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

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

As our understanding of the thalamocortical system deepens, the questions we face become more complex. Their investigation requires the adoption of novel experimental approaches complemented with increasingly sophisticated computational modeling. In this review, we take stock of current data and knowledge about the circuitry of the somatosensory thalamocortical loop in rodents, discussing common principles across modalities and species whenever appropriate. We review the different levels of organization, including the cells, synapses, neuroanatomy, and network connectivity. We provide a complete overview of this system that should be accessible for newcomers to this field while nevertheless being comprehensive enough to serve as a reference for seasoned neuroscientists and computational modelers studying the thalamocortical system. We further highlight key gaps in data and knowledge that constitute pressing targets for future experimental work. Filling these gaps would provide invaluable information for systematically unveiling how this system supports behavioral and cognitive processes.

Background and Objectives: In recent years Electronic scooters (E-Scooters) have gained popularity, whether for private use or as a publicly available transportation method. With introduction of these vehicles, reports of E-Scooter related accidents have surged, sparking a public debate and concern. Aim of this study was to analyse epidemiological data, characteristics and severity of traumatic brain injury (TBI) related to E-Scooter accidents. Materials and Methods: This retrospective case series evaluated 28 patients who were admitted to three largest neurosurgery clinics in Riga, Latvia between time period from April to October in two separate years – 2022 and 2023 after an E-Scooter related accident. Data were collected on patient demographics, time of the accident, alcohol consumption, helmet use, type of TBI, other related injuries, treatment and assessment at discharge. Results: A total of 28 patients were admitted with TBI related to E-Scooter use, with median age 30 years (Q1-Q3, 20.25-37.25), four individuals under age of 18, majority (64%) were male. In 23 cases the injury mechanism was fall, in 5 cases collision. None were wearing a helmet at time of injury. Alcohol intoxication was evident in over half of the patients (51.5%), with severe intoxication (&gt;1.2ppm) in 75% cases among them. Neurological symptoms upon admission were noted in 50% of cases. All patients had intracranial trauma - 50% brain contusions, 43% traumatic subdural hematoma, almost 30% - epidural hematoma. Craniofacial fractures were evident in 71% of cases, fractures in other parts of body in 3 patients. 6 patients required emergency neurosurgical intervention. Neurological complications were noted in 2 patients, 1 patient died. Conclusions: E-Scooter related accidents result in a significant number of brain and other associated injuries, with notable frequency linked to alcohol influence and lack of helmet use. Prevention campaigns to raise awareness of potential risks and implementation of more strict regulations should be done.

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

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

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

Proper blood-brain barrier (BBB) function is essential to maintain homeostasis keeping the central nervous system healthy. Disruption of the BBB has been described in nearly all neurological disease and injury contexts. Given the various physical and metabolic roles of the BBB and gliovascular unit (GVU), dysfunction should be assessed across multiple levels to pinpoint the exact nature of the impairment. Here we describe imaging techniques for evaluating various aspects of BBB and GVU integrity and function, tailored for use in mouse models with protocols for quantitative image analysis. These imaging techniques complement classical dye tracer experiments and reveal underlying molecular causes of BBB dysfunction.

This comprehensive review delves into the cognitive neuroscience of hypnosis and variations in hypnotizability by examining research employing functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG) methods. Key focus areas include functional brain imaging correlations in hypnosis, EEG band oscillations as indicators of hypnotic states, alterations in EEG functional connectivity during hypnosis and wakefulness, drawing critical conclusions, and suggesting future research directions. The reviewed functional connectivity findings support the notion that disruptions in the functional integration between different components of the executive control network during hypnosis may correspond to altered subjective appraisals of the agency during the hypnotic response, as per dissociated and cold control theories of hypnosis. Building upon existing EEG results, a promising exploration avenue involves investigating how frontal lobes' integrated functioning with other cortical regions influences hypnosis and individual differences in hypnotizability during both waking and hypnotic states. Future studies investigating the effects of hypnosis on brain function should prioritize examining distinctive activation patterns across various neural networks. A practical approach entails simultaneously analyzing the causal relationships in functional connectivity linked to cognitive, physiological, and behavioral responses.

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

The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumor from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making processes, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging state-of-the-art DL frameworks with transfer learning, we conduct a comprehensive evaluation encompassing both classification accuracy and interpretability. Using state-of-the-art DL frameworks with transfer learning, we conduct a thorough evaluation covering both classification accuracy and interpretability. We employ adaptive path-based techniques to understand the underlying decision-making mechanisms of these models. Grad-CAM and Grad-CAM++ highlight critical image regions where the tumors are located.

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

Background and Purpose: Oxidative stress is an important factor in the pathogenesis of neurological problems. One of the compounds causing oxidative stress is diazinon. In the present project, the protective role of gallic acid compound in reducing oxidative stress in brain tissue of mice exposed to diazinon was investigated. Method: In this study, eight groups of six series of mice including the first group (control) of normal saline, the second group of diazinon (40 kg/mg) as IP, three groups of diazinon with gallic acid (50, 100, 200 kg/mg), was prescribed as a single dose and for 24 hours. The atropine + pralidoxime group was also evaluated as a standard treatment group. At 24 hours after the last dose, the animals were sacrificed and brain tissue was examined to assess oxidative stress parameters, including glutathione content, protein carbonyl content, lipid peroxidation, reactive oxygen species (ROS) and mitochondrial function in mitochondria isolated from brain tissue. Results: The results of this research showed that oxidative stress biomarkers increased significantly (P<0.001) in the diazinon group compared to the control group. Oxidative stress was significantly reduced in the groups receiving gallic acid at a dose of 200 and 100 kg/mg of diazinon compared to the diazinon group. Conclusion: The results of the present study show that diazinon causes damage to brain tissue by inducing oxidative stress. Therefore, according to the reduction of oxidative stress by gallic acid compound, it can be concluded that this compound can protect against brain toxicity caused by diazinon in animal model by stimulating antioxidant and radical scavenging pathways.

This paper draws on data from a representative city-wide household food security survey of Nairobi conducted in 2017 to examine the importance of food remitting to households in contemporary Nairobi. The next section of the paper provides an overview of urbanization and the rapid growth of Nairobi which has led to growing socio-economic inequality, precarious livelihoods for the majority, and growing food insecurity, as context for the more detailed empirical analysis of food security and food remittances that follows. It is followed by a description of the survey methodology and sections analyzing the differences between migrant and non-migrant households in Nairobi. Attention then turns to the phenomenon of food remitting, showing that over 50% of surveyed households in the city had received food remittances in the previous year. The paper then uses multivariate logistic regression to identify the relationship between Nairobi household characteristics and the probability of receiving food remittances from rural areas. The findings suggest that there are exceptions to the standard migration and poverty-driven explanatory model of the drivers of rural-urban food remitting and that greater attention should be paid to other motivations for maintaining rural-urban connectivity in Africa.

Digital gait monitoring is increasingly used to assess locomotion and fall risk. The aim of this work is analyzing the changes in the foot-floor contact sequences of Parkinson’s Disease (PD) patients in the year following the implant for deep brain stimulation (DBS). During their best-ON condition, 30 PD patients underwent gait analysis at baseline (T0), at 3 months (T1), and 12 months (T2) after the DBS neurosurgery for bilateral high-frequency subthalamic nucleus stimulation. Thirty age-matched controls underwent gait analysis once. Each subject was equipped with bilateral foot-switches and a 5-minute walk was recorded, including both straight-line and turnings. The walking speed, turning time, stride time variability, percentage of atypical gait cycles, stance, swing, and double support duration were estimated. Overall, the gait performance of PD patients improved after DBS, as also confirmed by the decrease in the UPDRS-III score from 19.4±1.8 to 10.2±1.0 points (T0 vs. T2) (p < 0.001). In particular, the percentage change of atypical gait cycles of PD more affected side decreased at T1 (Straigh-line: -72%; Turnings: -43%) and at T2 (Straigh-line: -54%; Turnings: -20%). The percentage of atypical gait cycles proved an informative digital biomarker for quantifying PD gait changes after DBS, both in straight-line paths and turnings.

This study assesses the predictive performance of six machine learning models in forecasting tumor dynamics within three months following Gamma Knife Radiosurgery (GKRS) in 77 brain metastasis (BM) patients. The analysis meticulously evaluates each model before and after hyperparameter tuning, utilizing accuracy, AUC and other metrics derived from confusion matrices. Initial findings highlighted that XGBoost significantly outperformed other models with an accuracy of 0.95 and an AUC of 0.95 before tuning. Post-tuning, the Support Vector Machine (SVM) demonstrated the most substantial improvement, achieving an accuracy of 0.98 and an AUC of 0.98. Conversely, XGBoost showed a decline in performance after tuning, indicating potential overfitting. The study also explores feature importance across models, noting that features like “control at one year”, “age of the patient”, and “beam on time for volume V1 treated”, were consistently influential across various models, albeit their impacts were interpreted differently depending on the model’s underlying mechanics. This comprehensive evaluation not only underscores the importance of model selection and hyperparameter tuning but also highlights the practical implications in medical diagnostic scenarios, where the accuracy of positive predictions can be crucial.

This literature review investigates the importance of simulation and measurement techniques in the development of transmission lines and antennas, which are crucial to the reliability and efficiency of communication system. It emphasizes the necessity of exact measurement and computational approaches for simulating complicated electromagnetic process, which help to optimize designs before physical prototypes are built. The review examines a variety of research articles that offer innovative solutions to improving accuracy in antenna design, scalability and cost challenges in antenna measurements, and crosstalk measurement methods. It also explores how these methodologies affect antenna design parameters such as gain, bandwidth, and radiation patterns, emphasizing the importance of correct modeling to ensure the performance of transmission lines and antennas in modern communication system. The review serves as a valuable reference for guiding future research and technological advancements in these fields.

Countless disasters have resulted from climate change, causing severe damage to infrastructure and the economy. These disasters have significant societal impacts, necessitating mental health services for the millions affected. To prepare for and respond effectively to such events, it is crucial to understand people's emotions and the life incidents they experience both before and after a disaster strikes. In this case study, we mined the emotions and life incidents expressed on Twitter during the 2017 Hurricane Harvey. Between August 20 and August 30, we collected a dataset of approximately 400,000 public tweets related to the storm. Using a BERT-based model, we predicted the emotions associated with each tweet. Following emotion prediction, we applied various natural language processing techniques to analyze the content of the tweets, aiming to uncover the 'topics' discussed alongside specific 'emotions.' To efficiently identify these topics, we utilized the Latent Dirichlet Allocation (LDA) technique for topic modeling, which allowed us to bypass manual content analysis and extract meaningful patterns from the data. However, rather than stopping at topic identification, we further refined our analysis by integrating Graph Neural Networks (GNN) and Large Language Models (LLM). The GNN was employed to generate embeddings and construct a similarity graph of the tweets, which was then used to optimize clustering. This step ensured that related tweets were accurately grouped together based on their content and context. Subsequently, we used an LLM to automatically generate descriptive names for each event cluster. This advanced approach allowed us to identify and label life incidents automatically, providing a nuanced understanding of the key issues faced by the community during the hurricane. The results revealed significant emotional shifts as the hurricane reached its most devastating phase. These shifts coincided with pressing concerns and demands from the population, such as evacuation plans, animal safety, safety updates, public policy, help requests, flood impacts, and the spread of fake news. By combining GNN and LLM for automatic event name prediction, our study not only mapped out the emotions but also accurately categorized the specific life incidents that were most relevant during the disaster, offering critical insights for disaster preparedness and response strategies.

The Universe is treated as an unbound thermodynamic system in which two forces of Nature compete: Attractive gravity loss and repulsive entropic gain. Entropic gain, although not presently considered a force of Nature, is otherwise well known and can be expressed with the gas laws. The Universe at the time of last scattering was all gas. Its instant gain is expressed as gas pressure. When the Gibbs equation is properly applied to this unbound gas, the resulting atomic Hubble parameter is found to be exclusively dependent on baryon density, giving a constant and perpetual two-to-one gain/loss ratio. Instant loss to gravity cannot offset gas pressure in this “thermal equation”. By contrast, the Friedmann equation, a ΛCDM foundation, treats baryon mass as accreted, having little if any entropic gain. These two equations are actually equivalent. Expanding accreted matter at its comoving critical density is herein shown to behave exactly like a freely expanding gas. However, neither equation includes all of today’s Universal kinetic energy, and cannot by themselves fully account for observed stellar movement. The present paper proposes the plausible existence of highly energetic or “suprathermal” free electrons which comprise about half of the intergalactic medium’s kinetic total. It is this suprathermal energy which Λ expresses. The fluid equation, another ΛCDM foundation, eliminates entropic gain from the Gibbs equation. Additionally, rest mass’s Einstein energy E=Mc² is treated as a thermal variable. These two assumptions, isoentropy and energy conflation, led astronomers to populate Λ as they followed the prior art.

