In this paper, a distributed algorithm for reaching average consensus is proposed for multi-agent systems with tree communication graph, when the edge weight distribution is unbalanced. First, the problem is introduced as a key topic of core algorithms for several modern scenarios. Then, the relative solution is proposed as a finite-time algorithm, which can be included in any application as a preliminary setup routine, and it is well-suited to be integrated with other adaptive setup routines, thus making the proposed solution useful in several practical applications. A special focus is devoted to the integration of the proposed method with a recent Laplacian eigenvalue allocation algorithm, and the implementation of the overall approach in a wireless sensor network framework. Finally, a worked example is provided, showing the significance of this approach for reaching a more precise average consensus in uncertain scenarios.

This paper presents a dynamic homotopy technique that can be used to calculate a preliminary result for a power flow problem (PFP). This result can then be used as an initial estimate to efficiently solve the PFP using either the classical Newton-Raphson (NR) method or its fast decoupled version (FDXB) while still maintaining high accuracy. The preliminary stage for the dynamic homotopy problem is formulated and solved by employing integration techniques, where implicit and explicit schemes are studied. The dynamic problem assumes an initial condition that coincides with the initial estimate for a traditional iterative method such as NR. In this sense, the initial guess for the FPF is adequately set as a flat start, which is a starting for the case when this initialization is of difficult assignment for convergence. The static homotopy method requires a complete solution of a PFP per homotopy pathway point, while the dynamic homotopy is based on numerical integration methods. This approach can require only one LU factorization at each point of the pathway. Allocating these points properly helps avoid several PFP resolutions to build the pathway. The hybrid technique was evaluated for large-scale systems with poor conditioning, such as a 109272-bus model and other test systems under stressed conditions. A scheme based on the implicit backward Euler scheme demonstrated the best performance among other numerical solvers studied. It provided reliable partial results for the dynamic homotopy problem, which proved to be suitable for achieving fast and highly accurate solutions using both the NR and FDXB solvers.

This study examines the association of sociodemographic risk factors and the comorbidities of COVID-19. Most COVID-19-related studies were conducted in inpatient settings among patients with severe infection during the first and second waves of the pandemic. The third wave with less severe Omicron variant presented a unique opportunity to observe the adult patients treated in outpatient settings. We conducted a cross-sectional study (117 patients aged > 40 years presented with symptomatic COVID-19). We employed the Charlson Comorbidity Index (CCI) to compute comorbidity burden, Medical Outcomes Survey Short form (SF-12) to measure general health, and Self-care Self- Efficacy Scale (SCSES) to assess self-care ability. Fisher’s exact test and analysis of variance (ANOVA) were used to compute the relationship between comorbidities and demographics. This study report focuses on the distribution of the comorbidities and its relationship to demographic variables. The top five comorbidities were hypertension (42%), diabetes (31%), pulmonary disease/asthma (19%), depression (14%), and cancer (11%). Depression was significantly higher among younger age groups (p<0.01) and women (p<0.05). Age (p<0.0001) and income (p<0.01) were the most significantly associated demographics with the comorbidity burden. Incidence of pulmonary disease showed a positive association to educational status (p=0.05). This was the first study to report a significant prevalence of depression among adults with symptomatic COVID-19.

A smart precipitation estimation method based on FY-4B meteorological satellite data (FY-4B_AI) is proposed in this paper. This method spatiotemporally matches 125 features derived from multi-temporal and multi-channels of FY-4B satellite data with the precipitation at stations. Then a precipitation model using the Light Gradient Boosting Machine algorithm is constructed. Comparative results between FY-4B_AI and GPM/IMERG-L products for over 450 million station cases throughout 2023 shows that: 1) FY-4B_AI is superior to GPM IMERG-L in the average absolute error, root mean square error, relative error, correlation coefficient, probability of detection and critical success index. While in the mean error and false alarm rate, FY-4B_AI is slightly inferior to GPM/IMERG-L. 2) Evaluation of strong weather event applications reveals that both FY-4B_AI and GPM/IMERG-L can accurately represent the spatial distribution characteristics of precipitation, no matter in the southeast humid region or the northwest dry region. Notably, FY-4B_AI, due to its higher spatiotemporal resolution, provides a more detailed distribution of precipitation.

The paper examines the possibility of increasing the wear and corrosion resistance of a CP-Ti surface by duplex plasma electrolytic treatment (borocarburizing and polishing). The structure and composition of diffusion layers, their microhardness, surface morphology and roughness, wear resistance during dry friction and corrosion resistance in Ringer's solution were studied. The formation of a surface hardened layer up to 200 m thick with a microhardness of up to 950 HV, including carbides and a solid solution of boron and carbon, is shown. Subsequent polishing makes it possible to reduce surface roughness and remove weak areas of the porous oxide layer, which is formed during high-temperature oxidation in aqueous electrolyte vapor during borocarburizing. Changing the morphology and structural-phase composition of the CP-Ti surface helps reduce weight wear by 3 times (the mode of frictional interaction changes from microcutting to oxidative wear) and corrosion current density by 4 times after borocarburizing in a solution of boric acid, glycerin and ammonium chloride at 950 °C for 5 minutes and subsequent polishing in an ammonium fluoride solution at a voltage of 250 V for 3 minutes.

Streptococcus agalactiae is a highly contagious Gram- positive bacterium that causes mastitis, has a high infectivity for mammary epithelial cells, and is challenging to treat. However, the molecular interactions between it and mammary epithelial cells remains poorly understood. This study analyzed differential gene expression in mammary epithelial cells with varying levels of S. agalactiae infection using UID- Dual Seq and bioinformatics tools. This study identified 211 differentially expressed mRNAs (DEmRNAs) and 452 differentially expressed lncRNAs (DElncRNAs) in host cells, primarily enriched in anti-inflammatory responses, immune responses, and cancer-related processes. Additionally, 854 pathogen differentially expressed mRNAs (pDEmRNAs) were identified, mainly enriched in protein metabolism, gene expression, and biosynthesis processes. Mammary epithelial cells activate pathways, such as the ERK1/2 pathway to produce, to produce reactive oxygen species (ROS) to eliminate bacteria. However, the bacteria disrupt the host's innate immune mechanisms by interfering with the alternative splicing processes of mammary epithelial cells. Specifically, the bacterial genes of tsf, prfB, and infC can interfere with lncRNAs targeting RUNX1 and BCL2L11 in mammary epithelial cells, affecting the alternative splicing of target genes and altering normal molecular regulation.

An explicit mass formula for charged leptons is proposed which employes the $(2 \ell +1)$ degeneracy of the $SU(2)$ Laplacian of quantum mechanics. Possible cosmological implications are briefly discussed.

Artificial intelligence (AI) is transforming many healthcare fields including pediatric urology. This in-depth review explores recent advances in AI tools for pediatric urology, emphasizing how state-of-the-art technologies can be used to enhance patient treatment. An initial overview of pediatric urology is given to provide context for this interdisciplinary field and the wide spectrum of problems this entails, before discussing potential uses of AI to improve diagnostic accuracy, treatment planning, and surgical results. AI-powered predictive models are already changing pediatric clinical practice by guiding interpretation of medical images and supporting decision making. In addition to outlining the aims, methods, and conclusions of previous studies on AI applications in pediatric urology, this review also highlights knowledge gaps and priority areas for future study. Particular focus is given to specialized surgeries such pyeloplasty, where AI-guided treatment may help overcome specific technical challenges of this complex procedure. Also highlighted are practical, ethical, and legal aspects of integrating AI into pediatric urology, which will be crucial for responsible innovation in patient-centered treatment. Overall, this article aims to enlighten and inspire clinicians, researchers, and other healthcare stakeholders by offering insight into the future of AI in pediatric urology.

This paper proposes a novel facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depthwise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while also capturing the dependencies between different facial regions. Additionally, a novel loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets show that the proposed LFNSB model performs excellently in facial expression recognition tasks. It achieves high recognition accuracy while significantly reducing the number of parameters, thus substantially lowering model complexity.

Let $R$ be a commutative ring with identity. A proper ideal $I$ of a ring $R$ is called a square-difference factor absorbing primary ideal of $R$ if for $a,b\in R$, whenever $a^{2}-b^{2}\in I$, then $a+b\in\sqrt{I}$ or $a-b\in I$. Several characterizations and properties of this class of ideals are presented.Various examples are provided to illustrate the obtained results and demonstrate the applicability of our findings. Furthermore, the properties of this class of ideals are investigated in extensions of rings.

Diabetic peripheral neuropathy (DPN) is a prevalent complication of type 2 diabetes that affects a significant proportion of diabetic patients worldwide. Although the pathogenesis of DPN involves axonal atrophy and demyelination, the exact mechanisms remain elusive. Current research has predominantly focused on neuronal damage, overlooking the potential contributions of Schwann cells, which are the predominant glial cells in the peripheral nervous system. Schwann cells play a critical role in neurodevelopment, neurophysiology, and nerve regeneration. This review highlights the emerging understanding of the involvement of Schwann cells in DPN pathogenesis. This review explores the potential role of Schwann cell plasticity as an underlying cellular and molecular mechanism in the development of DPN. Understanding the interplay between Schwann cell plasticity and diabetes could reveal novel strategies for the treatment and management of DPN.

The elderly population is particularly susceptible to myocardial damage caused by heat stress (HS). With the continuous increase in global temperatures, this poses an undeniable challenge to human health. This study aimed to investigate the therapeutic effect of Shikimic Acid (SA) in treating HS-induced myocardial damage through network pharmacology, molecular docking, molecular dynamics (MD) simulations, and in vitro experiments. Network pharmacology analysis revealed that SA alleviates the inflammatory response in the heart muscle associated with HS by targeting 60 disease-related targets, such as TP53, TNF, and IL-6. The tumor proteoglycan, the PI3K-Akt signaling pathway, and hepatitis B were identified as the primary therapeutic pathways. Molecular docking and MD simulation results confirmed that SA can stably bind to both TNF (-6.642 kcal/mol) and IL-6 (-7.261 kcal/mol), and the complexes of Shikimic Acid-IL-6 and Shikimic Acid-TNF did not undergo significant changes within 100 ns. In addition, the in vitro experiment showed that 250 μM SA significantly increased the survival rate of HL-1 cells exposed to 43°C for 2 h by 71.1% and promoted the proliferation of HL-1 cells by 30.7%. Overall, these findings provide substantial evidence for the therapeutic potential of SA in HS-induced myocardial damage.

Accurate stock market predictions following earnings reports are crucial for investors. Traditional methods, particularly classical machine learning models, struggle with these predictions because they cannot effectively process and interpret extensive textual data contained in earnings reports and often overlook nuances that influence market movements. This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates ‘base factors’, such as financial metric growth and earnings transcripts, with ‘external factors’, including recent market indices performances and analyst grades, to create a rich, supervised dataset. This comprehensive dataset enables our models to achieve superior predictive performance in terms of accuracy, weighted F1, and Matthews correlation coefficient (MCC), especially evident in the comparison with benchmarks such as GPT-4. We specifically highlight the efficacy of the llama-3-8b-Instruct-4bit model, which showcases significant improvements over baseline models. The paper also discusses the potential of expanding the output capabilities to include a ‘Hold’ option and extending the prediction horizon, aiming to accommodate various investment styles and time frames. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.

Introduction – Elections are a cornerstone of democratic societies, enabling citizens to express their preferences and shape the direction of their governments. Economic conditions, political dynamics, and social factors often play pivotal roles in influencing voter behavior and determining electoral outcomes, making elections a key area of study in understanding governance and policy change. Objectives – This study examines how key economic factors like inflation, unemployment, and poverty influence election results in Ghana. It also looks at how these economic elements shape voter choices and predicts the possible outcomes of the 2024 general elections using past data. Methodology – The study used historical economic and political data from 1992 to 2023, applying statistical models like ARIMA (Auto-Regressive Integrated Moving Average) to forecast future election outcomes. Economic indicators such as inflation and unemployment were included to study their relationship with voter behavior. Findings - The results show that economic factors significantly impact voter turnout and party vote share. High inflation and unemployment rates tend to reduce voter support for incumbent parties. The model also predicts possible outcomes for the 2024 election based on these economic trends. Conclusion and Recommendation – The study concludes that improving economic conditions, such as reducing unemployment, could positively influence electoral outcomes for incumbents. It is recommended that policymakers focus on stabilizing the economy to enhance electoral success.

Withania somnifera (WS), also known as ashwagandha, is a popular botanical supplement used to treat various conditions including memory loss, anxiety and depression. Previous studies from our group showed an aqueous extract of WS root (WSAq) enhances cognition and alleviates markers for depression in Drosophila. Here, we sought to confirm these effects in the 5xFAD mouse model of β-amyloid (Aβ) accumulation. Six- to seven-month-old male and female 5xFAD mice were treated with WSAq in their drinking water at 0 mg/mL, 0.5 mg/mL or 2.5 mg/mL for four weeks. In the fourth week of treatment, spatial memory, anxiety and depressive-like symptoms were evaluated. At the conclusion of behavioral testing, brain tissue was harvested, immunohistochemistry was performed, and the cortical expression of antioxidant response genes was evaluated. Both concentrations of WSAq improved spatial memory and reduced depressive and anxiety-related behavior. These improvements were accompanied by a reduction in Aβ plaque burden in the hippocampus and cortex and an attenuation of neuroinflammatory markers. Antioxidant response genes were upregulated in the cortex of WSAq treated mice. Oral WSAq treatment could be beneficial as a therapeutic option in AD for improving disease pathology and behavioral symptoms. Future studies focused on dose optimization of WSAq administration and further assessment of the mechanisms by which WSAq elicits its beneficial effects will help inform the clinical potential of this promising botanical therapy.

This study employs a comprehensive computational approach to identify and evaluate potential inhibitors of gamma D crystallin (CRYGD), a key protein implicated in cataract formation. A library of compounds, including natural products and FDA-approved drugs, was screened using molecular docking simulations and binding energy calculations. The top-scoring compounds were further assessed for drug-likeness properties and potential toxicity. Molecular dynamics simulations were conducted on the most promising candidates to investigate their interactions with CRYGD at the atomic level. The study identified CNP0185903, CID 3741, and CNP0151361 as the most promising compounds, with CNP0151361 demonstrating the most favorable combination of binding affinity, structural stabilization, and conformational modulation of CRYGD. These findings provide a foundation for the development of novel pharmacological interventions for cataract treatment, potentially offering alternatives or complements to current surgical approaches.

