated and cause different human disease, but also offers multiple targetable points for designing therapeutic strategies. Cancer cells have evolved to use autophagy as an adaptive mechanism to survive under extremely stressful conditions within tumor microenvironment, but also to increase invasiveness and resistance to anti-cancer drugs such as chemotherapy. This review collects all clinical evidences of autophagy deregulation during cholangiocarcinogenesis together with all pre-clinical reports evaluating compounds that modulate autophagy to induce cholangiocarcinoma (CCA) cell death. Altogether, experimental data suggests an impairment of autophagy during initial steps of CCA development and increased expression of autophagy markers on established tumors and in invasive phenotypes. Pre-clinical efficacy of autophagy modulators promoting CCA cell death, reducing invasiveness capacity and resensitizing CCA cells to chemotherapy open novel therapeutic avenues to design more specific and efficient strategies to treat this aggressive cancer

There is a formal analogy between the evolution of the universe, when this is seen as a trajectory in the minisuperspace, and the worldline followed by a test particle in a curved spacetime. The analogy can be extended to the quantum realm, where the trajectories are transformed into wave packets that give us the probability of finding the universe or the particle in a given point of their respective spaces: the spacetime in the case of the particle and the minisuperspace in the case of the universe. The wave function of the spacetime and the matter fields, all together, can then be seen as a super-field that propagates in the minisuperspace and the so-called third quantisation procedure can be applied in a parallel way as the second quantisation procedure is performed with a matter field that propagates in the spacetime. The super-field can thus be interpreted as made up of universes propagating, i.e. evolving, in the minisuperspace. The analogy can also be used in the opposite direction. The way in which the semiclassical state of the universe is obtained in quantum cosmology allows us to obtain, from the quantum state of a field that propagates in the spacetime, the geodesics of the underlying spacetime as well as their quantum uncertainties or dispersions. This might settle a new starting point for a different quantisation of the spacetime.

Background and objectives: In the current context, the 2021 presidential elections in Peru distance from the social objective, not being objectively represented, that is why we analyze their validity, we determine the distances between the parties if they are extreme, the correspondences with the departments and their prospects, in the surveys we propose which departments influence the results. Methods: We use a mixed methodology, qualitative analysis, it will be multidimensional with the support of statistical methods and programs such as R Studio, worddj, Gephi, and Iramuteq. The quantitative analysis will be through factor analysis, correspondence and discriminant analysis to the data of the election Results and conclusions: The textual analysis mentions that there are dimensions such as the social issue, the results of the surveys and democracy that are far apart, regarding the electoral issue. This inculcates to work both on the part of the organisms that carry out these processes, as well as the initiative of the candidates, and the media. Regarding the quantitative analysis, it is detailed that the representative parties must be greater than 4.0 million voters, to make representative parties at the national level. The classification regarding the percentage of votes is given in three groups, highlighting the independence of the Peru Libre political party in these . In the correspondence analysis we can detail that both Fuerza Popular and Peru Libre are extremist parties. The vast majority of Peruvian citizens have an ideological tendency, intermediate between both parties. The prospective projections with a cross section give a victory to the Peru Libre party. In the discriminant analysis, the sample for the surveys focuses on 7 departments that does not include the Peruvian capital.

In this review, we aimed to understand the relationship between SARS-CoV-2 infection and preeclampsia severity in infected pregnant women. Pregnant women with SARS- CoV-2 infection have been shown to have a significantly increased risk of maternal death, ICU admission, preterm delivery, and stillbirth compared with those without infection. The risk of preeclampsia also increases in pregnant women infected with SARS-CoV-2, particularly in those with severe symptoms. We emphasize the im-portance of specialized clinical management to prevent poor pregnancy outcomes in this situation. The association between COVID-19 and preeclampsia (PE) is likely due to multiple mechanisms, including the direct effects of the virus on trophoblast function and the arterial wall, acute atherosis, local inflammation leading to pla-cental ischemia, exaggerated inflammatory responses in pregnant women, SARS-CoV-2-related myocardial injury, cytokine storm, and thrombotic microan-giopathy. Emphasis has been placed on the potential impact of COVID-19 on pregnant women, specifically in relation to thrombotic complications, placental dysfunction, and cardiovascular dysfunction. Undoubtedly, one of the diagnostic tools to differentiate between COVID-19-induced preeclampsia-like syndrome and true preeclampsia is the use of biomarkers, such as the sFlt-1/PlGF ratio. We wish to highlight the potential for COVID-19-induced myocardial injury in pregnant women and the associated in-crease in maternal mortality rate. Vaccination against COVID-19 in the general population and in pregnant women in particular, drastically reduced the severity of the disease. There is an urgent need to continue the follow-up of these women and their children to detect the appearance of sequelae of the disease or peristent COVID 19

The La Oroya metallurgical complex reveals a process of structural or power pathology, where economic and environmental social policies lead to a citizenry without systemic freedom, characterized by environmental contamination (PAMA executed on average 67%, some processes with 0%) and sanitary (plumbosis 8.39 to 10.28 μg / dL), the economic dependence of this industry is also observed, which caused the weakening of the communities to maintain autonomy over their own destinies.The Doe Run Peru, operated the metallurgical complex, creating added value to the extractive-export chain of mineral concentrate, but also took advantage of the legal loopholes, the little coordination between the State institutions and the little authority of the supervisory institutions to get the most out of it, complying with the law in some cases, but without moral quality in some others, such as health in the oroinos.Government institutions must have strong links that provide a normative framework with prospects with sustainable development, where the beneficiary is society and not only certain interest groups. The economic benefits of the metallurgical complex operation are multiple, which begin with labor, the purchase and sale of materials and services, the collection of taxes and energy.