We present a high-throughput nanoindentation study of in-situ bending effects on incipient plastic deformation behavior of polycrystalline and single-crystalline pure aluminum and pure copper at ultra-nano depths (<200nm). We find that hardness displays a statistically inverse dependence on in-plane stress for indentation depths smaller than 10nm, and the dependence disappears for larger indentation depths. In addition, plastic noise in the nanoindentation force and displacement displays statistically robust noise features, independently of applied stresses. Our experimental results suggest the existence of a regime in FCC crystals where ultra-nano hardness is sensitive to residual applied stresses, but plasticity pop-in noise is insensitive to it.

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

The therapeutic, attenuative, and morphological effects of proprietary neural stem cell-derived extracellular vesicles (NSC EVs, AB126) produced by Aruna Bio, Inc. were assessed in a range of in vitro and in vivo models of stroke. We emphasize that modulation of neuroinflammation is a key target and suggest further studies to advance our understanding of the mechanisms of action and ultimately how best to dose and monitor the benefits of AB126 in stroke patients.

Human milk (HM) feedings are associated with reduced risks of neurodevelopmental and neurocognitive delays and systemic inflammatory diseases in infants born prematurely and/or at a low birth weight. Utilizing a neonatal piglet model, HM feedings were compared to bovine milk-derived infant formula (IF)-feedings with the aim of understanding the underlying mechanisms involved in reducing these risks. Six male piglets at postnatal-day (PD)2 were randomized into two feeding groups: HM-fed (n = 3) or IF-fed (n = 3) for 28 days. At 3- and 4-weeks-old, piglet memory and learning performance were assessed using novel object recognition (NOR). At PD30 piglets were euthanized, and whole brains were excised, weighed, and preserved for further analyses, and brain microglial morphology, and systemic inflammatory cytokines were quantified. During NOR, HM-fed piglets had significantly more non-novel revisits at both 3- and 4-weeks-old compared to IF-fed piglets. Microglia of HM-fed piglets had longer processes, more branch points, more endpoints, and a larger territorial volume compared to IF-fed piglets at PD30. In a neonatal piglet model, HM-feedings may improve cognition and reduce brain inflammatory response compared to IF-feedings.

Involving industrial waste into economic circulation, in particular flotation waste from coal enrichment, is an urgent task. The coal preparation waste of ArcelorMittal Temirtau JSC was studied with a view to the possibility of using it as an alternative substitute for coke breeze in the sintering process. During a thermoanalytical study on a synchronous thermal analyzer STA 449 NETZSCH in dynamic mode with a heating rate of 10, 20, 30 °C/min in an air flow of 30 cm³/min in the temperature range 30–1000 °C, we determined the lower heat of combustion of carbon-containing material, change mass during calcination, change and rate of change in the mass of the sample, thermal effect of the process, the temperature of the beginning, end and maximum development of thermal effects. It has been established that the greatest mass loss is observed in the temperature range 580 °C- 610 °C, which is about 65–67% of the total mass of the organic component of coal. The maximum burnout rate corresponds to a temperature of 540–563 °C. The lowest calorific value of the studied waste was 15.9 MJ/kg. The results of thermal analytical research and technical analysis substantiate the possibility of using flotation waste in a mixture of up to 10% with coke breeze for combustion in the sintering layer for the production of sinter. Analysis of the composition of flotation waste showed that they have a high specific surface area (56.8∙10³ m²/kg), are highly dispersible, soggy, and capable of swelling, and the predominant amount of clay substance makes them a plastic material. The possibilities of using coal flotation waste as an alternative substitute for coke breeze have been studied after the regimes of its preparation for sintering have been developed and a rational regime of layer-by-layer combustion in the sintering layer has been established. The optimal ratio of coke and OPC in the fuel mixture of the sintering charge was determined.

The goal of the project was to understand how topography/bathymetry is imported and used in GeoFlood with an aim towards improving efficiency and computational cost of bathymetry handling in GeoFlood.GeoFlood is a software to simulate overland flooding basing on shallow water equations.Numerical methods and programming were used for this study.The GeoFlood was ran on a given data set (Malpasset data) and it was found out that indeed topography handling during simulations has to be improved to minimise the computational time and also improve the efficiency.Through this research, we also looked at the the shallow water equations, and the numerical techniques used to solve the equations

Establishing macrometastases at distant organs is a highly challenging process for cancer cells, with extremely high attrition rates. A very small percentage of disseminated cells have the ability to dynamically adapt to their changing micro-environments through reversibly switching to another phenotype, aiding metastasis. Such plasticity can be exhibited along one or more axes – epithelial-mesenchymal plasticity (EMP) and cancer stem cells (CSCs) being the two most studied, and often tacitly assumed to be synonymous. Here, we review the emerging concepts related to EMP and CSCs across multiple cancers. Both processes are multi-dimensional in nature; for instance, EMP can be defined on morphological, molecular and functional changes, which may or may not be synchronized. Similarly, self-renewal, multi-lineage potential, and anoikis and/or therapy resistance may not all occur simultaneously in CSCs. Thus, arriving at rigorous functional definitions for both EMP and CSCs is crucial. These processes are dynamic, reversible, and semi-independent in nature; cells traverse the inter-connected high-dimensional EMP and CSC landscapes in diverse paths, each of which may exhibit a distinct EMP-CSC coupling. Our proposed model offers a potential unifying framework for elucidating the coupled decision-making along these dimensions and highlights a key set of open questions to be answered.

Older adults with obesity have an increased risk for cognitive decline and subsequent Alzheimer’s disease. Previous research has focused on the impact of individual nutrients and overall dietary patterns to improve cognitive function. The Brain and Body Health (BB-Health) study aims to demonstrate the feasibility of a multicomponent nutritional supplement (MCNS) and weight loss (WL) intervention for individuals with overweight and obesity who are not consuming a healthy diet. BB-Health is a parallel-group randomized placebo-controlled 2x2 factorial pilot trial comparing the MCNS, WL, and MCNS+WL with a Control group. Eligible participants are 55 to 85 years of age with a body mass index 27.0 to 39.9 kg/m2. Major exclusion criteria include type 2 diabetes, moderate or severe cognitive impairment, and self-reported healthy dietary pattern. The BB-Health feasibility trial is registered as NCT05542199 at clinicaltrials.gov. The BB-Health objectives are to report: 1) recruitment and enrollment, including pace and descriptive statistics of the study sample; 2) adherence to the administered interventions; 3) successful data collection and performance of outcome assessments, including evaluation of change in key outcomes over time that are powered for this pilot study; and 4) summary statistics on outcomes for use in planning a subsequent, larger trial. The outcomes of interest are age-sensitive cognitive assessments, cerebral hemodynamics, and related health parameters. All measurements are collected at baseline, 3-, 6-, and 12-month visits. If the BB-Health protocol demonstrates feasibility, the novel MCNS with and without a parallel WL intervention has potential to be administered in a confirmatory randomized trial to evaluate ability to prevent age-related cognitive decline and improve brain health. The BB-Health Feasibility Study could significantly contribute to our understanding of the influence of diet and weight management on cognitive health

Oleuropein (OLE), a main constituent of olive, displays a pleiotropic beneficial dynamic in health and disease, based mainly on its antioxidant and hypolipidemic properties, and its capacity to protect myocardium during ischemia. Furthermore, OLE activates peroxisome proliferator-activated receptor (PPARα) in neurons and astrocytes, providing neuroprotection against noxious biological reactions that are induced following cerebral ischemia. Current study investigated the effect of OLE in the regulation of various neural plasticity indices, emphasizing the role of PPARα. For this purpose, 129/Sv wild-type (WT) and Pparα-null mice were treated with OLE for three weeks. The present findings revealed that chronic treatment with OLE up-regulated the brain-derived neurotrophic factor (BDNF) and its receptor TrkB in the prefrontal cortex (PFC) of mice via activation of ERK1/2, AKT and PKA/CREB signaling pathways. No similar effects were observed in the hippocampus. The OLE-induced effects on BDNF and TrkB appear to be mediated by PPARα, because no similar alterations were observed in the PFC of Pparα-null mice. Notably, OLE did not affect the neurotrophic factors NT3 and NT4/5 in both brain tissues. Fenofibrate though, a selective PPARα agonist, up-regulated BDNF and NT3 in the PFC of mice, whereas the drug induced NT4/5 in both brain sites tested. Interestingly, OLE provided neuroprotection in differentiated human SH-SY5Y cells against β-amyloid and H2O2 toxicity independently from PPARα activation. In conclusion, OLE and similar drugs acting either as PPARα agonists or via PPARα independent mechanisms could improve synaptic function/plasticity mainly in the PFC and to a lesser extent in the hippocampus, thus beneficially affecting cognitive functions.

Based on non-invasive neuroimaging techniques and graph theory-based network analyses, many researchers have tried to use small-world network model to elucidate sex differences in the brain. This manuscript aims to compile the related research findings from the past few years and summarize the sex differences in human brain networks from the perspective of small-world properties. We reviewed published reports examining altered small-world properties in both the functional and structural brain networks between males and females. Based on four patterns of altered small-world properties proposed--randomization, regularization, stronger small-worldization and weaker small-worldization, we found that current results point to a significant trend toward more regularization in females and more randomization in males in the functional brain networks. As for particular topological properties, the most consistent findings include a significant shift toward higher global efficiency in males and higher local efficiency in females in the functional network studies. On the other hand, there seems to be no consensus to date on the sex differences in small-world properties of the structural brain networks. Nevertheless, we noticed that the sample sizes in many published studies are limited, and future studies with larger samples are warranted to obtain more reliable results. We anticipate that the conclusions in this manuscript will contribute to a deeper understanding of neurobiological mechanisms underlying the sex differences in the brain.

In the dynamic landscape of 6G and smart cities, Visible Light Communication (VLC) assumes critical significance for IoT (Internet of Things) applications spanning diverse sectors. The escalating demand for bandwidth and data underscores the need for innovative solutions, positioning VLC as a complementary technology within the electromagnetic spectrum. This paper focuses on the relevance of VLC in the 6G paradigm, shedding light on its applicability across smart cities and industries. The paper highlights the growing efficiency of lighting LEDs in infrastructure, facilitating the seamless integration of VLC. The study then emphasizes VLC’s robustness in outdoor settings, demonstrating effective communication up to 10 meters. This resilience positions VLC as a key player in addressing the very last meter of wireless communication, offering a seamless solution for IoT connectivity. By introducing an a freely available open-source simulator combined to an alternative waveform, UFMC, the study empowers researchers to dimension applications effectively, showcasing VLC’s potential to improve wireless communication in the evolving landscape of 6G and smart cities.

This article explores the intricate functioning of the human brain, highlighting how it operates near a critical state to optimize memory storage, adaptability, and the generation of thoughts. Through complex, fractal-like neural networks and brain waves, the brain largely governs our actions via subconscious processes, allowing for rapid, automated responses while reserving conscious thought for novel or complex tasks. Reasoning and planning emerge from the interplay of episodic memory and various neural networks such as Default Mode Network (DMN), Salience Network (SN), and Executive Network (EN), guiding decisions and actions. The "self" and “consciousness” are portrayed as functional illusions, which is being continuously constructed by dynamic neural activity, while free will is examined as a deterministic illusion shaped by prior neural states. We have argued that the brain's response to the physical world is a blend of subconscious automation and conscious sensory processing, thus, reality navigating through an evolving neural plasticity that promotes creativity and the illusion of a coherent, freely acting self. We discuss how does the mind select what to think about, how does it begin? Online and offline state of mind. We address what is an intelligence, understanding, creativity. We propose individual neuronal cells might store information beyond synaptic changes, the pathways through which neuronal thoughts propagate and Darwinian-like evolution of thoughts in minimizing surprises. We have drawn some parallels between the human brain and Large Language Model (LLM) like ChatGPT in terms of their functional roles.