The cultural transmissions of a society like famous paintings, popular music, good poetry, and enduring religious rituals/doctrines/texts serve as mediums for the embodiment of algorithms that interact with the mind and body in looping, mostly unconscious interactions that are mutually beneficial to the participants of the society and the algorithms themselves. Useful algorithms are also found in the mind and bodies of other people, and individuals in a group can be used to outsource specialized tasks to benefit the society as a whole. Since useful algorithms must be embodied in a medium to work, there are physical limitations to the computations that can be performed in our universe.

The issue of climate change represents a significant global risk that demands immediate attention. However, higher education frequently fails to incorporate global perspectives and intricate transdisciplinary matters. In order to integrate global and transdisciplinary aspects into the curriculum of a technical university with a focus on computer science, a Master's programme has been developed. The programme's objective is to situate global risk at the core of the curriculum, thereby encouraging scientists to work beyond the boundaries of their respective disciplines. The paper considers the challenges that science and higher education must address to facilitate pathways towards a peaceful and sustainable future. It argues for a shift in focus from short-term and localised issues to a more holistic approach, emphasising the importance of considering universal problems. The curriculum, as developed, may be regarded as a guide for future university efforts in the fields of global risk and climate change.

Abstract: Background: We previously found that forest bathing significantly reduced the scores for negative emotions and in-creased the score for vigor in the profile of mood states (POMS) test in both males and females and increased the sero-tonin level in males, indicating the potential for beneficial effect on depressive status. In the present study, we investi-gated the effects of forest bathing on the levels of serotonin, oxytocin and insulin-like growth factor I (IGF-1) in blood, Self-Rating Depression Scale (SDS) scores, POMS, subjective fatigue symptoms and subjective sleep quality in female subjects with depression/depressive tendencies. Methods: Thirty-one female subjects aged 40.1±2.4 years with depression/depressive tendencies were recruited after obtaining informed consent. These subjects took day trips to a forest park, the birthplace of forest bathing in Japan named Akasawa Shizen Kyuyourin in Nagano Prefecture, and to a city area of Nagano Prefecture as a control in June 2023. On both trips, they walked 2.2-2.5 km for two hours each in the morning and afternoon on Saturday and Sunday, respectively. Blood was sampled in the afternoon before and after each trip. Concentrations of serotonin, oxytocin, IGF-1 and lactic acid in blood were measured. The SDS scores, POMS test, and questionnaires for subjective fatigue symptoms and subjective sleep quality were conducted before and after the trips. Ambient temperature and humidity were monitoring during the trips. The Nippon Medical School Central Ethics Committee approved this study. Results: Forest bathing significantly decreased SDS scores and the effect lasted for one week after forest bathing. Forest bath-ing also significantly increased the level of serotonin in serum in subjects who were not taking antidepressants, significantly increased the levels of oxytocin and IGF-1 in blood, significantly increased the scores for vigor-activity and friendliness and de-creased the scores for anger-hostility, confusion-bewilderment, fatigue-inertia, tension-anxiety, depression-dejection and total mood disturbance compared with city walking (all p

This study aimed to evaluate the effect of three yeast strains, Pichia guilliermondii (Levica 27), Candida norvegensis (Levazoot 15), and a commercial product (Levucell® SC 10) containing Saccharomyces cerevisiae, on in vitro rumen fermentation of two substrates: corn stover and a total mixed ration (TMR). Samples were taken 24 hours post-fermentation to measure pH, ammonia nitrogen (NH3-N), gas production, and volatile fatty acid (VFA) concentrations. The addition of yeasts did not affect gas production at 24 hours for either diet. For pH, differences between yeast strains were noted when TMR was used. The lowest NH3-N concentration (6.1 mM) was recorded with corn stover, showing similar results across strains. Conversely, NH3-N levels with TMR ranged from 47 mM to 66 mM. The inclusion of all three yeast strains increased the molar concentrations of total VFA, as well as acetic (C2), propionic (C3), and butyric (C4) acids in the rumen fluid. The C2:C3 ratio remained unchanged with corn stover but decreased with the inclusion of Levica 27 and Levazoot 15 in the TMR. With the commercial product Levucell® SC 10, this ratio was comparable to the control treatment. In conclusion, Levica 27 and Levazoot 15 demonstrated a stimulatory effect on in vitro rumen fermentation for the evaluated diets, evidenced by increased VFA concentrations.

Through the geometric relationships and force analysis of the main components of the panto-graph on high-speed trains, coefficients of aerodynamic forces and lift transmission between the pantograph and main components under crosswind conditions were derived. Based on the aer-odynamic forces acting on the pantograph at different crosswind speeds, wind angles, and oper-ating speeds, the aerodynamic lift of the pantograph and main components was ultimately de-termined. The results indicate that the aerodynamic lift of the pantograph is mainly distributed on the bow structure, with the aerodynamic lift of the upper frame all being negative values, while the absolute value of the aerodynamic lift of the lower arm rod is the smallest. The oper-ating speed of the pantograph and the wind angle of the crosswind have a significant impact on the aerodynamic lift of the main components, while the impact of the crosswind speed is rela-tively small. At the same operating speed of the pantograph, the lower the corresponding crosswind speed, the smaller the aerodynamic lift of the pantograph. The aerodynamic lift of the pantograph tends to decrease gradually with the increase of crosswind speed, and the impact of crosswind speed decreases gradually with the increase of pantograph operating speed. A com-prehensive relationship formula between the aerodynamic lift of the pantograph and the oper-ating speed, crosswind speed, and wind angle is obtained, and the empirical formula for the contact force of the bow net and train operating speed is modified. The research results are of great significance and value for the study and application of lift forces on pantographs under crosswind conditions.

As businesses increasingly adopt advanced technologies to enhance productivity and safety, the integration of human-robot collaboration (HRC) emerges as a transformative approach. This paper explores the role of GPU-accelerated computer vision and generative AI in optimizing HRC within business environments. By leveraging the computational power of GPUs, real-time data processing, and advanced machine learning algorithms, robots can better interpret complex visual cues and adapt to dynamic workspaces. Generative AI further enables the design of intelligent robotic systems capable of anticipating human actions, optimizing task allocation, and ensuring safety through predictive modeling. This research highlights the potential of these technologies to improve operational efficiency, reduce human error, and create safer, more adaptable workplaces. The implications of such advancements are discussed, providing insights into the future of HRC in various industries.

Photocatalytic ammonia decomposition to produce N2 and H2 was achieved using single-walled carbon nanotube (SWCNT) nanohybrids. Physical modification of ferrocene-dye-encapsulated CNTs by amphiphilic C60-dendron yielded nanohybrids with a dye/CNT/C60 coaxial heterojunction. Upon visible light irradiation, an aqueous solution of NH3 and dye@CNT/C60-dendron nanohybrids produced both N2 and H2 in a stoichiometric ratio of 1:3. Action spectra of this reaction clearly demonstrated that the encapsulated dye acted as the photosensitizer, with an apparent quantum yield (AQY) of 0.22% at 510 nm (the λmax of the dye). This study presents the first example of dye-sensitized ammonia decomposition and offers a new avenue for developing efficient and sustainable photocatalytic hydrogen production systems.

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, extracting the surface area affected by precipitation using a threshold method constrained by confidence intervals derived from microwave remote sensing images; Second, collecting disaster information from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms was proposed (TextCNN-Attention Disaster Classification); Finally, matching traffic road information on social media with basic geographic data, visualizing the classification of traffic road disaster risk levels, and completing the assessment of traffic road disaster levels based on multi-source heterogeneous data. Using the "7.20" rainstorm event in Henan Province as an example, this study categorizes the disaster impact on traffic roads into five levels: particularly severe, severe, moderate, mild, and affected, as interpreted from remote sensing images. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

The coronavirus SARS-CoV-2 is the causative agent for the COVID-19 first registered in Wuhan, China and responsible for more than 6 million deaths worldwide. Currently, RT-PCR is the gold standard method for diagnosing COVID-19. However, serological tests are needed for screening acute disease diagnosis and screening large populations during the COVID-19 outbreak. Herein we described the development of an in-house enzyme-linked immunosorbent assay (ELISA) for detecting levels of anti-Spike-1-RBD IgM antibody in well-defined serum/plasma panel for screening and identifying subjects infected with SARS-CoV-2 in a Latin population. We found that, compared to the RT-PCR as standard method, the in-house CovIgM-ELISA displayed sensitivities of 96.15% and 93.22% for samples collected up to 30 or 60 days after infection, respectively, as well as 95.59% specificity with 97.3% accuracy. The agreement kappa value (κ) of our CovIgM-ELISA was substantial when compared to RT-PCR (κ =0.873) and to SCoV-2 DetectTM IgM ELISA (κ = 0.684). The IgM levels detected in the population positively correlate with the neutralizing activity against the Wild-type, Alpha and Delta variants but failed to neutralize Omicron. These data indicate that our in-house CovIgM-ELISA is a compatible performing assay for the detection of SARS-CoV-2 infection.

Continuous efforts are required to find ways to protect crop production against the toxicity of petroleum hydrocarbons, such as diesel, contamination of soils. There is a need for identification of candidate plants that are tolerant to diesel toxicity that might also have the potential for remediation of diesel-contaminated soils. In this study, petunia, a popular ornamental plant and a model experimental plant in research on phytoremediation of environmental pollutants, was used to evaluate a novel method for rapidly assessing diesel toxicity based on the tolerance of shoots generated through in vitro plamt cell culture selection. Petunia shoot lines (L1 to L4) regenerated from diesel-treated callus were compared with those from non-diesel-treated callus (control). Significant morphological differences were observed among the tested lines and control, notably with L1 and L4 showing superior growth. In particular, L4 exhibited remarkable adaptability, with increased root development and microbial counts in a diesel-contaminated potting mix, suggesting that the shoots exhibited enhanced tolerance to diesel exposure. Here, this rapid bioassay has been shown to effectively identify plants with varying levels of tolerance to diesel toxicity and could therefore assist accelerated selection of superior plants for phytoremediation. Further research is needed to understand the genetic and physiological mechanisms underlying tolerance traits, with potential applications beyond petunias to other environmentally significant plants.

Objectives: The global quotient of psychological development, along with the motor skills, coordination, speech, and social behavior scores, was analyzed in children born prematurely with varying degrees of intracranial hemorrhage (grades I–IV), considering the unilaterality or bilaterality and dimensions of the left and right lateral ventricles. Materials and Methods: The study included 120 children divided into four groups based on gestational age: Group 1 (25–29 6/7 weeks), Group 2 (30–32 6/7 weeks), Group 3 (33–34 6/7 weeks), and Group 4 (35–37 weeks). The maximum dimensions of the right and left lateral ventricles were recorded via ultrasound. At 3 years of age, psychomotor development was assessed using the Brunet–Lezine scale. Statistical analysis was performed using appropriate modules in the SPSS 23.0 for Windows software package, along with Pearson’s ꭓ2 test and analysis of variance (ANOVA). Results: Differences in the global development quotient (QR) between the groups were statistically significant (p < 0.01). In Group 1, a larger dimension of the left lateral ventricle was associated with a decrease in motor skills, while in Group 2, a larger dimension of the right ventricle resulted in a significant decrease in coordination. In Group 3, all observed variables—that is, motor skills, coordination, speech, and social behavior—showed minimal values with a wider right ventricle, but without statistical significance. In Group 4, a significantly lower speech score was recorded with the dilation of the left lateral ventricle. Conclusions: The sizes of the left and right lateral ventricles after the resorption of intracranial hemorrhage affect the values (scores) obtained using the Brunet–Lezine scale in children at the age of 3 years.

In modern times, technology has become more than just a tool; it is a vital component of our lives, driving society and humanity towards new horizons, better living standards, and solu-tions for complex, global challenges. This article explores the world of dependable systems, specifically focusing on system design, software solutions, and architectural decisions that fa-cilitate collaborative work. It aims to dive into the intricacies of designing robust and effective document collaboration software. Throughout this paper, readers will gain insights into the proposed design process, enabling them to understand, learn from, and critically evaluate the examined methodologies and outcomes. Like everything else in the software technology field, there are multiple solutions to any given problem, and this work is no exception to this rule. The objective of this study is to provide a detailed architectural overview of the microservices pattern in an orchestrated cloud environment based on independent containers. To examine the pros and cons of such a system, the paper will detail how a real-time document collaboration system can benefit from such an architecture regarding availability, elasticity, scaling, and disaster recovery. Moreover, the paper will analyze how cloud-native technologies can be used to create a scalable platform solution, capable of handling traffic, dynamic resource storage, asynchronous com-munication, and application scaling. The intricate nature of this system renders this paper a valuable resource for prospective investigations within the domain of dependable systems, cloud computing, and distributed systems. This paper not only showcases the design and implementation process but also sets a founda-tion for future research and innovation in dependable systems, collaborative technologies, sustainable solutions, and distributed system architecture.

Psoriasis is a multifactorial, chronic, and inflammatory disease that severely impacts patients’ quality of life. The disease is caused by genetic irregularities affected by the epigenetic and environmental factors. Some of these factors may include the seasonal changes, such as solar radiation, air pollution and humidity, and changes in circadian rhythm, especially in the temporal and polar zones. Thus, some psoriasis patients report seasonal variability of symptoms. Through a comprehensive review, we aim to delve deeper into the intricate interplay between seasonality, environmental factors, and the genetic and epigenetic landscape of psoriasis. By elucidating these complex relationships, we strive to provide insights that may inform targeted interventions and personalized management strategies for individuals living with psoriasis.

Anxiety in older adults is a prevalent yet under-recognized condition associated with significant societal and individual burdens. This study used a machine learning approach to compare the relative importance of 57 risk and protective factors for anxiety symptoms in a population-representative sample of middle-aged and older European adults (N = 65,684; ages 45–103 years; 55.7% women; 15 countries represented). Results revealed loneliness and self-rated poor health as primary risk factors (Nagelkerke R2 = .272), with additional predictive contributions from country of residence, functional limitations, financial distress, and family care burden. Notably, follow-up analysis showed that none of 16 social network variables were associated with loneliness; rather, cohabitating with a partner/spouse was most strongly associated with reduced loneliness. Further research is needed to elucidate directional associations between loneliness and anxiety (both general and sub-types). These findings underscore the imperative of addressing loneliness for mitigating anxiety and related mental health conditions among aging populations.

