Bacterial infections pose a serious threat to human health, and the overuse of antibiotics has led to the rise of multidrug-resistant bacteria. Numerous current wound dressings, which may cause skin irritation or allergies are insufficient in handling large wounds, often leading to infections. This highlights the need for better antibacterial materials. Nypa fruticans (Nipa palm), native to Southeast Asia, has long been utilized for various purposes because of its multiple benefits such as its antimicrobial properties, presence of probiotics, and high cellulose content. This study evaluates the use of cellulose hydrogel from nipa fronds, loaded with probiotics (ProbioGel), as an antimicrobial agent to prevent infections. The study involved an in-vitro assay using the Kirby-Bauer method to evaluate the gel’s antimicrobial properties against common wound pathogens. Additionally, whole genome sequencing and promoter analysis were done to identify genes supporting their biochemical properties. The results showed ProbioGel exhibited significant antimicrobial activity against Staphylococcus aureus, Escherichia coli, Staphylococcus epidermidis, and Candida albicans due to the presence of genes encoding lysozyme, carnobacteriocin B2, and fatty acid ABC transporter ATP binding in the Lacticaseibacillus paracasei BCRC-16100. This study highlighted the potential of ProbioGel as an innovative and effective antimicrobial and anti-infection agent for wound care. The discovery of genes in the probiotic strain responsible for encoding antimicrobial peptides further supports its therapeutic potential.

In today’s fast-paced technological landscape, businesses must adapt to evolving business models and harness the power of data to thrive. Small and Medium-sized Enterprises (SMEs) face significant challenges in aligning IT infrastructure with business objectives while navigating digital transformation. This systematic review, conducted using PRISMA 2020 guidelines, investigates the role of Enterprise Architecture (EA) and Information Management (IM) in driving IT growth and sustaining competitive performance in SMEs. Through a synthesis of academic research, industry analysis, and case studies from the last decade, this review identifies key frameworks—such as TOGAF, Zachman, and FEAF—that support the alignment of IT capabilities with organizational goals. The integration of IM within EA frameworks is found to enhance decision-making, resource allocation, and operational efficiency. Emerging technologies like Artificial Intelligence (AI) and cloud computing are highlighted for their transformative impact, enabling real-time data analysis, improved scalability, and enhanced agility. Our review reports that 43.44% of the studies focused on cloud-based solutions, while 24.59% adopted hybrid models, offering SMEs scalable and flexible IT infrastructures. The findings emphasize the necessity of strong governance frameworks to ensure compliance, adaptability, and long-term sustainability in a dynamic environment. This research contributes to a comprehensive roadmap for SMEs, enabling them to leverage EA and IM for sustained growth and competitive advantage in the digital era.

The etiology of autism spectrum disorder (ASD) has not yet been completely elucidated. Through time, multiple attempts have been made to uncover the causes of ASD. Different theories have been proposed, such as that it is caused by “Gods Wrath,” alterations in the gut–brain axis with an emphasis on gut dysbiosis, post vaccine complications, and genetic or even autoimmune causes. In this review we present data covering over 170 000 participants at age ranging from 2 to 14 years focusing on human cell changes rather than using animal models found in previous studies. The male/female ratio was 4:1. Data collection occurred in many countries covering ethnically diversified subjects. Moreover, we aimed to show how the progress in genetic techniques influences the explanation of medical White Papers in ASD area.

There is an extensive body of research in the literature focusing on predicting the mechanical properties of concrete, such as compressive strength. Summarizing the current studies following the valuable contributions of researchers will serve as a guide for future studies and researchers. To this end, this study aims to identify the key authors, sources, institutions, and countries that have contributed to the prediction of concrete compressive strength. Additionally, it aims to provide researchers with comprehensive information on prominent research themes, trends, and gaps in the literature related to the prediction of concrete compressive strength. For this purpose, 2319 articles on the prediction of concrete compressive strength published from 2000 to 19th August 2024 were identified through the Scopus Database. The scientific measurement analyses were conducted using VOSviewer software. Upon reviewing the relevant research, it was found that machine learning methods are frequently used in predicting concrete compressive strength. In this context, the study will make significant contributions to the literature by examining leading institutions, countries, authors, and sources in the field, synthesizing data, and highlighting research areas, gaps, and trends related to concrete compressive strength prediction.

World Health Organization is encouraging countries to include contact screening and single dose of rifampicin administration as preventive chemotherapy for contacts of leprosy patients in their leprosy control activities. However, no study has been conducted to assess the safety of SDR-PEP and acceptability and feasibility of this intervention in Togo. To assess the safety of SDR-PEP we used a cohort design and for acceptability and feasibility we used a mixed method combining a quantitative study to assess the safety of SDR-PEP in a cohort of contacts from recently diagnosed leprosy patients followed by a qualitative study to identify the social, cultural or institutional factors that would influence the adoption of single dose rifampicin as post-exposure prophylaxis for contacts of leprosy patients in Togo. For the quantitative study, all identified index patients agreed to the disclosure of their status to their contacts and provided a list of their contacts. All the contacts found agreed to take part in the study and an appointment was made for screening. However, some contacts were absent on the screening day for no reason. All eligible contacts agreed to take SDR and were followed up after taking the drug. No severe adverse events were reported during follow-up. For the qualitative study, 72 interviews (66 semi-structured interviews and 6 focus groups) were carried out, and it emerged that overall, opinions were favorable on the acceptability and feasibility of implementing single dose rifampicin as post-exposure prophylaxis for contacts of leprosy patients in Togo. However, a number of conditions need to be considered for more effective results.

Background/Objectives: Current guidelines recommend intravenous thrombolysis (IVT) followed by mechanical thrombectomy (MT) for patients with acute ischemic stroke (AIS) caused by large vessel occlusion (LVO). This combined approach, known as bridging therapy (BT), is believed to increase the likelihood of a favorable functional outcome when administered within 4.5 hours of symptom onset. However, the benefits of BT over direct mechanical thrombectomy (d-MT) remain debated. This study aimed to compare the outcomes of AIS-LVO patients undergoing MT within 6 hours of symptom onset, with and without prior IVT. Methods: Within the prospective TINL STROKE-registry, AIS-LVO patients admitted to the Department of Neurology, University of Pécs, between February 2023 and June 2024 were investigated. The primary endpoint was the proportion of patients reaching functional independence at 90 days, defined as a modified Rankin Scale (mRS) score of 0-2. Secondary endpoints included clinical improvement at 72 hours (National Institute of Health Stroke Scale [NIHSS] score of ≤1 or a change from baseline [ΔNIHSS] of ≥4) and successful recanalization (modified Thrombolysis in Cerebral Infarction [mTICI] score ≥2). Safety outcomes were evaluated based on thrombus migration and intracranial hemorrhage (ICH). Results were compared using linear and logistic regression analyses adjusted for baseline variables. Results: Of 82 patients, 51 (62.2%) received BT, while 31 (37.8%) underwent d-MT. The BT group showed a significantly higher rate of functional independence (45.7% vs. 17.2%, p=0.014) and a lower 90-day mortality rate (13.7% vs. 35.5%, p=0.029). Multivariate analysis revealed that IVT was independently associated with favorable functional outcome (p=0.011) and reduced mortality (p=0.021). No significant differences were observed in terms of clinical improvement at 72 hours, successful recanalization, thrombus migration, or hemorrhagic transformation between the groups. Conclusions: This study supports current guidelines recommending BT for thrombectomy-eligible AIS-LVO patients, offering new insights into the ongoing clinical debate.

The objective of the study was to analyze the climate change experienced in Spain between 1971 and 2022 and to estimate the future climate (2050). The main objectives were: 1) to analyze the temporal evolution of temperature from 1971 to the present, to quantify the warming process experienced in the case study and to evaluate the increase in ex-treme heat events (heatwaves); 2) to study the evolution of the precipitation regime to determine whether there is a statistically representative trend towards a drier climate and an increase in extreme precipitation; 3) to investigate the interaction between annual precipitation and the continuous increase in temperature; and 4) to estimate the future climate scenario for mainland Spain and the Balearic Islands towards 2050, analyzing the trends in land aridity and predicting a possible change from a Mediterranean climate to a warm steppe climate, according to the Köppen classi-fication. In this study, we used the E-OBS dataset provided by the European Climate Assessment and Dataset (ECA&D) project. E-OBS is a high-resolution gridded daily climate dataset. Its advantage over other databases is that it is based on direct climate information from the weather stations included in the Copernicus ECA&D. Furthermore, a detailed interpolation and refinement process allows a complete, uniform view of the studied territory. This dataset has pro-vided daily weather information since 1950 and is presented as a complete dataset of the European region. It is availa-ble on a regular grid of 0.1 and 0.25 degrees for the elements of daily average temperature (TG), daily minimum tem-perature (TN), daily maximum temperature (TX) and the sum of daily precipitation (RR), among other climatic varia-bles. In this study, we obtained daily temperature data, including the average, maximum and minimum temperature, and precipitation from 1971 to 2022, using the 27.0e dataset. We used information from E-OBS from 1 January 1971 to 31 December 2022, with a resolution of 0.25 degrees. The main purpose of this study was to analyze whether there is a temporal evolution of the precipitation regime in Spain towards less precipitation, and to study the relationship between annual precipitation and the tendency to-wards progressive warming. Specifically, the aim was to test the hypothesis that the increase in temperature resulting from the global warming process implies a tendency towards progressive drought. The hypothesis is + T  - RR. Given the extreme annual variability of the climate, in addition to the ordinary least squares methodology, the techniques mainly used in this study were the Mann-Kendall test and the Kendall-Theil-Sen regression. The Mann-Kendall test confirmed the statistical significance of the relationship between RR and TX, with a statistical relia-bility close to 99.9%. The KTS regression predicted a reduction of 38.25 mm for each 1°C increase. If the warming trend experienced in recent years (1973– 2022) continues, it is foreseeable that by 2050 there will be a reduction in precipitation in Spain of between 14% and 23% with respect to the precipitation of the reference period (understood as the average between 1971 and 2000). Spain’s climate is likely to vary from Mediterranean to warm steppe in the Köppen classification system (from “C” to “B”).

Background : The multisystem manifestations of Fabry disease can create major challenges in patient care. Although enzyme replacement therapy with recombinant agalsidase beta has demonstrated clinical benefits, the standard fortnightly, multihour infusion regimen imposes a substantial burden on patients. Methods : We assessed the safety and feasibility of shortening the agalsidase beta infusion time to 90 minutes in adult patients with classic or later-onset Fabry disease in the absence of premedication. A total of 39 consecutive adult patients (agalsidase-naive: n=6; significant comorbidities: n=15) with no recent infusion-associated reactions underwent a total of 85 agalsidase beta infusions in our tertiary reference center for lysosomal diseases. Each infusion was administered at a constant rate (between 0.78 and 1.17 mg/min, depending on the patient’s body weight). Results : No adverse events of any type (including discomfort and infusion-associated reactions) were reported during or after infusions. The patients’ vital signs remained stable, and patient satisfaction was high. Seven patients requested even shorter infusions (50 to 60 minutes) under strict medical supervision. Conclusions : Our results suggest that shortening the agalsidase beta infusion time to 90 minutes is safe and feasible in stably treated adult patients with Fabry disease and no recent infusion-associated reactions.