In this note, I analyze the data generated by M. Fodje's (2013) simulation programs "epr-simple" and "epr-clocked". They were written in Python and published on Github. Inspection of the program descriptions shows that they make use of the detection-loophole and the coincidence-loophole respectively. I evaluate them with appropriate modified Bell-CHSH type inequalities: the Larsson detection-loophole adjusted CHSH, and the Larsson-Gill coincidence-loophole adjusted CHSH (NB: its correctness is conjecture, we do not have proof). The experimental efficiencies turn out to be approximately eta = 81% (close to optimal) and gamma = 55% (far from optimal). The observed values of CHSH are, as they should be, within the appropriately adjusted bounds. Fodjes' detection-loophole model turns out to be very, very close to Pearle's famous 1970 model, so the efficiency is close to optimal. The model has the same defect as Pearle's: the joint detection rates exhibit signaling. Fodje's coincidence-loophole model is actually a clever modification of his detection-loophole model. Because of this, however, it cannot lead to optimal efficiency.

This article reevaluates the Woesean concept of crossing a ‘Darwinian threshold’ from pre-genomic communality, as prevailing in an ancestral ‘progenote’ state, to vertically stable lineages of autonomous and self-similar cells. This transition from collective trunk-line evolution to Darwinian speciation is dependent on the generation of modular organismal genomes. The same general principle should be valid at subcellular levels, allowing the emergence of semi-autonomous genomic agents, such as viruses and plasmid-carrying endogenous vesicles with organelle-like properties. As compartmentalized agents of endogenous nature could start with smaller genomes than those required for fully autonomous cells, it is conjectured that stable subcellular lineages emerged earlier than their cellular counterparts. Referring to the recent ‘pre-endosymbiont hypothesis’, it is proposed that free-living bacteria (the first ‘prokaryote’ cells) arose by ‘lineage escape’ from plasmid-bearing organelle-like compartments, evolving inside the internally complexifying ‘paracells’ of the progenote community. The double-membrane envelopes of diderm bacteria may have resulted from cell-biological processes facilitating cellular lineage escape. The later emergence of archaeal cells (resembling bacteria in ‘prokaryote’ appearance with unichromosomal genomes) and eukaryotic organisms (with compartmented cells and multichromosal genomes) can also be interpreted in terms of this modified progenote hypothesis. Conceivably, the multichromosomal genomes of eukaryotes were bundled in endogenous nuclear compartments to organize a ‘nuclear-cytoplasmic lineage’, which became vertically stable by perfecting mitosis/meiosis-like divisions and yet retained some intra-species population confluence by sexual division-fusion cycles.

Mpox (monkeypox) is a zoonotic disease that has been endemic in African countries for decades, with a recent outbreak in several countries around the world. A 39-year-old male with HIV-HBV coinfection and poor adherence to antiretroviral treatment, who was severely immunocompromised and had a concurrent diagnosis of Mpox infection, presented to our hospital with disseminated dermatosis (over 350 lesions), perianal ulcers, odynophagia, oral intolerance, diarrhea, and soft-tissue bacterial superinfection of the lower extremities. Laboratory results were consistent with HBV infection, with an absolute CD4 cell count of 40 cells/uL and a positive PCR result for Mpox. An abdominopelvic CT scan showed evidence of severe proctitis and perineal soft-tissue infection. After 65 days of Mpox PCR, new lesions in the vesicular stage continued to appear, eventually developing hemodynamic instability and sepsis, resulting in a fatal outcome. Our case highlights the importance of intentionally looking for risk factors such as HIV/HBV coinfection and evaluating immune status (CD4 cell count) in patients with severe Mpox infection because it could be related to higher mortality.

Does the choice of proxy for stock market development matter? This paper suggests that the growth effect of stock market development is sensitive to the choice of proxy and using alternative financial development indicators have practically no influence on the results. We found that using either the stock market capitalization to GDP ratio or the stock market returns; have a positive and significant effect on growth. However, we cannot make same conclusion when one uses either the ratio of total value of trades on the major stock exchanges to GDP or stock market turnover ratio to proxy for stock market development as the coefficient on these variables were found to be statistically insignificant. The indexes extracted from principal component analysis confirm the sensitivity of the effect to the choice of proxy. This finding suggest that stock market development is a conceptual terms, thus, representing it with single indicators make it impossible to identify which stock market development indicators have a significant positive growth effects.

Developmental hazard evaluation is an important part of assessing chemical risks during pregnancy. Toxicological outcomes from prenatal testing in pregnant animals result from complex chemical-biological interactions, and while New Approach Methods (NAMs) based on in vitro bioactivity profiles of human cells offer promising alternatives to animal testing, most of these assays lack cellular positional information, physical constraints, and regional organization of the intact embryo. Here, we engineered a fully computable model of the embryonic disc in the compucell3d.org modeling environment to simulate epithelial-mesenchymal transition of epiblast cells and self-organization of mesodermal domains (chordamesoderm, paraxial, lateral plate, posterior/extraembryonic). Cell fate in the model is determined by an autonomous homeobox (HOX) clock driven by morphogenetic signals (e.g., FGF, WNT, ATRA, CDX). Executing the model renders a quantitative cell-level computation of mesodermal subpopulations and consequences of perturbation based on known embryogeny. For example, synthetic perturbation of the control network rendered altered phenotypes (cybermorphs) mirroring experimental mouse embryology, with 50% reductions in FGF4, FGF8 and BMP4 signaling resulting in 86%, 98% and 59% reductions, respectively in the posterior mesodermal population, while ATRA exposure also resulted in a 78% decrease in this population. This model enables integration of in vitro chemical bioactivity data for specific molecular targets with known embryology to test mechanistic veracity and quantitative prediction of altered development.