Background/Objectives: The latest WHO classification underscores the importance of the complete blood count (CBC) and cell morphology assessment in diagnosing lymphoid neoplasms. This study evaluated the diagnostic role of Research Use Only (RUO) parameters from the BC-6800 Plus Mindray analyzer in differentiating cell chronic lymphocytic leukemia (CLL), acute lymphoblastic leukemia (ALL), and other lymphoproliferative disorders. Methods: CBCs were performed on 90 patients (66% male, median age 67 years) with pathological blood counts (ALL, n=14; CLL, n=47; other lymphoproliferative disorders, n=29) at Novara’s University Hospital. Univariable and multivariable logistic regression assessed the association of basic (WBC, Hb, RDW, NE#, LY#, MO#, PLT) and research cell parameters (NLR, NMR, LMR, NeuX, NeuY, NeuZ, LymX, LymY, LymZ, MonX, MonY, MonZ). Results: In multivariable analysis, Hb (p=0.02), NeuY (p=0.04), and MonY (p=0.01) independently predicted CLL versus ALL, correctly classifying 93.4% of cases (AUC 0.91, 95% CI 0.81-1.0). MO# (p=0.003), LymY (p<0.0001), and MonY (p=0.004) were independent predictors of CLL versus other lymphoproliferative disorders, classifying 77.6% of cases (AUC 0.86, 95% CI 0.78-0.95). NeuZ (p=0.01) and NeuY (p=0.04) independently predicted ALL versus other lymphoproliferative disorders, classifying 93% of cases (AUC 0.98, 95% CI 0.95-1.0). Conclusions: The use of RUO parameters offers valuable, cost-effective support in the early diagnosis of ALL, CLL, and other lymphoproliferative disorders.

This work aims to investigate the effect of process temperature and catalyst content by thermochemical degradation of municipal solid waste (MSW) fraction (organic matter + paper + plastic) on the yield of reaction products (bio-oil, biochar, H2O and gas), physi-cochemical properties and chemical composition of bio-oils, as well as on the morphol-ogy and crystalline phases of biochar in laboratory scale. The organic matter, paper and plastic segregated from the gravimetric composition of total waste sample were subject-ed to the pre-treatments of drying, crushing and sieving. Thermal pyrolysis was per-formed at 400, 450 and 475 °C and 1.0 atmosphere, and catalytic pyrolysis at 450°C and 1.0 atmosphere, using 5.0, 10.0 and 15.0% (wt.) of FCC zeolite, bath mode, using a laboratory-scale borosilicate glass reactor. The bio-oil was characterized for acidic val-ue. The chemical functions present in the bio-oil identified by FT-IR and the composi-tion by GC-MS. Biochar was characterized by SEM/EDS and XRD. Thermal pyrolysis of the MSW fraction shows bio-oil yields between 9.44 and 9.24% (wt), aqueous phase yields between 21.93 and 18.78% (wt), solid phase yields between 67.97 and 40.34% (wt) and gas yields between 28.27 and 5.92% (wt). The bio-oil yield decreased with in-creasing process temperature. For the experiments using FCC, the biochar and gas yields slightly increased with increasing FCC content, while that of bio-oil decreased and the H2O phase remained constant. GC-MS of bio-oils identified the presence of hy-drocarbons and oxygenates, as well as nitrogen-containing compounds including am-ides and amines. The acidity of the bio-oil increased with increasing temperature and with the aid of FCC as a catalyst. The presence of hydrocarbons in bio-oil was identified by the addition of FCC catalyst due to the deoxygenation of carboxylic acids, followed by decarboxylation and decarbonylation reactions, producing aliphatic and aromatic hydrocarbons.

Background: In high-risk breast cancer patients with skin infiltration the administration of a uniform dose to superficial tissues is fundamental in order to reduce local skin relapse. A personalized bolus may prevent the potential standard bolus’ inadequate dose distribution due to air gaps between the bolus and the skin. In this pilot study, we introduced in clinical practice the use of a personalized 3D printed bolus filled with ultrasound transmission gel. Methods: Seven patients undergoing radiotherapy after mastectomy were selected. 3D printed bolus dosimetric assessment was performed with MOSFET dosimeters on an anthropomorphic phantom and subsequently on three selected cases with increasing bolus shape irregularity. Acute/late toxicity and local control was assessed. Results: Overall, for the clinical cases the percentage median difference between measured and calculated doses was -2.7% (-7.0%÷4.9%). Median follow-up was 21 months. After two years, one patient showed G2 pain, one patient manifested G1 telangiectasia, one patient showed G1 hyperpigmentation, and two patients had no relevant toxicity. Conclusions: Personalized 3D-printed bolus filled with ultrasound gel may easily reproduce the standard bolus’ consistency providing an accurate coverage of the target area with tolerable acute/late toxicity grades. This is a pilot study and further investigations are needed.

Clustering is a very common analysis of data present in large datasets, with the aims of understanding and summarizing data, and discovering similarities, among others. However, despite the present success of subsymbolic methods for data clustering, the description of the obtained clusters cannot rely on the intricacies of the subsymbolic processing. For clusters of data expressed in the Resource Description Framework (RDF) we extend and implement an optimized previously proposed logic-based methodology which computed an RDF structure — called a Common Subsumer — describing the commonalities among all resources. We tested our implementation with two open, and very different RDF datasets: one devoted to Public Procurement, and the other devoted to drugs in Pharmacology. For both datasets, we were able to provide reasonably concise and readable descriptions of clusters up to 1800 resources. Our analysis shows the viability of our methodology and computation, and paves the way for general cluster explanations to lay users.

This study addresses a gap in the literature by conducting a comparative analysis of statistical and artificial intelligence techniques for predicting bankruptcy in small and medium-sized enterprises (SMEs). Using a comprehensive database of 7,104 Belgian SMEs, our findings demonstrate that intelligent techniques offer superior predictive accuracy for bankruptcy. Notably, our results indicate that a firm's position in the global value chain significantly affects prediction accuracy, with lower performance for firms operating upstream in the production process. This suggests that bankruptcy risk may spread upward along the value chain. This insight is particularly valuable for practitioners, as it highlights the importance of focusing on specific performance factors based on the type of firms they manage, advise, or collaborate with.

By tonnage, corn (Zea Mays L.) is the #1 crop produced globally, and recent research has suggested that no-till (NT) systems can lead to reduced yields of this important crop. Additionally, there is a lack of long-term data about the effects of tillage and N management on cropping systems. Corn is the most nitrogen (N)-fertilized crop in the USA, and N losses to the environment contribute to significant impacts on air and water quality. We conducted long-term studies on conventionally tilled (CT), and conservation tillage systems such as strip tillage (ST) and NT, under different N rates. We found that immediately after conversion to NT, yields from NT were significantly lower than yields from CT (P<0.1), but after five years of NT, the NT yields were 1.5% higher than the CT yields (P<0.1). Initially, the NT yields were lower than the ST (P<0.01), but after seven years of NT, the NT yields were comparable to ST grain yields. Although the total aboveground N uptake with NT immediately after conversion to NT was lower than with CT and ST, these differences were not significant in the long run. The nitrogen use efficiency (NUE) with NT increased over time. The present work highlights the importance of long-term research for determining the cumulative impacts of best management practices such as NT. We found that NT becomes a more viable practice after five or seven years of implementation, demonstrating the high importance of long-term research.

The structure and diversity of tropical vegetation are shaped by biotic and abiotic factors, which function as environmental filters affecting plant performance in different spatial and temporal scales. We compared soil traits, vegetation structure and leaf traits (i.e., specific leaf area, polyphenols and chlorophyll content a/b and total) in early, intermediate and late successional stages of a tropical dry forests in southeastern Brazil. For this purpose, we measured leaf traits of the most abundant species in the same 18 plots (50 x 20m/ six per successional stage) in 2009 and 2018. We tested whether tree species growing in early forests have a greater investment in conservative traits related to chemical defenses and tolerance to desiccation compared to late-stage tree species. We observed contrasting results when comparing the chronosequence differences in leaf traits both in 2009 and 2018 and the temporal changes along this period. Specific leaf area was lower than expected for all successional stages while polyphenol content increased along time, contrary to other studies in tropical dry forests. These results suggest that contrasting environmental conditions such as soil water holding capacity and light availability are responsible of the observed pattern. Total chlorophyll content increase lightly, while the a/b chlorophyll ratio doubled in all successional stages, contrary to our hypothesis. Therefore, we suggest that the conservative-acquisitive spectrum in successional gradients of TDFs should be further investigated for a better understanding of plant community assemblages.

Background/Objectives: Advancements in surgical stapling devices play a crucial role in improving outcomes for bariatric procedures. This study evaluates the performance and safety of a new endoscopic stapler (EnDrive®BelugaTM) regarding perioperative results across multiple bariatric surgery types. Methods: A retrospective analysis was conducted on 112 patients who underwent bariatric procedures using the Beluga stapler at two centres in the UAE and Kenya over 6-month period (June-December 2023). Procedures included laparoscopic sleeve gastrectomy (LSG), Roux-en-Y gastric bypass (LRYGB), mini gastric bypass (OAGB-MGB), and revisions. Perioperative outcomes, complications, and haemoglobin changes were assessed. Results: The cohort included 29 males and 83 females, with a mean age of 32.8 years and preoperative BMI of 41.1 kg/m². Procedures performed were 88 primary LSG, three primary LRYGB, 12 primary OAGB-MGB, and nine revision surgeries. No conversions, deaths, bleeding, or leaks occurred. Minor complications were observed in 4 patients. One patient required reoperation for intestinal obstruction. The mean hospital stay was 1.5 days. Haemoglobin levels showed minimal change from preoperative (12.8 g/dL) to postoperative day 1 (12.3 g/dL). Conclusions: The new Beluga endoscopic stapler demonstrated safe and effective performance across various bariatric procedures, with low complication rates and minimal blood loss. Its enhanced articulation capabilities may offer advantages in specific cases. Further studies are warranted to compare long-term outcomes with established stapling devices.

The border between Niger and Libya, often described as an "ungoverned space," epitomizes the complex interplay of economic, political, and security challenges within the Sahel region. Authorities and international actors have made various efforts to establish control, but tribal structures predominantly manage governance in these areas. These structures lack formal institutions and rely heavily on external security interventions to combat terrorism and irregular migration. The increasing militarization of the region raises significant concerns about the long-term efficacy of these approaches in achieving sustainable security. This study argues that enduring stability in such contexts requires a nuanced balance of economic, political, and social factors. The research methodology includes a comprehensive literature review of security issues, governance, state fragility, and counter-terrorism, utilizing scientific journals, books, reports, and newspapers. The study investigated the dual nature of ungoverned spaces, examining their role in facilitating terrorism and organized crime while highlighting the critical need for governance to enhance citizen welfare and support state functions. Recent developments in Libya and the Sahel under-score the urgency of addressing security and developmental challenges. The study concludes by advocating for sustainable political and development initiatives over militarization, emphasizing the necessity of a new social contract to address the security challenges in the Sahel region effectively.

This article explores the relationship between Wang Yangming's philosophy and the new quality productivity, revealing how the new quality productivity, while inheriting the core ideas of Yangming's philosophy, achieves a higher level through innovation and development. The new quality productivity emphasizes the integration and innovation of knowledge, technology, and information, characterized by knowledge-driven innovation, information technology support, sustainable green development, and global collaborative development. The fusion of Yangming's philosophy and the new quality productivity holds significant theoretical and practical implications.

Cholera is a diarrheal disease prevalent in populations without access to clean water. Cholera is caused by Vibrio cholerae, which colonizes the upper small intestine in humans once ingested. A growing number of studies suggests the gut microbiome composition modulates animal behavior. Zebrafish are an established cholera model that can maintain a complex, mature gut microbiome during infection. Larval zebrafish, which have immature gut microbiomes, provide the advantage of high-throughput analyses for established behavioral models. We identified the effects of V. cholerae O1 El Tor C6706 colonization at 5 days post-fertilization (dpf) on larval zebrafish behavior by tracking startle responses at 10 dpf. We also characterized the larval gut microbiome using 16S rRNA sequencing. V. cholerae-infected or uninfected control groups were exposed to either an alternating light/dark stimuli or a single-tap stimulus, and average distance and velocity were tracked. While there was no significant difference in the light/dark trial, we report a significant decrease in distance moved for C6706-colonized larvae during the single-tap trial. This suggests that early V. cholerae colonization of the larval gut microbiome has a dampening effect on sensorimotor function, supporting the idea of a link between the gut microbiome and behavior.

Objectives: Imaging exams are requested in intensive care units as a diagnostic, evaluation, and clinical monitoring method. While Chest Computed Tomography is the gold standard, Chest X-ray is the exam usually performed in routine care. Lung ultrasound has been gaining ground as it is a reliable exam performed at the bedside without exposure to radiation and at a low cost. This study aimed to evaluate the accuracy of bedside lung ultrasounds performed by pediatric intensivists as a diagnostic method. Methods: A prospective, diagnostic test-type study comparing bedside lung ultrasound performed by a pediatric intensivist with the evaluation of chest X-ray and lung ultrasound performed by a radiologist. Result: 48 patients on mechanical ventilation, aged from 1 month to 14 years, were analyzed, corresponding to 95 chest X-ray exams and 95 lung ultrasound exams. The Kappa agreement coefficients between the pediatric intensivist and the radiologist, both analyzing the lung ultrasound, were: normal report (1.0), atelectasis (1.0), pneumothorax (1.0), consolidation (0.97), pleural effusion (0.95), and pulmonary edema (0.90). Sensitivity, specificity, and accuracy of lung ultrasound were, respectively, normal report (50%; 91.8%; 82.1%), consolidation (88.3%; 57.1%; 76.8%), atelectasis (11.1%; 90.7%; 83.2%), edema (0; 70.2%; 70.5%), pneumothorax (83.3%; 94.4%; 93.7%), and pleural effusion (71.4%; 73%; 72.6%). Conclusion: The pediatric intensivist was skilled in examining and diagnosing lung injuries. Lung ultrasound showed good sensitivity, specificity, and accuracy when the pediatric intensivist performed when diagnosing consolidation, pleural effusion, and pneumothorax. Furthermore, lung ultrasound demonstrated better accuracy in evaluating pulmonary edema when compared with chest X-ray.