Introduction: Migraine is a highly disabling neurological condition with substantial variability in its clinical presentation. This study aimed to explore the influence of familial transmission of comorbid medical conditions on the phenotype of chronic and episodic migraine. Methods: A total of 500 patients who presented to a private tertiary headache clinic were categorized into episodic and chronic migraine groups based on the International Classification of Headache Disorders-3 criteria. Data on demographics, clinical symptoms, and familial medical history were collected. Statistical analysis included chi-square and independent sample t-tests to evaluate the impact of familial comorbidities on migraine phenotypes. Results: Of the participants, 63% were diagnosed with episodic migraine and 37% with chronic migraine. Significant findings included higher rates of medication overuse (63.31%) and allodynia (58.82%) in patients with chronic migraine. Notably, patients with a maternal history of hypercholesterolemia showed a higher prevalence of chronic migraine (78.6%), whereas those with both parents affected tended to have episodic migraine. Educational levels are inversely correlated with the prevalence of chronic migraine, suggesting a socioeconomic influence on migraine chronification. Discussion: This study highlights significant associations between familial health conditions and migraine phenotypes. The findings suggest that the maternal transmission of comorbidities, particularly hypercholesterolemia, may influence the development of chronic migraine. These results underscore the importance of considering familial medical history in the management and understanding of migraine.

A proof is given showing the Collatz Conjecture is true for all positive integers. The proof was possible once the perspective of the proof was changed from looking at the pattern of the positive integers to looking at the conjecture rules. The conjecture rule for even numbers organizes all positive integers into unique sets and the rule for odd numbers interconnects the unique sets into dendritic pathways to “1.” Infinite loops, other than the minor 4-2-1 loop after reaching “1,” and pathways to infinity are shown to be mathematically impossible. The proof predicted a general equation that shows all positive integers ultimately reach a final value of “1," and calculates the values and locations of the odd positive integers during the iterations for each tested positive integer.

Investigation of functional magnetic resonance imaging (fMRI) data with machine learning (ML) techniques, including also deep learning (DL) methods, have been widely used to study Autism Spectrum Disorder (ASD). This disorder is characterized by symptoms that affect the individual’s behavioral aspects and social relationships. Early diagnosis is crucial for intervention, but the complexity of ASD poses challenges for treatment development. This study compares traditional ML techniques with deep learning (DL) methods in the analysis of functional connectivity measures obtained from the time series of multicentric ABIDE dataset. Specifically, Support Vector Machines (SVM) classifiers, with both linear and Radial Basis Function (RBF) kernels, as well as eXtreme Gradient Boosting (XGBoost) classifiers, are compared against the TabNet classifier, which is a DL architecture customized for tabular data analysis and a Multi Layer Perceptron (MLP). The findings suggest that DL classifiers may not be optimal for the type of data analyzed, as their performance trails behind that of standard classifiers. SVMs achieve performances, in terms of AUC, around 75%, compared to the best TabNet and MLP results, which are 65% and 71%, respectively. Additionally, this work investigates the brain regions that contribute most to the classification task, which are found to be those primarily responsible for sensory and spatial perception, as well as attention modulation, known to be altered in ASD.

The detection of early biomarkers and molecular mechanisms in Parkinson’s disease (PD) remains a challenge. Recent research has pointed to potential roles for peptidylarginine deiminases (PADs), a family of calcium activated enzymes, and associated post-translational protein citrul-lination/deimination in early stages of the disease, with a focus on hippocampal and cortex re-gions of the brain. Further analysis of brain region specific changes in PAD isozymes and identi-fication of brain-region specific citrullinomes, therefore remain to be studied. This study assessed brain-region specific PAD isozyme expression (PADs 1-4; PAD6) and associated citrullinated protein targets in the 6-hydroxydopamine (6-OHDA) induced rat model of pre-motor PD. Cortex, hippocampus, striatum, midbrain, cerebellum and olfactory bulb were compared between con-trols/shams and the PD model. In cortex, a significant increase of PAD2 and PAD3 was observed in the PD model. In hippocampus, PAD3, PAD4 and PAD6 showed strongest levels, but with no significant changes observed between PD and controls. In striatum, a significant reduction in all PAD isozymes was observed in the PD model. In the olfactory bulb, PAD3 was significantly el-evated in PD. In midbrain, PAD2, PAD4 and PAD6 showed strongest signal but with no signifi-cant changes between PD and controls. In cerebellum, a significant increase was seen in PAD3, PAD4 and PAD6 in the PD model. Citrullinated protein hits were most abundant in cortex and hippocampus, while for the PD model an increase in citrullinated hits was particularly observed in cortex and cerebellum, compared to controls. For all brain regions there was a considerable difference in citrullinated protein IDs between the PD model and the controls. Citrullinome as-sociated KEGG pathways differed in the six brain regions; some were overlapping for controls and PD, some were identified for the PD model only, and some were identified in control brains only. KEGG pathways identified in PD brains onlywere: “Axon guidance”; “Spinocerebellar ataxia”; “Hippo signalling pathway”; “NOD-like receptor signalling pathway”; “Phosphatidyl-inositol signalling system”; “Rap1 signalling pathway”; “Platelet activation”; “Yersinia infection”; “Fc gamma R-mediated phagocytosis”; “Human cytomegalovirus infection”; “Inositol phosphate metabolism”; “Thyroid hormone signalling pathway”; “Progesterone-mediated oocyte matura-tion”; “Oocyte meiosis”; and “Choline metabolism in cancer”. Our findings highlight differences in PAD isozymes, citrullinated proteins and associated KEGG pathways between brain regions, in pre-motor PD.

The pivotal role of the basolateral amygdala (BLA) in the emotional modulation of hippocampal plasticity and memory consolidation is well established. Specifically, multiple studies have demonstrated that the activation of the noradrenergic (NA) system within BLA governs these modulatory effects. However, most of the current evidence has been obtained by direct infusion of synthetic NA or beta-adrenergic agonists. In the present study, we aimed to investigate the effect of endogenous NA release in the BLA, induced by a natural aversive stimulus (coyote urine), on memory consolidation for a neutral hippocampal-dependent task. We combined a weak version of the object location task (OLT) with mild predator odor exposure (POE) for our experiments. To investigate the role of the BLA in memory modulation, a subset of the animals (Wistar rats) were treated with the non-selective beta-blocker propranolol at the end of the behavioral procedures. Hippocampal tissue was collected either 90 minutes after drug infusion or 24 hours after the OLT test. The samples were used to determine the levels of phosphorylated CREB (pCREB) and activity-regulated cytoskeleton-associated protein (Arc) - two molecular markers of experience-dependent changes in neuronal activity. The result suggests that POE is a behavioral paradigm suitable for studying the interaction between BLA and the hippocampus in memory prioritization and selectivity.

The cytoskeleton is a master organizer of cell cortex, organelles positioning and membrane trafficking. Therefore, playing a decisive role in establishment of apico-basal polarity. Septins are critical cytoskeleton components which function in apico-basal polarity remain weakly investigated. Here, using 3D culture system, we demonstrate that septin9 localizes at basolateral membrane (BM). Its depletion induces an inverted polarity phenotype, decreasing -catenin at BM and increasing the expression of the transforming growth factor (TGF) and the epithelial-to-mesenchymal transition (EMT) markers. Similar effects were observed upon deleting its two polybasic domains, and the mutant become cytoplasmic and apical. The cysts with an inverted polarity phenotype display an invasive phenotype, with src and cortactin accumulating at the peripheric membrane. Inhibition of TGF-receptor and RhoA rescues the polarized phenotype. Although the cysts from overexpressed-septin 9, overgrowth and present a filled lumen. Both phenotypes correspond to tumor features. This suggests that septin 9 expression and assembly through the two PB domains are essential for establishing and maintaining apico-basal polarity against tumor development through a mechanism involving TGF-dependent EMT and RhoA activity.

Cancer cells can acquire a spectrum of stable hybrid epithelial/mesenchymal (E/M) states during epithelial-mesenchymal transition (EMT). Cells in these hybrid E/M phenotypes often combine epithelial and mesenchymal features and tend to migrate collectively commonly as small clusters. Such collectively migrating cancer cells play a pivotal role in seeding metastases and their presence in cancer patients indicates an adverse prognostic factor. Moreover, cancer cells in hybrid E/M phenotypes tend to be more associated with stemness which endows them with tumor-initiation ability and therapy resistance. Most recently, cells undergoing EMT have been shown to promote immune suppression for better survival. A systematic understanding of the emergence of hybrid E/M phenotypes and the connection of EMT with stemness and immune suppression would contribute to more effective therapeutic strategies. In this review, we first discuss recent efforts combining theoretical and experimental approaches to elucidate mechanisms underlying EMT multi-stability (i.e. the existence of multiple stable phenotypes during EMT) and the properties of hybrid E/M phenotypes. Following we discuss non-cell-autonomous regulation of EMT by cell cooperation and extracellular matrix. Afterwards, we discuss various metrics that can be used to quantify EMT spectrum. We further describe possible mechanisms underlying the formation of clusters of circulating tumor cells. Last but not least, we summarize recent systems biology analysis of the role of EMT in the acquisition of stemness and immune suppression.

Interhemispheric and frontoparietal functional connectivity have been reported to increase during explicit information processing. However, it is unclear how and when interhemispheric and frontoparietal functional connectivity interact during explicit semantic processing. Here, we tested the neural coupling hypothesis that explicit semantic processing promotes neural activity in the nondominant right hemispheric areas owing to synchronization with enhanced frontoparietal functional connectivity at later processing stages. We analyzed electroencephalogram data obtained using a semantic priming paradigm, which comprised visual priming and target words successively presented under direct or indirect attention to semantic association. Scalp potential analysis demonstrated that the explicit processing of congruent targets reduced negative event-related potentials, as previously reported. Current source density analysis showed that explicit semantic processing activated the right temporal area during later temporal intervals. Subsequent dynamic functional connectivity and neural coupling analyses revealed that explicit semantic processing increased the correlation between right temporal source activities and frontoparietal functional connectivity in later temporal intervals. These findings indicate that explicit semantic processing increases neural coupling between the interhemispheric and frontoparietal functional connectivity during later processing stages.

Green spaces in residential community is important, yet understudied, feature as an urban ecological system. While large urban parks and remnant wildlands in urban areas tend to receive a public attention from conservation and management perspectives, less is known for the importance of spatial and ecological characteristics of the community-scale landscapes. This study investigates natural elements in four planned communities in Phoenix metropolitan area, Arizona, two of which represent conventional type of neighborhood and the other two exemplify the community development with a proclaimed vision of sustainability. These distinct type of communities, which capture variations in age, location, open space type, and a cross-section of housing density, are compared with regard to landscape connectivity as a means of gauging ecological condition for community sustainability. Using Geographical Information System and landscape connectivity indices, each community’s landscape features including size, physical connectedness and ecological potential were examined. Furthermore, a questionnaire survey was performed to examine the perceptional differences between the two types of community residents. The findings demonstrate that the green spaces in conventional communities are more physically connected than the counterparts, but the naturalness and ecological quality manifested by the amount of the land that may serve as potential urban desert habitats were higher in the sustainable communities. The results of the survey indicated that the respondents inhabiting in sustainable communities possess a higher level of satisfaction than the people in conventional type of community due mainly to the amount of, easy access to, and perceived ecological values of, green spaces in their neighborhoods and surrounding areas. The study concludes that careful community design with ecological consideration can help create sustainable communities which can benefit both site-scale ecosystems and perceived human well-being.

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.

Traumatic brain injury (TBI) is an important global clinical issue, requiring not only prevention but also effective treatment. Following TBI, diverse parallel and intertwined pathological mechanisms affecting biochemical, neurochemical, and inflammatory pathways can have severe impact on the patient’s quality of life. The current review summarizes evidence for utility of amantadine in TBI in connection to its mechanism of action. Amantadine combining multiple mechanisms of action may offer both neuroprotective and neuroactivating effects in TBI patients. Indeed, use of amantadine in TBI has been encouraged by several clinical practice guidelines/recommendations. Amantadine is also available as infusion which may be of particular benefit in unconscious patients with TBI, due to immediate delivery to the central nervous system and the possibility of precise dosing. In other situations, orally administered amantadine may be used. There are several questions that remain to be addressed: Can amantadine be effective in disorders of consciousness requiring long-term treatment and in combination with drugs approved for treatment of TBI? Do the observed beneficial effects of amantadine extend to disorders of consciousness due to factors other than TBI? Well controlled clinical studies are warranted to ultimately confirm its utility in the TBI and provide answers these questions.