The transition to renewable and sustainable energy sources is critical to solving the environmental and socioeconomic problems associated with the use of fossil fuel. This study uses an interdisciplinary approach to analyze the evolution, challenges and prospects of a sustainable energy transition in the context of recent discovery and exploitation of fossil resources. We study the case of Senegal from 2000 to 2027 and the role of natural gas in its energy transition. In 2000, Senegal's energy mix consisted of about 97% for fossil energy and only 3% for renewable energy. Nowadays, although fossil fuels are still dominant, but they account for about 70% of total electricity production. The country has indeed undertaken tremendous efforts to develop renewable energy sources including, solar, hydro and wind power, which currently account for about 30% of the total energy mix. Socio-economic indicators show that over the past two decades, Senegal's population and electricity production have grown significantly, leading to an increased energy demand/need. This growth has resulted in a five-fold increase in per capita energy consumption over the same period. Combining the strategic use of natural gas as a transition energy source with the continued development of renewable energy sources could make Senegal as a model for West Africa’s energy transition. The projections for 2027 suggest a structural shift in the energy mix, with natural gas, oil, and renewables at 47%, 32%, and 21%, respectively.

This study analyzed the influence of attitude toward innovation and creativity, entrepreneurial self-efficacy, and institutional support in the consolidation of an entrepreneurial culture among Peruvian university students, using the Theory of Planned Behavior (TPB) as the theoretical framework. Data were collected from 948 students through a validated 29-item questionnaire, and the data analysis was conducted using the PLS-SEM method. The results showed that both attitude toward innovation and entrepreneurial self-efficacy have a significant positive impact on entrepreneurial culture, and entrepreneurial self-efficacy acted as a partial mediator in this relationship. However, external resources and institutional support were not significant mediators between entrepreneurial intention and entrepreneurial culture. This study expands the understanding of TPB in the Peruvian context, highlighting the importance of fostering self-efficacy and an innovative attitude in students to strengthen entrepreneurial culture in universities. Educational institutions should consider these key factors when designing programs that promote entrepreneurship.

Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems, with a strong impact on their natural regeneration. This study presents a comprehensive analysis of post-fire forest recovery using Landsat time series data from 2000 to 2023 in the Middle Volga region of the Russian Federation. The analyses utilised the LandTrendr algorithm in Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To assess the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator was applied. The results suggested that the post-fire spectral recovery is significantly influenced by the degree of the BS on affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belong to the BS classes of moderate and high severity. A regression model showed that land surface temperature (LST) was more significant to post-fire recovery than the variability in precipitation (Pr), explaining about 65% of the variance in post-fire recovery. This study provides new insights into the post-fire forest recovery dynamics and scientific bases for the cost-effective management strategies under changing climate conditions.

This study introduces a novel, robust, and efficient method for the simultaneous quantitative determination of three sunscreen filters namely 4-methylbenzylidene camphor, octyl methoxycinnamate, and avobenzone, in a moisturizing sunscreen cream specifically designed for acne-prone skin. The method employs high performance liquid chromatography with photodiode-array detection, providing reliable separation of the analytes. Chromatographic separation was achieved using a Fortis Phenyl analytical column (150.0 x 2.1 mm, 5 μm), with isocratic elution at a flow rate of 0.4 mL/min. The mobile phase was composed of a 57/43 (v/v) mixture of acetonitrile/45 mM aqueous ammonium formate solution, ensuring sufficient resolution and peak symmetry for the target compounds. The method was validated comprehensively for critical performance parameters, including linearity, precision, accuracy, and robustness. Linearity was established across a suitable range for all three analytes, with high correlation coefficients. Precision was confirmed with intra-run and total precision coefficients of variation of ≤ 4.6 %, while accuracy assessments yielded a percent recovery between 98.6 and 100.4, for all quality control levels. Additionally, the method was able to effectively separate the sunscreen filters from other cosmetic ingredients, such as [β-(1.3), (1.6)-D-glucan], Low Molecular Weight (LMW) hyaluronic acid and plant extracts ensuring specificity in complex formulations. This straightforward and time efficient sample preparation process, involving methanol extraction followed by serial dilution, makes the method suitable for routine quality control in cosmetic laboratories. The method was successfully applied to the analysis of two different lots of a commercial sunscreen cream, achieving excellent recovery for all filters, ranging between 94.6% and 99.8%, thus demonstrating its reliability and applicability for the quality control of cosmetics.

Hypertrophic cardiomyopathy (HCM) poses a significant health burden worldwide, characterized by myocardial hypertrophy and increased risk of sudden cardiac death. Recent studies have revealed the involvement of exosome-derived microRNAs (miRNAs) in the pathogenesis of HCM, shedding light on novel regulatory mechanisms in cardiac remodeling and dysfunction. This literature review synthesizes current evidence on the role of exosome-derived miRNAs in HCM. It discusses key miRNAs identified from diverse cellular origins, including cardiomyocytes, stem cells, and conduction cells, elucidating their contributions to hypertrophic signaling pathways, fibrosis, and changes in cellular metabolism. Notable miRNAs highly expressed in exosomes such as miR-1, miR-133, and miR-208 are highlighted for their implications in HCM pathophysiology. Moreover, the review explores the diagnostic and therapeutic potential of exosome-derived miRNAs as biomarkers and therapeutic targets in HCM management. The studies summarized in this review demonstrate that exosome-derived miRNAs play a crucial role in orchestrating the molecular events underlying HCM, offering new insights into disease mechanisms and potential therapeutic avenues. Understanding the intricate interplay between exosome-mediated miRNA communication and HCM pathophysiology holds promise for the development of personalized diagnostic tools and targeted therapies to improve patient outcomes in HCM.

In this paper, a completely different metaheuristic algorithm called the Orangutan Optimization Algorithm (OOA) is introduced, which replicates the complex and adaptive behaviors of Orangutans in their natural habitat. The main inspiration for OOA lies in the strategic foraging techniques of Orangutans, alongside their highly developed nesting abilities. More words are used to provide a detailed theoretical explanation of OOA, followed by a comprehensive modeling of the algorithm’s implementation in two key phases: exploration and exploitation. These phases are mathematically structured to ensure the balance required for optimal search processes. The performance of OOA is rigorously evaluated using twenty-nine benchmark functions from the CEC 2017 test suite, with problem dimensions of 10, 30, 50, and 100. More sentences are included to elaborate on the optimization outcomes, which reveal that OOA effectively balances the exploration and exploitation stages, yielding suitable and competitive solutions across the benchmark functions. Additionally, to further validate its quality, the results from OOA are compared against twelve other well-established metaheuristic algorithms. The analysis of these simulation results demonstrates that OOA consistently outperforms competing algorithms by achieving superior results across most benchmark functions. Furthermore, to assess its real-world applicability, OOA is implemented on a set of twenty-two constrained optimization problems drawn from the CEC 2011 test suite. The extended simulation results clearly indicate that OOA is highly efficient in solving real-world optimization challenges, consistently delivering a superior performance when compared to other competitor algorithms.

This study explores the synergistic integration of Artificial Intelligence (AI) in information technology with photochemical methods to optimize nanoparticle synthesis. By leveraging machine learning algorithms and predictive modeling, we demonstrate significant enhancements in the precision, efficiency, and scalability of nanoparticle production. AI-driven analysis of reaction parameters, optical properties, and structural characteristics enables real-time monitoring and adaptive optimization of photochemical reactions.The developed framework utilizes deep learning techniques to correlate reaction conditions with nanoparticle size, shape, and composition, facilitating the synthesis of tailored nanoparticles for various applications. Results show improved monodispersity, increased yield, and reduced synthesis time compared to traditional methods.This innovative approach paves the way for the rapid development of high-performance nanoparticles in fields such as biomedical imaging, energy storage, and catalysis. By harnessing the power of AI in information technology, this research unlocks new possibilities for the precise and efficient synthesis of nanoparticles via photochemical methods.

Federated unlearning represents a sophisticated evolution in the domain of machine learning, particularly within federated learning frameworks. In financial applications, where data privacy and security are paramount, federated unlearning allows institutions to selectively remove or “unlearn” specific data from trained models without compromising their overall performance and accuracy. This capability is essential for ensuring that models remain adaptable, secure, and compliant with regulatory requirements, while minimizing the need for expensive retraining. In this paper, we explore various financial applications where federated unlearning can have a significant impact, including fraud detection, portfolio management, and credit risk modeling. By allowing targeted removal of outdated, erroneous, or sensitive data, federated unlearning enhances the agility of financial models, enabling institutions to keep pace with the dynamic financial landscape. Using practical numerical examples, we demonstrate how unlearning improves model accuracy and decision-making while maintaining data privacy across distributed systems. This paper underscores the critical role of federated unlearning in addressing the challenges of modern financial institutions, offering insights into its practical applications and future potential.

The integration of advanced nanotechnology solutions in healthcare presents unparalleled opportunities for transformative care and cost savings. However, market penetration remains hindered by regulatory, technological, and adoption barriers. This study explores business analytics strategies facilitating successful market integration of nanotechnology innovations in healthcare. A mixed-methods approach combining qualitative expert interviews, quantitative survey analysis, and data-driven modeling is employed.Key findings highlight the critical role of data-driven decision-making, collaborative partnerships, and tailored market segmentation in overcoming adoption hurdles. Specifically, predictive analytics and machine learning algorithms are identified as essential tools for optimizing nanotechnology product development, pricing strategies, and supply chain efficiency. Furthermore, strategic partnerships with healthcare stakeholders and tailored market segmentation enable targeted commercialization and accelerated diffusion.This research contributes to the existing literature by providing actionable insights for nanotechnology firms, policymakers, and healthcare administrators seeking to harness the transformative potential of nanotechnology in healthcare. The proposed business analytics framework offers a valuable roadmap for navigating the complex healthcare landscape and ensuring the sustainable market integration of advanced nanotechnology solutions.

Within education, we make assumptions the correct manner of teaching a student, and with that, we assume further that regardless of the intellectual content that a student should engage with, the acculturation for the customary (and always rather traditional) means of religious apprehension for the purpose of an assumedly universal and beneficial role. This article instead promotes the view that such determinations are (1) unsuitable for modern society and (2) causal to a disjunction with a truly proper manner of education in the form of the matheticism – a term rather unfortunately applied in educational theory.

The integration of nanoparticles in biomedical applications requires precise analysis of their photochemical properties. However, computational simulations of these properties are computationally intensive, hindering rapid advancement. This study explores the implementation of Graphics Processing Unit (GPU) acceleration techniques to enhance computational efficiency in analyzing nanoparticle photochemistry within bioinformatics frameworks.By leveraging GPU parallel processing capabilities, we developed optimized algorithms for photochemical property simulations, achieving significant speedup over traditional Central Processing Unit (CPU)-based methods. Our GPU-accelerated framework demonstrated substantial performance gains in computing photochemical properties, such as absorption spectra and energy transfer rates.

This research investigates the influence of yoga on social intelligence and academic performance of business students. Employing a randomised controlled experiment with Executive MBA participants, the research explores the mediating role of mindfulness in these relationships. Results indicate that yoga positively influences social intelligence, decision-making skills as reflected in case study scores, and overall academic performance. Mindfulness is found to partially mediate these relationships, accounting for a significant portion of the observed effects. The study reveals that yoga directly contributes 43% to increased social intelligence, with improved mindfulness accounting for 57%. Similarly, yoga directly accounts for 46% of improved academic achievement, while enhanced mindfulness contributes 54%. These findings suggest that incorporating yoga into business education curricula could potentially enhance both the interpersonal capabilities and academic outcomes of students, preparing them more holistically for the challenges of the modern corporate world.