With the offshore oil-gas fields entering decline phase, high-efficiency separation of oil-gas-water mixtures becomes a significant challenge. As essential equipment for separation, the three-phase separators play a key role in Offshore Oil-Gas production. However, level control is critical in the operation of three-phase gravity separators on offshore facilities, as it directly affects the efficacy and safety of the separation process. This paper introduces an advanced deep deterministic policy gradient with adaptive learning rate weights (ALRW-DDPG) control algorithm, which improves the convergence and stability of the conventional DDPG algorithm. An adaptive learning rate weight function is meticulously designed, and an ALRW-DDPG algorithm network is constructed for simulating three-phase separator liquid level control. The effectiveness of the ALRW-DDPG algorithm is subsequently validated through simulation experiments. The results show that the ALRW-DDPG algorithm achieves a 15.38% improvement in convergence rate compared to the traditional DDPG algorithm, and the control error is significantly smaller than that of PID and DDPG algorithms.

This study considers the features of the chemical composition, internal structure, and oscillatory zoning of sulfides, sulfosalts and sulfates in the epithermal high-intermediate sulfidation type Au-Ag-Te Emmy deposit (Khabarovsk Territory), Russia. In this deposit the most widespread sulfide is pyrite occurring in two generations: pre-ore pure pyrite (I) with cubic crystal habit and dimensions from 20 μm to 0.5 mm from the early barren pre-ore hydrothermal stage, and pyrite (II) of the ore stage, which is represented by (a) colloform aggregates (IIa) formed as a result of rapid precipitation from the solution, and (b) crystalline pyrite (IIb), which is precipitated due to continuous decrease in temperature. Both pyrite (II) morphological varieties contain copper (0.06-6.03 wt.%). The crystalline variety of pyrite is associated with goldfieldite, hemusite and kawazulite which were deposited during the early mineralizing event in the deposit.

This study presents a comprehensive examination of the transformative potential of Islamic finance in advancing sustainable development goals (SDGs). It underscores the unique position of Islamic finance, with its ethical and moral foundations, to contribute significantly to environmental sustainability, social equity, and economic development. The paper highlights innovative financial instruments like Zakat, Waqf, and green Sukuk, which not only align with but also actively promote SDGs. Through a multidisciplinary approach, it advocates for a paradigm shift towards integrating sustainability into the core of financial systems. The analysis includes challenges in harmonizing Human Development Indexes (HDIs) with Ecological Footprints (EFs), proposing strategies for sustainable development, ecological conservation, and educational reforms. This study is pivotal for policymakers, financial institutions, and educators, providing a roadmap for leveraging Islamic finance towards achieving a balanced, sustainable, and inclusive global economy.

Commuting to work by private vehicle is one of the main sources of air pollution in cities, mainly from NO2 and particulate matter (PM2.5 and PM10). With the spread of telework, traffic congestion during peak hours is reduced on certain days of the week, improving air quality. This study an-alyzes the relationship between the improvement of air quality improvement and urban traffic resulting from teleworking activities after the COVID-19 pandemic in Madrid, Spain. The article considers road traffic and teleworking before the COVID-19 pandemic (2018 and 2019), during the pandemic (2020 and 2021) and in the period after (2022 and 2023) in the city center and the in-fluence on certain environmental factors. Daily NO2, PM2.5, PM10, and O3 concentration data were collected at air quality stations in Madrid municipality, traffic data and some meteorological variables such as wind speed, precipitation and temperature were considered. When conducting correlation and regression analysis among the variables it shows a clear association between NO2 and traffic before the pandemic, which is lower for both PM and O3. This correlation was main-tained during the pandemic, except for O3, whose association increased during this period and then decreased in the later period due to various motives. These results seem to indicate the ex-istence of a relevant relationship between urban mobility and air quality and an especially rele-vant relationship with telework, suggesting the need for policies aimed at promoting sustainable mobility in the future.

The modern era has numerous issues associated with power generation, especially with old fashioned combustion power plants. Since fossil fuels are primarily responsible for the emission of greenhouse gasses, there is a desperate need for adaptation and sustainability. Looking at those challenges, geothermal energy which civilization had a long time ago becomes the desirable solution. This paper aims at establishing the feasibility of geothermal power plants in meeting the challenges of energy intensity and environmental conservation. This study also illustrates that the categories of geothermal energy systems which include the dry steam, flash steam and the binary cycle power plants are capable of achieving high capacity factors, initiating feasibility studies, and have relatively low impacts on the environment. While it is likely to cost more in the initial stages of construction, geothermal power plants provide more long-term cost benefits and significantly fewer toxic emissions relative to the fossil fuel plants. Switching to geothermal energy not only means that the world will have new sources of energy in the near future but also that it will be clean and environmentally friendly. Through incremental steps, geothermal power plants may well open the door to a full energy source infrastructure for subsequent generations.

This study investigates the collapse of the medial longitudinal arch (MLA) as a risk factor for medial tibial stress syndrome (MTSS), hypothesizing that overuse of extrinsic foot muscles to prevent MLA collapse can lead to disability. Twenty healthy adults (39 feet) with a foot posture index score below 6 and no recent lower extremity orthopedic history participated. Ultrasonog-raphy measured foot muscle cross-sectional areas, while three-dimensional motion analysis us-ing VICON assessed foot kinematics during gait, focusing on navicular height at initial contact (ICNH) and dynamic navicular drop (DND) during the stance phase. Hierarchical cluster analy-sis based on ICNH and DND compared muscle cross-sectional areas between clusters using ANOVA or Kruskal–Wallis test. The analysis indicated that ICNH was lower in clusters 1 and 3 than in cluster 2, and DND was smaller in clusters 1 and 2 than in cluster 3. Although there was no significant difference in muscle cross-sectional area between the clusters, the flexor hallucis longus tended to be thicker in cluster 1 than in cluster 3 (p = 0.051). The findings suggest that the flexor hallucis longus may help prevent MLA compression during loading, indicating that over-use of extrinsic foot muscles may contribute to MTSS development.

Astragalus plants belong to the legume family and have obvious rhizosphere effect. The northwestern area of Yunnan China is the main distribution region of Astragalus plants with high application value, but low recognition and resource reserves. The aim of this research is to explore the species diversity of cultivable rhizosphere fungi derived from A. acaulis, A. forrestii, and A. ernestii growing in special high-cold environment of northwest Yunnan, and discover anti-infective components from these fungi. A total of 93 fungal strains belonging to 38 species in 18 genera were isolated and identified. Antibacterial and antimalarial screening yielded 10 target strains. Among them, the ethyl acetate crude extract of the fungus Aspergillus calidoustus AA12 from A. acaulis fermentation substance showed a broad antibacterial spectrum and the best antimalarial activity. Further chemical investigation led to the first discovery of seven compounds from the species of A. calidoustus, including a sesterterpine 6-epi-ophiobolin G (1), two sesquiterpenes penicisochroman A (2), pergillin (3) and 7-methyl-2-(1-methylethylethlidene)-furo[3,2-H]isoquinoline-3-one (4), and three polyketides trypacidin (5), 1,2-seco-trypacidin (6) and questin (7). Compound 1 exhibited moderate to strong antibacterial activity against six Gram-positive pathogens with the minimum inhibitory concentration (MIC) ranging from 25 to 6.25 μg/mL, and prominent inhibitory effect on the biofilm of Streptococcus agalactiae at MIC value of 3.125 μg/mL. This compound also displayed potent antimalarial activity against Plasmodium falciparum strains 3D7 and chloroquine-resistant Dd2 at the half maximal inhibitory concentration (IC50) values of 3.319 and 4.340 µmol/L at 72 h, respectively. This study contributed to our understanding of the cultivable rhizosphere fungi Astragalus-derived in special high-cold environment and further increased the library of fungi available for natural anti-infectious products screening.

Background/Objectives: Rehabilitation helps reduce disability in dementia. The Australian National Dementia Action Plan identifies a gap in clear treatment pathways post-diagnosis, affecting the quality of life for those with dementia. This study assessed the impact of a one-day dementia training course and follow-up on GPs' and practice nurses' knowledge, attitudes, and confidence regarding dementia rehabilitation. Methods: The training, led by two experienced GPs and an academic physiotherapist, covered dementia diagnosis, allied health roles, care planning, and referrals. The follow-up involved applying the learnt material and completing a reflective task. Three longitudinal surveys (Dementia Knowledge Assessment Scale - DKAS, General Practitioners’ Attitudes and Confidence towards Dementia Survey - GPACS-D, and Dementia Rehabilitation Scale) and Likert-scale statements were conducted pre-course, post-course, and at 4-month follow-up, alongside a focus group. Descriptive and regression analyses were applied to survey data, and content analysis was used for focus group data. Results: Seventeen participants (14 GPs, 3 nurses) completed the pre-post course survey, with eight (6 GPs, 2 nurses) participating in follow-up and focus group discussions. Post-course, DKAS scores increased by 12.1%, GPACS-D by 10.1%, and the dementia rehabilitation scale by 9.4%. Likert-scale statements improved by 8-79%. At the four-month follow-up, there was a slight, non-significant decline in most measures. Focus groups highlighted the training’s impacts, useful components, barriers, and suggestions for improvement. Conclusion: Training GPs and practice nurses in dementia rehabilitation enhances knowledge, awareness, and confidence. Ongoing efforts are needed to sustain benefits and address referral barriers for better access to dementia rehabilitation services.

It is vital to analyze ship collision risk for preventing collisions and improving safety at sea. This paper takes Ningbo-Zhoushan Port, a typical complex navigable water, as the research object, propose a probabilistic conflict detection method to estimate the potential collision risk by using a dynamic domain model driven by AIS data. Combined with the algorithm of fast modularity optimization and spectral clustering, a ship group extraction method from the perspective of water navigation management was proposed. Aiming at ship traffic characteristics in complex navigable waters, conduct dynamic calculation of ship collision risk from individual, regional and local multi-scale perspectives. The results show that the proposed method can detect the collision risk in a timely, reliable and effective manner under complex dynamic conditions. As such, they provide valuable insights for ship collision risk prediction and the development of risk mitigation measures.

Background: Obesity remains a widely debated issue, often criticized for the limitations in its identification and classification. This study aims to compare two distinct systems for classifying obesity: Body Mass Index (BMI) and body fat percentage (BFP) as assessed by bioelectrical impedance analysis (BIA). By examining these measures, the study seeks to clarify how different metrics of body composition influence the identification of obesity-related risk factors. Methods: The study enrolled 1255 adults, comprising 471 males and 784 females, with a mean age of 36 ± 11.90 years. Participants exhibited varying degrees of weight status, including optimal weight, overweight, and obesity. Body composition analysis was conducted using the TANITA Body Composition Analyzer BC-418 MA III device (T5896, Tokyo, Japan), evaluating the following parameters: current weight, basal metabolic rate (BMR), adipose tissue (%), muscle mass (%), and hydration status (%). Results: Age and psychological factors like cravings, fatigue, stress, and compulsive eating were significant predictors of obesity in the BMI model but not in the BFP model. Additionally, having a family history of diabetes was protective in the BMI model (OR: 0.33) but increased risk in the BFP model (OR: 1.66). The BMI model demonstrates exceptional predictive ability (AUC=0.998). In contrast, the BFP model, while still performing well, exhibits a lower AUC (0.975), indicating slightly reduced discriminative power compared to the BMI model. Conclusions:BMI classification demonstrates superior predictive accuracy, specificity, and sensitivity. This suggests that BMI remains a more reliable measure for identifying obesity-related risk factors compared to the BFP model.

As the aging of the world accelerates, clarifying the relationship between cultural differences and ageism is an urgent issue. Therefore, in this study, we conducted a correlation analysis between the six cultural scales of Hofstede et al. [1] and the objective and subjective ageism scales calculated from data on 35,232 people from 31 countries included in the World Values Survey Wave 6 by Inglehart et al. [2]. The results of a partial correlation analysis controlling for economic and demographic factors showed that power distance (PDI) and indulgence versus restraint (IVR) are positively and negatively correlated with ageism, respectively. This is the first study to show that these two cultural scales that previous studies have not paid much attention to are related to ageism.

Potentially, traditional medicine is used to manage multidrug resistance (MDR) bacterial infections. This study determined the antioxidant and antibacterial activities of Thymus schimperi (Ts), Rhamnus prinoides (Rp) and Justicia schimperiana (Js) from Addis Ababa against MDR-Uropathogenic Bacteria. Hydromethanolic extracts were prepared. Quantitative determination of phytochemicals and DPPH, ABTS and FRAP assays for antioxidant activities were carried out. In vitro, antibacterial activity and minimum inhibitory concentration (MIC) were performed. Ts had the highest total phenolic (TPC), flavonoid (TFC) and proanthocyanidin content. GC-MS analyses identified 14 bioactive-compounds: in Ts extract, with hexanedioic acid, bis(2-ethylhexyl) ester, thymol, and o-cymen-5-ol being the most predominant, respectively; in Rp extract (6 compounds; hexanedioic acid, bis(2-ethylhexyl) ester, β-D-glucopyranoside, methyl and desulphosinigrin respectively); and in Js extract (5 bioactive-compounds: hexanedioic acid, mono (2-ethylhexyl) ester, debrisoquine, and 8,11,14-heptadecatrienoate, methyl ester) being predominant, respectively. The extracts' TPC showed a strong negative correlation with the DPPH assay (r= -0.999; p=0.023), while its TFC correlated significantly with the ABTS assay (r=0.999; p= 0.032). Ts showed the highest antibacterial activity against clinical isolates of Escherichia coli and Klebsiella pneumoniae ESBL at 1000 mg/mL, and TS had the lowest MIC (4 mg/mL) among evaluated extracts against E. coli (ATCC25922). In conclusion, Ts and Rp showed activity against patient-isolated MDR-uropathogenic E. coli and K. pneumoniae ESBL.

The clinical application of collagen-based biomaterials is expanding rapidly, especially in tissue engineering and cosmetics. While oral supplements and injectable skin boosters are popular for enhancing skin health, clinical evidence supporting their effectiveness remains limited. Injectable products show potential in revitalizing skin, but safety concerns persist due to challenges in sterilization and the risk of biological contamination. Traditional methods of sterilization (heat and irradiation) can denature collagen. This study addresses these issues by introducing a novel technique: double filtration and low-temperature steam sterilization of a collagen gel. In vitro tests document sterility and confirm that the collagen structure remained intact without modification. An in vivo study found no adverse effects in rats or significant lesions at the implantation site over 13 weeks. These results suggest that this novel method to process collagen gels is a safe and effective skin booster. Advanced processing methods are likely to mitigate safety risks associated with injectable collagen products, though further research is needed to validate their biological effectiveness and clinical benefits.