To show that underlying Schmitt's account of fascist politics lies a Gnostic-like metaphysical dualism separating the realms of value and power. Contrary to the normative political tradition of the West, which defends an ethical politics, Schmitt - jurist and theorist of the Nazis - aligns himself with Machiavelli and Hobbes to defend realpolitik: where sovereignty is ultimately a function of the Dictator's will alone. This paper shows the contradiction within such a position, which criticizes values in politics but by its advocacy, and its defense of the Dictator's willing, relies on valuation, choice, and hence the ethical.

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

There is scientific consensus that organismal aging did not evolve by natural selection (NS) because it lacks individual benefit. Nonetheless it exists, leading to much speculation about its origins, and when the diminishing force of selection begins. Both concepts are based upon two misconceptions; that aging occurs in and of itself and is caused by the declining strength of NS during the reproductive lifespan. Although lacking individual benefit, aging evolved by NS as a tradeoff of survival for reproduction. Based upon regulatory dynamics that participate in this tradeoff, aging begins once reproductive success has been achieved through offspring nurturing. Thereafter, the strength of NS wanes to exponentially accelerate aging, leading to death. Assumptions of the theory are that: (1) a life-long, “holistic” regulatory mechanism whose genic expression is modified epigenetically, originates in ontogenesis; (2) the regulatory mechanism of the last developmental stage becomes redundantly expressed during “morphostasis”, a non-aging, life interval of peak vitality to ensure completion of reproduction through nurturing, and (3) thereafter, loss of regulatory redundancy causes aging which reduces the strength of natural selection and allows accumulation of randomly occurring somatic damage.

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

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

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

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

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

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

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

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

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

Background/Objectives: The anesthetic block of the sensory branches of the superficial cervical plexus (SCP) renders a specific area of the face, head, and anterior neck insensible and painless. Chronic pain in these areas can be difficult to diagnose and treat. In this report, we briefly review the existing evidence on the topic of the SCP block (SCPB) to set the context for our research. We then share our own clinical experience with the SCPB for managing chronic pain syndromes from both cancerous and non-cancerous etiologies. Methods: We first performed a comprehensive literature search and narrative review of clinical cases and studies that utilized the SCPB as an analgesic technique. We then conducted a retrospective case series of all patients who received a SCPB at our pain clinic since 2020. Results: Our literature review found only a few cases reported, with most of them focusing on acute painful emergencies and perioperative pain syndromes and only very few addressing chronic pain. In our pain clinic, 14 patients received one or more SCPBs for chronic pain management. In 42% of these cases, the pain was related to cancer. The most common areas of pain corresponded to the regions supplied by the transverse cervical and greater auricular nerves. The procedures were uneventful in all cases, and patients rated them as effective and worthwhile 71% of the time. Conclusions: Despite the lack of high-quality studies on SCPBs in pain management, the authors’ experience suggests that it is a valid minimally invasive alternative for managing chronic face, head and neck pain.

The purpose of the study was to evaluate the success of procedural conscious sedation, using inhaled equimolar nitrous oxide-oxygen (NOIS – EMONO), in patients undergoing routine dental and oral surgery procedures in a Swiss university hospital setting. Materials and methods: The authors conducted a retrospective cohort study of patients that underwent NOIS-supported procedures between 2018 and 2022 at the oral surgery department of the University Hospital of Geneva (HUG), Switzerland. The primary outcome was the measurement of the procedure success and efficacy as defined by the European Society of Anesthesiology. Secondary objectives included the analysis of the types of treatments performed, their indications, patient behavior, and the patient - clinician satisfaction score. Results: 55 patients were included in the study, 85% underwent surgical procedures, the remaining 15% underwent restorative and preventive procedures. The overall treatment success rate was 98.2%, and 97.9% for surgically treated patients. 62% of patients appeared relaxed, calm, and serene, while 16% expressed pain or fear during the procedure. Infiltrative administration of local anesthesia caused stress in 22% of patients, this was significantly lower in sub cohorts who received local topical anesthetics (0%) or a combination of systemic and local topical analgesics (7%). Patients (75%) and clinicians (91%) were satisfied with the procedure. Conclusion: Inhaled equimolar nitrous oxide-oxygen procedural sedation used during dental procedures and oral surgery results in high treatment success and satisfaction rates. The administration of additional topical anesthetics helps reduce the anxiety and stress related to infiltrative anesthesia. Further dedicated studies and prospective trials are needed to confirm these findings.

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

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

Pregnancy-related morbidity and mortality remains high across the United States and the majority of deaths are preventable. Misdiagnosis and delay in diagnosis are thought to be contributors to preventable harm. These diagnostic errors in obstetrics are understudied. We present selected research methods to ascertain rates of and harm associated with diagnostic errors, the challenges in investigating them, and present future steps toward achieving diagnostic excellence in obstetrics.

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

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

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

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

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.