The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as ‘mild’, ‘moderate’ or ‘severe’ based on neurological impairment. Yet stroke survivors argue that the classification of ‘mild’ does not represent the holistic experience and impact of stroke on their daily lives. In this observational cohort study, we aimed to identify different types of impairment among stroke survivors classified as mild. We used mild stroke survivors’ data from the START longitudinal stroke cohort (n=73) with measures related to sensorimotor, cognition, depression, functional disability, physical activity, work and social adjustment over 12 months. Given the multi-source, multi-granular and unlabelled nature of data, we utilised a structure-adapting, unsupervised machine learning approach, the Growing Self-organising Map (GSOM) algorithm to generate distinct clinical profiles. These diverse impairment profiles revealed that mild stroke survivors experience varying degrees of impairment and impact (cognitive, depression, physical activity, work/social adjustment) at different time points, despite the uniformity implied by their NIHSS-classified ‘mild’ stroke. This emphasises the necessity of creating a holistic and comprehensive representation of mild stroke survivors’ needs over the first-year post-stroke to improve rehabilitation and post-stroke care.

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

Bio-absorbable stents are a novel tool which relieve stenosis and then resorb as the patient grows, avoiding obligatory re-intervention. This case describes the novel use of an iron bio-absorbable stent in a child with surgically repaired Tetralogy of Fallot who post-operatively developed a severely stenotic right ventricular outflow tract.

Recent advancements in brain stimulation and nanomedicine have ushered in a new era of therapeutic interventions for psychiatric and neurodegenerative disorders. This review explores the cutting-edge innovations in brain stimulation techniques, including their applications in alleviating symptoms of main neurodegenerative disorders and addiction. Emphasis is placed on the underlying main neurotransmitter modifications and their specific brain area location, particularly focusing on the dopaminergic system, which plays a critical role in these conditions. Furthermore, the review delves into the groundbreaking developments in nanomedicine, highlighting how nanotechnology can be utilized to target aberrant signaling in neurodegenerative diseases, with a specific focus on the dopaminergic system. The discussion extends to emerging technologies such as magnetoelectric nanoparticles (MENPs), which represent a novel intersection between nanoformulation and brain stimulation approaches. These innovative technologies offer promising avenues for enhancing the precision and effectiveness of treatments by enabling non-invasive, targeted delivery of therapeutic agents as well as on site, on-demand stimulation. By integrating insights from recent research and technological advances, this review aims to provide a comprehensive understanding of how brain stimulation and nanomedicine can be synergistically applied to address complex neuropsychiatric and neurodegenerative disorders, paving the way for future therapeutic strategies.

Sechium compositum (Cucurbitaceae) is a wild species that is distributed in the Soconusco region, Chiapas, Mexico, and the border with Guatemala. This species has an intangible biochemical value resulting from the pharmacological relevance of its secondary metabolites. However, as a consequence of the lack of knowledge about its importance, it is being displaced from its habitat at an accelerated rate, incurring the risk of genetic loss. Therefore, an in vitro culture protocol with two experimental phases was evaluated to propagate, conserve, and regenerate this species. The first phases considered the multiplication of seedlings, adding seven concentrations (0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2 mg mL-1) of 6-benzylaminopurine (BA) and thidiazuron (TDZ) and evaluating the number of buds, shoots, and seedling height. The best multiplication response was recorded with 0.1, 0.2, 0.4, and 1.0 mg L-1 of BA and 0.1 mg L-1 of TDZ, as well as the MS base culture medium. With 0.1 mg L-1 of BA, 52-mm height, 1.36 shoots, and 9.22 buds were obtained, surpassing the MS control (MS culture medium alone); likewise, twice more roots (80%) and a 14% reduction in the bud structure were packed in 50 repetitions. The second phase consisted of inducing callus formation from stem and leaf explants through the addition of 0.5, 1.0, and 2.0 mg L-1 of TDZ and 2,4-Dichlorophenoxyacetic acid (2,4-D) to the medium. The results demonstrated that the induction of callogenesis in S. compositum has a better start from the stem in a medium with 2.0 mg L-1 of 2,4-D, whose value was 4.89 —equivalent to 97.8% callus formation around the explant. The addition of 500 mg L-1 of polyvinylpyrrolidone (PVP) is also suggested to reduce oxidation. This protocol represents a significant advance in the conservation, multiplication, and callogenesis of S. compositum and contributes to its rescue and revaluation in the face of the danger of extinction.

Typhulaceae Jülich is one of the cold-adapted fungal families in basidiomycetes. Typhula (Pers.) Fr. and Pistillaria Fr. representing the family are distinguished from the morphology of a stipe to a hymenium (Fries 1821). The hymenium of Typhula is distinguishable from the stipe, but indistinguishable and consecutive in the genus Pistillaria. This taxonomic criterion is ambiguous, and consequently the opinion of Karsten (1882) has been widely accepted; I the genus Typhula, basidiomata develop from sclerotia, while basidiomata develop directly from substrata in the genus Pistillaris. But, Corner (1970) observed basidiomata of Pistillaria petasitis S. Imai in Hokkaido, Japan, from sclerotia. We collected irregular sclerotia of Typhula hyperborea H. Ekstr. from Upernavik, West Greenland. This specimen had a stipe-like structure on a Poaceae plant, and sclerotia developed on its tip. In August, P. petasitis in Hokkaido, Japan, formed basidiomata directly from substrates on the ground. Similar phenomena were found from other Typhula spp. in Japan. All of these fungi had the potential to produce sclerotia in culture, and as well as the capacity of mycelial growth at ambient air temperatures in each locality when samples were collected. These findings suggest that Typhula spp. developed basidiomata not only from the sclerotia dispersed by the basidiospores but also mycelia generated by the spore germination formed basidiomata multiple times depending on their growth environments.

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

Parkinson's disease (PD) is a prevalent neurodegenerative disorder marked by progressive motor and non-motor symptoms. Traditional treatments primarily manage symptoms rather than address the disease's root causes, highlighting the need for novel therapeutic approaches. Natural phytochemicals, particularly phenolic compounds, have shown potential due to their neuroprotective effects. Antiaris africana is commonly used in traditional African medicines to treat several illnesses including neurodegenerative disorders. However, its neuroprotective mechanism remains elusive. This study aimed to employ network pharmacology-based prediction to identify key targets and pathways through which A. africana exerts its effects on PD. Targets associated with Parkinsonism were identified and analyzed for interaction with compounds from A. africana. Protein-protein interaction (PPI) networks were constructed, and key hub genes were identified using Cytoscape. Functional enrichment analysis highlighted significant pathways involved in PD. Molecular docking was performed to evaluate the binding affinities of the compounds to PD-related targets. Our results reveal that compounds such as caffeic acid, chlorogenic acid, catechin, ellagic acid, epigallocatechin, gallic acid, isoquercitrin, kaempferol, quercetin, quercitrin, and rutin interact with key proteins implicated in PD, including APP, EGFR, CASP3, ESR1, AKT1, and MAPK8. Notably, rutin exhibited strong binding affinity for EGFR, and chlorogenic acid showed potential in modulating the APP pathway. These findings suggest that phenolic compounds in A. africana may exert neuroprotective effects through multiple molecular mechanisms, offering potential therapeutic strategies for PD. This study underscores the importance of integrating molecular docking and network pharmacology to elucidate the therapeutic potential of natural compounds in complex diseases like Parkinson's disease

In this work, the properties of a composition based on synthetic rubber ethylene-propylene ter-polymer and polymers with functional groups of the mixture are studied. Low efficiency of some compositions based on ethylene and propylene copolymer limits their use in the production of tires and rubber products. Therefore, we conducted a study to improve the disadvantages of SREPT-60 using butyl rubber + choro polyisoprene + butadiene-nitrile + butadiene styrene rubber and adding a plasticizer to the composition = α, methacrylate-(bis-diethylene glycol phthalate) -(btegf-9) The data obtained showed that a number of its indicators of rubber based on them were improved. Tear resistance, resistance to repeated stretching increased, adhesion of the composition to metal, chemical resistance increased, the degree of swelling in benzene and gasoline-benzene mixture decreased. Compositions based on the mixture SREPT-60 using butyl rubber + choro polyisoprene + butadiene-nitrile + butadiene styrene rubber and adding a plasticizer to the composition = α, methacrylate-(bis-diethylene glycol phthalate) -(btegf-9) +Dioctyl phthalate: (70:20:10) have high resistance to swelling in an aggressive environment (Benzene, gasoline, alcohol, acid, oil, etc.) mixture.

The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper introduces the Multi-Metric Scoring Dynamic Source Routing (MMS-DSR), a novel enhancement of the Dynamic Source Routing (DSR) protocol, designed to meet the demands of 6G-enabled MANETs and the dynamic environments of VANETs. MMS-DSR integrates advanced technologies and methodologies to enhance routing performance in dynamic scenarios. Key among these is the use of a CNN-LSTM based beamforming algorithm, which optimizes beamforming vectors dynamically, exploiting spatial-temporal variations characteristic of 6G channels. This enables MMS-DSR to adapt beam directions in real-time based on evolving network conditions, improving link reliability and throughput. Furthermore, MMS-DSR incorporates a multi-metric scoring mechanism that evaluates routes based on multiple QoS parameters, including latency, bandwidth, and reliability, enhanced by the capabilities of Massive MIMO and the IEEE 802.11ax standard. This ensures route selection is context-aware and adaptive to changing dynamics, making it effective in urban settings where vehicular and mobile nodes coexist. Additionally, the protocol uses machine learning techniques to predict future route performance, enabling proactive adjustments in routing decisions. The integration of dynamic beamforming and machine learning allows MMS-DSR to effectively handle the high mobility and variability of 6G networks, offering a robust solution for future wireless communications, particularly in smart cities.

The main purpose of this article is to identify brain diseases such as Alzheimer's and Parkinson's in an intelligent way using Python software and processing the function of neurons in the neural network areas of the brain, which are classified into different components, and the data is based on the information obtained from FMRI images using algorithms artificial intelligence can intelligently diagnose the type of disease. Therefore, by using spatial clustering based on the deep learning method with noise algorithm (DBSCAN), fMRI brain image and fMRI brain surface complexity are classified, and then image preprocessing will be applied to fMRI images by using artificial intelligence neural networks (ANN) algorithm. became. Then, by using the ANN algorithm and Deep CNN topology of the brain neural network, the type of disease in the brain can be diagnosed by simulating the performance of an artificial neural network (ANN) in Python software. Deep machine learning-based methods, especially algorithms based on neural networks, have shown great success in medical image analysis for various tasks including brain tumor detection, segmentation, and classification, and Alzheimer's and epilepsy. In this research, we propose an integrated model based on a convolutional neural network for simultaneous recognition and classification of brain diseases for intelligent diagnosis of tumors and epilepsy. The average accuracy obtained from our proposed model is 97.89 percent accuracy and 91.66 percent specificity.

Traumatic Brain Injury represents a significant public health concern due to its role in traumatic death and disability, often caused by head impacts or rapid accelerations. Despite advancements in Finite Element Head Models, existing models primarily represent adult males, neglecting anatomical and physiological differences between genders and age groups. Additionally, the mechanistic roles of specific intracranial tissues, such as the dura mater, have conventionally been overlooked. To better understand the mechanical behavior of the brain and its injured regions in the context of TBI, including the dura mater, this study proposes the development of a Female Finite Element Head Model. This model, representative of a middle-aged female subject, was developed using medical image-derived geometry and finite element modeling techniques. The validation results demonstrate a similarity between the numerical displacement curves and those obtained experimentally. In the model with dura mater, the numerical results show analogous behavior to the experimental results, despite minimal variations in the amplitude of the curves. These advancements highlight the significance of including the dura mater in biomechanical brain modeling. Incorporating the dura mater in Finite Element Head Models produces more realistic results than models without it. This emphasizes the importance of enhancing biomechanical models to better represent anatomical complexity.