Regular exercise has been shown to have a positive effect on the health outcomes of individuals with Type-2 diabetes mellitus (T2DM), however, it is still underutilised in developing countries. This study investigated diabetes-related knowledge, attitudes, and practice toward exercise and its benefits among individuals with T2DM. A total of one hundred and ninety-nine participants (male= 21.6% and female= 78.4%) with T2DM, aged between 18- 75, 43.77 (SD ±14.78) years participated in the study. A validated diabetes-related knowledge, attitude, and practice questionnaire, evaluated by true/false or unsure was utilised in the study. Descriptive and inferential statistics analysis was used to analyze data with the significance set at p<0. The study results demonstrated significantly poor diabetes-related knowledge of exercise (p<0.001). The majority of the study participants 163 (81.90%) with T2DM did not know that physical activity and exercise are different. Regarding the attitude of the individuals with T2DM, 158 (70.40%) of the study participants felt that their regular work was an adequate substitution for exercise. More than 50% of the study participants demonstrated a positive practice towards exercise and its benefits towards T2DM (p<0.001). The study revealed that participants’ knowledge and attitudes were poor towards exercise and its benefits, but their practice was good.

Traditional terrain analysis has relied on Digital Topographic Maps produced by national agencies and Digital Elevation Model (DEM) created using Airborne LiDAR. However, these methods have significant drawbacks, including the difficulty in acquiring data at the desired time and precision, as well as high costs. Recently, advancements and miniaturization in LiDAR technology have enabled its integration with Unmanned Aerial Vehicle (UAV), allowing for the collection of highly precise terrain data. This approach combines the advantages of conventional UAV photogrammetry with the flexibility of obtaining data at specific times and locations, facilitating a wider range of studies. Despite these advancements, the application of UAV LiDAR in terrain analysis remains underexplored. This study aims to assess the utility of UAV LiDAR for terrain analysis by focusing on the doline features within karst landscapes. In this study, we analyzed doline terrain using three types of data: 1:5000 scale digital topographic maps provided by the National Geographic Information Institute (NGII) of Korea, Digital Surface Models (DSM) obtained through UAV photogrammetry, and DEM) acquired via UAV LiDAR surveys. The analysis results indicated that UAV LiDAR provided the most precise three-dimensional spatial information for the entire study site, yielding the most detailed analysis outcomes. These findings suggest that UAV LiDAR can be utilized to represent terrain features with greater precision in the future which is expected to be highly useful not only for generating contours but also for conducting more detailed topographic analyses, such as calculating area and slope of the study sites.

Prevalence estimates of Parkinson’s disease (PD) – the fastest growing neurodegenerative disease – are underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating gaps between patient symptoms and diagnosis. Early-stage PD (n=82) and age-matched comparison (n=50) participants completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) Random Forest classification model (AUC=0.92) that performed well across environmental contexts. These findings provide robust support for further exploring consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.

The energy sector relies on numerical model output forecasts for operational purposes on a short-term scale, up to 10 days ahead. Reducing model errors is crucial, particularly given that coarse resolution models often fail to account for complex topography, such as that found in Costa Rica. Local circulations affect wind conditions at the level of wind turbines, thereby impacting wind energy production. This work addresses a specific need of the Costa Rican Institute of Electricity (ICE) as a public service provider for the energy sector. The developed and implemented product in this study serves as a proof of concept that could be replicated by WMO Members. It demonstrates a product for wind speed forecasting at wind power plants by employing a novel strategy for input selection based on large-scale indicators to enhance Artificial Intelligence-based forecasting methods. The product is developed and implemented based on the full-value chain framework for weather, water, and climate services for the energy sector introduced by the WMO. The results indicate a reduction of wind forecast RMSE by approximately 55% compared to the GFS grid values. The conclusion is that combining coarse model outputs with regional climatological knowledge through AI-based downscaling models is an effective approach for obtaining reliable local short-term wind forecasts up to 10 days ahead.

The integration of drone technology into healthcare logistics presents a significant opportunity to enhance the speed, reliability, and efficiency of medical sample transportation. This study inves-tigates the current advancements in drone technology, focusing on its application in the rapid and secure delivery of medical samples, particularly in urban and remote regions where traditional transportation methods often face challenges. Utilizing a combination of recent technological de-velopments such as AI-driven navigation systems, real-time monitoring, and secure payload management, the study examines how drones can mitigate logistical barriers like traffic conges-tion and geographical isolation. The research methodology includes a comprehensive review of recent case studies from various regions, illustrating the practical applications and benefits of drones in healthcare. The results demonstrate a substantial reduction in transportation time and costs, along with improved accessibility to healthcare services in underserved areas. The study concludes that, while challenges such as regulatory hurdles and privacy concerns remain, the on-going advancements in drone technology and supportive regulatory frameworks have the poten-tial to revolutionize medical logistics, ultimately improving patient outcomes and healthcare de-livery.

The role of astro- and microglial cells in the pathogenesis of epilepsy is currently under active in-vestigation. It is proposed that the activity of these cells may be regulated by agonists of peroxi-some proliferator-activated nuclear receptors (PPARs). The objective of this study was to investi-gate the effects of the PPAR β/δ agonist GW0742 (Fitorine) on the behavior and gene expression of astro- and microglial proteins involved in the regulation of epileptogenesis in rat brain in a lithi-um-pilocarpine model of temporal lobe epilepsy (TLE). A seven-day administration of GW0742 at a dose of 5 mg/kg/day has been shown to suppress TLE-related behavioral hyperactivity in the Open Field test and to attenuate the TLE-induced increase in gene expression and protein levels of astro- and microglial activation markers Gfap and Aif1 in the dorsal hippocampus and temporal cortex. Furthermore, GW0742 reversed the TLE-related increase in Lcn2 gene expression (a marker of the A1 phenotype of astrocytes) in the temporal cortex, the increase in the ratio of Nos2/Arg1 gene expression (markers of the M1/M2 phenotypes of microglia), and the decrease in Arg1 gene ex-pression in the dorsal hippocampus. The results suggest that GW0742 may mitigate behavioral and biochemical abnormalities in a lithium-pilocarpine model of TLE.

Exposure to even low levels of the environmental pollutant cadmium (Cd), increases the risk of kidney damage. The body burden of Cd at which kidney damage occurs is not, however, reliably defined. Here, multiple-regression and mediation analyses were applied to data from 737 non-diabetic Thai nationals, of which 32.2% had hypertension and 9.1% had an estimated glomerular filtration rate (eGFR) ≤ 60 mL/min/1.73 m2 (low eGFR). The excretion rates of Cd (ECd) and N-acetyl-β-D-glucosaminidase (ENAG), a marker of tubular injury, were normalized to creatinine clearance (Ccr) as ECd/Ccr and ENAG/Ccr. Doubling ECd/Ccr increased the risks of having a low eGFR [POR = 2.71 (95% CI:1.97, 3.74), p &lt; 0.001] and severe tubular injury [POR = 4.80 (95% CI: 1.35, 17.1), p = 0.015]. ENAG/Ccr was strongly associated with ECd/Ccr in both men (β = 0.447, p &lt;0.001) and women (β = 0.394, p &lt;0.001), while showing a moderate inverse association with eGFR only in women (β = −0.178, p = 0.002). A moderate association of ENAG/Ccr and ECd/Ccr was found in the low- (β = 0.287, p = 0.001), and the high-Cd body burden groups (β = 0.145, p = 0.004), but ENAG/Ccr was inversely associated with eGFR only in the high-Cd body burden group (β = −0.223, p &lt;0.001). These findings together with an insignificant mediated effect suggested that Cd-induced injury that causes release of NAG plays little or no role in the nephron destruction that reduces GFR.

Assembly theory provides a promising framework to explain the complexity of systems such as molecular structures and the origins of life, with broad applicability across various disciplines. In this study, we explore and consolidate different aspects of assembly theory by introducing a simplified toy model to simulate the autocatalytic formation of complex structures. This model abstracts the molecular formation process, focusing on the probabilistic control of catalysis rather than the intricate interactions found in organic chemistry. We establish a connection between probabilistic catalysis events and key principles of assembly theory, particularly the probability of a possible construction path in the formation of a complex object, and examine how the assembly of complex objects is impacted by the presence of autocatalysis. Our findings suggest that autocatalysis tends to favor longer consecutive construction sequences in environments with low catalysis probability, while this bias diminishes in environments with higher catalysis probabilities, highlighting the significant influence of environmental factors on the assembly of complex structures.

The study surveyed the use of academic libraries and information seeking behaviour of students at Lusaka Apex Medical University. The objectives of the study were to determine how often LAMU students used the libraries, to ascertain the purpose for which students used the library and to determine the challenges faced in accessing library services. Using a survey research design, 230 students selected using simple random sampling participated. Findings revealed that there were more male (51.1%) students who participated. The study also found that the majority of students (54.7%) were occasionally using the library. Results on the purpose for using the library showed that the majority (39.6%) were using it to read in order to improve personal competence. In relation to the challenges, majority students (42.6%) stressed the inadequacy of relevant materials. The need for librarians to make themselves available to aid students that seek information for different purposes was recommended. This was seen as a way through which many would have access to the available information in their field. There is a need for proper orientation and sensitization to students on how to access both physical and online materials. This would increase the library use as well as improve the performance of students. Lastly, it was recommended that a study on the digital literacy of students be done in order to ascertain their competence and search skills in sourcing for information.

A contemporary technique for calculating seismic risk is earthquake nowcasting (EN), which analyzes the progression of the earthquake (EQ) cycle in fault systems. 'Natural time' is a novel idea of time that serves as the foundation for EN assessment. The earthquake potential score (EPS), which has been discovered to have useful uses regionally and worldwide, is a unique tool EN uses to predict seismic risk. For the estimation of the EPS for the occurrences with the highest magnitude among these applications, we have since 2021 concentrated on Greece territory, applying sophisticated Artificial Intelligence (AI) algorithms, both wise (semi)supervised and unsupervised models, along with a customized dynamic sliding window technique that performs as a stochastic filter able to fine-tune the geo seismic occurrences. Long short-term memory (LSTM) neural networks, random forests, and clustering (geospatial) models are three machine learning techniques that are particularly good at finding patterns in vast databases and may be used to enhance earthquake prediction performance. In this study, we attempt to forecast whether practical Machine-learning and AI/Game-theoretic-based approaches can help predict big earthquakes and the normal future seismic cycle for 6-12 months. Specifically, we focus on answering two questions for a given region: (1) Is there a chance that a significant earthquake—say, one with a magnitude of M ≥ 6.0 will happen in the upcoming year? (2) What is the largest earthquake magnitude predicted in the upcoming year, and with which exact geographic coordinates (GCS)? Our results are quite promising and project a high precision accuracy score (≥ 98%) for seismic nowcasting in terms of four predictive parameters: the approximate (a) latitude, (b) longitude, (c) focal depth, and (d) magnitude of the phenomena.

The purpose of this project is to explore the potential of Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN), as a tool for making directional predictions for assets. The focus will be on using LSTM for classifying up/down moves in stock indices, stocks, and FX rates (EUR/USD). Various financial ratios, advanced technical indicators, and volatility estimators will be used as features to enhance the predictive power of the model. Incorporating credit spreads and news indicators can also improve the model. The advantage of LSTM is its ability to handle sequences of arbitrary length, which means that longer time frames such as 5-day, 10-day, or 1-week returns can be used for prediction. Before using RNNs, exploratory data analysis (EDA) will be performed to understand the underlying patterns in the data. This may include dimensionality reduction techniques such as autoencoding, self-organizing maps, or decision tree regression. The results of the EDA will be used to guide the selection of features for the LSTM model. It is important to note that long memory and stationarity are common characteristics of time series data, such as interest rates or economic indicators. Power-law autocorrection, Hurst exponent fractional, or Markov processes may be used to model these data. Predicting 5-day, 10-day returns for equities or 1-week, 1-month returns for financial factors presents two challenges. First, a large amount of data is required, typically 7–10 years of data. Second, positive autocorrelation in the 5-day or 10-day returns in equity time series must be isolated.

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus (DM), often leading to hospitalization and non-traumatic amputations in the United States. It is particularly prevalent among individuals of Hispanic descent, with a prevalence rate exceeding 10% [1, 2]. The target of this study is to identify markers of severity of DFU. This work follows a strategy that uses first Electronic Health Records and machine learning to identify risk factors reported as measurements of blood tests, such as cholesterol, blood sugar, and specific proteins in a cohort of thousands of patients. After this RNA risk factors are predicted using samples of bulk and single-cell gene expression across patients with different levels of DFU severity. We found the Albu-min/Creatinine Ratio (ACR) test as a key factor in severe DFUs. We also found a cluster of cells that harbor high expression of Apolipoprotein E protein (APOE) and it is significant in non-healing DFU cases. Overall, the study shows how the use of hundreds of thousands of EHR can reduce and inform the search space of molecules in a few samples of bulk-single cell transcriptomics and identify molecular markers of severity of DFU.

Mulberries is a type of fruit tree that is rich in nutrients and possesses both medicinal and dietary properties, deeply loved by consumers. The fragrance is a significant factor in the sensory quality of mulberries, playing a crucial role in consumer purchasing behavior. 'Baichang' mulberry is a unique variety of mulberry that emits a strong, creamy-like fragrance, which has enormous potential market value. However, the key metabolites and mechanisms responsible for the formation of this fragrance are still unclear. In this study, we utilized GC-MS technology and RNA-seq to examine the Volatile Organic Compounds (VOCs) and gene expression in 'Baichang' (Morus macroura) mulberries and 'Hongchang' (Morus laevigata) mulberries, the latter of which do not have a distinct fragrance. The results showed that 661 volatile metabolites were detected in both mulberry varieties. Most of these were found in higher relative amounts in the 'Baichang' mulberry variety, with 47 types being present only in the 'Baichang' mulberries, suggesting a rich composition of VOCs in the 'Baichang' mulberry. 312 metabolites were identified as differential metabolites; combining the sensory annotation information and relative content of VOCs, 70 candidate VOCs were annotated as related to the formation of 'sweet, waxy, coconut, and oily' sensations. They could potentially be the key metabolites contributing to the unique aroma of 'Baichang' mulberries. The relative odor activity value (rOAV) analysis revealed that 2(3H)-Furanone, 5-hexyldihydro- was the key VOC contributing to the formation of the special aroma of 'Baichang' mulberries. By RNA-seq, 19 key structural genes related to ester synthesis were identified, including AAT , ACX , ALDH, CYP, EHL, FAD, HPL, and LOX. The high expression of these structural genes in the "Baichang" mulberry fruit is the main reason for its unique creamy aroma. This study provides insights into the aroma differences among different kinds of mulberrie.