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

The three-dimensional structure of Gulf of Mexico’s warm-core rings, detaching from the Loop Current, is investigated using satellite altimetry and a large set of ARGO float profiles. Reconstruction of the Loop Current rings (LCRs) vertical structure from sea surface height observations is made possible by the use of the gravest empirical modes method (GEM). The GEMs are transfer functions that associate a value of temperature and salinity for each variable pair {dynamic height;pressure}, and are computed by estimating an empirical relationship between dynamic height and the vertical thermohaline structure of the ocean. Between 1993 and 2021, 40 LCRs were detected in the altimetry and their three-dimensional thermohaline structure was reconstructed, as well as a number of dynamically relevant variables (geostrophic and cyclogeostrophic velocity, relative vorticity, potential vorticity, available potential energy and kinetic energy density etc.). The structure of a typical LCR was computed by fitting an analytical stream function to the LCRs dynamic height signature and reconstructing its vertical structure with the GEM. The total heat and salt contents and energy of each LCR were computed and their cumulative effect on the Gulf of Mexico’s heat, salt and energy balance is discussed. We show that LCRs have a dramatic impact on these balances and estimate that residual surface heat fluxes of -13 Wm−2 are necessary to compensate their heat input, while the fresh water outflow of the Mississippi river approximately compensates for their salt excess input. An average energy dissipation of O[10−10−10−9] Wkg−1 would be necessary to balance their energy input.

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

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

Regular inspections during construction work verify that the work completed is consistent with the plans and specifications and ensure that it is within the planned time and budget. This requires frequent physical site observations to independently measure and verify the completion percentage of the construction progress performed over periods of time. The current computer vision-based (CV) techniques for the measurement of as-built elements, predominantly use 3D laser scanning or 3D Photogrammetry modelling to determine the geometrical properties of as-built elements on construction sites. Both techniques require data acquisition from several positions and angles to generate sufficient information about the element’s coordinates making the deployment of these techniques on dynamic construction project sites a challenging task. In this paper, we propose a pipeline for automating the measurement of as-built components using artificial intelligence (AI) and computer vision (CV) techniques. The pipeline requires a single image obtained with a stereo-camera system to measure the size of selected objects or as-built components. We demonstrate our approach by measuring the size of concrete walls and columns. The novelty of this work is attributed to the fully automated CV-based method for measuring any given element using a single image only. The proposed solution is suitable for use in measuring the sizes of as-built components of built assets. It has the potential to be further developed and integrated with BIM models for use on construction projects for progress monitoring.

Spinal cord injury (SCI) is a condition that affects between 8.8 and 246 people in a million and, unlike many other neurological disorders, it affects mostly young people, causing deficits in sensory, motor, and autonomic functions. Promoting regrowth of axons is one of the most important goals for the neurological recovery of patients after SCI, but it is also one of the most challenging goals. A key event after SCI is the formation of a glial scar around the lesion core, which is mainly comprised of astrocytes, NG2+-glia, and microglia. Traditionally, the glial scar has been regarded as detrimental for recovery because it may act as a physical barrier for axon regrowth and release various inhibitory factors. However, more and more evidence now suggest that the glial scar is beneficial for the surrounding spared tissue after SCI. Here, we review experimental studies that used genetic and pharmacological approaches to ablate specific populations of glial cells in rodent models of SCI, in order to understand their functional role. The studies showed that ablation of either astrocytes, NG2+-glia or microglia may result in disorganization of glial scar, increased inflammation, extended tissue degeneration and impaired recovery after SCI. Hence, glial cells and glial scar appear as important beneficial players after SCI.

Regulatory agencies across the Latin American Region have strengthened the regulatory science through the development of new tools, standards and various other related parameters to evaluate and assess safety, efficacy, quality and performance. The former have been implemented to promote and incorporate new drugs and technologies, which still, are a challenge to well-established regulatory frameworks. Furthermore, in today’s environment, the existing regulatory framework protecting public’s health creates barriers for market entry of novel drugs and medical devices. This article aims to the pioneering work that Cuban Regulatory Agency (CECMED) has been developing with the aim to build a strong regulatory framework geared to accelerated innovation and the successful transition from research and development to clinical development. The Office of Innovation recently established at the CECMED is the first flagship in Latin America and the Caribbean region. Its aim is to play a leading role as a driving force for the national and regional biopharmaceutical innovation. This article will discuss the Office of Innovation its conceptualisation and management taking into account the Latin American regional and national Cuban context.

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

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

(1) Background: Painful nervus intermedius neuropathy (e.g., geniculate neuralgia) involves continuous or near-continuous pain affecting the distribution of the intermedius nerve(s). The diagnosis of this entity is challenging, particularly when the clinical and demographic features do not resemble the typical presentation of this condition. To the best of our knowledge, only three case reports have described the occurrence of nervus intermedius neuropathy in young patients. (2) Case Description: A 13-year-old female referred to the Orofacial Pain clinic with a complaint of pain located deep in the right ear and mastoid area. The pain was described as a constant throbbing and dull, with an intensity of 7/10 on numerical rating scale, characterized by superimposed brief paroxysms of severe sharp pain. The past treatments included ineffective pharmacological and irreversible surgical approaches. After a comprehensive evaluation, a diagnosis of idiopathic painful nervus intermedius neuropathy was given, which was successfully managed with the use of gabapentin. (3) Conclusions and Practical Implications: The diagnosis and treatment of neuropathic pain affecting the nervus intermedius can be challenging due to the complex nature of the sensory innervation of the ear. The diagnosis can be even more challenging in cases of atypical clinical and demographic presentations, which in turn may result in unsuccessful, unnecessary, and irreversible treatments. Multidisciplinary teams and constant knowledge update are fundamental to provide good quality of care to our patients and to not overlook any relevant signs or symptoms.