The study aims to explain the different effects between the multiplication, addition, division and subtraction equations. In this sense, multiplication and addition equations develop a new element or fact, meanwhile, division equations express information within the element or fact, subtraction is the selection of element or fact. Furthermore, for a multiplication and addition equation results in the new element or fact, it is necessary to depend on specific physical concepts, without extreme alteration, as it would result in the alteration of the element or fact. It is worth highlighting the importance of fixed values ​​in the equation to describe the specific nature of the element or fact. The study makes it possible to understand open equations (without solutions) in mathematics and physics, in addition to understanding existing equations. The study makes reference to the theory of obligatory necessity and the theory of differences between elements.

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

Traditional urban design often overlooks the synchronisation of human and ecological connectivities, typically favouring corridors for ecological continuity. Our study challenges this convention by introducing a computational design approach, Meta-Connectivity, leveraging the deep generative models performing cross-domain translation to integrate human-wildlife landscape connectivity in urban morphology amidst the planetary urbanisation. Utilising chained Pix2Pix models, our research illustrates a novel meta-connectivity design reasoning framework, combining landscape connectivity modelling with conditional reasoning based on deep generative models. This framework enables the adjustment of both human and wildlife landscape connectivities based on their correlative patterns in one single design process, guiding the re-materialisation of urban landscapes without the need for explicit prior ecological or urban data. Our empirical study in East London demonstrated the framework's efficacy in suggesting wildlife connectivity adjustments based on human connectivity metrics. The results demonstrate the feasibility of creating an innovative urban form in which the land cover guided by the connectivity gradients replaces the corridors based on simple geometries. This research thus presents a methodology shift in urban design, proposing a symbiotic approach to integrating disparate yet interrelated landscape connectivities within urban contexts.

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

Intratumoral heterogeneity is a major ongoing challenge in the effective therapeutic targeting of cancer. Accumulating evidence suggests that a fraction of cells within a tumor termed Cancer Stem Cells (CSCs) are primarily responsible for this diversity resulting in therapeutic resistance and metastasis. Adding to this complexity, recent studies have shown that there can be different subpopulations of CSCs with varying biochemical and biophysical traits resulting in varied dissemination and drug-resistance potential. Moreover, cancer cells can exhibit a high level of plasticity or the ability to dynamically switch between CSC and non-CSC states or among different subsets of CSCs. The molecular mechanisms underlying such plasticity has been under extensive investigation and the trans-differentiation process of Epithelial to Mesenchymal transition (EMT) has been identified as a major contributing factor. Besides genetic and epigenetic factors, CSC plasticity is also shaped by non-cell-autonomous effects such as the tumor microenvironment. In this review, we discuss the recent developments in understanding CSC plasticity in tumor progression at biochemical and biophysical levels, and the latest in silico approaches being taken for characterizing cancer cell plasticity with implications in improving existing therapeutic approaches.

Waddington’s epigenetic landscape, a famous metaphor in developmental biology, depicts how a stem cell progresses from an undifferentiated phenotype to a differentiated one. The concept of “landscape” in the context of dynamical system theory represents a high-dimensional cell state space, in which each cell phenotype is considered as an “attractor” that is determined by interactions among multiple variables (molecular players), and is buffered against environmental fluctuations. Further, biological noise is thought to play an important role during these cell-fate decisions and in fact controls transitions between different phenotypes. Here, we discuss these phenotypic transitions in cancer from a dynamical systems perspective and invoke the concept of “cancer attractors” – hidden stable states of the underlying regulatory network that are not occupied by normal cells. Using epithelial-to-mesenchymal transition (EMT), cancer stem-like properties, metabolic reprogramming and the emergence of drug/hormone resistance as examples, we illustrate how phenotypic plasticity in cancer cells enables them to acquire hybrid phenotypes (such as hybrid epithelial/mesenchymal and hybrid metabolic phenotypes) that tend to be more aggressive and notoriously resilient to drug/hormone treatment. Furthermore, we highlight multiple factors that may give rise to phenotypic plasticity in cancer cells, such as (a) multi-stability or oscillatory behaviors governed by underlying regulatory networks involved in cell-fate decisions in cancer cells, and (b) network rewiring due to conformational dynamics of intrinsically disordered proteins (IDPs) that are highly enriched in cancer cells. We conclude by discussing why a therapeutic approach that promotes ‘recanalization’, i.e. the exit from “cancer attractors” and re-entry into “normal attractors”, is more likely to succeed rather than a conventional approach that targets individual molecules/pathways.

A hybrid organic molecules as ligands and inorganic NCs in this research combines the chemical properties of organic semiconductors with the chemical stability of inor- ganic nanoparticles like PbS. It is possible to improve the ligand exchange and PbS-NCs' distribution by adding Amino phynel anchor group to TIPs-pentacene and Carboxylic anchor group to Tetraphenylporphyrin molecules. By adding anchor groups to one or two sides of these molecules, they would convert the molecules to ecient ligands to obtain superlattice coupled organic-inorganic nanostrcuture semicondcutor layers (COINs). Adding on side group would cap the PbS nanoparticles while adding two an- chor group would play a linker role between two PbS NCs. This morophology enhance- ment paves the way to manipulate the physical properties of the COINs and fabricate solar cells. Singlet ssion phenomena demonstrates pentacene molecule's advantage for high eciency in organic solar cells and its ability to overcome the Schockley-Queisser limit. Ultraviolet photo-emission spectroscopy (UPS) determines the highest occupied molecular orbital (HOMO) and the work function of COINs, while the lowest unoccu- pied molecular orbital (LUMO) of COINs were calculated subsequently by adding both the exciton binding energy and the optical band gap to the measured HOMO energy. The current versus voltage characteristics and impedance spectroscopy were studied for these molecules as well.

In large-scale parallel computing and communication systems, an interconnect network structure is usually modeled as a graph, in which vertices and edges correspond to processors and communication links, respectively. In a graph, the vertices and edges are likely to fail, so we must think about the fault tolerance of a graph. Connectivity is an important parameter in the study of faulty tolerance for a graph. In this paper, we study a special class of connectivity: $m$-component connectivity, this is a natural generalization of the classical connectivity of graphs defined in terms of the minimum vertex-cut. Let $F$ is a vertex set of $G$ $(i.e, F\subseteq V(G))$, if the following conditions are satisfied, we say $F$ is a $m$-component cut: $(1)$ $G-F$ is disconnected; $(2)$ the number of components in $G-F$ is greater than or equal to $m$. In another word, the $m$-component connectivity $c\kappa_{m}(G)$ is defined as $min\{|F|~|~F\subseteq V(G)$ and $F$ is a $m$-component cut$\}$. Determining $m$-component connectivity is still unsolved in most interconnection networks even for small $m'$s. Leaf-sort graph is a special Cayley graph, it has many special properties that are different from other Cayley graphs. So we need to pay attention to some of it's special properties when we study it. In this paper, we can get the values: $c\kappa_{3}(CF_{n})=3n-6$ $(n$ is odd$)$ and $c\kappa_{3}(CF_{n})=3n-7$ $(n$ is even$)$ for $n\geq3$; $c\kappa_{4}(CF_{n})=\frac{9n-21}{2}$ $(n$ is odd$)$ and $c\kappa_{4}(CF_{n})=\frac{9n-24}{2}$ $(n$ is even$)$ for $n\geq4$; $c\kappa_{5}(CF_{n})=6n-16$ $(n$ is odd$)$ and $c\kappa_{5}(CF_{n})=6n-18$ $(n$ is even$)$ for $n\geq5$.

In this work, the transport equations of ionic species in concrete are studied. First, the equations at the porescale are considered, which are then averaged over a representative elementary volume. The so obtained transport equations at the macroscopic scale are thoroughly examined and each term is interpreted. Furthermore, it is shown that the tortuosity-connectivity does not slow the average speed of the ionic species down. The transport equations in the representative elementary volume are then compared with the equations obtained in an equivalent pore. Lastly, comparing Darcy’s law and the Hagen-Poiseuille equation in a cylindrical equivalent pore, the tortuosity-connectivity parameter is obtained for four dierent concretes. The proposed model provides very good results when compared with the experimentally obtained chloride profiles for two additional concretes.

Many marine species use different habitats at different stages of their life cycle. Functional connectivity, the degree to which the seascape facilitates or impedes movement between habitat patches, is poorly studied in marine systems. We reviewed the scientific literature to explore the various barriers preventing functional connectivity between marine habitats and how the removal of these barriers may restore connectivity. To our knowledge, this is the first systematic review to investigate functional connectivity between life cycle habitats for a range of marine species. A total of 4,499 records were identified and screened, leaving 69 publications eligible for review. The results highlighted a range of distances between nursery and adult habitats that limited functional connectivity for a number of species, predominantly reef fishes. For some species, adults were absent on reefs >9km from the closest nursery habitat, suggesting a threshold for connectivity. Similarly, increased distance between spawning and settlement habitats decreased settling success of larvae of various taxa. Pelagic larval duration, seascape topography and climate change were also shown to impact functional connectivity during the larval phase. The removal and mitigation of barriers preventing functional connectivity, including dams and habitat fragmentation, restored connectivity between disconnected life cycle habitats, but the efficacy of these approaches differed between species and studies. The results of this review deepen our understanding of marine functional connectivity between life cycle habitats via larval, juvenile, and adult dispersal. These findings have implications for the design and management of marine reserve networks.

Assessment of health-related quality of life (HRQoL) after pediatric traumatic brain injury (TBI) has been limited in children and adolescents due to a lack of disease-specific instruments. To fill this gap, the Quality of Life after Traumatic Brain Injury for Children and Adolescents (QOLIBRI-KID/ADO) was developed for the German-speaking population. Reference values from a comparable general population are essential for comprehending the impact of TBI on health and well-being. This study examines the validity of the German QOLIBRI-KID/ADO in a general pediatric population in Germany and provides reference values for use in clinical practice. Overall, 1997 children and adolescents aged 8-17 years from the general population and 300 from the TBI population participated in the study. The questionnaire was tested for reliability and validity. A measurement invariance (MI) approach was used to assess the comparability of the HRQoL construct between both samples. Reference values were determined by percentile-based stratification according to factors that significantly influenced HRQoL in regression analyses. The QOLIBRI-KID/ADO demonstrated strong psychometric properties. The HRQoL construct was measured largely equivalently in both samples, and reference values could be provided. The QOLIBRI-KID/ADO was considered reliable and valid for assessing HRQoL in a general German-speaking pediatric population, allowing for clinically meaningful comparisons between general and TBI populations.

By defining a constant probabilistic orbit of and iterated functions, the stability dynamics of these functions in possible interactions through connectivity provides the formation of a dynamic fixed point as a metric space between both iterated functions. The presence of a dynamic fixed point identifies qualitatively phases of iteration time lengths and interaction orbits of the event. Qualitative results show that the greater the average distance from one of the functions to the fixed point of the other (all possible solutions), the higher the iteration expression on time (false asymptotic effect) of one of the functions and in the opposite hand, the lower the average distance, the higher orbit’s interactions proximity between iterated functions (stability). This feature reveals asymptotic (well-defined) behavior between functions f and g within a well-defined Lyapunov stability.

Transketolase (TKT) is an essential thiamine diphosphate (ThDP)-dependent enzyme of the non-oxidative branch of pentose phosphate pathway, with the glucose-6P flux through the pathway regulated in various medically important conditions. Here, we characterize the brain TKT regulation by acylation in rats with perturbed thiamine-dependent metabolism, known to occur in neurodegenerative diseases. The perturbations are modelled by administration of oxythiamine inhibiting ThDP-dependent enzymes in vivo, or by reduced thiamine availability in the presence of metformin and amprolium, inhibiting intracellular thiamine transporters. Compared to control rats, chronic administration of oxythiamine does not significantly change the modification level of the two detected TKT acetylation sites (K6 and K102), but doubles malonylation of TKT K499, concomitantly decreasing 1.7-fold the level of demalonylase sirtuin 5. The inhibitors of thiamine transporters do not change average levels of TKT acylation or sirtuin 5. TKT structures indicate that the acylated residues are distant from the active sites. The acylations-perturbed electrostatic interactions may be involved in conformational shifts and/or formation of TKT complexes with other proteins or nucleic acids. Acetylation of K102 may affect the active site entrance/exit and subunit interactions. Correlation analysis reveals that the action of oxythiamine is characterized by significant negative correlations of K499 malonylation or K6 acetylation with TKT activity, not observed upon the action of the inhibitors of thiamine transport. However, the transport inhibitors induce significant negative correlations between the TKT activity and K102 acetylation or TKT expression, absent in the oxythiamine group. Thus, perturbations in the ThDP-dependent catalysis or thiamine transport manifest in the insult-specific patterns of the brain TKT malonylation and acetylations.