Strong yet ductile alloys have long been desired for industrial applications to enhance structural reliability. This work produced two (CoCrNi)93.5Al3Ti3C0.5 medium-entropy alloys with exceptional strength-ductility combinations, via short/medium (3 min/30 min)-time annealing after hot-rolling. Three types of intergranular precipitates including MC, M23C6 carbides, and L12 phase were detected in both the samples. Noticeably, the high density of intragranular L12 precipitates were only founded in the medium-time annealed sample but not in another one. Upon inspection of deformed substructure, it was revealed that the plane slip is the dominant deformation mechanism of both the alloys. This is related to the lower stacking fault energy, higher lattice friction induced by C solute, and slip plane softening caused by intragranular dense L12 precipitates. Additionally, we noted that the stacking fault and twinning act as the mediated mechanisms in deformation of the short-time annealed alloy, while only the former in the medium-time annealed one. It is attributed to the higher energy stacking faults and the increased critical twinning stress caused by intragranular dense L12 precipitates that inhibited the twinning tendency. Our present findings provide not only a guidance for optimizing the mechanical properties of high/medium-entropy alloys, but also a fundamental understanding of deformation mechanisms.

In the design of real-world networks, researchers evaluate various structural parameters to assess vulnerability, including connectivity, toughness, and tenacity. Recently, the tightness metric has emerged as a potentially superior vulnerability measure, although many related theorems remain unknown due to its novelty. Harary graphs, known for their maximum connectivity, are an important class of graph models for network design. Prior work has evaluated the vulnerability of three types of Harary graphs using different parameters, but the tightness metric has not been thoroughly explored. This article aims to calculate the tightness values for all three types of Harary graphs. First, it will attempt to calculate the lower bound for the value of the tightness parameter in Harary graphs using existing lemmas and theorems. Then, by presenting new lemmas and theorems, we will try to find the exact value or upper bound for this parameter in Harary graphs. For the first type of Harary graph, the tightness is precisely determined, while for the second and third types, upper bounds are provided due to structural complexity. The lemmas, theorems, and proof methods presented in this research may be used to calculate other graph and network parameters. However, the newness of the tightness parameter means that further research is needed to fully characterize its properties.

Lactococcus lactis is a potential bacterial cell factory to develop delivery systems for vaccines and therapeutic proteins. Large progress has been made in applications using engineered L. lactis against, e.g., inflammatory bowel disease and cervical cancer, but improvement of secretion and cell anchoring efficacy is still desirable. A double-labelling method based on biarsenical hairpin binding and nickel-polyhistidine affinity was used for visualization of protein trafficking and quantification of targeted proteins on the cell surface and in the cytoplasm. To investigate the importance of mature domain targeting signals (MTSs), we generated truncated constructs encoding 126, 66, and 26 amino acid residues from the N-terminus of the basic membrane protein A (BmpA) and fused those with the gene for the human papillomavirus serotype 16 (HPV16) E7 oncoprotein. Overexpression of fusion proteins was observed to come at a cost of cell proliferation. L. lactis cells produced and displayed the shortest fusion protein only with difficulty, suggesting that the entire absence of a homologous sequence containing MTSs significantly impedes export and surface anchoring of fusion proteins. With 40 amino acids following the signal peptide and containing one MTS, effective translocation was possible. Mutations of MTSs towards increased hydrophobicity resulted in an increase of secreted and surface-displayed fusion protein, suggesting the potential to design rationally improved constructs.

Textile heaters are made of knitted conductive yarns integrated into the fabric, making them stretchable, washable, breathable, and suitable for close-to-skin wear. The non-zero resistance in the lead wires, however, causes non-uniform power distribution, presenting a design challenge. To address this, the electrical performance of the heaters is modelled as an n-ladder resistor network. Using the finite difference method, simple, closed-form expressions are derived for networks with the power source connected to input terminals A1B1 and A1Bn, respectively. The results are exact and are used to derive approximations and design criteria. Solutions for ladder networks as presented in this paper apply to a wider class of physical problems, such as irrigation systems, transformer windings, and cooling fins.

Importance: Neuropsychiatric disorders like schizophrenia and bipolar disorder lack objective diagnostic markers, hindering accurate diagnosis and treatment. A novel approach using patient-derived cerebral organoids and digital analysis of electrophysiological recordings could provide much-needed objective biomarkers.Objective: To develop a digital analysis pipeline that can identify distinct electrophysiological signatures of schizophrenia and bipolar disorder using multi-electrode array recordings from patient-derived cerebral organoids.Design: This was an experimental study using previously published data. The study type can be classified as a case-control study, comparing cerebral organoids and 2D neuronal cultures derived from patients with schizophrenia, bipolar disorder, and healthy controls. The study utilized multi-electrode array recordings for analysis. Setting: The study was conducted in a laboratory setting using previously recorded data. Main Outcomes and Measures: The primary outcome was the accuracy of a support vector machine classifier in distinguishing between healthy control, schizophrenia, and bipolar disorder samples based on electrophysiological features extracted from the recordings. Key cellular-digital biomarkers were identified using minimum redundancy maximum relevance feature selection.Results:Our Support Vector Machine classifier achieved 95.8% accuracy in distinguishing SCZ from control samples in 2D neuronal cultures under both baseline and post-electrical stimulation (PES) conditions. For cerebral organoids, classification accuracy was 83.3% under baseline conditions, improving to 91.6% under PES when distinguishing between control, SCZ, and BPD samples. Key features for classification included channel-specific measures such as median, covariance, autocorrelation, and kurtosis of neural activity. Confusion matrices visualize classification performance, with most misclassifications occurring in the BPD class under baseline conditions. These results demonstrate that our digital analysis pipeline can effectively distinguish between healthy control and patient-derived samples using electrophysiological features from multi-electrode array recordings, particularly under PES conditions.Conclusions and Relevance: This digital analysis pipeline demonstrates the potential to identify objective electrophysiological signatures of schizophrenia and bipolar disorder using patient-derived cerebral organoids. This approach could lead to improved diagnostic accuracy and personalized treatment strategies for neuropsychiatric disorders.

Within the phenomenology of particle physics, the theoretical model of 4-zero textures is validated using a chi-square criterion that compares experimental data with the computational results of the model. Traditionally, analytical methods that often imply simplifications, combined with computational analysis, have been used to validate texture models. In this paper, we propose a new meta-heuristic variant of the differential evolution algorithm that incorporates aspects of the particle swarm optimization algorithm called "HE-DEPSO" to obtain chi-squared values are less than a bound value, which exhaustive and traditional algorithms cannot obtain. The results show that the proposed algorithm can optimize the chi-square function according to the required criteria. We compare simulated data with experimental data in the allowed search region, thereby validating the 4-zero texture model.

Background/Objectives: This literature review, on occasion of the reported case report, explores the complex interplay between gabapentin (GBP), a gamma-aminobutyric acid (GABA) analog, and the hypothalamic-pituitary-adrenal (HPA) axis in patients undergoing major surgical procedures. It specifically investigates the potential impact of GBP on cortisol levels, stress responses, and infection risk, illustrated by a detailed clinical case. Methods: This review combines a comprehensive literature search with a case report of a 17-year-old male with osteosarcoma who experienced transient adrenal insufficiency and infections while receiving GBP. The case is analyzed in the context of existing literature on GBP and the HPA axis. Results: The findings highlight the intricate relationship between GBP use, adrenal insufficiency, and infection susceptibility. It underscores the need for further research and clinical vigilance when prescribing GBP to patients with underlying medical conditions, particularly in the context of major surgical procedures. The review underscores the need for further research and clinical vigilance when prescribing GBP, particularly in perioperative settings. Conclusions: GBP’s effects on the HPA axis and immune responses are complex and multifaceted. Clinicians should exercise caution when prescribing GBP, especially for patients with underlying conditions undergoing major surgery. Further research is needed to elucidate the mechanisms of GBP's influence on cortisol levels and stress responses.

Despite the potential benefits of generative Artificial Intelligence (genAI), concerns about its psy-chological impact on medical students, especially with regard to job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed to examine the specific fears, anxieties, mistrust, and ethical concerns medical students could harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 medical students studying in Jordan across various academic years, employing a structured self-administered questionnaire with an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, and Ethics and comprising 12 items, with three items for each construct. The results indicated variable levels of anxiety towards genAI among the participating medical students: 34.1% reported no anxiety about genAI role in their future careers (n = 56), while 41.5% were slightly anxious (n = 61), 22.0% somewhat anxious (n = 36), and 2.4% extremely anxious (n = 4). Among the FAME constructs, Mistrust was the most agreed upon (mean: 12.35±2.78), followed by Ethics construct (mean: 10.86±2.90), Fear (mean: 9.49±3.53), and Anxiety (mean: 8.91±3.68). Sex, academic level, and Grade Point Average (GPA) did not significantly affect the students’ perceptions of genAI. However, there was a notable direct association between the students’ general anxiety about genAI and elevated scores in the Fear, Anxiety, and Ethics constructs of the FAME scale. Prior exposure to genAI and its previous use did not significantly modify the scores of the FAME scale. These findings highlighted the critical need for refined educational strategies to address the integration of genAI in medical training. The results demonstrated a pervasive anxiety, fear, mistrust, and ethical concerns among medical students regarding the deployment of genAI in healthcare, indicating the necessity for curriculum modifi-cations that focus specifically on these areas. Interventions should be tailored to increase genAI familiarity and competency, which would alleviate apprehension and equip future physicians to engage with this inevitable technology effectively. The study also highlighted the importance of incorporating ethical discussions into medical courses to address mistrust and concerns about the human-centered aspects of genAI. Conclusively, the study calls for a proactive evolution of medical education to prepare students for AI-driven healthcare practices shortly to ensure that physicians are well-prepared, confident, and ethically informed in their professional interactions with genAI technologies.

Renal replacement therapy (RRT), widely used in the treatment of renal injury in the course of sepsis, aims to eliminate the toxins and proinflammatory cytokines, involved in the pathomechanism underlying septic shock. Dialysis filters characterised by a high adsorption potential for cytokines in RRT when septic renal injury is the case. It is of key importance in the antibiotics treatment of sepsis to achieve desired values of PK/PD indices. Continuous renal replacement therapy (CRRT) may affect antimicrobial clearance, increasing their elimination in some cases. The aim of this study was to determine the degree of adsorption for linezolid, on three different types of filters used in CRRT. In our in vitro study a continuous veno-venous hemofiltration (CVVH) was conducted using 3 types of filters: polysulfone (PS), polyethyleneimine-treated polyacrylonitrile (PAN PEI) and non-PEI-treated polyacrylonitrile (PAN) filters. Each type of filter was used in 3 CVVH cycles, involving the use of 600 mg of linezolid dissolved in 700 ml of bovine blood or in 700 ml of 0.9% NaCl. In each case the total volume of the obtained solution was 1000 ml. Blood samples, at particular time points, were collected in order to measure drug concentration. The differences in mean drug/NaCl adsorption and drug/ blood adsorption were determined using one-way ANOVA with multiple comparisons by Tukey post-hoc test. A p-value of < 0.05 was considered to be significant. A significant adsorption of linezolid was found for PAN PEI filter, both in samples obtained from bovine blood and 0.9% NaCl solutions, at the endpoint. No significant adsorption was demonstrated on PAN or PS filters. There is a need for in vivo research to confirm the effect of filter type on linezolid concentration in patients undergoing CRRT.

Titanium alloys, notably Ti6Al4V, are frequently employed in biomedical applications due to their high mechanical strength and biocompatibility. But improving their surface qualities like biocompatibility and resistance to corrosion remains a crucial topic of study. This study investigates the effects of rhenium-carbon coatings deposited on Ti6Al4V substrates using magnetron sputtering technique with a molybdenum anchoring layer. Despite successful formation of rhenium carbides, elemental rhenium, and rhenium oxides confirmed by EDS and XRD analysis, the coatings exhibited poor adhesion, resulting in significant delamination as observed in SEM images. Corrosion resistance tests revealed that the coatings adversely affected the Ti6Al4V substrate, as indicated by increased corrosion current densities and more negative corrosion potentials in potentiodynamic polarization tests. Biocompatibility assessments using PK15 cells showed a notable reduction in cell viability and metabolic activity, particularly with coatings of higher surface roughness. These findings underscore the need for improved surface preparation and coating processes to enhance adhesion and biocompatibility of rhenium-carbon coatings on Ti6Al4V substrates. Future research should focus on optimizing coating adhesion, investigating cytotoxicity mechanisms related to surface roughness, and expanding biocompatibility evaluations to various cell types and biological environments.

Ischemic stroke is a leading cause of mortality worldwide, and intravenous thrombo-lysis, while improving functional outcomes, still leaves a significant mortality rate. This study aimed to investigate the clinical and pathological data of thrombolysed stroke patients who subsequently died and underwent autopsy, focusing on hemorrhagic transformation (HT). Over a 10-year period, 1426 acute ischemic stroke patients re-ceived thrombolysis at our center, with an in-hospital mortality rate of 11.7%. Autop-sies were performed on 98 of the 167 deceased patients. HT was found in 47% of these cases, only less than half occurring within a day of thrombolysis. Significant inde-pendent predictors of HT included higher lactate dehydrogenase (LD) levels and higher INR values at admission. HT directly caused death in 30% of cases, often through herniation, while other complications (pulmonary embolism, pneumonia) were also common. These findings underscore the importance of postmortem investi-gations to accurately determine the incidence of HT and contributing factors. Our data indicate that in the vast majority of HT cases, the role of contributing factors other than rt-PA may be important. Of the routinely assessed clinical and laboratory parameters at admission only LD and INR were found to be independent predictors of HT in the autopsied studied cohort.