The purpose of this study is to analyse the drivers and outcomes of the brand love derived from consumption experiences in the automobile retail sector. We examine the effects of consumers’ perceptions of perceived service quality and the trust they feel towards a car dealership (cognition) on emotions and brand love (affection), and the effects of brand love on consumers´ behavioural intentions (conation). This research provides insights into customer brand love in the automobile industry.

Quantum-dot cellular automata (QCA) is an emerging transistor-less field-coupled nanocomputing (FCN) approach to ultra-scale ‘nanochip’ integration. In QCA, to represent digital circuitry, electrostatic repulsion between electrons and the mechanism of electron tunnelling in quantum dots are used. QCA technology can surpass conventional complementary metal-oxide semiconductor (CMOS) technology in terms of clock speed, reduced occupied chip area, and energy efficiency. To develop QCA circuits, irreversible majority gates are typically used as the primary components. Recently, some studies have introduced reversible design techniques, using reversible majority gates as the main building block, to develop ultra-energy efficient QCA circuits. However, this approach resulted in time delays, an increase in the number of QCA cells used, and an increase in the chip area occupied. This work introduces a novel hybrid design strategy employing QCA irreversible, reversible, and partially reversible gates to establish an optimal balance between power consumption, delay time, and occupied area. The hybrid technique allows the designer to have more control over the circuit characteristics to meet different system needs. A combination of reversible, irreversible, and innovative partially reversible majority gates is used in the proposed hybrid design method. We have evaluated the hybrid design method by examining the half-adder circuit as a case study. We have developed four hybrid QCA half-adder circuits, each of which simultaneously incorporates various types of majority gates. The QCADesigner-E 2.2 simulation tool was used to simulate the performance and energy efficiency of the half-adders. This tool provides numerical results for the circuit input/output response and heat dissipation at the physical level, within a microscopic quantum mechanical model.

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

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

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

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

This paper presents data supporting the IC Regimen for potentiating the effectiveness of lorlatinib in treating ALK positive cancers, and specifically non-small cell lung cancer. Lorlatinib is an effective treatment for ALK driven non-small cell lung cancer and other ALK driven cancers. Lorlatinib inhibits ALK kinase and achieves good brain tissue levels, a common site for metastases in lung cancers. However, resistance to lorlatinib usually supervenes and the cancer awakens and starts growing again, resistant to lorlatinib. This paper analyses data indicating that adding two common generic drugs, itraconazole and cilostazol, to lorlatinib treatment may delay resistance development. Itraconazole is marketed worldwide as a generic antifungal drug that also inhibits Hedgehog signaling, CYP3A4, and the p-gp efflux pump. Cilostazol is a generic anti-thrombosis, phosphodiesterase 3 inhibiting drug that carries minimal bleeding risk. Cilostazol may enhance lorlatinib by deprivation of trophic growth factors supplied by platelets. Itraconazole may enhance lorlatinib effectiveness by reducing or stopping a Hedgehog centered amplifying feedback loop with ALK kinase. The combination of metastatic non-small cell lung cancer being a low-survival disease and the general safety itraconazole plus cilostazol augmentation, make a clinical trial of this trio worthwhile.

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

Autism spectrum disorder (ASD) is a common condition with lifelong implications and a strong hereditary component suggesting genetic underpinnings. The last decade has seen dramatic improvements in DNA sequencing and related bioinformatics and databases. We analyzed the raw DNA sequencing files on the Variantyx® bioinformatics platform for the last 50 ASD patients evaluated with trio whole genome sequencing (trio-WGS). “Qualified” variants were defined as coding, rare, and evolutionarily conserved. Primary Diagnostic Variants (PDV) additionally were in genes directly linked to ASD and matched clinical correlation. A PDV was identified in 34/50 (68%) of cases, including 25 (50%) cases with heterozygous de novo and 10 (20%) with inherited variants. De novo variants in genes directly associated with ASD were far more likely to be Qualifying than non-Qualifying versus a control group of genes not associated with ASD (P = 0.0002, odds ratio 29), validating that most are indeed disease related. Only 14/34 (41%) of PDV cases had the variant listed on the laboratory report, and reanalysis increased diagnostic yield from 28% to 68%. Variants that we assigned as PDVs yet not on the report were predominately de novo in genes not yet reported as ASD associated. Many subjects both with and without a PDV had inherited Qualifying variants in known ASD-associated genes, suggesting polygenic inheritance. Thirty-three subjects (66%) had treatment recommendation(s) based on DNA analyses. Our results demonstrate high yield of trio-WGS for revealing molecular diagnoses in ASD that is greatly enhanced by re-analyzing DNA sequencing files. In contrast to previous reports, de novo variants dominate the findings, mostly representing novel conditions. This has implications to the cause and rising prevalence of autism.

In recent years, online social networks have received important consideration in spatial modelling fields given the critical information that can be extracted from them for events in real time; one of the most latent issues is that regarding various natural disasters such as earthquakes. Although it is possible to retrieve data from these social networks with embedded geographic information provided by GPS, in many cases this is not possible. An alternative solution is to reconstruct specific locations using probabilistic language models, more specifically those based on Name Entity Recognition (NER), which extracts names from a user’s description about an event occurring in a specific place (e.g., a collapsed building on a specific avenue). In this work, we present a methodology to use twitter as a social sensor system for disasters. The methodology scores NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space is proposed. The proposed methodology is evaluated with tweets related to the 2017 earthquake in Mexico.