Background/Objectives: The COVID-19 pandemic has significantly impacted global health, with varying vaccine effectiveness (VE) across different regions and vaccine platforms. In Africa, where vaccination rates are relatively low, inactivated vaccines like Sinopharm and Sinovac have been widely used. This study evaluated the real-world effectiveness of licensed inactivated COVID-19 vaccines in Zimba-bwe during a period dominated by Omicron variants. Methods: We conducted a prospective test-negative case-control study among symptomatic adults across six Zimbabwean provinces from November 2022 to October 2023. Participants were categorized based on vaccination status, and na-sopharyngeal swabs were collected for SARS-CoV-2 PCR testing. Vaccine effective-ness was assessed using conditional logistic regression, adjusting for various co-variates such as age, sex, and comorbidities. Results: Among 5,175 participants, 701 tested positive for SARS-CoV-2, and 4,474 tested negative. The overall adjusted VE against symptomatic COVID-19 was 31% (95% CI: 5.3%-49.7%) among verified vaccinated individuals. Boosted individuals demonstrated a higher VE of 59.8% (95% CI: 40.3%-72.9%). VE decreased signifi-cantly to 24% (95% CI: -4.1%-44.8%) in individuals vaccinated over a year prior. Similar VE was observed for Sinopharm (36.8%, 95% CI: 11.4%-54.9%) and Sino-vac (38.1%, 95% CI: 16.3%-54.2%). Conclusions: The study indicates modest protection from inactivated COVID-19 vaccines against symptomatic Omicron infection, with significant enhancement fol-lowing booster doses. These findings highlight the need for continued vaccine eval-uation, particularly in resource-limited settings, to inform public health strategies and optimize vaccination programs.

The rat osteosarcoma cell line UMR-106 is widely used for the study of bone cancer biology. We have characterized UMR-106 with a combination of optical genome mapping (OGM), long-read sequencing (for identification of variants and 5mC methylation), and short-read RNA sequencing for expression. Genome sequence was compared to a Sprague-Dawley control animal, the strain from which UMR-106 was derived, and expression data were compared to a public rat osteoblast dataset. Using the COSMIC database to identify the most affected genes in human osteosarcomas (OS) we found somatic mutations in Tp53 and H3f3a in UMR-106. OGM identified a relatively small number of differences between the cell line and a strain-matched control animal but did detect a ~45 Mb block of amplification that included Myc on chromosome 7 which was confirmed by long-read sequencing. The amplified region showed several blocks of non-contiguous rearranged sequence implying complex rearrangements during their formation and included 14 genes reported as biomarkers in human osteosarcoma, many of which also showed increased transcription. A comparison of 5mC methylation from the nanopore reads of tumor and control identified several genes with distinct differences including the OS marker Cdkn2a. This dataset illustrates the value of long DNA methods for the characterization of cell lines and how inter-species analysis can inform about the genetic nature underlying mutations that underpin specific tumor types. The data should be a valuable resource for investigators studying osteosarcoma, in general, and specifically the UMR-106 model.

Tussilago farfara L. is a traditional medicinal plant valued for its potentially health-promoting metabolites. Its herbal raw material has been recognized and used since ancient times and continues to be widely used in traditional medicine. Introducing these plants to in vitro cultivation is a challenging task, but once the protocol is developed, such cultures can provide an abundant and inexhaustible source of plant material. In this study, we report the successful induction and growth of vigorous T. farfara L. callus in vitro. Callus induction was achieved on solid media in darkness, whereas it appeared inefficient under light conditions and in suspension culture. We present a detailed description of callus growth kinetics, morphological analysis, photosynthetic activity and biochemical parameters (including protein content and photosynthetic pigments) supported by histological studies. Furthermore, we observed potential for organogenesis and somatic embryogenesis. This method for in vitro propagation of T. farfara, along with callus culture maintenance, offers a wide range of applications in pharmacy and could benefit the environment by minimizing the depletion of natural populations of this species.

Non-immunoglobulin based scaffold proteins (SPs) represent one of the key therapeutic target-specific and high-affinity binders in modern medicine. Among their cellular targets are signaling receptors, in particular, receptor tyrosine kinases, whose dysfunction leads to the development of cancer and other serious diseases. Successful applications of SPs have been reported for HER receptor type 2 (HER2), the member of the human epidermal growth factor receptor family that regulates the cell growth and differentiation. To extend blood residence of SPs and prevent their high accumulation in kidney, these proteins are often fused with serum albumin. Promising results for the HER2-binding activity were obtained for SP G3 from DARPins (Designed Ankyrin Repeat Proteins) family fused with albumin-binding domain (ABD) [1]. Interestingly, detected HER2-G3 binding strongly depended on the position of G3 module in the sequence of the constructs. Further improvement of these constructs for biomedical applications requires deciphering the molecular mechanism responsible for this effect. Here, we investigate the structural and dynamic aspects of ABD-G3 and G3-ABD chimeras using NMR spectroscopy and molecular modeling. Based on biophysical data, we came to the conclusion that extensive interdomain contacts form in both constructs, although the binding interfaces and complex stability are somewhat different. Also, it was shown that the domain linker plays an important role – it limits the accessibility of detected protein-protein binding sites depending on the order of the domains in chimeric molecules. The results create a solid structural basis for the rational design of new effective SP-constructs targeting signaling receptor in the cell.

Is there a qualitative difference between apes’ and human Theory-of-Mind (or ability to estimate others’ mental states)? After opting for the idea that expectations are empty profiles that recognize a particular content when it arrives, I apply the same description to ‘vicarious expectations’ –very probably present in apes. Thus, (empty) vicarious expectations and one’s own (full) contents are distinguished without need of meta-representation. Then, three proposals are made. First, vicarious expectations are enough to support apes’ Theory-of-Mind (including ‘spontaneous altruism’). Second, since vicarious expectations require a profile previously built in the subject that activates them, this subject cannot activate any vicarious expectation of mental states that are intrinsically impossible for him. Third, your mental states that think of me as a distal individual are intrinsically impossible states for me, and therefore, to estimate them, I must estimate your mental contents. This ability (the original nucleus of human Theory-of-Mind) is essential in human lifestyle. It is involved in unpleasant and pleasant self-conscious emotions, which respectively contribute to ‘social order’ and to cultural innovations. More basically, it makes possible the human (prelinguistic or linguistic) communication, since it originally made possible the understanding of others’ mental states as states that are addressed to me, and that are therefore impossible for me.

Despite offering several potential benefits over standard prosthetic aortic valve replacement, the use of the pulmonary autograft has been limited to date, due to concerns over the risk of pulmonary autograft expansion and the need for reintervention. Several techniques using materials with biomimetic potential have been developed to reduce this complication. The incidence, risk factors and pathophysiology of pulmonary autograft dilatation are discussed in this article. This seminar will provide an overview of the techniques of external pulmonary autograft support and their advantages and limitations. It also considers future directions for further investigation and future clinical applications of external pulmonary autograft support. Dilatation of the autograft is more likely to occur in patients with aortic regurgitation and a dilated aortic annulus. External scaffolding may prevent autograft stretching and expansion in these specific cases. However, from a biomimetic point of view, any permanent scaffold potentially restricts the movement of the autograft root. This reduces some of the benefits associated with the use of autologous tissue, which is the priority of the Ross procedure. To address this issue, several bioresorbable matrix could be used to support the root during its initial adaptive phase. Control of blood pressure with aggressive therapy is the first line to avoid this problem in the first year after pulmonary autograft implantation, together with support of the annular and sino-tubular junction in some selected cases. This is the best way to maintain stable autograft root dimensions while preserving root dynamics. However, to determine the efficacy of this combined external support and best medical management, it is important to perform regular imaging and clinical follow-up.

Gene therapy is a medical approach capable of correcting genetic and epigenetic alterations in pathological conditions, including cancer. Different strategies have been pursued for cancer gene therapy, aiming to restore the function of tumor suppressor genes or inhibit oncogenes. These comprise the use of CRISPR/Cas9 gene editing technology and therapeutic oligonucleotides, such as microRNAs, anti-microRNAs, small interfering RNAs, DNAzymes, and epigenetic modifiers. However, despite great potential, their broad clinical use is hindered due to the lack of safe and efficient systems for targeted delivery to cancer cells. To this purpose, aptamers could represent an ideal tool. They are short single-stranded oligonucleotides that bind receptors selectively present or overexpressed on cancer cell membranes, often undergoing cell-specific internalization. Thanks to this last feature, numerous internalizing aptamers have been investigated as targeting moiety to deliver therapeutic oligonucleotides, viral vectors, and nanoparticle-based systems in preclinical studies. Here, we summarize the state-of-the-art of aptamer applications in cancer gene therapy, providing key examples and discussing their advantages, limitations, and clinical perspectives.

The immune system attempts to eliminate foreign substances, such as pathogens and viruses, that invade the body. As normal cells transform into cancerous cells, the immune system can eliminate these cells and suppress cancer onset. The immune system is regulated so that it does not become overactive or attack normal cells. When cancer cells transform from normal cells, they acquire various characteristics, and some cancer cells influence the regulatory function of the immune system to suppress it and escape immune attack. Therefore, treatments have been developed to eliminate the suppression of the immune system by cancer cells and to restore the immune system's ability to eliminate cancer cells. Immunotherapies include immune checkpoint inhibitors, cytokines, cancer vaccines, and effector cell therapies. Cytokine therapy activates the immune system by injecting substances produced by immune cells (such as interleukin 2 and interferon alpha) into the body, thereby increasing the ability of the immune system to attack cancer cells. Cancer vaccine therapy enhances the attack on cancer cells by injecting substances (antigens) that serve as markers for cancer cells into the body to make it easier for immune cells to detect the cancer. Depending on the type of antigen, cancer peptides, tumor cells, and dendritic cell vaccines are available. Effector cell therapy is a treatment method in which immune cells that directly attack cancer cells (CD8+ T cells, NK cells, etc.) are taken from the patient's body, expanded outside the body, activated, and returned to the body to attack cancer cells. To develop these treatments, it is essential to understand the cells and molecules related to immunity as well as the local tumor environment. In this article, we consider the factors related to antitumor immunity.

Cow milk is rich in proteins, fats, carbohydrates, and minerals, and its nutrient content depends on various factors. We tested differences in fatty acid and protein contents of milk from seven breeds and regions, including three dairy breed samples: Holstein milk from Beijing, China (BH); Holstein milk from Hebei province, China; Jersey milk (JS); and four dairy/meat cow samples: Sanhe milk, Xinjiang brown milk (XH), Simmental milk, and Shu Xuanhua milk. Breed significantly affected total fat, fatty acid, and protein contents. The region significantly affected the contents of different fatty acids, α-lactalbumin, and lactoferrin. JS had the highest total fat, most fatty acids, and casein contents. XH performed well with significantly higher unsaturated fatty acid content than BH, which was not significantly different from JS, and its protein composition of low β-lactoglobulin and high lactoferrin was more similar to breast milk, which may thus be favorable to the growth and development of children. Our results may inform the development of dairy products from different cow breeds and advance the process of accurate breed identification.