The classification and summarization of online reviews using machine learning and deep learningbased approaches are the subject of this systematic literature review. Consumers, companies, and researchers can now find vital information from online reviews. Automatically categorizing and summarizing these reviews can offer useful information and facilitate decision-making. The purpose of this study is to offer a thorough overview of the current methodology, approaches, and developments in this area. The review concentrates on machine learning-based methods for categorization and summarizing tasks, highlighting their advantages, drawbacks, and new developments. This systematic review provides insights into the most recent methodologies and indicates prospective directions for further investigation by synthesizing the data from a wide range of pertinent research publications

Background: Drought, N deficiency and herbivory are considered the most important stressors caused by climate change in the agro- and eco-systems and varied in space and time shaping a highly dynamic and heterogeneous stressful environments. This study aims to evaluate the to-mato morpho-physiological and metabolic responses to combined abiotic and herbivory at dif-ferent within-plant spatial levels and temporal scales. Methods: Leaf-level morphological, gas exchange traits and VOC profiles were measured in to-mato plants exposed to N deficiency and drought, T. absoluta larvae and their combination. Ad-ditive, synergistic or antagonistic effects of the single stress when combined were also evaluat-ed. Morpho-physiological traits and VOC profile were also measured on leaves located at three different positions along the shoot axes. Results: The combination of the abiotic and biotic stress has been more harmful than single stress with antagonistic and synergistic but non-additive effects for the morpho-physiological and VOC tomato responses, respectively. Combined stress also determined a high within-plant phenotypic plasticity of the morpho-physiological responses. Conclusions: These results suggest that the combined stress in tomato determined a “new stress state” and a higher within-plant phenotypic plasticity which could permit an efficient use of the growth and defence resources in the heterogeneous and multiple stressful environmental condi-tions.

Migraine has a high prevalence worldwide and is one of the main disabling neurological diseases in individuals under the age of 50. In general, treatment includes the use of oral analgesics or non-steroidal anti-inflammatory drugs (NSAIDs) for mild attacks, and, for moderate or severe attacks, triptans or 5-hydroxytryptamine/serotonin (5-HT)1B/1D receptor agonists. However, the administration of antimigraine drugs in conventional oral pharmaceutical dosage forms is a challenge, since many molecules have difficulty crossing the blood-brain barrier (BBB) to reach the brain, which leads to bioavailability problems. Efforts have been made to find alternative delivery systems and/or routes for antimigraine drugs. In vivo studies have shown that it is possible to administer drugs directly into the brain via the intranasal (IN) or the nose-to-brain route, thus avoiding the need for the molecules to cross the BBB. In this field, the use of lipid nanoparticles, in particular solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC), has shown promising results, since they have several advantages for drugs administered via the IN route, including increased absorption and reduced enzymatic degradation, improving bioavailability. Furthermore, SLN and NLC are capable of co-encapsulating drugs, promoting their simultaneous delivery to the site of therapeutic action, which can be a promising approach for the acute migraine treatment. This review highlights the potential of using SLN and NLC to improve the treatment of acute migraine via the nose-to-brain route. First sections describe the pathophysiology and the currently available pharmacological treatment for acute migraine, followed by an outline of the mechanisms underlying the nose-to-brain route. Afterwards, the main features of SLN and NLC and the most recent in vivo studies investigating the use of these nanoparticles for the treatment of acute migraine are presented.

We discuss three key points: defining mathematically the notions of phase, symmetry breaking, topological defects and their stability in a formal language of homotopy theory and fiber bundle formalism; postulating and proving two theorems for successive symmetry breaking scenarios in condensed matter physics and cosmology, and demonstrating application of theorems to superfluid Helium 3 and Kibble-Lazarides-Shafi (KLS) walls, supporting and mathematically justifying the results of [Volovik, 2020].

Brain pathological changes impair cognition early in disease etiology. There is an urgent need for understanding aging-linked mechanisms of early memory loss to develop therapeutic strategies and prevent the development of cognitive impairment. Tusc2, also named Fus1, is a mitochondrial-resident protein regulating Ca2+ fluxes to and from mitochondria impacting overall health. We reported that Tusc2-/- female mice develop chronic inflammation and age prematurely, causing age- and gender-dependent spatial memory deficits at 5 m.o. Therefore, we investigated Tusc2-dependent mechanisms of memory impairment in mice, comparing changes in resident and brain-infiltrating immune cells. Interestingly, Tusc2-/- female mice demonstrated proinflammatory increase in astrocytes, IFN-γ expression in CD4+T cells and Granzyme-B in CD8+T cells. We also found fewer FOXP3+ T-regulatory cells and Ly49G+ NK and Ly49G+ NKT cells in female Tusc2-/- brain, suggesting a weakened anti-inflammatory response. Moreover, Tusc2-/- hippocampi exhibited Tusc2- and sex-specific protein changes associated with brain plasticity, including mTOR activation, and Calbindin and CamKII dysregulation affecting intracellular Ca2+ dynamics. Overall, data suggest that dysregulation of Ca2+-dependent processes and proinflammatory brain microenvironment heighten in Tusc2-/- mice could underlie cognitive impairment. Thus, strategies to modulate the brain’s Tusc2-, Ca2+, and mitochondria-dependent pathways should be explored to improve cognitive health.

This experiment was conducted to study the effects of Cyfluthrin (Cy) exposure on neurobehaviour, hippocampal tissue and synaptic plasticity in Wistar rats. First, high-dose Cy exposure can cause nerve injury with symptoms such as deficits in learning and memory ability, spatial exploration and autonomic motor ability. Moreover, it was found that medium- and high-dose Cy exposure can cause disordered energy metabolism in hippocampal cells and an abnormal release of the neurotransmitter Glu. Second, pathological brain sections showed that the middle and high doses of Cy caused deformation, reduction and disorder of hippocampal pyramidal cells, a decrease in the number of Nissl bodies, pyknosis of the hippocampal cell nuclear membrane and serious damage to organelles, indicating that exposure to these doses of Cy may cause hippocampal tissue damage in rats. Third, with the increase in exposure dose, the morphological changes in hippocampal synapses were more obvious, including blurred synaptic spaces, decreased synaptic vesicles and a decreased number of synapses. Moreover, the expression levels of the key synaptic proteins PSD-95 and SYP also decreased in a dose-dependent manner, indicating obvious synaptic damage. Finally, the study found that medium and high doses of Cy could upregulate the expression of A2AR in the hippocampus, and the expression levels of inflammatory factors and apoptosis-related proteins increased in a dose-dependent manner. Moreover, the expression of A2AR mRNA was correlated with neurobehavioural indicators, inflammatory factors, synaptic plasticity factors and apoptosis factors, suggesting that Cy may cause nerve damage in rats and has a close relationship with A2AR.

Visual Sensor Networks (VSNs) have been the focus of numerous studies, emphasizing the quality of the information they generate. The primary goal of these networks is to capture data from an area, extract the pertinent information from the visual scene, and facilitate communication. However, the presence of objects in the study area can impacted the network's efficiency, linking the quality of acquired data directly to the positioning of the cameras. Strategically positioning multiple cameras in a potentially complex area, while ensuring effective communication between nodes, presents a challenging issue in VSNs applications. This paper introduces a specific method to enhance the deployment of visual sensors in indoor environments. The research addresses the theme of multi-objective optimization of VSN deployment, integrating various criteria: maximizing overall visual coverage, maximizing the observation of zones of interest, maximizing observation redundancy of zones of interest and ensuring communication connectivity for data transfers. To tackle this problem, we simulate the observed environment incorporating cameras, obstacles, and considering zones of interest. Camera positioning is performed while considering visual constraints and the scene's characteristics. The quality of positioning is evaluated based on the defined objective functions. Initially, a comparative study is conducted with a state-of-the-art approach, integrating the first three objective functions. Subsequently, we incorporate the fourth objective function to ensure data transfers. The proposed method demonstrates efficient camera network deployments in indoor areas, considering global coverage and observation redundancy. In contrast to the state-of-the-art, these results are achieved in areas with visual obstacles while ensuring communication connectivity between the different cameras.

Brain-computer interfaces (BCI) from electroencephalography (EEG) provide a practical approach to support human-technology interaction. In particular, motor imagery (MI) is a widely-used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, named Kernel-based Regularized EEGNet (KREEGNet), leveled on Centered Kernel Alignment and Gaussian Functional Connectivity, explicitly designed for EEG-based MI classification. The approach proactively tackles the challenge of intra-subject variability brought on by noisy EEG records and the lack of spatial interpretability within end-to-end frameworks applied for MI classification. KREEGNet is a refinement of the widely accepted EEGNet architecture, featuring an additional kernel-based layer for regularized Gaussian functional connectivity estimation based on CKA. The superiority of KREEGNet is evidenced by our experimental results from binary and multi-class MI classification databases, outperforming the baseline EEGNet and other state-of-the-art methods. Further exploration of our model’s interpretability is conducted at individual and group levels, utilizing classification performance measures and pruned functional connectivities. Our approach is a suitable alternative for interpretable end-to-end EEG-BCI based on deep learning.

Clonal evolution and cellular plasticity are the genetic and non-genetic driving forces of tumor heterogeneity that in turn determines the tumor cell response towards therapeutic drugs. Several lines of evidence suggest that therapeutic interventions foster the selection of drug resistant neural crest stem-like cells (NCSCs) that establish minimal residual disease (MRD) in melanoma. Here we established a dual reporter system enabling the tracking of NGFR expression and mRNA stability, providing insights into the maintenance of NCSC-states. We observed that the transcriptional reporter that contained a 1kb fragment of the human NGFR promoter was activated only in a minor subset (0.72±0.49%, range 0.3-1.5) and ~2-4% of A375 melanoma cells revealed stable NGFR mRNA. The combination of both reporters provided insights into phenotype switching and revealed that both cellular subsets gave rise to cellular heterogeneity. Moreover, whole transcriptome profiling and gene set enrichment analysis (GSEA) of the minor cellular subset revealed hypoxia-associated programs that might serve as potential drivers of an in vitro switching of NGFR-associated phenotypes and relapse of post-BRAF inhibitor treated tumors. Concordantly, we observed that the minor cellular subset increased in response to dabrafenib over time. In summary, our reporter-based approach provided insights into plasticity and identified a cellular subset that might be responsible for the establishment of MRD in melanoma.

Constitutive models for plastic deformation of metals are typically based on flow rules determining the transition from elastic to plastic response of a material as function of the applied mechanical load. These flow rules are commonly formulated as a yield function, based on the equivalent stress and the yield strength of the material, and its derivatives. In this work, a novel mathematical formulation is developed that allows the efficient use of machine learning algorithms describing the elastic-plastic deformation of a solid under arbitrary mechanical loads and that can replace the standard yield functions with more flexible algorithms. By exploiting basic physical principles of elastic-plastic deformation, the dimensionality of the problem is reduced without loss of generality. The data-oriented approach inherently offers a great flexibility to handle different kinds of material anisotropy without the need for explicitly calculating a large number of model parameters. The applicability of this formulation in finite element analysis is demonstrated, and the results are compared to formulations based on Hill-like anisotropic plasticity as reference model. In future applications, the machine learning algorithm can be trained by hybrid experimental and numerical data, as for example obtained from fundamental micromechanical simulations based on crystal plasticity models. In this way, data-oriented constitutive modeling will also provide a new way to homogenize numerical results in a scale-bridging approach.