Green Infrastructure promotes the use of natural functions and processes as potential solutions to reduce negative effects derived from anthropocentric interventions such as urbanization. In cities of Latin America, for example, the need for more nature-sound infrastructure is evident due to its degree of urbanization and degradation of ecosystems, as well as the alteration of the local water cycle. In this study, an experimental approach for implementation of a prototype is presented. The experiment took place in a highly urbanized watershed located in the Metropolitan Area of Costa Rica. Initially, understanding the characteristics of the study area at different scales was achieved by applying the Urban Water System Transition Framework to identify the existing level of development of the urban water infrastructure, and potential future stages. Subsequently, preferences related to spatial locations and technologies were identified from different local decision-makers. Those insights were adopted to identify a potential area for implementation of the prototype. The experiment consisted on an adaptation of the local sewer to act as a temporal reservoir to reduce the effects derived from rapid generation of stormwater runoff. Unexpected events, not considered initially in the design, are reported in this study as a means to identify necessary adaptations of the methodology. Our study shows from an experimental learning-experience that the relation between different actors advocating for such technologies influences the implementation and operation of non-conventional technologies. Furthermore, the perception of security associated to green spaces was found as a key driver to increase the willingness of residents to modify their urban environments. In consequence, those aspects should be carefully considered as factors of designs of engineering elements when they are related to complex socio-ecological urban systems.

Background: and objectives: Covid-19 is a global pandemic that requires a culturally integrated response in all Latin American government systems. In this study, we investigate the multidimensional associations of actions in Latin American indigenous peoples against Covid-19 Methods: We performed a data mining analysis of scientific literature records from 19 academic journals, using the topics Covid-19, mortality, indigenous peoples, and future measures, rights and actions. The literature is analyzed in a multidimensional way, either isolated or combined. It uses switchable statistical methods, the R Studio, worddj, Gephi, and Iramuteq for both textual and multidimensional analysis. Results and Conclusions: In this analysis, the literature is classified into 4 linked groups which are the indigenous people, the rights of territorial protection, the indigenous health culture, the Covid-19 pandemic with its protection efforts. Taking the indigenous peoples as an axis, we observe that the covid pandemic and the protection of their territories do not have the same force in the current context, the indigenous people are more linked to the protection of their territory. Indigenous peoples have a link of cultural strength in traditional health independent of Covid treatment. It should be noted that government agencies are taking measures regarding the covid pandemic with an effort not marked within the health culture of indigenous peoples.

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

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

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

Guava (Psidium guajava L.) is a species is widely distributed in Córdoba, despite this, there are few studies that have been carried out to understand its genetic diversity. For this reason, the objective of this research was to evaluate the genetic diversity and variability of guava (Psidium guajava L) using microsatellite markers in Montería, Córdoba-Colombia. Forty-five accessions were evaluated. For DNA extraction, the CTAB 2x extraction protocol was used with modifications. The extracted DNA was amplified by the PCR technique using seven microsatellite markers. A total of 46 alleles were found with an average of 6.5 alleles per locus. A total of 28 private alleles were identified in the populations, possibly fixed by different evolutionary forces. The evaluated population presented an average expected heterozygosity of 0.572 and the observed heterozygosity was lower (0.058). The fixation index presented a total average of 0.906, indicating that in Montería there is an excess of homozygotes. The PIC values allowed us to determine that the markers mPgCIR9, mPgCIR11, mPgCIR13, mPgCIR16, mPgCIR19 and mPgCIR22 are highly informative, while mPgCIR23 is moderately informative. The guava population in Montería presented a low genetic diversity within the subpopulations, which could be related to inbreeding events such as self-pollination..

Since December 2019, the coronavirus disease (COVID-19) has rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, the authors use statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national level in Mexico. Two types of models are proposed: first, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model is used to estimate the middle and the end of the outbreak. Model selection will be performed using Vuong’s test. The proposed models show overall fit similar to predictive models (e.g. time series, and machine learning); however, the interpretation of parameters is less complex for decision-makers and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue.

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

In the same manner that there are several statements of the second law of thermodynamics but all of them are equivalent, it is possible that most of the entropic methods applied to the nervous system and brain in particular share a similar outcome, or essence, that helps understand some aspects of the fundamentals of basic neurodynamics. In this short review focused on certain aspects of the entropic metrics some results are examined that indicate the fundamental importance of the natural tendency towards a maximal energy distribution for healthy brain activity and thus cognition to emerge.

The Flack Parameter is now almost universally reported for all chiral materials characterized by X-ray crystallography. It’s elegantly simplicity was an inspired development by Flack, and although the original algorithm for its computation has been strengthened by other workers, it remains an essential outcome for any crystallographic structure determination. As with any one-parameter metric, it needs to be interpreted in the context of its standard uncertainty.

Simulated crime scene investigation is an essential component of forensic science education, but its implementation poses challenges relating to cost, accessibility and breadth of experience. Virtual reality (VR) is an emerging technology which offers exciting prospects for teaching and learning, especially for imparting practical skills. We document here a multidisciplinary experimental study in which a bespoke VR crime scene app was designed and implemented, after which it was tested by both undergraduate student and staff/postgraduate student cohorts. Through both qualitative and quantitative analyses, we demonstrate that VR applications support learning of practical crime scene processing skills. VR-based practical sessions have the potential to add value to forensic science courses through offering cost-effective practical experience and the ability to work in isolation, in a variety of different scenarios. Both user groups reported high levels of satisfaction with the process and reports of adverse effects (motion sickness) were minimal. With reference to user feedback, we proceed to evaluate the scalability and development challenges associated with large-scale implementation of VR as an adjunct to forensic science education.