Oncolytic measles virus (MeV) is a promising anti-cancer treatment. However, the production of high titers of infectious MeV (typically 10⁷–10⁹ TCID₅₀ per dose) is challenging because the virus is unstable under typical production conditions. The objective of this study was to investigate how the multiplicity of infection (MOI) and different media – serum-containing (SCM), serum-free (SFM) and chemically-defined (CDM) – affect MeV production. We infected Vero cells at MOIs of 0.02, 0.2 or 2 TCID₅₀ mL^-1 and the lowest MOI resulted in the largest number of infected cells towards the end of the production period. However, this did not equate to higher maximum MeV titers, which were similar for all MOIs. The medium had a moderate effect, generating maximum titers of 0.89–2.17 × 10⁶, 1.08–1.25 × 10⁶ and 4.58–9.90 × 10⁵ TCID₅₀ mL^‑1 for SCM, SFM and CDM, respectively. Infection at a low MOI often required longer process times to reach maximum yields. Our findings show that SCM, SFM and CDM are equally suitable for MeV production in terms of yield and process time. This will allow MeV production in serum-free conditions, addressing the safety risks and ethical concerns associated with the use of serum.

Despite improvements in participation in population-based screening programs, colorectal cancer remains a major cause of cancer-related mortality worldwide. Targeted interventions are desirable to reduce the health and economic burden of this disease. Two-dimensional monolayers of colorectal cancer cell lines represent the traditional in vitro models for disease and are often used for diverse purposes, including delineation of molecular pathways associated with disease aetiology or to gauge drug efficacy. The lack of complexity in such models, chiefly the limited epithelial cell diversity and differentiation, attenuated mucus production, lack of microbial interactions and mechanical stresses, has driven interest in development of more holistic and physiologically relevant in vitro model systems. In particular, established ex vivo patient-derived explant and patient-derived tumour xenograft models have been supplemented by progress in organoid and microfluidic organ-on-a-chip cultures. Here we discuss the applicability of advanced culturing technologies, such as organoid systems, as models for colorectal cancer and for testing chemotherapeutic drug sensitivity and efficacy. We highlight current challenges associated with organoid technologies and discuss their future for more accurate disease modelling and personalized medicine.

Evaluating aviation emissions and examining the dispersion properties of contaminants are crucial for understanding atmospheric pollution. To more properly assess the pollutant emissions and dispersion of aircraft during the landing and take-off (LTO) cycle, and to more effectively address air pollution surrounding the airport resulting from flight operations, this study assessed emissions throughout the LTO phase utilizing Quick Access Recorder (QAR) data in conjunction with the first-order approximation method. An improved Gaussian diffusion model for mobile point sources is employed to examine the diffusion characteristics of contaminants. Additionally, CFD calculation outcomes for various exhaust velocities and wind speeds were utilized to validate the trustworthiness of the improved Gaussian model. The discussion also encompasses the influence of diffusion time, wind direction, wind speed, temperature gradient, and particle deposition on the concentration distribution of contaminants. The findings indicated that the Gaussian diffusion model aligned with the results of the CFD calculations. The diffusion distribution of contaminants around airports varies over time and is significantly influenced by atmospheric environmental factors, including wind direction, wind speed, and atmospheric stability.

Older people with sleep disturbances also have other chronic pathologies that may interfere with these disturbances. One of the comorbidities that is frequently present is type II diabetes. Objetives: This research aims to find out whether type 2 diabetes mellitus present in elderly people affects the level of severity of obstructive sleep apnoea syndrome (OSAS). Methods: Cross-sectional descriptive study, with a sample of 75 elderly people who attended the Sleep Unit of Doctor Sagaz Hospital in Jaen to be diagnosed with OSAS, and classified according to severity. Results: Significant differences were obtained in the severity of OSAHS between the participants who had type II diabetes mellitus and those who did not, with the former showing higher scores (U=326, p=0.02). Conclusions: Therefore, the presence of type 2 diabetes in the el-derly in our study was associated with more severe OSA. The main discrepancies when compar-ing this research with other studies result from the lack of research addressing these variables in this population group, as well as from the great heterogeneity of methods and techniques used in the different studies (different sample sizes, age range of the population, and diagnostic methods used), causing great variability in the results found. Given the findings obtained in this research, we hope to contribute to future translational research and improve the quality of life of this population.

Approximate unlearning is an emerging field in machine learning that addresses the challenge of efficiently removing specific data points from models without the need for full retraining. In the financial sector, where models are built on sensitive customer data and market information, the ability to unlearn data is crucial for meeting privacy regulations like GDPR and ensuring model adaptability in real-time environments. This paper explores the categorization of approximate unlearning into two main approaches: data-driven approximation and model-driven approximation. Data-driven approximation focuses on selectively retraining parts of the dataset to simulate the removal of data, while model-driven approximation adjusts the model’s internal parameters to nullify the influence of unwanted data points. Both methods offer computational and memory-efficient ways to balance the need for privacy compliance with the performance demands of financial models. The paper discusses practical applications in algorithmic trading, fraud detection, and risk assessment, and highlights the challenges associated with each approach. Through case studies and literature references, we demonstrate how approximate unlearning can be applied to maintain system efficiency, data privacy, and regulatory compliance in the financial domain. This research provides a roadmap for future developments in approximate unlearning, particularly in the context of real-time financial systems and federated learning environments.

Human activities continuously alter the delivery of ecosystem services (ESs), which play a crucial role in human well-being. There is a pressing need for effective ecological management strategies that consider the spatial heterogeneity of ESs to support the transition from trade-offs to synergies. This study focuses on the Haba River Basin and examines characteristics of land use change and the shift from trade-offs to synergies. The results indicate that from 1990 to 2000, the initial phase of land development, 10.65% of the land experienced change. Subsequently, during the intensive period of land development from 2000 to 2010, 30.29% of the land underwent significant transformation, with approximately 78% of grassland, sparse grassland, forested land, and desert converted into arable land. However, between 2010 and 2020, as the focus shifted towards the establishment of native vegetation and the intensity of land development decreased, only a small percentage (3.65%) of the total area underwent changes. Based on an in-depth analysis of spatial heterogeneity from 1990 to 2020, it is believed there has been a shift from trade-offs to co-benefits between 2000-2010 and 2010-2020. The years 2010 and 2020 were pivotal time nodes for the transition from trade-offs to synergies and for reducing trade-offs, with NPP identified as a critical driving factor for Comprehensive ES （CES） functions. By considering the trade-offs/synergies relationship and hotspots of ecological service functions, combined with unified water resource management policies, comprehensive ecological management measures tailored to different regions are proposed. These measures have facilitated the implementation of robust ecological protection policies to shift ES development from trade-offs to synergies in arid areas, thereby enhancing overall ecosystem service functions in the Haba River Basin. The research findings offer crucial scientific support and guidance for ecosystem management in arid areas, particularly within Central Asia.

Music has been proposed for a long time as a treatment for mental disorders. However, some special types of music or stimulating sounds might induce negative emotions. Herein, 8-week-old male mice were exposed to two specific genres of music, hip-hop (HH) and heavy metal (HM), and construction noise (CN), and a series of behavioral experiments were performed to evaluate the impact of these music or sounds on the emotion of mice. The experiment lasted 1 week in which 8-week-old male mice were used as animal models. We found that long term exposure HM, HH, and CN induced depressive symptoms with a decrease in dopamine receptor 1 and BDNF in the mixture of hippocampus and frontal cortex of mice. Our results have highly addressed that certain special types of sound or music might induce negative emotions and loss of dopaminergic neurons in mice, and it is necessary to intervene in adolescences who are exposed to these music or sounds for a long time.

Cutaneous squamous cell carcinoma (cSCC) is the second most common form of skin cancer, with an increasing global incidence. While most cases are successfully treated with surgical excision, a subset can metastasize, leading to significant morbidity and mortality. Current staging systems based on clinical and histopathological features have shown limitations in accurately predicting metastatic risk. This review examines the role of gene expression profiling (GEP), particularly the 40-gene expression profile (40-GEP) test, in improving risk stratification and management of cSCC. We assess the prognostic value of the 40-GEP test, its integration with current staging systems, and its impact on clinical decision-making. Recent studies suggest that incorporating GEP results with traditional staging methods can enhance the identification of high-risk patients, potentially leading to more personalized treatment strategies. The review also explores the challenges of implementing GEP in routine clinical practice, including cost-effectiveness considerations and the need for standardization. Finally, we discuss the implications for future cSCC management and highlight areas for further research. As molecular profiling techniques continue to evolve, GEP represents a promising approach to optimizing care for cSCC patients, aligning with the growing emphasis on personalized medicine in oncology.

The infections of bacterial origin represent a significant problem to public healthcare worldwide both in clinical and community settings. Recent decade was marked by limiting treatment options for bacterial infections due to growing antimicrobial resistance (AMR) acquired and transferred by various bacterial species, especially the ones causing healthcare-associated infections, which has become a dangerous issue noticed by the World Health Organization. Numerous reports shown that the spread of AMR is often driven by several species-specific lineages usually called the ‘global clones of high risk’. Thus, it is essential to track the isolates belonging to such clones and investigate the mechanisms of their pathogenicity and AMR acquisition. Currently, the whole genome-based analysis is increasingly used for these purposes, including epidemiological surveillance and analysis of mobile elements involved in resistance transfer. However, in spite of the exponential growth of available bacterial genomes, their representation usually lack uniformity and availability of supporting metadata, which creates a bottleneck for such investigations. In this dataset, we provide the results of a comprehensive genomic epidemiology analysis of 61,857 genomes of a dangerous bacterial pathogen Klebsiella pneumoniae. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), assignment of the isolates to known global clones, capsular (KL) and lipooligosaccharide (O) types, presence of CRISPR-Cas systems, and cgMLST profiles are given, and the presence of AMR and virulence genes within the genomes is also reported. This dataset will be useful for researchers in the field of K. pneumoniae genomic epidemiology, resistance analysis and prevention measure development.

This paper introduces an open-source active high-precision signal amplifier designed for enhancing biodata acquisition, specifically focusing on EEG signals. The compact device, with a diameter of 18 mm, seamlessly integrates electronics, signal processing, and meticulous design considerations. Incorporated within the single board are circuits for low-noise amplification, an analog interface equipped with bandpass filters, and an integrated ADC for precise digitization. A comprehensive evaluation of signal quality has been conducted, encompassing aspects such as noise levels, electrode bias tolerance, and common mode rejection ratio. To facilitate this evaluation, the widely recognized Silver Chloride electrode (Ag/AgCl) has been employed. Additionally, this paper delves into an extensive discussion concerning diverse sources of noise, along with strategies to curtail input noise and minimize noise originating from electrodes and amplifiers. Leveraging the substantial advancements achieved in the design of dry contact electrodes, this specific device is poised to carve a notable niche in the realm of brain-computer interface.