Porphyromonas gingivalis is the major pathogenic bacteria found in the subgingival plaque of patients with periodontitis, leading to neuroinflammation. The bacteria destroy periodontal tissue through its virulence factors which are retained in the bacteria’s outer membrane vesicles (OMV). The purpose of this study was to determine the real time effect of intraperitoneal in-jection of P. gingivalis OMV on the production and expression of inflammatory markers as well as histopathological changes in the central nervous system (CNS) of adult zebrafish. Fol-lowing the LD50 (21 µg of OMV), the zebrafish were injected intraperitoneally with 18 µg of OMV and control group with normal saline at 7 different time points. Brains of experimental zebrafish were dissected at desired time points for colorimetric assays, ELISA and histology. This study discovered that nitric oxide and PGE2 were significantly increased at 45 minutes while IL-1β and IL-6 were expressed at subsequent 12- and 24-hours. respectively. Histopatho-logical changes such as blood coagulation, astrocytosis, oedema, spongiosis, and necrosis were also observed between 6- and 24-hours. The two apoptotic enzymes, caspases 3 and 9 were not expressed at any time point. In summary, the OMV induced neuroinflammatory responses and histopathological changes in adult zebrafish were time point dependent

In recent years the field of computer science has experienced great changes, due to the incredible advances in the field of artificial intelligence. Deep Learning models are responsible for most of them since the biggest milestones occurred in 2012 when AlexNet won the image classification challenge called ImageNet. These models have demonstrated great performances in different types of complex tasks like image restoration, medical diagnosis or object recognition. Their disadvantages are related to their high data dependency, which forces experts in the field to follow a precise methodology to obtain accurate models. In this paper, we describe a complete workflow that begins with the management of the raw data until the in-depth interpretation of the performance of the models. This should be taken as a high-level consultation document describing good practices that should be applied. Apart from the step-by-step methodology, we present different use cases that correspond to the two main problems of the field: classification and regression.

Ecological connectivity, as a research method related to spatial ecology and conservation biology, has attracted increasing attention from researchers at home and abroad in recent years. Habitat connectivity, as a key link in ecological connectivity, is of great significance to promote offshore ecological restoration and protection. However, there has been less systematic research about habitat connectivity, which lacks corresponding theories and practices. Therefore, this paper discusses habitat connectivity from three aspects: (1) the concept of habitat connectivity is introduced and clarified, (2) the application of connectivity in artificial habitat and adjacent waters and its relationship with biodiversity conservation are reviewed and illustrated with examples, and (3) the future development trends of this research direction are summarized and prospected, in order to provide a scientific basis and useful reference for the related work of offshore restoration projects in China. Generally, this paper argues that an increase in human behavior irreversibly destroys the connectivity of marine habitats and threatens the temporal scales of biodiversity and ecosystem services. Therefore, the theoretical research results and practical experience of ecological connectivity should be fully applied to marine ecosystems, and the restoration of degraded ecosystems should be encouraged and supported in ways that promote natural recovery.

Abstract: Social media has become a vital tool in ensuring productivity among Undergraduates, as studies have shown an increased number of users globally. This study assessed the impact of social media usage on undergraduates’ academic performance and working memory. Participants from three different universities such as the University of Georgia (UG), Eastern European University (EEU), and Batumi State University (BSU) completed this study using online google forms. UG and EEU are in the capital city, and BSU is in Batumi City. The study questionnaires were approved by the institutional review board before the data collection. Descriptive analyses were assessed for all study populations, characteristics, frequencies, percentages, means, and standard deviations using Statistical Package for Social Science (SPSS Inc., Chicago, IL, version 25.0 for Windows) software. 41.3% were male students, as most of the participants were foreign students living and studying in Georgia. Male students had the risk of ‘conflict’ with families and friends because of social media. Female students are at increased risk of using social media as an ‘escape’ from negative feelings. 89.7% of students that perform physical activities, considering their academic workload had the risk of preoccupation complaints. 87.9% felt dissatisfied regularly because they wanted to spend more time on social media. Utilization of social media differed based on the characteristics of the participants such as age, gender, and geography. We observed the increase in prevalence of social media addiction, its effect on academic performance and working memory among undergraduate students.

This investigation aimed to explore the impact of acute and regular physical exercise on Brain-Derived Neurotrophic Factor (BDNF) levels in individuals with obesity. Using the Population, Intervention, Comparator, Outcome, and Study design (PICOS) approach, the inclusion criteria were defined as follows: i) population - individuals with overweight or obesity of any age or gender; ii) intervention - acute or chronic interventions involving aerobic or anaerobic methodologies; iii) comparator - for acute effect studies, pre- and post-evaluations served as comparators, while interventions included at least one experimental group or the inclusion of a control group; iv) outcome - BDNF variability concerning the applied methodology; v) study design - restricted to experimental studies exclusively. Conducted in August 2022, the searches were carried out on PubMed, Academic Search Complete, and Web of Science. The risk of bias was assessed using the Risk of Bias in Non-randomized Studies (RoBANS) tool. Out of 98 potentially relevant articles, 16 met the eligibility criteria, encompassing 23 trials. Substantial heterogeneity was observed for both acute (I2 = 80.4%) and long-term effects (I2 = 88.7%). Additionally, RoBANS analysis indicated a low risk of bias for the included studies. The study's findings revealed a significant increase in the concentration of circulating BDNF in individuals with obesity compared to the control group following acute exercise. However, such an effect was not evident in the context of long-term physical exercise, with a small effect size (ES = 0.49) recorded in the latter scenario. The observed significant increase in BDNF levels post-acute exercise suggests a potential acute response mechanism contributing to the intricate interplay between exercise and neurotrophic factors in this population. Nevertheless, the absence of a similar effect in the context of long-term physical exercise implies a potential adaptation or ceiling effect. These findings underscore the importance of considering both the duration and nature of exercise interventions when exploring the neurobiological responses associated with obesity and exercise. The study provides valuable insights for future research and clinical implications in understanding the nuanced relationship between exercise, neurotrophic factors, and obesity.

Recent research has highlighted associations between sleep and microbial taxa and pathways. However, the causal effect of these associations remains unknown. To investigate this, we performed a two-sample bidirectional Mendelian randomization (MR) analysis using summary statistics of genome-wide association studies (GWAS) from 412 gut microbiome traits (N = 7,738) and GWAS studies from seven sleep-associated traits (N = 345,552 to 386577). We employed multiple MR methods to assess causality, with Inverse Variance Weighted (IVW) as the primary method. We further applied Cochran’s Q statistical analysis, MR-Egger intercept, and MR-PRESSO for heterogeneity and pleiotropy assessment, alongside a Bonferroni correction ((P

A recent study demonstrated that noninvasive measurements of cortical hemodynamics and metabolism in the resting human prefrontal cortex can facilitate quantitative metrics of unilateral mitochondrial-hemodynamic coupling and bilateral connectivity in infraslow oscillation (ISO) frequencies in young adults (YA). The ISO includes three distinct vasomotions with endogenic (E), neurogenic (N), and myogenic (M) frequency bands. The goal of this study was to prove the hypothesis that there are significant differences between young and older adults (OA) in the unilateral coupling (uCOP) and bilateral connectivity (bCON) in the prefrontal cortex. Accordingly, we performed measurements from 24 OA (67.2 ± 5.9 years of age) using the same 2-channel broadband near-infrared spectroscopy (bbNIRS) setup and resting-state experimental protocol as those in the recent study (Shahdadian et al, Cerebral Cortex Communications, 2022). After quantification of uCOP and bCON in three E/N/M frequencies and statistical analysis, we demonstrated that OA had significantly weaker bilateral hemodynamic connectivity but significantly stronger bilateral metabolic connectivity than YA in the M band. Furthermore, OA exhibited significantly stronger unilateral coupling on both prefrontal sides in all E/N/M bands, particularly with a very large effect size in the M band (&gt; 1.9). These age-related results clearly support our hypothesis and were well interpreted following neurophysiological principles. The key finding of this paper is that the neurophysiological metrics of uCOP and bCON are highly associated with age and may have the potential to become meaningful features for human brain health and be translatable for future clinical applications, such as early detection of Alzheimer’s disease.

In an era of global connectivity, language and literature education faces the challenge of navigating multilingualism, cultural diversity, and technological advancements. This paper explores the challenges and opportunities in language and literature education in the era of global connectivity. Through a qualitative research approach, interviews, focus group discussions, and classroom observations were conducted with 30 Arts and Sciences students and teachers. The findings reveal significant themes related to cultural diversity, multilingualism, and the integration of technology in language learning. The study emphasizes the importance of inclusive pedagogies, critical thinking development, and the incorporation of authentic and diverse literary works. Recommendations are provided for educators and policymakers to promote intercultural understanding and enhance language and literature education. The limitations of the study suggest areas for future research, including long-term language proficiency and diverse contextual considerations. Overall, this paper calls for ongoing innovation and collaboration to meet the evolving needs of language and literature education in an interconnected world.

The dorsal column nuclei complex (DCN-complex) includes the dorsal column nuclei (DCN, referring to the gracile and cuneate nuclei collectively), external cuneate, X, and Z nuclei, and the median accessory nucleus. The DCN are organised by both somatotopy and modality, and have a diverse range of afferent inputs and projection targets. The functional organisation and connectivity of the DCN implicate them in a variety of sensorimotor functions, beyond their commonly accepted role in processing and transmitting somatosensory information to the thalamus, yet this is largely underappreciated in the literature. To consolidate insights into their sensorimotor functions, this review examines the morphology, organisation, and connectivity of the DCN and their associated nuclei. First, we briefly discuss the receptors, afferent fibres, and pathways involved in conveying tactile and proprioceptive information to the DCN. Next, we review the modality and somatotopic arrangements of the remaining constituents of the DCN-complex. Finally, we examine and discuss the functional implications of the myriad of DCN-complex projection targets throughout the diencephalon, midbrain, and hindbrain, in addition to their modulatory inputs from the cortex. The organisation and connectivity of the DCN-complex suggest that these nuclei should be considered a complex integration and distribution hub for sensorimotor information.

This study aimed to elucidate the similarities and differences between amyloid-forming corpora amylacea (CA) in the prostate and lung, examine the nature of CAs in cystic tumors of the atrioventricular node (CTAVN), and clarify the distinctions between amyloid-forming CA and spheroid-type amyloid deposition. We conducted proteomics analyses using liquid chromatography–tandem mass spectrometry with laser microdissection and immunohistochemistry to validate the characteristics of CAs in the lung and prostate. Our findings revealed that CAs in these organs primarily consist of common proteins (β2-microglobulin and lysozyme) and locally produced proteins. Moreover, we observed a discrepancy between the histopathological and proteomic analysis results in CTAVN-associated CAs. In addition, while the histopathological appearance of amyloid-forming CAs and spheroid-type amyloid deposits were nearly identical, the latter deposition lacked β2-microglobulin and lysozyme, and exhibited evident destruction of surrounding tissue. A literature review further supported these findings. These results suggest that amyloid-forming CAs in the lung and prostate are formed through a shared mechanism, serving as waste containers (wasteosomes) and/or storage for excess proteins (functional amyloids). In contrast, we hypothesize that while amyloid-forming CA and spheroid-type amyloid deposits are formed, in part, through common mechanisms, the latter are pathological.

Background: Stroke is one of the leading causes of death and disability worldwide. The diagnosis of stroke remains largely clinical, yet widely used stroke scoring systems and brain imaging do not satisfactorily allow distinction of ischaemic stroke (IS) patients from stroke mimics (SM). Blood biomarkers are promising tools that could facilitate clinical triage. Methods: This study recruited 66 patients with IS and 24 SM. The levels of Glial fibrillary acidic protein (GFAP), Neuron-specific enolase (NSE), Neurofilament light chain (NfL) and blood-brain barrier (BBB) proteins [Occludin (OCLN), Zonula occludens 1 (ZO-1), Claudin-5] in blood serum were measured by enzyme-linked immunosorbent assay technique. Biomarker levels in IS patients and SM was compared using the Mann–Whitney U test. Multivariable logistic regression analysis was used to evaluate the diagnostic performance of biomarkers in combination with the National Institutes of Stroke Scale (NIHSS) score. Results: Significant differences in circulating GFAP, NfL, OCLN, ZO-1, and Claudin-5 but not NSE in IS patients compared to SM. The combination of circulating ZO-1, Claudin-5, and OCLN with NIHSS score gives the highest diagnostic accuracy, sensitivity, and specificity. Conclusion: A prediction model with Circulating BBB proteins in combination with NIHSS score differentiates between IS patients and SM.

Rabies is a viral zoonotic disease caused by the lyssavirus and endemic to most tropical and sub-tropical areas of the world. This study reports on the occurrence of rabies in dogs and relates the results to the vaccination status of the dogs. A total of each of the 50 brain and 50 saliva samples from 100 dogs were tested for Lyssavirus using RT-PCR and visualized using gel electrophoresis. Additionally, a histopathological diagnosis of rabies was made on the brain samples. A lyssavirus positivity of 34 % was realized with 29/50 from brain and 5/50 from saliva being positive for rabies. Statistical significance was associated with vaccination (p=0.011) and health (p=0.0) statuses and attack by a stray dog prior to exhibition of signs of rabies (p=0.002). The rabid positive dogs had bitten at least 15 humans and a dog before their demise. This study confirms vaccination as an effective tool for the prevention of rabies. However, elimination of the disease is possible if there is an established and effective collaboration between veterinary and human medicine through one health.