Introduction: The COVID-19 pandemic highlighted the necessity for rapid, efficient testing at large-scale events. Developing rapid and efficient testing methods was critical, especially for events with a high risk of virus spread. The queueing theory offers a powerful tool to manage the flow of people through testing centers. This study uses the M/M/c model to examine how different floor plan configurations impact the efficiency of RADT testing centers. Aim of the study: This research applies queueing theory to optimize the Rapid Antigen Diagnostic Test (RADT) processes at mass gatherings. The study uses the M/M/c queueing model to evaluate the dynamics of RADT centers configured in U-shaped and straight-line layouts. The primary goal is to assess the feasibility of using RADTs efficiently at events and mass gathering entrances, aiming to enhance throughput and minimize wait times. Methodology: The study involved 500 healthy participants, managed by medical staff across a U-shaped and a straight floor plans. Agile management techniques were employed to enhance operational efficiency. The process stages included queue number issuance, registration, sample collection, sample mixing, and results dissemination. Results: Both floor plans enabled the completion of RADT screening in approximately 2 minutes per participant. The U-shaped layout slightly outperformed the straight-line setup in efficiency, demonstrating its effectiveness in optimizing the testing process. Conclusion: This study confirms the feasibility of implementing Rapid Antigen Diagnostic Test (RADT) processes at mass gatherings. It was found that the U-shaped floor plan configuration is particularly effective, indicating its potential suitability for future mass testing scenarios.

Background: The Government of Bangladesh is currently providing COVID-19 vaccines free of cost, but for a massive population, the free vaccination program’s sustainability is challenging. In this regard, assessing willingness to pay can help to explore vaccine pricing, subsidizing strategy, and vaccine demand. The study aimed to evaluate the prevalence of willingness to pay (WTP) for the COVID-19 vaccine and its associated factors to ensure the sustainability of the vaccination program. Methods: A cross-sectional research design was adopted to collect data from 1497 respondents through online and face-to-face interviews. Data were analyzed using the chi-square test and point-biserial correlation for descriptive statistical analysis, and multiple logistic regression analysis was also used to identify the factors associated with the dependent variable in the study. Results: The prevalence of WTP towards a COVID-19 vaccine was 50.9 %, and mean and median WTP were respectively 754.55 BDT (US$ 8.93), 300 BDT (US$ 3.55). WTP of other religions was significantly higher than Muslims (aOR=1.5, P=0.037). WTP was also higher among Graduate (aOR=2.2, P=0.007), Masters & MPhil/PhD (aOR=2, P=0.030), higher family income group (aOR=1, P=0.039) and respondents with higher knowledge about the vaccine (aOR=1.1, P=0.003), behavioral practice (aOR=1.1, P< 0.001), higher subjective norms (aOR=1.2, P=0.009), higher anticipated regret (aOR=1.2, P=0.005) and higher perceived benefits from the vaccine (aOR=1.1, P=0.029). On the contrary, WTP is lower among respondents who had a higher negative attitude toward vaccines (aOR= 0.9, P<0.001) and higher behavioral control (aOR=0.9, P=0.006). The participants who had experienced severe morbidity and who belonged to a higher income group, they have highly tended to WTP's highest amount of money for the COVID-19 vaccine. Conclusion: The study found that nearly half of the respondents were not interested in paying for the COVID-19 vaccine. It emphasizes higher education, proper knowledge dissemination about the COVID-19 vaccine, positive behavioral practice, reducing negative attitudes towards it, and higher household income to increase WTP for the COVID-19 vaccine. Health promotion materials should be developed to improve COVID-19 vaccine knowledge and reduce negative attitudes regarding vaccines. A subsidized scheme for lower-income groups should be initiated to minimize the catastrophic income challenge regarding WTP for the COVID-19 vaccine.

This article investigates the fundamental principles that establish universal limits in physical phenomena, such as the uniformity of physical laws and Energy conservation, leading to the concepts of maximum speed, locality, and maximum density. These principles guide me to explore Quantum Black Holes—relativistically rotating, miniature charged black holes with a singular core at the highest possible density. Uniquely, these Quantum Black Holes maintain all quantized charges, including gravitational ones. The study further demonstrates that quarks and leptons are composed of these Quantum Black Holes, redefining them as the actual elementary particles.Exploring these particles reveals that they emit Energy outward, modifying the curvature of nearby space-time. This discovery provides new insights into local space-time dynamics, characterized by a single equation that includes the influence of all charges and hidden Energy. By the conclusion of this article, I show that Quantum Black Holes offer explanations for several Standard Model deficiencies: the nature of luminous, dark, and anti-Matter; the mass, size, and quantized charges of all elementary particles (including gravitational); the nature of neutrino oscillations; and the mechanism by which electric charges remain bound even though the same type of electric charge components must repeal each other.

The dynamic response of the shed-tunnel structure under rockfall impact attracts much attention in recent years. However, the research on the dynamic response of the shed-tunnel structure under rockfall impact mostly focuses on the rockfall single impact, while in practical engineering, the shed-tunnel structure often encounters the rockfall repeated impacts during its service life. Therefore, this research focuses on exploring the dynamic response characteristics of the shed-tunnel structure under the rockfall repeated impacts. First of all, based on the model test of the shed-tunnel under rockfall impact as a reference, the FEM (Finite Element Method)-SPH (Smoothed Particle Hydrodynamics) coupling numerical calculation model is established based on ANSYS/LS-DYNA finite element code. The numerical simulation of the dynamic response of the shed-tunnel structure under rockfall impact is realized, and the rationality of the model is verified. Then, with this model and the full restart technology of LS-DYNA code, the effects of four factors, e.g. rockfall mass, rockfall impact velocity, rockfall impact angle and rockfall shape, on the impact force and impact depth of the buffer layer, the maximum plastic strain and axial force of the rebar, the shed roof vertical displacement and the shed-tunnel plastic strain are studied. These results show that the cumulative deformation and damage of the shed-tunnel structure caused by the rockfall repeated impacts play an important role in the destruction of the shed-tunnel structure, and the special attention should be paid to this issue.

Epidemiologic studies have shown an association between tuberculosis and lung cancer. The al-tered tumor microenvironment after tuberculosis infection appears to contribute to cancer pro-gression. Pleural effusions are enriched in exosomes, which act as mediators of intercellular communication. We hypothesized that tuberculous pleural effusion (TPE)-derived exosomes mediate intercellular communication. Then we examined the interaction between TPE-derived exosomes and cancer cells. Exosomal miRNA profiling of TPE was performed using a microRNA array. An in vitro lung cancer cell experiment and an in vivo mouse xenograft tumor model were used to evaluate the effects of select exosomal microRNAs. TPE-derived exosome treatment en-hanced the growth of A549 cells both in vitro and in a nude mouse xenograft model. Neighboring cancer cells were observed to take up TPE-derived exosomes, which promoted cancer cell inva-sion. Exosome-mediated transfer of select microRNAs, including miR-130b-3p and miR-423-5p, to A549 lung cancer cells activated cyclin D1 signaling and increased the expression of phos-phorylated p65, a cyclin D1 transcription factor. Inhibitors of miR-130b and miR-423-5p sup-pressed the promotion of lung cancer by TPE derived exosomes and reduced the expression of p65 and cyclin D1. These results suggest that TBE-derived exosomal miRNAs can serve as a novel therapeutic target in tuberculous fibrosis-induced lung cancer.

Introduction: The environmentally acquired aerobic spore-forming (EAS-Fs) bacteria that are ubiquitous in nature (e.g. soil) are transient colonisers of the mammalian gastro-intestinal tract. Without regular exposure, their numbers quickly diminish. These species of bacteria have been suggested to be essential to the normal functioning of metabolic and immunogenic health. The modern Western lifestyle restricts exposure to these EAS-Fs, possibly explaining part of the pathogenesis of many Western diseases. To date, the only animal studies that address specific microbiome modelling are based around germ-free animals. We have designed a new animal model that specifically restricts exposure to environmental sources of bacteria. Methodology: A new protocol, termed Super Clean, that involves housing mice in autoclaved individually ventilated cages (IVCs), with autoclaved food / water, and strict ascetic handling practice was first experimentally validated. The quantification of EAS-Fs was assessed by heat-treating faecal samples and measuring colony forming units (CFUs). This was then compared to mice in standard-conditions. Mice were housed in their respective groups from birth until 18 months. Stool samples were taken throughout the experiment to assess for abundance in transiently acquired environmental bacteria. Clinical, biochemical, histological, and gene expression markers were analysed for diabetes, hypercholesterolaemia, obesity, inflammatory bowel disease, and non-alcoholic fatty liver disease (the “diseases of the West”). Results: Our results show that stringent adherence to the Super Clean protocol produces a significantly decreased abundance of aerobic spore-forming Bacillota after 21 days. This microbiomic shift was correlated with significantly increased levels of obesity and impaired glucose metabolism. There was no evidence of colitis, liver disease or hypercholesterolaemia. Conclusion: This new murine model successfully isolates EAS-Fs, and has the potential utility for future research allowing for the investigation into the clinical impact living in relative hygienic conditions.

The Galápagos archipelago is a vast reservoir of terrestrial and marine biodiversity, owing in large part to its relatively recent volcanic genesis and colonization by humans. This unique ecological system is particularly susceptible to human, animal, and environmental impacts. Climate change, globalization, and the blurring of human-domestic animal-wildlife interfaces are poised to bring new threats and challenges to the region. A One Health perspective that simultaneously considers human, animal, and environmental health is imperative in assessing and mitigating the challenges facing the Galápagos Islands.In Part I of this review, we provide the historical context for biodiversity in the archipelago; discuss the role of invasive species in habitat destruction, fragmentation, and competition with endemic species; and summarize the established and emerging infectious disease threats. We also discuss the imperative to implement research, surveillance, and preventative measures to identify and manage future threats from a One Health perspective, with a specific emphasis on implications for wildlife health. In Part II, we outline the socioeconomic context of life in the Galápagos Islands, evaluate the current and predicted effects of climate change, and discuss direct anthropogenic factors affecting Galápagos biodiversity, such as tourism, fishing, pollution, and the illegal wildlife trade. We also examine the impact of the COVID-19 pandemic in the region. Throughout this two-part review, we build a cohesive picture of One Health in the Galápagos Islands by integrating past work, current needs, and emerging threats. We also consider overarching goals for conservation, ecosystem management, and socioeconomic sustainability that have been previously defined by both governmental and non-governmental stakeholders, and we identify discrete, implementable, and interdisciplinary recommendations that will facilitate achievement of those goals.

Geographic Information Systems (GIS) played an important role in understanding the dynamics of the territorial spread of COVID-19 during the pandemic. Even today, it is important to understand such dynamics in view of the imminent risk of new epidemics and pandemics. This study aimed to identify space-time clusters of incidence and mortality due to COVID-19 during the pandemic, analyzing socioeconomic and demographic characteristics in order to highlight the priority areas for control and surveillance actions. This study was conducted in the Metropolitan Region of São Paulo (MRSP) from March 2020 to February 2022. To detect the clusters, a multidimensional punctual process was created to perform multiple tests for every geographic point using the SaTScan™ software. Socioeconomic and demographic differences were analyzed using nonparametric Mann-Whitney and Kruskal-Wallis tests. High-risk clusters of incidences were observed in May 2020 (RR=2; p-value <0.001) and March to June 2021 (RR=2.6; p-value <0.001) in the capital of São Paulo and neighboring municipalities, with statistically significant differences between the socioeconomic variables analyzed. No low-risk cluster of COVID-19 incidence was found, but low-risk clusters of mortality from COVID-19 were found in the period from July to December 2020 in the central region of the capital (RR=0.33; p-value <0.001), which concentrates the highest incomes and the lowest percentages of Black, Brown (Mixed-race), and Indigenous people in the MRSP.

The inclusion of active materials in 3D-printed objects presents many potential applications in a variety of fields, including medicine and agriculture. Through a new three-phase coating system, we fabricate 3D objects that are printed via fused deposition modeling (FDM), with embedded functional materials. The method, namely Continuous Material Deposition on Filaments (CMDF) is based on passing a filament through a solution in which the functional substance is dissolved together with the polymer from which the filament is made. As model cases, three different types of materials were tested, Rhodamine B, ZnO nanoparticles (NPs), and Ciprofloxacin (Cip) while dissolved or dispersed in a PLA-containing solution. It was found that the properties of the coating materials are not affected by the coating and printing steps and they are maintained on the surface of the printed structure. This enabled, for example, achieving an antibacterial activity by the ZnO NPs and the controlled release of the antibiotics, Cip. Since the approach does not alter the mechanical properties of the printed structures, it offers a simple and convenient method for the introduction of a wide range of materials into 3D-printed objects.

Sleep is a crucial aspect of geriatric assessment for hospitalized older adults, and implementing AI-driven technology for sleep monitoring can significantly enhance the rehabilitation process. Sleepsense, an AI-driven sleep-tracking device, provides real-time data and insights, enabling healthcare professionals to tailor interventions and improve sleep quality. This study explores the perspectives of an interdisciplinary hospital team on implementing Sleepsense in geriatric hospital care. Using the Interpretive Descriptive approach, we conducted focus groups with physicians, nurses, care aides, and an activity worker. The Consolidated Framework for Implementation Research (CFIR) informed our thematic analysis to identify barriers and facilitators to implementation. Among 27 healthcare staff, predominantly female (88.89%) and Asian (74.1%) and mostly aged 30-50 years, themes emerged that Sleepsense is perceived as a timesaving and data-driven tool that enhances patient monitoring and assessment. However, barriers such as resistance to change and concerns about trusting the device for patient comfort and safety were noted, while facilitators included training and staff engagement. The CFIR framework proved useful for analyzing implementation barriers and facilitators, suggesting future research should prioritize effective strategies for interdisciplinary team support to enhance innovation adoption and patient outcomes in rehabilitation settings.

Adhesive-invasive E. coli have been suggested to be associated with the development of Crohn's disease (CD). It is assumed that they can provoke the onset of the inflammatory process as a result of the invasion of intestinal epithelial cells, and then, due to survival inside macrophages, stimulate chronic inflammation and the development of granuloma. In previous reports, we have shown that passage of the СD isolate ZvL2 on minimal medium M9 supplemented with sodium propionate (PA) as a carbon source stimulates, and with glucose - inhibits the adherent-invasive properties and the ability to survive in macrophages. This effect was reversible and not observed for the laboratory strain K12 MG1655. We were able to compare the isogenic strain AIEС in two phenotypes - virulent (ZvL2-PA) and non-virulent (ZvL2-GLU). We showed that, unlike ZvL2-GLU, the ZvL2-PA activates the production of ROS and cytokines when interacting with neutrophils. The laboratory strain does not cause a similar effect. Differences in neutrophil NADH-oxidase activation and ζ potential for ZvL2-GLU and ZvL2-PA are associated with changes in the abundance of membrane proteins. Using differential 2D electrophoresis and LC-MS, we identified membrane proteins that may be associated with the activation of the virulence properties of the СD isolate ZvL2.