Contaminated soils are a challenge for implementing biotechnology in bioremediation, recovery of Critical and Strategic Raw Materials (CRMs and SRMs), and food security. European Union (EU) Governments have established strict limits on As, Pb, Cd, and Hg in foods (Document 32023R0915), and requested the recovery of 34 CRMs within a circular economy (CE) (5th CRMs list). This study proposed a biotechnological tool for the decontamination of soil with heavy metal(loids) by arbuscular mycorrhizal (AM)-assisted phytoextraction and subsequent recovery of CRMs, or by phytostabilization to prevent their entry into the food chain. It consisted of placing Baccharis salicifolia plants, inoculated or non-inoculated with AM fungi, into Bioreactors (BRs) containing mining soil with Cd, Ni, and Cu, according to the argentinian Patent (AR090183B1). The bioextractive potential (BP) was also estimated at the highest Technological Readiness Level (TRL) using a Vegetable Depuration Module (VDM, TRL 6). Inoculated plants showed significantly higher aerial bioaccumulation coefficients (Cd: 68.62; P: 2.99; Ni: 2.51; Cu: 0.18) in BRs, and the BP values reached 1.16 g, 9.75 g, 2.40 g, and 213.1 g for Ni, Cd, Cu, and P, respectively. Finally, those CRMs and SRMs could be recovered from biomass through hydrometallurgy within a CE framework.

In the industrial sector, intelligent sensors in fault diagnosis are becoming more critical in recent technological improvements. The prediction accuracy can be enhanced in fault diagnosis using 3-dimensional, sequential, real-time, and image data. Sensors that capture the vibration, sound, and image data are more critical in predicting unbalancing, tool wear, crack, misalignment, etc, in the rotating machinery to increase productivity and to provide an effective maintenance management system. Due to the fast development of industry 4.0 techniques, monitoring of mechanical machinery is experiencing explosive growth and getting more attention in the area of fault diagnosis (FD). Machine learning and Deep learning methods give promising results and accuracy in predicting faults in rotating machinery on shop floors. The success of AI-based models is due to the availability of comprehensive labeled data. Federated Learning (FL) is the machine learning subfield aiming to train an algorithm with a heterogeneous dataset. Data transmission from local facilities to a central server in AI models creates data privacy and security issues. Heterogeneous data analysis is a complicated process in predicting the machine fault in the central server because of millions of data during real-time condition monitoring. Decentralized data handling and analysis is mandatory in condition monitoring because of heterogeneity in data processing, data privacy, and security advantages. The application of federated learning in fault diagnosis has been getting more attention in recent days, and this study is a review of FL applications that address fault diagnosis in rotating machinery in the first phase. A comparison between the types of FL approaches in FD and the use of aggregation algorithms and their applications will also be discussed in Phase 1. In phase 2, a novel methodology has been proposed using Federated learning to diagnose rotating machinery faults. The proposed method, FLOACOS, addresses how prediction challenges are solved using federated learning approaches by optimizing and standardizing the data at local facilities. This work will be helpful for future condition monitoring researchers and gives an overview of a novel method of the FL technique used in predicting faults and the progress made in the maintenance management of rotating machinery.

In this study, we provided an empirical testability of the FF3FM against the co-moment-augmented FF3FM using the Newey–West GMM methods to account for potential endogeneity. The results showed that value risk is not significantly priced, and systematic skewness and systematic kurtosis play more important roles in explaining asset return than market and size factors. We discovered that there is much to gain in moving from the incumbent FF3FM to the co-moment-augmented FF3FM. By extending this study to Nigeria and using out-of-sample data that span across the recent COVID-19 period, the study has contributed to existing knowledge significantly. The study concludes that the co-moment-augmented FF3FM is superior to the incumbent FF3FM in pricing assets, and the anomalies associated with the return-size risk factor relationship are reduced by accounting for systematic skewness and systematic kurtosis in the FF3FM. Therefore, we recommend that investors should take a long position in size-hedged portfolios and hold systematic skewness and kurtosis as constant, and vice visa.

Sex differences in brain metabolism and their relationship to neurodegenerative diseases like Alzheimer's are an important emerging topic in neuroscience. Intrinsic anatomic and metabolic differences related to male and female physiology have been described, underscoring the importance of considering biological sex in studying brain metabolism and associated pathologies. The hippocampus is a key structure exhibiting sex differences in volume and connectivity. Adult neurogenesis in the dentate gyrus, dendritic spine density, and electrophysiological plasticity contribute to the hippocampus' remarkable plasticity. Glucose transporters GLUT3 and GLUT4 are expressed in human hippocampal neurons, with proper glucose metabolism being crucial for learning and memory. Sex hormones play a major role, with the aromatase enzyme that generates oestradiol increasing in neurons and astrocytes as an endogenous neuroprotective mechanism. Inhibition of aromatase increases gliosis and neurodegeneration after brain injury. Genetic variants of aromatase may confer higher Alzheimer's risk. Oestrogen replacement therapy in postmenopausal women prevents hippocampal hypometabolism and preserves memory. Insulin is also a key regulator of hippocampal glucose metabolism and cognitive processes. Dysregulation of the insulin-sensitive glucose transporter GLUT4 may explain the comorbidity between type II diabetes and Alzheimer's. GLUT4 colocalizes with the insulin-regulated aminopeptidase IRAP in neuronal vesicles, suggesting an activity-dependent glucose uptake mechanism. Sex differences in brain metabolism are an important factor in understanding neurodegenerative diseases and future research must elucidate the underlying mechanisms and potential therapeutic implications of these differences.

The idea of developing a robot controlled by iris movement to assist physically disabled individuals is indeed innovative and has the potential to significantly improve their quality of life. This technology can empower individuals with limited mobility and enhance their ability to interact with their environment. Disability of movement has huge impact on the life of physically disabled people. Therefore, there is need to develop a robot which can be controlled using iris movement. The main idea of this work revolves around iris recognition from an eye image, specifically identifying the centroid of the iris. The centroid's position is then utilized to issue commands to control the robot. This innovative approach leverages iris movement as a means of communication and control, offering a potential breakthrough in assisting individuals with physical disabilities. The proposed method aims to improve the precision and effectiveness of iris recognition by incorporating advanced segmentation techniques and fuzzy clustering methods. The fast gradient filters using a fuzzy inference system (FIS) is employed to separate the iris from its surroundings. Then, the bald eagle search (BES) algorithm is employed to locate and isolate the iris region. Subsequently, the Fuzzy KNN algorithm is applied for the matching process. This combined methodology aims to improve the overall performance of iris recognition systems by leveraging advanced segmentation, search, and classification techniques. The results of the proposed model are validated using True Success Rate (TSR) and compared to other existing models. The results highlight the effectiveness of the proposed method for the 400 tested images representing 40 people.

Trajectory planning determines whether a UAV can successfully complete its mission, and the reasonable planning of UAV trajectory in 3D environment is a complex global optimization problem, which needs to take into account many constraints such as urban environment, mountain terrain, obstacles, no-fly zones, flight boundaries, flight distances, and trajectory change rates. In view of the shortcomings of the whale optimization algorithm in 3D trajectory planning, such as slow convergence speed, low accuracy and easy to fall into the local optimum, this paper increases the diversity of the initial population through the introduction of the reverse learning mechanism, and optimizes the coordination of the global and local search ability by integrating the nonlinear convergence factor and the random number generating mechanism, so as to realize the improved whale optimization algorithm that can cope with the higher degree of freedom and the more complex constraints. The simulation results show that the optimization algorithm has improved the convergence accuracy by 22.1% and reduced the standard deviation by 74.1%, which can effectively deal with the 3D UAV path planning problem.

Objectives: The aim of this study was to evaluate the levels of physical literacy (PA) in primary school children in Santiago, Chile, and to explore the relationship between PA and body mass index (BMI). In addition, gender and age differences were analyzed in relation to PA levels in a context where childhood obesity remains a public health challenge. Methods: 439 students in grades 5-8 were assessed using the Canadian Assessment of Physical Literacy - 2 (CAPL-2) questionnaire. Demographic variables (sex, age, and BMI) were correlated with levels of motivation to engage in physical activity and physical activity knowledge. Chi-square were applied with acceptable significance p<0.050 and good p<0.010. Results: The results revealed a significant correlation between motivation for physical activity and gender, with boys showing higher levels of motivation than girls. In addition, an inverse relationship was observed between BMI and motivation, suggesting that more motivated children tend to have a healthier BMI. Knowledge about physical activity increased with age, but did not have a significant influence on BMI. Conclusions: The findings highlight the need to design gender-specific interventions to promote PA and physical activity adherence, with motivation being a key factor in promoting a healthy lifestyle. Public health policies should address these factors to improve pediatric health in Chile.

— An extreme bandgap Al0.64Ga0.36N quantum channel HEMT with Al0.87Ga0.13N top and back barriers grown by MOCVD over bulk AlN substrate demonstrated a critical breakdown field of 11.37 MV/cm, higher than 9.8 MV/cm expected for the channel Al0.64Ga0.36N material. We show that the fraction of this increase is due to the quantization of the 2D electron gas. The polarization field maintains electron quantization in the quantum channel even at low sheet densities in contrast to conventional HEMT designs. An additional increase in the breakdown field is due to quantum-enabled real space transfer of energetic electrons into high-Al barrier layers in high electric fields. These results show the advantages of quantum channel design for achieving record high breakdown voltages and enabling superior power HEMT devices.

Rice cultivation stands as a cornerstone of Sri Lanka's economy, serving as a vital source of em-ployment for rural communities. However, the constraints of limited land availability have prompted an escalating dependence on agrochemicals, notably for pest management, thereby posing significant threats to human health and the environment. This review delves into the ex-ploration of silica nanoparticles as a promising eco-friendly substitute for conventional pesticides in the context of Sri Lankan rice farming. It comprehensively examines various aspects including the synthesis methods of silica nanoparticles, their encapsulation with synthetic pesticides, and an evaluation of their efficacy in pest control. Furthermore, it sheds light on the innovative utilization of agricultural waste such as rice husk and straw in the production of silica-based nano-pesticides. This approach not only demonstrates a shift towards sustainable agricultural practices but also aligns with the principles of green chemistry and circular economy, offering a holistic solution to the challenges faced by the rice farming sector in Sri Lanka.

The rapid increase in unstructured data across various fields has made multi-document comprehension and summarization a critical task. Traditional approaches often fail to capture relevant context, maintain logical consistency, and extract essential information from lengthy documents. This paper explores the use of Long-context Large Language Models (LLMs) for multi-document summarization, demonstrating their exceptional capacity to grasp extensive connections, provide cohesive summaries, and adapt to various industry domains and integration with enterprise applications/systems. The paper discusses the workflow of multi-document summarization for effectively deploying long-context LLMs, supported by case studies in legal applications, enterprise functions such as HR, finance, and sourcing, as well as in the medical and news domains. These case studies show notable enhancements in both efficiency and accuracy. Technical obstacles, such as dataset diversity, model scalability, and ethical considerations like bias mitigation and factual accuracy, are carefully analyzed. Prospective research avenues are suggested to augment the functionalities and applications of long-context LLMs, establishing them as pivotal tools for transforming information processing across diverse sectors and enterprise applications.