Cardiovascular diseases (CVDs) are the leading cause of death in the globally. Morbidity and disability related to non-fatal events are increasing exponentially. There are several symptoms that may arise after invasive therapeutic approaches such as coronary artery bypass graft (CABG), including chronic pain in anatomical areas connected to the mediastinum; these pains can be found not only initially after surgery, but also years later. We present a case where non-specific neck pain (NNP), in a patient undergoing CABG five years earlier, was approached with an osteopathic technique, working the pericardial area. To the knowledge of the Authors, it is the first case report where osteopathy is associated with a gentle treatment on the pericardial area to resolve a NNP.

At 2.3 megabases in length, the dystrophin gene is enormous: transcription of a single mRNA requires approximately 16 hours. Principally expressed in skeletal muscle, the dystrophin protein product protects the muscle sarcolemma against contraction-induced injury, and dystrophin deficiency results in the fatal muscle-wasting disease, Duchenne muscular dystrophy. This gene is thus of key clinical interest, and therapeutic strategies aimed at eliciting dystrophin restoration require quantitative analysis of its expression. Approaches for quantifying dystrophin at the protein level are well established, however study at the mRNA level warrants closer scrutiny: measured expression values differ in a sequence-dependent fashion, with significant consequences for data interpretation. In this manuscript we discuss these nuances of expression and present evidence to support a transcriptional model whereby the long transcription time is coupled to a short mature mRNA half-life, with dystrophin transcripts being predominantly nascent as consequence. We explore the effects of such a model on cellular transcriptional dynamics, and then discuss key implications for the study of dystrophin gene expression, focussing both on conventional (qPCR) and next-gen (RNAseq) approaches.

A developed six-port reflectometry (SPR) system was integrated to measure the relative permittivity of tumor and normal breast tissue for medical diagnostic purpose. In order to obtain an accurate and precise measurement, the calibration process was done to the SPR using the well-known three-standard technique. Next, the studied dielectric probe was connected to the calibrated measurement-port of the SPR. The open end of the probe aperture was dibbed into the normal and tumor synthetic breast tissue samples to measure the synthetic breast tissues dielectric constant, ɛrʹ, and loss factor, ɛrʺ in the frequency range of 1.5 GHz to 3.3 GHz. Finally, the comparative studies were conducted between commercial VNA with Keysight 85070E dielectric probe and the studied SPR-probe system based on the measured magnitude of the reflection coefficient, phase shift, dielectric constant, and loss factor of the synthetic breast samples. The maximum absolute errors of the measured reflection coefficient magnitude, phase shift, dielectric constant, and loss factor were found to be 0.01, 1.07°, 1.12, and 0.75, respectively. It was ascertained that the predicted dielectric constant, ɛrʹ, is able to differentiate between normal, (ɛrʹ &lt; 50) and tumor, (ɛrʹ &gt; 50) breast tissues.

In our investigation of the time evolution of energy states Einstein’s interpretation from more than a century ago is found to have greater internal consistency than that of non-relativistic quantum mechanics. We choose to align more closely with his methods by abandoning the non-relativistic Hamiltonian method, which has been the standard for nearly a century, in favor of a relativistically correct Lagrangian method. Integral equations of motion for the absorption and emission of radiation are derived that underlie and anticipate the differentially motivated Schrödinger equation. This new interpretation applies to a large volume of experimental evidence of both classical and quantum mechanical origin. Among the examples discussed in support are Planck’s law describing black body radiation, matrix mechanics, the function of the simplest quantum mechanical system an electron cyclotron, chaos theory, and evolutionary biology.

Detailed evaluation is provided for the likelihood statistics intrinsic to interlocking Sequential Bayes analysis, which allows estimation of evidential support for dendrograms charting the macroevolution of taxa. It involves complexity functions, such as fractal evolution, to generate well-supported evolutionary trees. Required are data on trait changes from ancestral species to descendant species, which is facilitated by reduction of large genera to monothetic groups (one ancestral species each), most conveniently named as separate genera. Assigning each species one Shannon informational bit per new trait, then adding the traits as sequential Bayesian analysis (each posterior probability used as the prior for the next iteration of Bayes Formula), then interpretation with an odds chart, provides a posterior probability that the ancestor-descendant order is correct, that is, entirely follows evolutionary theory involving adaptation and rarity of total trait reversals. The key fact is that the most recently acquired traits of the ancestral species are selectively inviolate and passed on without change to each descendant species. The details of sequential Bayesian analysis were clarified by calculation of likelihood ratios and Bayes factors that compare optimal models with the next most likely model. Such analysis demonstrated that the optimum arrangement of ancestor and descendant species leads to high support values for fitting evolutionary theory, comparable to statistical support levels reported for molecular evolutionary trees. The fact that phylogenetic analysis does not offer an ancestor-descendant model in is found to be conducive of precision at the expense of accuracy, and leads to wrong ancestor-free models with likelihoods explaining data at high credible levels. The number of advanced traits in the outgroup as a Bayesian prior greatly enhances posterior probability. The method is simple, free of special computer analysis, and well-suited to standard taxonomic practice.