Contradictory reports are available on vaccine-associated hyperstimulation of the immune system, provoking the formation of pathological autoantibodies. Despite being interconnected within the same network, the role of the quieter, yet important non-pathological; natural autoantibodies (nAAbs) is less defined. We hypothesize that upon a prompt immunological trigger, also physiological nAAbs exhibit a moderate plasticity. We investigated their inducibility through aged and recent antigenic triggers. Anti-viral antibodies (anti-MMR; n=1739 and anti-SARS-CoV-2 IgG; n=330) and nAAbs (anti-citrate synthase IgG, IgM; n=1739) were measured by in-house and commercial ELISAs, using Croatian (Osijek) anonymous samples with documented vaccination background. Results were subsequently compared for statistical evaluation. Interestingly, IgM isotype nAAb showed a statistically significant connection with anti-MMR IgG seropositivity (p< 0.001 in all cases), while IgG isotype nAAb levels were elevated in association with anti-SARS CoV-2 specific seropositivity (p= 0.019) and in heterogeneous vaccine regimen recipients (unvaccinated controls; vector/mRNA vaccines p= 0.002). Increasing evidence supports the interplay between immune activation and the dynamic expansion of nAAbs. Consequently, further questions may emerge regarding the ability of nAAbs silently shaping the effectiveness of immunization. We suggest re-evaluating the impact of nAAbs on the complex functioning of the immunological network.

The great pipefish Syngnathus acus is one of the most representative European syngnathid, being highly associated with seagrass and macroalgal beds. Surprisingly, the ecology of this ovoviviparous marine fish has received scanty attention. The population inhabiting three sites on Cíes Archipelago (Atlantic Islands National Park, NW Spain) was monitored in 2017-2018 for spatial and temporal changes in abundances, reproduction traits, trophic niche occupancy and dietary regimes across reproduction states through an isotopic (δ13C and δ15N) approach. Abundances were highly variable across seasons and sites, decreasing significantly from mid-autumn. The population consisted almost exclusively of large adults that migrate by the end of the breeding season, which extended from mid-spring to summer. Operational sex ratios suggest that the species is sex-role reversed. S. acus is a secondary consumer (Trophic position= 3.36 ± 0.05) preferring amphipods but displaying annual and seasonal dietary plasticity. Mature fish were less selective than immatures (especially females) with a higher preference for amphipods (36-68%) in the former. The second most preferred prey were carideans, copepods or isopods, depending on the year and reproduction state. Overall, the wider trophic niches in females and immature specimens compared to males and mature fish would indicate a higher variability in both the use of prey resources and/or their origin. The present study highlights the trophic plasticity and unique features of S. acus population in Cíes Archipelago, especially regarding the outstanding size of the fish and the exceptional occurrence of breeders.

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.

The connectivity level of last-generation vehicles is constantly on the rise. The combined use of Vehicle-To-Everything (V2X) connectivity and autonomous driving can provide remarkable benefits through the optimization of the route and speed trajectory. In this framework, this paper focuses on vehicle eco-driving optimization in a connected environment. The virtual test rig of a premium segment passenger car was used for generating the simulation scenarios. The benefits, in terms of energy and time savings, that the introduction of V2X communication, integrated with cloud computing, can have in a real-world scenario were assessed. The Reference Scenario is a pre-defined Real Driving Emissions (RDE) compliant route, while the simulation scenarios were generated by assuming two different penetration levels of V2X technologies. The associated energy minimization problem is formulated and solved by means of a global optimization algorithm, i.e., Dynamic Programming (DP). The optimization framework includes information coming from the surrounding environment, e.g., traffic lights state, speed limits, distance to travel, etc. The simulations show that introducing a smart infrastructure along with optimizing the vehicle speed in a real-world route can potentially reduce the required energy by 54% while shortening the travel time by 38%. Finally, a sensitivity analysis is performed on the bi-objective optimization cost function to find a set of Pareto optimal solutions, between energy and travel time minimization.

Background Content marketing is increasingly important for online branding. Brand popularity can be more easily determined online than sales-based measures but is not yet well-explained from a content marketing perspective. Promising predictors are open data syndication policies, connectivity to e-commerce platforms, product reviews, data health, and the depth and width of a brands product portfolio. A predictive content marketing model can help brand owners to understand their e-commerce potential. Methods We used brand popularity (Brand Popularity Rank) and catalog data in combination with product reviews from an independent content aggregator. For all datasets, we selected the overlapping dataset for brand popularity and brand reviews based on a period of 90 days from June 10, 2022, till September 24, 2022 (n = 333 brands). Backward stepwise multiple linear regression was used to develop a predictive content marketing model of the Brand Popularity Rank. Results Through stepwise backward multiple linear regression five highly significant (p < 0.01) predictive factors for brand rank are selected in our content marketing model: the brand’s data syndication policy, the number of connected e-commerce platforms, a brand’s number of products, its number of products per category, and the number of product categories in which it is active. Our model explains 78% of the variance of Brand Popularity Rank and has a good and highly significant fit: F (5, 327) = 233.5, p < 0.00001. Conclusions We conclude that a content marketing model can adequately predict a Brand Popularity Rank based on online popularity. In this model an open content syndication policy, more connected e-commerce platforms, and catalog size, i.e., presence in more categories and more products per category are each related to a better (lower) Brand Popularity Rank score.

The study of the brain networks using analysis of electroencephalography (EEG) data based on statistical dependencies (functional connectivity) and mathematical graph theory concepts is common in neuroscience and cognitive sciences for examinations of patient and healthy individuals. The Consciousness Fields according to Taheri theory and applications in the optimization of system under study have been investigated in various studies. In this study, we examine the results of working with Faradarmani Consciousness Field (FCF) in the brain of Faradarmangars. Faradarmangars are one of the necessary components in mind mediation of the function of Faradarmani Consciousness Fields according to Taheri. For this purpose, the functional and effective connectivity and the corresponding brain graphs of EEG from the brain of Faradarmangars is compared with that of non Faradarmangar groups during FCF connection. According to the results of the present study, the brain of the Faradarmangars shows significant decreased activity in delta (BA8), beta2 (BA4/6/8/9/10/11/32/44/47) and beta3 (in 34 of 52 BA) frequency bands mainly in frontal lobe and after that in parietal and temporal lobes in the comparison with the non Faradarmangars. Moreover, the functional and effective connectivity analysis in the frontal network shows dominant multiple decreased connectivity mainly in the case of beta3 frequency band in all parts of the frontal network. On the other hand, the graph theory analysis of the Faradarmangar brain shows an increase in the activity of the O2-T5-F4-F3-FP2-F8 areas and significant decrease in the characteristic path length and increases in global efficiency, clustering coefficient and transitivity. In conclusion, the unique higher graph function efficiency and the reduction in the brain activity and connectivity during the Faradarmani Consciousness Field mind mediation, shown the passive and detector like function of the human brain in this task.

Traumatic brain injury (TBI) is a global healthcare concern affecting millions, with wide-ranging symptoms including sensory and behavioral changes that can persist long-term. Due to similarities with human brain cytoarchitecture and inflammation, minipig models are advantageous for translational TBI research. However, gaps in knowledge exist regarding their behavioral and sensory sequelae following injury. Therefore, in this study, we assessed changes in approachability using a forced human approach task (FHAT) and mechanical nociception using the von Frey Test in adult male and female Yucatan minipigs for up to one week following a sham or central fluid percussion injury (cFPI). Specifically, the FHAT assessed each animal’s response to a forced interaction with either a known or unknown experimenter. To evaluate changes in nociceptive sensory sensitivity, von Frey monofilaments ranging from 0.008 to 300 grams of force were applied to the pinna of the ear or base of the tail. We found that forced approachability was affected by experimenter familiarity as well as cFPI in a sex-specific manner at subacute timepoints. We also found reductions in sensitivity following cFPI on the ear in male minipigs and on the tail in female minipigs. Overall, the current study demonstrates that cFPI produces both behavioral and sensory changes in minipigs up to one-week post-injury.

Phenotypic switching in cancer cells has been found to be present across tumor types. Recent studies on Glioblastoma report a remarkably common architecture of four well-defined phenotypes coexisting within high levels of intra-tumour genetic heterogeneity. Tumours grown from any cell type recapitulate the original phenotypic composition. Similar dynamics have been shown to occur in breast cancer, melanoma and likely across further cancer types. Given the adaptive potential of phenotypic switching (PHS) strategies, understanding how it drives tumour evolution and how to break down these architectures is a major priority. Here we model the ecological dynamics behind PHS. The model is able to reproduce experimental results, and specific conditions for cancer progression and clearance reveal novel features of plastic tumours and its direct consequences on therapy resistance. Following our results we discuss \textit{transition} therapy as a novel scheme to target not only combined cytotoxicity but also the rates of phenotypic switching.

Background: Early diagnosis of postoperative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. Prognostic models based on various data could help in the early diagnosis of postoperative complications after cardiac surgery. Objectives: The purpose of the study was to determine whether an integrated approach based on clinical data along with metabolites and biomarkers had greater predictive value than the models built on less data. Methods: The study included patients (n=62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass), who were retrospectively divided into two groups depending on the presence (n=26) or absence (n=36) of postoperative complications of all types. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers (interleukin-6, procalcitonin, NT-proBNP, high-sensitivity troponin T) and metabolites (8 aromatic and 2 dicarboxylic acids were detected using gas chromatography-mass spectrometry). Results: Patients before surgery, compared with healthy donors, differed in the concentration of metabolites (p &lt; 0.05), and had concentrations of a number of biomarkers exceeding the corresponding reference values. Multivariate PLS-DA models, predicting the presence or absence of postoperative complications, were built using clinical data (ROC-AUC=0.71), concentrations of metabolites and biomarkers (ROC-AUC=0.60), and the entire data set (ROC-AUC=0.75). For comparison we built univariate models using the EuroScore2 (ROC-AUC=0.59) and SOFA (ROC-AUC=0.76) scales; concentrations of lactate (ROC-AUC=0.71), the dynamic changes of 4-hydroxyphenyllactic acid (ROC-AUC=0.69), and the sum of three sepsis-associated metabolites (ROC-AUC=0.70). The proposed complex model using entire data set was comparable to the SOFA scale, which confirms the expediency of searching for new predictive models based on a variety of factors.

The $k$-set tree connectivity, as a natural extension of classical connectivity, is a very important index to evaluate the fault-tolerance of interconnection networks. Let $G=(V, E)$ be a connected graph and a subset $S\subseteq V$, an $S$-tree of graph $G$ is a tree $T=(V',E')$ that contains all the vertices of $S$. Two $S$-trees $T$ and $T'$ are internally disjoint if and only if $E(T)\cap E(T')=\varnothing$ and $V(T)\cap V(T')=S$. The cardinality of maximum internally disjoint $S$-trees is defined as $\kappa_{G}(S)$, and the $k$-set tree connectivity is defined by $\kappa_{k}(G)=\min\{\kappa_{G}(S)|S\subseteq V(G)\ \text{and} \ |S|=k\}$. In this paper, we show that the $k$-set tree connectivity of hierarchical folded hypercube when $k=4$, that is, $\kappa_{4}(HFQ_{n})=n+1$, where $HFQ_{n}$ is hierarchical folded hypercube for $n\geq 7$.

This paper presents technical solutions to increase Industry 4.0 maturity. Within the “5G-Enabled Manufacturing” project, a 5G network has been deployed at the shop floor to enable fast and scalable connectivity. This network was used to connect a grinding machine to a private cloud to enable visibility and transparency of the production data, which is the basis for Industry 4.0 and smart manufacturing. Results indicate a present lack of commercially available product independent solutions for interconnection and data transfer despite the availability of open standards and well-documented demonstrator projects. The solution is described and discussed regarding technical interoperability, focusing on the system layout, communication standards, and open systems. From the discussion, it is derived that manufacturing end-users are in need to expand and further internalize knowledge on future information and communication technologies to reduce their dependency on equipment and technology providers.