This study investigated the characteristics and outcomes of drug-resistant tuberculosis patients in selected rural healthcare facilities in the Eastern Cape, South Africa. A retrospective review of clinical records from 456 patients, covering the period from January 2018 to December 2020, revealed that a statistically significant relationship between DR-TB types and age groups (Chi-square statistic: 30.74, p-value: 0.015). Younger adults (19-35 years) and middle-aged adults (36-50 years) are more frequently affected by RR-TB and MDR-TB, which are the most prevalent forms of DR-TB. Less common types, including Pre-XDR, XDR, and INH TB, were observed in smaller numbers. The study suggests that DR-TB imposes a heavy burden on the working-age population. Gender analysis shows that while the frequency of DR-TB differs between males and females, the percentage distribution of DR-TB types is relatively equal. Both genders are predominantly affected by RR-TB and MDR-TB, which together account for nearly 90% of cases. Pre-XDR, XDR, and INH-resistant TB are much less common, comprising only a small percentage of cases in both males and females. High-risk behaviors such as smoking and drinking are linked to a wider diversity of DR-TB types, while occupations like mining and prison work show higher rates of RR-TB and MDR-TB. In HIV-positive individuals, DR-TB is more common, but the distribution of DR-TB types between HIV-positive and negative groups shows no statistically significant difference. However, HIV-positive individuals have a 20% lower survival rate (65%) compared to HIV-negative patients (85%). Financial stability and comorbidities also significantly influence outcomes, with patients having stable income and fewer high-risk comorbidities experiencing better survival and treatment outcomes. The findings underscore the importance of addressing socioeconomic disparities and strengthening healthcare infrastructure to improve DR-TB treatment outcomes in rural Eastern Cape.

Alzheimer's disease (AD) is the most prevalent form of dementia. With the global aging population, the incidence of AD and related dementias is projected to rise dramatically, posing significant public health challenges. Traditionally, the diagnosis of AD has relied on the detection of amyloid-beta (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau in the brain, assessed through invasive cerebrospinal fluid (CSF) analysis and costly positron emission tomography scans. However, these methods are not feasible for widespread screening or use in low-resource settings. Recent advances in blood-based biomarkers offer a promising alternative for the early detection and monitoring of AD. These biomarkers provide a minimally invasive, cost-effective means of tracking the pathological progression of AD before the onset of clinical symptoms, potentially enabling earlier and more accurate diagnosis. Blood-based biomarkers, including those related to Aβ, phosphorylated tau, and neurofilament light polypeptide, have demonstrated significant potential in reflecting the underlying neuropathology of AD and correlating with traditional CSF and imaging biomarkers. This review synthesizes the latest research on blood-based biomarkers in AD, highlighting their role in the disease's etiopathogenesis, diagnostic accuracy, and prognostic value. We discuss the emerging evidence on proteinopathies, neuroinflammation, vascular pathology, and bioenergetic profiles, emphasizing the clinical applicability of these biomarkers. Furthermore, we explore the integration of blood-based biomarkers with advanced neuroimaging techniques and their potential to revolutionize the diagnostic landscape of AD, particularly in resource-limited settings. We have integrated protein-protein interaction (PPI) and miRNA network analyses to enhance the analysis. By incorporating these, we can explore the interactions and relationships between different proteomic, genomic, and epigenomic biomarkers, potentially unveiling new insights into the underlying biological mechanisms of AD. The continued development and validation of these biomarkers could lead to more accessible and equitable AD diagnostics, ultimately improving patient outcomes.

Ceratitis capitata (Wiedemann) (medfly) strongly affects Argentinean fruit production and export. Augmentative biological control using the exotic parasitoid Diachasmimorpha longicaudata (Ashmead) is currently applied. The ability to find and parasitize medfly larvae on a wide diversity of fruit host species is a key issue to be analyzed. This research assessed the effect of the physical features of fruit on the preference of foraging D. longicaudata females and the influence of varying release density on parasitoid performance as a pest mortality factor in three fruit species. Trials were performed inside field cages under semi-arid environmental conditions in Argentina's central–western fruit-growing region. Sweet orange, peach, and fig were tested. Fruits were inoculated with third-instar larvae of the Vienna-8 temperature-sensitive lethal medfly strain. Naïve, 5-d-old mated D. longicaudata females were released in cages at 20, 40, 80 and 160 parasitoid densities. The highest levels of medfly mortality and parasitoid emergence were recorded in fig and peach, although D. longicaudata also induced mortality in orange, a fruit with few physical features favorable to parasitism. The medfly mortality in all fruit host species significantly increased with increased parasitoid females released into field cages. Diachsmimorpha longicaudata has high potential as a medfly biocontrol agent.

Waveform generation as part of an on-chip built-in self-test (BIST) circuitry often necessitates sufficient linearity without expensive hardware overhead. Achieving high linearity is critical for accurate signal generation, especially in applications requiring high precision, such as biomedical and instrumentation. Currently, achieving the high linearity and precision required in signal generators often relies on costly hardware such as automated test equipment (ATE). This paper presents a DAC based hardware synthesizable arbitrary waveform generator. We use a low-cost DAC and a fully digital on-chip testing and calibration approach to nullify the effect of the DAC’s non-linearity on the generated waveform. The ultra-low cost and high linearity benefit of the proposed waveform generator makes it highly suitable for integration into resource-constrained systems. The proposed approach is validated using simulation results of the small-area DAC designed in TSMC 0.18um technology and the testing and calibration algorithms implemented in MATLAB. The DAC, designed with a matching accuracy at only the 5-bit level, is able to generate a signal with an ENOB of 12 bits alongside a SFDR and THD surpassing 100 dB. This high level of signal purity is consistently maintained across 100 Monte-Carlo simulations, demonstrating the robustness of the architecture against PVT variations as well as random mismatches.

Amphibians, which are essential components of ecosystems, are susceptible to pharmaceutical contamination, a phenomenon of increasing concern owing to the widespread consumption and detection of pharmaceutical compounds in environmental matrices. This review investigates oxidative stress (OS) as the primary mechanism of drug toxicity in these organisms. The evidence gathered reveals that various pharmaceuticals, from antibiotics to anesthetics, induce OS by altering biomarkers of oxidative damage and antioxidant defense. These findings underscore the deleterious effects of pharmaceuticals on amphibian health and development and emphasize the necessity of incorporating OS biomarkers into ecotoxicological risk assessments. Although further studies on diverse amphibian species, drug mixtures, and field studies are required, OS biomarkers offer valuable tools for identifying sublethal risks. Furthermore, the development of more refined OS biomarkers will facilitate early detection of adverse effects, which are crucial for protecting amphibians and their ecosystems. Ultimately, this review calls for continued research and mitigation strategies to safeguard biodiversity from pharmaceutical contamination.

Let $\mathcal{M}(\Spin(8,\mathbb{C}))$ be the moduli space of $\Spin(8,\mathbb{C})$-Higgs bundles over a compact Riemann surface $X$ of genus $g\geq 2$. The triality automorphism of $\Spin(8,\mathbb{C})$ acts on $\mathcal{M}(\Spin(8,\mathbb{C}))$ and those Higgs bundles that admit a reduction of structure group to $G_2$ are fixed points of this action. This defines a map of moduli spaces of Higgs bundles $\mathcal{M}(G_2)\rightarrow\mathcal{M}(\Spin(8,\mathbb{C}))$. In this work, the action of the triality automorphism is extended to an action on the Hitchin integrable system associated to $\mathcal{M}(\Spin(8,\mathbb{C}))$. In particular, it is checked that the map $\mathcal{M}(G_2)\rightarrow\mathcal{M}(\Spin(8,\mathbb{C}))$ restricts to a map at the level of Prym varieties. Necessary and sufficient conditions are also provided for the Prym varieties associated with the moduli spaces of $G_2$ and $\Spin(8,\mathbb{C})$-Higgs bundles to be disconnected. Finally, some consequences are drawn from the above results in relation to the geometry of the Prym varieties involved.

In Belgium, around a quarter of M. genitalium infections are resistant to both macrolides and fluoroquinolones -termed multi-drug-resistant (MDR) infections. It is unclear what the best treatment is for these infections. We report the first two cases of MDR M. genitalium urethritis treated with combination therapy of minocycline, metronidazole, methenamine and pristinamycin. In both cases, this treatment resulted in microbiological and clinical cure.

This research investigates the impact of Al and Ti elements on the formation of self-generated ordered phase in high-entropy alloys (HEA), which have been receiving widespread concentration in present times thanks to their remarkable multifunctional properties and wide range of component selection. By adjusting the dual elements of Al and Ti, the study explores the regulation of quantity and distribution of the in-situ self-generated ordered phases in the alloy. The results of the annealing heat treatment process demonstrated that, through accession Al and Ti elements, the Cr-Fe-Ni medium-entropy alloy transfer from BCC phase to BCC+FCC phase and the precipitation of new phases such as B2, L21, and σ. This led to a synergistic impact of second phase strengthening, and also of solid solution strengthening, resulting in improved compressive properties of HEA.

The adoption of social media metrics by small and medium-sized enterprises (SMEs) has become a critical tool for engaging customers and enhancing strategic outcomes. However, many SMEs face challenges in effectively tracking and evaluating social media investments, making it difficult to assess the effectiveness of their strategies. This systematic review identifies and analyses key social media activities and performance metrics that contribute to SME strategic success. Through an extensive review of literature from Google Scholar, Web of Science, and Scopus, we selected 138 studies published between 2014 and 2024. The analysis reveals that engagement metrics (e.g., likes, shares), brand awareness (e.g., mentions), and conversion metrics (e.g., click-through rates) significantly influence organizational performance and long-term success. SMEs leveraging detailed engagement metrics reported a 25% increase in customer retention, while brand awareness metrics led to a 30% improvement in customer acquisition. The review highlights areas requiring further research, including a need for sector-specific metric analysis and a deeper understanding of emerging social media platforms. These results offer practical insights for SMEs to enhance their strategic use of social media and adapt to rapidly changing market dynamics.

This study presents a novel predictive model to address the churn problem in the motor insurance sector using a dual-phase framework. The first phase employs a cave-degree perturbation technique for effective feature selection, while the second phase applies the Magnetic Force Perturbation Technique (MFPT) to optimize the search process and avoid local optima traps. Two metaheuristics are proposed: the Adaptive Random Forest-Assisted Large Neighborhood Feature Optimizer (ARALFO) and the Adaptive Random Forest Particle Swarm Optimizer (ARFPSO) to enhance churn prediction accuracy. The model was evaluated on two real-world motor insurance datasets, achieving a 95% accuracy rate and outperforming state-of-the-art algorithms. An ablation study confirmed the significant impact of the cave-degree and MFPT techniques in boosting predictive performance.

Aim of study: to evaluate the therapeutic impact of topically applied curcumin as Alternative or Complementary to Triamcinolone Acetonate in Treatment of Oral Lichen Planus. Background: Oral lichen planus (OLP) is a chronic inflammatory disorder affecting the mucous membranes, believed to be an immune-mediated condition. Its persisting nature can lead to extended discomfort for those affected. The primary symptoms are pain and a burning sensation, with a potential risk of malignancy if not properly managed, necessitating lifelong clinical monitoring. Presently, OLP is considered a significant challenge due to the absence of a definitive cure. Patients & patients: Thirty patients with oral lichen planus were divided into three groups, 10 patients each: Group A: ten patients with OLP were received topical triamcinolone acetonide. Group B: ten patients with OLP were received topical curcumin. Group C: ten patients with oral lichen planus were received topical triamcinolone acetonide combiend with topical curcumin. For six weeks. Each patient was examined at baseline, 1 and 3months of therapy. Pain was scored using the visual analogue scale. Salivary level of interleukin 6 were estimated at baseline, 1 and 3months evaluation periods. Results: Significant decrease in pain score was observed in all groups and there was a statistically significant difference between them at all treatment periods in favor to group C . 2 Additionally, all groups showed a decrease in the size of lesions which was greater in group A,C compared to the B group. A significant reduction of the mean interlukin6 continued down to 3 months in all groups with statistically insignificant difference between them at three months, while statistically significant difference in favor to group C at 1month was observed. Conclusion: The results proved the benefit of curcumin as a promising medication when used in combination with conventional therapy in treatment of OLP patients. However, further longterm studies with large sample size are needed.