In the past thirty years, childhood obesity rates have risen significantly worldwide, affecting over 340 million children in affluent nations. This surge is intricately tied to metabolic disorders, notably insulin resistance, type 2 diabetes mellitus (T2DM), and the continually evolving spectrum of metabolic-associated (dysfunction) steatotic liver disease (MASLD). This review underscores the alarming escalation of childhood obesity and delves comprehensively into the evolving and dynamic changes of nomenclature surrounding diverse conditions of hepatic steatosis, from the initial recognition of non-alcoholic fatty liver disease (NAFLD) to the progressive evolution into MASLD. Moreover, it emphasizes the crucial role of pediatric endocrinologists in thoroughly and accurately investigating MASLD onset in children with T2DM where each condition influences and exacerbates the progression of the other. The review critically highlights the inadequacies of current screening strategies and diagnosis, stressing the need for a paradigm shift. A proposed solution involves the integration of hepatic magnetic resonance imaging assessment in the diagnostic arsenal for children showing insufficient glycaemic control and weight loss post-T2DM diagnosis, thereby complementing conventional liver enzyme testing. This holistic approach aims to significantly enhance diagnostic precision, fostering improved outcomes in this vulnerable high-risk pediatric population.

Regulations and guidelines on water quality are used according to physicochemical but scarcely biological variables to protect ecosystems and control water use. This study innovates using in-sects as bioindicators to establish the health status of an aquatic ecosystem in the Latin American's lentic bodies. Water quality in urban lentic bodies in the Metropolitan Region, Chile, was evaluated from of aquatic insect assemblages and physicochemical variables for conserving aquatic life. Evaluations were carried out in parallel in four sampling stations in three water bodies, two seasons, and 2–3-year series, with three replicates. Aquatic insects were randomly sampled and identified by families; abundance, and richness differences were compared with non-parametric tests. Family Biotic Indices (FBI) were calculated. Physicochemical variables were measured using portable multiparametric and laboratory chemical analysis to determine water quality. A new physicochemical variable (total suspended solids) was established, which had a greater incidence with the results obtained for the FBI. Based on the nitrogen and phosphorus levels in the water bodies, all of them were eutrophics. Given the ecosystem diversity and complexity, studies should delve deeper into wetlands to establish methods that contribute to determining water quality, using insects as bioindicators and physicochemical variables.

In the Euclidean form of the theory of gravity, where there is no dedicated time parameter, a generalized canonical form of the principle of least action is proposed. On its basis, the quantum principle of least action is formulated, in which the “dynamics” of the universe in the Origin is described by the eigenvector of the action operator - the wave functional on the space of 4D Riemannian geometries and configurations of matter fields in some compact region of 4D space. The corresponding eigenvalue of the action operator determines the initial state for the world history of the universe outside this region, where the metric signature is Lorentzian and thus the time parameter exists. The boundary of the Origin region is determined by the condition that the rate of change of the determinant of the 3D metric tensor is zero on it. A modification of quantum theory is proposed that eliminates the uncertainty of the sign of the Hilbert-Einstein action in the Euclidean region of the Origin. On this basis, we formulate a minimum principle for the eigenvalue of the action operator, similar to the principle of minimum energy for the ground state in quantum mechanics. It has been suggested that in the initial state the universe contains a certain distribution of its own mass, which is not directly related to the fields of matter.

Background: Chronic kidney disease (CKD) is a significant cardiovascular (CV) risk factor, with dialysis-dependent CKD (DD-CKD) patients facing high mortality rates. Hypercholesterolemia is another crucial CV risk factor, typically managed with lipid-lowering therapy, though its efficacy in DD-CKD remains uncertain. Evidence shows mixed results regarding the benefits of statins in these patients. Citrate-based dialysates are known to reduce inflammatory biomarkers compared to acetate-based ones, potentially impacting lipid profiles and immune responses. This study aimed to determine the effects of citrate versus acetate dialysate on lipid profiles and immunophenotypes in DD-CKD patients. Methods: This unicentric, cross-over, prospective study included 21 hemodialysis patients (10 males, 11 females, average age 62.25 years). Each patient underwent 24 dialysis sessions (12 with each dialysate) and acted as their own control. Lipid profiles, immunological parameters, and nutritional and inflammatory markers were measured before the last session with each dialysate. Results: Compared to acetate dialysate (AD), citrate dialysate (CD) resulted in significantly higher LDL (75.71 vs. 67.05 mg/dL, p=0.042) and HDL (51.19 vs. 47.14 mg/dL, p=0.013), and lower TG (97.86 vs. 110.38 mg/dL, p=0.046). CD also led to higher NK cells (19.24 vs. 16.95%, p=0.035), higher complement C3 levels (115.14 vs. 107.81 mg/dL, p=0.009) and lower CD3+ CD8+ and CD16+56+ lymphocytes. Finally, total lymphocytes were lower with AD than with CD. We found no difference in predialysis nutritional nor inflammatory parameters (IL-6 and hs-CRP) except for ESR, which was higher when subjects used CD than AD. Conclusion: There are significant differences in lipid and immunophenotypic profiles that should increase the CV risk of patients who use CD. However, numerous studies have shown no differences, or even a benefit in mortality in certain HD subpopulations that use CD. Further studies are needed to understand if the observed changes may be counterbalanced by other mechanisms potentially provided by citrate (e.g., reducing vascular calcification).

Recent studies have shown the potential of Transformer-based neural networks in increasing prediction capacity. However, classical transformers present several problems such as computational time complexity and high memory requirements, which make Long Sequence Time-Series Forecasting (LSTF) challenging. The contribution to the prediction of time series of flood events using deep learning techniques is examined, with a particular focus on evaluating the performance of the Informer model (a particular implementation of transformer architecture), which attempts to address the previous issues. This study explores the predictive capabilities of the Informer model compared to statistical methods, stochastic models and traditional deep neural networks. The accuracy, efficiency as well as the limits of the approaches are demonstrated via numerical benchmarks relating to real river streamflow applications. Using daily flow data from the River Test in England as the main case study, we conduct a rigorous evaluation of the Informer efficacy in capturing the complex temporal dependencies inherent in flood time series. Among other things, the present work extends its analysis to encompass diverse time series datasets from various locations (>100) in the United Kingdom, providing insights into the generalizability of the Informer. The results highlight the significant superiority of the Informer model over established forecasting methods, especially regarding the LSTF problem. Furthermore, it is observed that the structure of time series, as expressed by climacogram, affects the performance of the Informer network.

Carbamate insecticides have been used for over four decades to control brown planthopper, Nilaparvata lugens, but resistance has been reported in many countries, including Korea. The test results on resistance to fenobucarb showed that the LC₅₀ values were 3.08 for the susceptible strain, 10.61 for the 2015 population, and 73.98 for the 2019 population. Compared to the susceptible strain, the 2015 and 2019 populations exhibited resistance levels 3.4 and 24.2 times higher, respectively. To elucidate the reason for the varying levels of resistance to fenobucarb in these populations, mutations in the ace1 gene, the target site of carbamate, were investigated, but no previously reported mutations were confirmed. Through RNAseq analysis focusing on the expression of detoxification enzyme genes as an alternative resistance mechanism, it was found that the carboxylesterase gene Nl-EST1 was overexpressed 2.4 times in the 2015 population and 4.7 times in the 2019 population compared to the susceptible strain. This indicates a strong correlation between the level of resistance development in each population and the expression level of Nl-EST1. Previously, Nl-EST1 was reported in an organophosphorus insecticide-resistant strain of Sri Lanka 2000. Thus, Nl-EST1 is crucial for developing resistance to organophosphorus and carbamate insecticides. Resistance-related genes such as Nl-EST1 could serve as expression markers for resistance diagnosis, and it can apply to integrated resistance management of N. lugens.

The antimicrobial resistance to clinically approved antibiotics is rapidly increasing worldwide. Staphylococcus aureus is a critical contributor to deaths associated with antibiotic resistance. In this regard, the search for natural compounds is quite active, that alone or in combination with other agents can be effective as new antibacterial agents. The antibacterial activity of essential oils has presented an increasing interest during the last years and was effective even on multidrug-resistant strains. The present study aims to investigate the interaction effects of oregano essential oil (OEO) with some conventional antibiotics against Staphylococcus aureus tested by agar methods. For this purpose, we selected S. aureus ATCC 29213 and five clinical isolates representative of the species. The essential oil was analyzed using gas chromatography coupled with mass spectrometry (GC-MS). The analysis of the Oregano essential oil revealed the main component to be carvacrol (81,20%). Concentration 2.5% (v/v) OEO was combined with drugs proposed by EUCAST, 2024, specifically penicillin, cefoxitin, erythromycin, gentamycin and tetracycline. The research results showed that the combination of OEO with penicillin, cefoxitin, erythromycin, and tetracycline enhanced the antimicrobial activity of the antibiotics against S. aureus in almost all cases. The area of suppression remained identical (but not smaller) in the unaffected cases. When comparing the effects of the GEN-OEO combination and GEN against most S. aureus strains, a decrease in antimicrobial activity was reported by the diffusion agar method.

Background: Pharmacists need up-to-date knowledge and decision-making support in HIV care. We aim to develop MARVIN-Pharma, an adapted artificial intelligence-based chatbot initially for people with HIV, to assist pharmacists, considering evidence-based needs. Methods: From Dec 2022 to Dec 2023, an online needs-assessment survey evaluated Québec pharmacists' knowledge, attitudes, involvement, and barriers relative to HIV care, alongside perceptions relevant to the usability of MARVIN-Pharma. Recruitment involved convenience and snowball sampling, targeting National HIV and Hepatitis Mentoring Program affiliates. Results: Forty-one pharmacists (28 community, 13 hospital-based) across 15 Québec municipalities participated. Participants perceived their HIV knowledge as moderate (M=3.74/6). They held largely favorable attitudes towards providing HIV care (M=4.02/6). They reported a “little” involvement in the delivery of HIV care services (M=2.08/5), most often ART adherence counseling, refilling, and monitoring. The most common barriers reported to HIV care delivery were a lack of time, staff resources, clinical tools, and HIV information/training, with pharmacists at least somewhat agreeing that they experienced each (M≥4.00/6). On average, MARVIN-Pharma's acceptability and compatibility were in the ‘undecided’ range (M=4.34, M=4.13/7, respectively), while pharmacists agreed to their self-efficacy to use online health services (M=5.6/7). Conclusion: MARVIN-Pharma might help address pharmacists' knowledge gaps and barriers to HIV treatment and care, but pharmacist engagement in the chatbot’s development seems vital for its future uptake and usability.

As an important part of urban terminal delivery, automated guided vehicle (AGV) has been widely used in the field of takeout delivery. Due to the real-time generation of takeout orders, the delivery system is required to be extremely dynamic, so the AGV needs to be dynamically scheduled. At the same time, the uncertainty in the delivery process (such as the meal preparation time) further increases the complexity and difficulty of AGV scheduling. Considering the influence of these two factors, the method of embedding stochastic programming model into rolling mechanism is adopted to optimize the AGV delivery routing. Specifically, To handle real-time orders under dynamic demand, an optimization mechanism based on rolling scheduling framework is proposed, which allows the AGV route to be continuously updated. Different from most VRP models, open chain structure is used to describe the dynamic delivery path of AGV. In order to deal with the impact of uncertain meal preparation time on route planning, a stochastic programming model is formulated with the purpose of minimizing the expectation of order timeout rate and the total customer waiting time. In addition, an effective path merging strategy and after-effects strategy are also considered in the model. In order to solve the proposed mathematical programming model, a multi-objective optimization algorithm based on NSGA-III framework is developed. Finally, a series of experimental results demonstrate the effectiveness and superiority of the proposed model and algorithm.

Background: School nursing plays a crucial role in promoting the health and well-being of students in educational settings. Previous research has highlighted its protective effect across various health domains, including substance use, mental health, violence, and sexual health. Despite its importance, the job satisfaction of school nurses and the factors influencing it have been insufficiently addressed in scientific research, underscoring the need to better understand their challenges and barriers. Therefore, the objective of this research is to evaluate the perceived job satisfaction and professional quality of life of school nurses at a national level in Spain. Methods: A descriptive cross-sectional study was conducted using the Font-Roja Job Satisfaction Questionnaire and the CPV-35 Professional Quality of Life Questionnaire. The sample included 553 school nurses from different geographical regions in Spain, selected through school nursing groups and associations. Data were collected from November 2023 to February 2024. Results: The majority of the nurses were women (68%), with a median age of around 40 years and predominantly less than 5 years of work experience (56%). Significant differences were observed in job satisfaction and professional quality of life depending on the employing entity (healthcare and educational settings). Factors such as workload, institutional support, and work experience influenced the perception of job satisfaction. Conclusions: We affirm that this study, being one of the first of its kind at a national level in Spain, highlights the need to develop effective strategies to improve the job satisfaction and professional quality of life of school nurses. School nurses in Spain present a moderately high level of job satisfaction, although there are significant differences in factors such as status and job monotony depending on the sector of employment (Healthcare, Education, Others). Nurses in educational contexts report lower levels of job satisfaction, influenced by the nature of the work, interpersonal relationships, opportunities for professional development, and institutional support. Additionally, work experience impacts satisfaction, being more positive for nurses with greater experience. We also affirm that the professional quality of life of school nurses is homogeneous, with an average score of 6.000 on the CPV-35. Although professional competence is uniformly perceived, work pressure is a common concern. Working more than 37.5 hours per week is associated with higher professional quality of life, although also with lower job satisfaction. Only half of the schools have a school nurse, highlighting the need for policies that ensure equity in service provision. There is also a recognized need for health training for teachers, which could improve support for nurses and student health. Active interaction with the educational community reinforces the integral role of school nurses and emphasizes the importance of the socioeconomic context in interventions and resource allocation. Keywords: School nursing, job satisfaction, professional quality of life, work factors, educational settings.