Differences between the quantum mechanical and relativistic concepts of time observed in GPS satellites are accounted for by applying the equivalence principle to the transitioning electron of an atomic clock. This allows a differential equation of motion to be derived for the electron in the Minkowski space between electron shells. By applying Hamilton’s principle we transform the differential equation into a relativistically correct integral equation of motion, the time integral of a Lagrangian. This newly derived equation accounts for the abstract mathematics of the more familiar non-relativistic model by means of a physical interpretation. Abstract rotations in Hilbert space are replaced by real rotations of particle field geometries in Minkowki space that describe the electron transition and emission of a photon. Because the properties of energy are universal the same integral equations are able to be used to describe the time evolution of galaxies despite vast differences in lifetime. The comparison of gravitational and electromagnetic energy exchanges by means of radial and transverse fields leads to an alternative to the standard model that is based on a field model. It explains the observed properties of galaxies as a superposition of neutrinos localized within the horizon of a black hole with spins aligned. The particle properties of free neutrinos are accounted by means of a classical field geometry; specifically, a spin that is oriented in space with rotational inertia due to energy. A test of the standard model is proposed based on differing views for flavor oscillation.

During glioblastoma treatment, the pharmaceutical monoclonal antibody to VEGF, bevacizumab, has improved quality of life and delayed progression for several months but has not, or only marginally prolonged overall survival. In 2017 several dramatic research papers appeared that are crucial to our understanding of glioblastoma vis a vis the mode of action of bevacizumab. As a consequence of these papers, a new, potentially more effective, treatment protocol can be built around bevacizumab. This is the ADZT Regimen where four old drugs are added to bevacizumab. These four are apremilast, marketed to treat psoriasis, dapsone, marketed to treat Hansen’s disease, zonisamide, marketed to treat seizures, and telmisartan, marketed to treat hypertension. The ancillary attributes of each of these drugs has been shown to augment bevacizumab. This paper will detail the research data supporting that contention.

Hash tables are widely used. They rely on good quality hash functions. Popular data structure libraries either provide no hash functions or weak hash functions for sets or maps, making it impossible or impractical to use them as keys in other tables. This article presents three algorithms for hashing a set, two of which are simple to implement, practically fast, and can be combined. The quality evaluations follow the method of [1, chapter 2]. The insight that we are looking for commutative semigroups suggests that even better methods than symmetric polynomials may be found.

In recent years, online social media information has been subject of study in several data science fields due to its impact on users as a communication and expression channel. Data~gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users opinions and make predictions about real events. Cyber attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ1 regularization.

This study is based on the fatty acid and amino acid profile of 7 edible insect species: Acheta domesticus, Alphitobius diaperinus, Blaptica dubia, Galleria mellonella, Locusta migratoria, Tenebrio molitor and Zophobas morio. The fatty acid distribution within lipid classes (neutral lipids, glycolipids and phospholipids) was studied, while the amino acid profile was used to calculate the species-specific nitrogen-protein conversion factor (Kp). The profiles were used to evaluate the nutritional quality of the lipids and proteins. Oleic acid was the predominant fatty acid in all insects except for A. domesticus, in which a larger percentage of linoleic acid was found. The majority of the lipids were neutral lipids. L. migratoria showed a remarkably high content of α-linolenic acid in its phospholipid fraction, while T. molitor phospholipids were the only fraction in which a measurable amount of docosahexaenoic acid was found. Overall, in most insects, the phospholipid fraction had the highest polyunsaturated fatty acid content compared to the other classes, which may be protective in terms of autoxidative stability. Kp values in the range of 4.17 to 6.43 were obtained. Within the nutritional quality indices, all insects showed healthy fatty acid and high quality amino acid profiles.

Introduction: Individuals with gout are at higher risk of developing chronic conditions, such as diabetes, chronic kidney disease (CKD), and cardiovascular diseases. In this study, the association between urate-lowering therapy (ULT) use and the prevalence of these conditions was evaluated. Methods: This observational, cross-sectional pharmacoepidemiologic study used the 2013-2018 biannual cycles of the National Health and Nutrition Examination Survey. The inclusion criteria were adults 30 years of age and older with a diagnosis of gout. The association between ULT treatment status and dyslipidemia, coronary heart disease, heart failure, hypertension, and chronic kidney disease was evaluated, as well as associations with select clinical laboratory biomarkers. Results: Use of ULT was 28.9% (95%CI 24.3%-33.9%). There was no significant association between ULT use and the prevalence of heart failure, coronary heart disease, hypertension, or dyslipidemia (p>0.05). Those on ULT had a lower mean eGFR compared to those not on treatment (68.03 versus 74.74 mL/min/1.73m², p=0.014). LDL- and HDL- and total cholesterol were significantly lower among those receiving ULT treatment (p<0.05). Conclusion: ULT use continues to be low among those with gout. Those on ULT were more likely to be diagnosed with CKD, college graduates and above, older males, and obese. Clinical guidelines conditionally recommend ULTs in co-morbid kidney disease; while our results may reflect guidelines recommendation for ULT use in CKD patients, worsening kidney function while taking ULT is unlikely. Further research is necessary to determine the long-term impact of ULTs on kidney function and cardiovascular biomarkers.

Recent advances in microscopy imaging and image analysis motivate more and more institutes world-wide to establish dedicated core-facilities for bio-image analysis. To maximize the benefits research groups at these institutes gain from their core-facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core-facilities can offer. We also discuss potential conflicts of interests between the targeted missions and implementations of services to guide decision makers and core-facility founders to circumvent common pitfalls.