The United States is facing an alarming surge in cyberattacks, with data breaches rising by 72% since 2021, presenting grave national security risks. Notable incidents, including Chinese hackers attempting to access high-level government information and Iranian-linked cyber groups targeting U.S. water systems, highlight the fragility of critical infrastructure. These attacks pose dangers not only to national security but also to economic stability and public health, amplified by the rapid growth of interconnected technologies and global digitalization. In response to these escalating threats, the U.S. must take a multifaceted approach. Key strategies include strengthening cybersecurity education and workforce development, enforcing more rigorous regulatory frameworks, and enhancing preparedness through incident response and recovery planning. Additionally, increased international cooperation is essential to address state-sponsored cyber threats and foster a global framework for cybersecurity defense. As cyber threats continue to evolve, future efforts will rely on technological advancements such as artificial intelligence (AI) for real-time threat detection, the adoption of zero-trust security models, and the development of adaptive, resilient cybersecurity infrastructures. Collaboration between the public and private sectors, coupled with ongoing innovation and education, will be critical to maintaining national security and promoting economic growth in an increasingly interconnected digital landscape.

This study examines the degradation of agglomerate particles in treated produced water used for hydraulic fracturing in the Bakken Formation. By analyzing d90 values and pump speeds from different manufacturers, the research quantifies particle size reduction rates, revealing how degradation contributes to pore throat blocking in tight formations. The study found that agglomerate particles in hydraulic fracturing fluids ranged from 9 to 85 microns in size, with degradation occurring most rapidly within the first five minutes of pump action. As time progresses, degradation slows, indicating that pore clogging is most likely at the beginning of the fracturing process. The research emphasizes that pump speed is a critical factor in determining the rate of particle degradation. To minimize formation damage, it is recommended to scale up particle size and use coarser particles in suspension. However, both large and small particles pose risks: larger particles can reduce porosity, while smaller particles may penetrate pore throats and form internal filter cakes. The study highlights the importance of selecting the appropriate pump speed and particle size distribution to optimize fracturing fluid design, maintain formation permeability, and reduce the risk of formation damage. Lower pump speeds and fewer coarse particles are advised to avoid pore clogging during the fracturing process.

The emergence of Artificial Intelligence (AI) has transformed the way historical data is interpreted, uncovering previously unseen trends and fundamentally altering modern management practices. This shift has significantly impacted the analysis of business history and leadership studies. However, there is still a notable gap in research regarding the effects of AI-driven reinterpretations of historical events on contemporary business strategies, leadership frameworks, and ethical considerations. This study aims to address this gap by exploring the methodologies and implications of AI in business history analysis, focusing on case studies of Walmart and JPMorgan Chase. Integrating foundational theories such as evolutionary theory, path dependency, transformational, and authentic leadership reveals how AI-driven insights can refine strategic planning, advance leadership development, and promote equitable management practices. It also highlights the ethical challenges in using AI for historical analysis, particularly the risks of perpetuating existing biases. Findings indicate that AI's capacity to analyze large volumes of historical data significantly reshapes our understanding of business evolution and leadership effectiveness. The study concludes with actionable recommendations for adopting ethical AI practices, outlining future research directions, including developing AI models for contextual analysis and exploring AI’s role in promoting diverse leadership models. This research advances academic discourse by providing a theory-driven framework for leveraging AI in business history and leadership studies. It underscores AI's transformative potential and complex implications for future business practices.

This study aimed to test the hypothesis that the use of theories in open access resources during the COVID-19 pandemic would be greater than in copyrighted resources. Using the Scopus database from August 18-28, 2023, the search terms “COVID-19,” “Coronavirus,” “SARS-CoV-2,” and “2019-nCoV” retrieved 511,920 results, of which 17,487 were selected. After filtering, 8,254 studies were analyzed. These were categorized as "Both Theory and Model," "Only Theory," and "Only Model." Using MS Excel, the researchers sorted studies based on titles, abstracts, and keywords to assess the magnitude of theory and model use. The study found that theories and models were employed across various disciplines and indexed accordingly. Further analysis included term categories, year-wise distribution, and citation patterns in relation to access categories. The study recommended future research to explore the connection between theories/models and factors such as publication trends, geography, and funding. The study concluded that, journals should encourage authors to include theories/models used in their studies in titles, especially during health emergencies, to provide early insights into handling pandemics.

Hearing-loss (HL) is a major public health issue albeit data is limited. Objectives: were to determine the frequency and factors. Design: was descriptive cross-sectional using web-based self-administered google-form questionnaire. Data analyzed by-IBM SPSS-V-24-Windows. Results: In 789 responses, 6.6 % reported HL, 62.4% from Eastern-region of which 41.2% were employed and 31.7% students. Despite 36.5% reported HL &gt; 5-years, 21.2% sought treatments. 19.2% reported diabetes and 51.9% had family-history. Ages 11– 50 years showed female predominance (females 59.1%, males 41%). Age was significant in HL (P &lt;0.001) irrespective of gender (males 6.8%, females 6.4%, P value =0.838) reaching peaks at 17.2% 51-60 years and 25% in 71-80 implying a potential risk (OR = 1.43, 95% CI: 0.78-2.81, p = 0.032). 34.6% indicated genetics then noise (26.9%), while infections, stroke, and fevers made 19.2%. Air or sea travels (7.7%) were insignificant (P = 0.628) while head injuries (11.5%), or low- and high-altitudes showed 21.2%. Noise-hazard (P = 0.048) and medications (P = 0.017) were significant in HL. Conclusion: thus, we show geriatric age, noise, and inheritance-history as major predispositions. However, the study was limited by small sampling. Future large cohort multicenter studies on factual clinical data would gain more insights into the HL.

Indoleamine 2,3-dioxygenase 1 (IDO1) and IDO2, originated from gene duplication before vertebrate diver-gence. While IDO1 has a well-defined role in immune regulation, the biological role of IDO2 remains unclear. Discovered in 2007, IDO2 is located near the IDO1 gene. Because of their high sequence similarity, IDO2 was initially thought to be a tryptophan (Trp)-degrading enzyme like IDO1. Differently from what expected, IDO2 displays extremely low catalytic activity toward Trp. Nevertheless, many studies, often contradictory, tried to demonstrate that IDO2 modulates immune responses by catabolizing Trp into kynurenine, a hypothesis un-convincing and, let’s say, linked to the first life of IDO2. Here, we review its “new life”, the one in which IDO2 has a functional role beyond Trp metabolism. The IDO2’s evolutionary persistence across species, despite be-ing almost inactive as an enzyme, suggests it has some relevant biological importance. IDO2 expression in human normal cells is poor, but significant in various cancers, with two prevalent SNPs. Overall, the com-parison of IDO2 to IDO1 as a Trp-degrading enzyme may have led to misunderstandings about IDO2’s true physiological and pathological roles. New insights suggest that IDO2 might function more as a signaling molecule, particularly in cancer contexts, and further studies could reveal its potential as a target for cancer therapy.

Small and Medium-scale Enterprises (SMEs) need a platform that actively enables collaboration with Research institutions and consultants since SMEs lack the financial resources to conduct independent research. Such a platform will require a verifiable manipulation-free system for enabling, executing, and recording collaboration activities and tracking reputations among the organizations and individuals that use the platform. Blockchain provides an opportunity to build such a collaborative platform by enabling the verifiable recording of outcomes from collaborations, aggregating the resulting reputation of the collaborating parties, and offering tokenised incentives for rewarding positive contributions to the platform. Cryptocurrencies from which blockchain-based tokens are highly unstable, thereby, reducing business organisations’ interest in blockchain applications. Hence, there is a need to design a self-sustaining valuable token model that incentivises user behaviours that positively contribute to the platform. This paper explores the application of game theory in analyzing token-based economic interactions between the various groups of users in a blockchain-based collaboration platform to design and simulate a token distribution system that provides a fair reward mechanism for the users while also providing a dynamic pricing model for the utility value provided by the platform tokens. Furthermore, the paper analyzed potential risks on the token model design and propose mitigation strategies.

Aging is the important risk factor for tumorigenesis. Metabolic reprogramming is hallmark of both aging and tumor initiation. However, how the crosstalk between aging and metabolic reprogramming affects the tumor microenvironment (TME) to promote tumorigenesis was poorly explored. We utilized a computational approach proposed by our previous work, termed as MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to depict age-related metabolic plasticity using pan-cancer bulk RNA-seq data. Through MMP3C analysis across 17 age-related cancer types, a high metabolic organized heterogeneity was found across those cancer types. Subsequent analysis revealed a higher degree of glycolysis and impaired oxidative phosphorylation in older patients. Similar phenomenon was also observed at the single cell resolution. Many energy generation pathways, i.e., TCA cycle, oxidative phosphorylation were weakened in activated T cells and macrophages, while it increased in exhausted T cells, immunosuppressive macrophages, and Tregs in older patients. It was suggested that aging-induced metabolic switches alter the utilization of glucose, thereby influencing immune function and resulting in the remodeling of the tumor microenvironment. This study provides new insights into the associations between tumor metabolism and TME mediated by aging, linking with novel strategies for cancer therapy.

For almost 75 years, the general solution for the Schrödinger equation was assumed to be generated by an exponential or a time-ordered exponential known as the Dyson series. We study the unitarity of this solution in case of singular Hamiltonian and provide a new methodology that is not based on the assumption that the underlying space is L2(R). Then, an alternative operator for generating the time evolution is suggested that is manifestly unitary, regardless of the choice of the Hamiltonian. The new construction involves an additional positive operator that normalizes the wave-function locally and allows us to preserve unitary even on indefinite norm spaces. Our considerations show that Schrödinger's and Liouville's equations are, in fact, two sides of the same coin, and together they become the unified description of quantum systems.

This paper presents a case study of Fulani herdsmen in Nigeria, whose traditional ethnoveteri-nary practices risk being lost as the country transitions to more intensive and enclosed livestock practices. We use a planetary health framing to make visible the value of indigenous practices that are less damaging to the environment, animal welfare and human health. Through ethnographic observation, focus group discussions (FGD) and key stakeholder interviews, we show that the Fulani use a complex system of herbal medicines and traditional herding practices to maintain herd health, manage and treat animal disease when it arises. However, their traditions often sit uncomfortably with commercial farming practices. As traditional Fulani grazing lands are eroded, dispossessed Fulani take employment from businessmen farmers but both parties’ inex-perience with shed hygiene, artificial feed and less environmentally resilient crossbreeds leads to increased incidence of infectious disease. This in turn drives higher use of antibiotics. There is a ‘causal chain’ of underlying drivers that leads, through poorer environmental, animal and hu-man health, to increased use of antibiotics. The antibiotic resistance that emerges from this chain threatens human health now and into the future. Through a planetary health framing we advocate for more respect for Fulani herdsmen and their traditional ethnoveterinary practices as an alter-native to increasing antibiotic use.

Species of the genus Kalanchoe have a long history of therapeutic use in ethnomedicine linked to their remarkable medical properties. One of them is Kalanchoe blossfeldiana succulents occurring in tropical regions. Despite the great interest in this plant, there are no reports about its therapeutic effects on the skin. In this study, the antioxidant properties and skin permeation of a topical hydrogel containing K. blossfeldiana ethanol extract was assessed. Additionally, the content of active compounds in the K. blossfeldiana extract was evaluated by UHPLC-MS and HPLC-UV. The extract was analysed with three antioxidant assays: ABTS, DPPH and FRAP. Furthermore, antielastase and antihialuronidase properties of the tested extract were assessed. The ex vivo penetration studies were performed using the Franz diffusion cells. The obtained results show that antioxidant properties of K. blossfeldiana extract were similar to ascorbic acid, while antielastase and antihialuronidase tests indicated strong antiaging and anti-inflammatory activity (IC50 was 26.8 ± 0.13 and 77.31 ± 2.44 µg/mL, respectively). Moreover, active ingredients contained in K. blossfeldiana extract penetrated through human skin and accumulate in it. In conclusion, the hydrogel containing K. blossfeldiana extract can be considered as an interesting and new alternative to dermatologic and cosmetic preparations.

With the rising awareness of environmental issues, concerns about pollution and global warming have gained importance, and electricity generation costs continue to increase. As a result, research on energy recovery using thermoelectric modules has attracted attention in recent years. Given the lack of significant breakthroughs in thermoelectric materials, improving the structure of thermoelectric modules has become a key direction for enhancing efficiency. Common thermoelectric module types include flat plate (Flat Plate TEG), annular (Annular TEG), flexible (Flexibility TEG), segmented, and cascaded designs. Factors influencing thermoelectric performance include the number, shape, height, contact area of thermoelectric elements, temperature difference, copper plate length, contact resistance, and the geometric arrangement of the elements. This study is based on the commercially available TGM1-127-1.0-0.8 thermoelectric module. By simulating the output performance of both flat plate and annular thermoelectric module structures under identical conditions—such as total volume of thermoelectric material, shape, and height of the thermoelectric legs, the total contact area with the heat source, temperature differential, and total volume of the connecting copper plate—we observed that the number of thermoelectric elements not only determines the open-circuit voltage but also influences the output power. The trend of output power remains consistent under varying temperature differences, regardless of changes in the load resistance.

This paper proposes the use of Bayesian inference techniques to search for and obtain valid instruments in dynamic panel data models where endogenous variables may exist. The use of Principal Component Analysis (PCA) allows for obtaining a reduced number of instruments in comparison to the high number of instruments commonly used in the literature, and Monte Carlo Markov Chain (MCMC) methods enable efficient exploration of the instrument space, deriving accurate point estimates of the elements of interest. The proposed methodology is illustrated in a simulated case and in an empirical application, where the partial effect of a series of determinants on the attraction of international bank flows is quantified. The results highlight the importance of promoting and developing the private sector in these economies, as well as the importance of maintaining good levels of credit worthiness.

An outbreak of esophagitis and ingluvitis caused by Salmonella Typhimurium in passerines seized from illegal wildlife trafficking is described. This illegal activity causes stress and leads to lowered immunity in the birds. Additionally, inadequate hygiene conditions predispose the birds to diseases such as salmonellosis. Few studies report the occurrence of Salmonella-induced lesions in the esophagus and crop of wild birds; thus, this study aimed to describe the disease in these species, investigate its presence in the environment, and determine the antimicrobial resistance profile. Three birds of the genus Sporophila were necropsied. In the esophagus and crop, yellowish plaques corresponding to necrosis with bacterial aggregates were observed. Salmonella spp. was isolated from these lesions, with genus confirmation via MALDI-TOF. Environmental samples were collected from the enclosures and cages where the animals were quarantined, and the same bacterium was isolated. In two birds, the serotype S. Typhimurium was identified. Antibiograms performed on the strains from the birds and the environment revealed resistance to antibiotics in the classes of Penicillins, Sulfonamides, Aminoglycosides, Monobactams, Tetracyclines, and first and third-generation Cephalosporins. To the authors' knowledge, this is the first report of this agent causing death in Sporophila due to esophagitis and ingluvitis. It is also the first report of salmonellosis in three species of passerines in Brazil. The study underscores the importance of understanding the pathogens circulating in wild animals, especially within the context of One Health.

The high performance of liquid chromatography(HPLC) has long been an indispensable tool for vigilance and analysis across range disciplines such as pharmaceuticals, environmental monitoring, food safety attributes etc. We will discuss about HPLC method development and validation, types of liquid chromatography methods focusing on proper use of a mobile phase like an eluent selection, how to optimize the conditions as well take it through its various procedures required for approval from regulatory bodies. Strategies to improve method robustness, sensitivity and reproducibility are highlighted. The future breakthroughs in HPLC technology and some innovative trends have also been speculated to limit this fundamental methodology.

Dengue fever is a persistent public health issue in tropical regions, including Vietnam, where climate variability plays a crucial role in disease transmission dynamics. This study focuses on developing climate-based machine learning models to forecast dengue outbreaks in Ba Ria Vung Tau (BRVT) province, Vietnam, using meteorological data from 2003 to 2022. We utilized four predictive models—Negative Binomial Regression (NBR), Seasonal AutoRegressive Integrated Moving Average with Exogenous Regressors (SARIMAX), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM)—to predict weekly dengue incidence. Key climate variables, including temperature, humidity, precipitation, and wind speed, were integrated into these models, with lagged variables included to capture delayed climatic effects on dengue transmission. The NBR model demonstrated the best performance in terms of predictive accuracy, achieving the lowest Mean Absolute Error (MAE) compared to other models. The inclusion of lagged climate variables significantly enhanced the model’s ability to predict dengue cases. SARIMAX and LSTM models, although effective in capturing seasonal trends, struggled with overfitting and failed to accurately predict short-term outbreaks. XGBoost exhibited moderate predictive power but was sensitive to overfitting, particularly without fine-tuning. Our findings confirm that climate-based machine learning models, particularly the NBR model, offer valuable tools for forecasting dengue outbreaks in BRVT. However, improving the models’ ability to predict short-term peaks remains a challenge. The integration of meteorological data into early warning systems is crucial for public health authorities to plan timely and effective interventions. This research contributes to the growing body of literature on climate-based disease forecasting and underscores the need for further model refinement to address the complexities of dengue transmission in highly endemic regions.

Geothermal energy generation faces challenges in efficiency, partly due to restrictions on reinjection temperatures caused by scaling issues. Therefore, developing strategies to prevent scaling is critical. This study aims to simulate the scaling tendencies and corrosion effects of geothermal fluids on various construction materials used in scaling reactor/retention tank systems. A range of materials, including carbon steel, austenitic stainless steel, duplex stainless steel, two proprietary two-part epoxy coatings, and thermally sprayed aluminium (TSA), were tested in a simulated geothermal brine. Experiments were conducted in a laboratory vessel designed to replicate the wall shear stress conditions expected in a scaling reactor. The tests revealed varying scaling tendencies among the materials, with minimal corrosion observed. The dominant scale formed was calcium carbonate, consistent with geochemical modelling. The findings suggest that despite the high operating temperatures, the risk of corrosion remains low due to the brine's low chloride content, while the wettability of materials after immersion may serve as a useful indicator for selecting those that promote scaling.

Biofilm formation in unicellular eukaryotes, such as Candida albicans and Saccharomyces cerevisiae, represents a significant challenge in various sectors, including healthcare, agriculture, and industry. These biofilms are resistant to conventional antimicrobial treatments due to their protective extracellular matrix and complex regulatory mechanisms, such as quorum sensing (QS) and cyclic AMP (cAMP) signaling. Recent advances in the understanding of biofilm formation pathways have led to the identification of novel compounds that target these regulatory mechanisms. This review highlights breakthroughs from 2024 research, focusing on the inhibition of quorum sensing, disruption of extracellular matrix synthesis, and transcriptional/post-transcriptional regulation of biofilm-related genes. The application of these discoveries in Vietnam is particularly promising, given the country's challenges with biofilm-related infections in healthcare and productivity losses in agriculture and aquaculture. Natural product-based approaches, especially those derived from Vietnam's marine biodiversity, offer environmentally sustainable solutions for biofilm control. However, translating these laboratory findings into scalable, cost-effective applications faces challenges such as resistance development, environmental sustainability, and regulatory constraints. This review provides a comprehensive overview of compounds targeting biofilm regulatory mechanisms, evaluates their potential applications in Vietnam, and discusses future research directions, emphasizing the need for interdisciplinary collaboration to address the complexities of biofilm control.

Deuterium is a heavy nonradioactive isotope of hydrogen, having a neutron as well as a proton, making it twice as heavy. It is a natural element, present at 156 parts per million in seawater. The ATPase pumps in the mitochondria utilize proton motive force to drive ATP synthesis, and deuterons damage the pumps, producing a stutter that can cause reactive oxygen release and inefficiencies in ATP synthesis. Cellular metabolism incorporates several novel mechanisms to assure low deuterium content in the mitochondria and other organelles. Nicotinamide adenine dinucleotide (NAD) is a major carrier of deuterium depleted (deupleted) protons to supply the mitochondria. Many enzymes, especially flavoproteins, are able to use proton tunneling to fractionate out deuterium. In this paper, we argue that the amino acid proline is able to trap and sequester deuterium, and that peptidyl prolyl isomerases (PPIases) play a central role in facilitating deuterium trapping in proline-rich proteins, most notably collagen. The endothelial glycocalyx also sequesters deuterium in gelled water lining the blood vessels, creating a battery and promoting low deuterium in the circulation. Excess deuterium promotes cancer growth, and cancer cells release large quantities of lactate via aerobic glycolysis to help reverse deuterium overload, systemically.

The enormous diversity of hair fibers found in human population is striking, while the basic structure of human fibers is often the same! But why do some fibers curl? Curling is related to the machinery and processes that produce different keratin filament orientation in relation to the axis of hair growth, influenced by developmental and molecular control. It is established that a curly hair shaft is produced by a curved hair follicle that have an angulated dermal implantation, with a bulb bent in the shape of golf club, while the outer root sheath is dissymmetrical; have an outer side that is convex bulging and thin and a concave side in which the cells express alpha-smooth muscle protein reflecting internal mechanical force. Yet, the exact cause of hair curling is not known. All the previous studies were short of impacting curly hair treatment. My hypothesis is that the curling mechanical force is generated by a hypersensitive contracted Arrector Pili muscle (APM) of the hair follicle. In curly hair, each APM fiber is shortened; pulling its attachment spot upward towards the epidermis and obliquely or laterally; curling the outer root sheath to produce the curved follicle described above. This contraction has the influence at a time when the young hair root is being formed and keratin microfibrils are being oriented and packed. APM is a multi-unit muscle wherein each fiber contracts independently to create the enormous diversity of curly hair types. APM contraction can be modulated by oral administration of selective smooth muscle relaxing treatment that relax the curved follicle from within. Further studies are needed to confirm the role of APM in shaping curly hair follicle including AI studies, i.e., computer-aided target identification to demonstrate APM active site that can interact with herbal small molecules.

This paper presents the novel development of a shape memory polymer foam based on polymer-polymer nanocomposites. Herein, polylactide (PLA)/biosourced polyamide (PA) foams are fabricated by in-situ fibrillation of polymer blends and subsequent supercritical CO2 foaming technique. In this system, PLA serves as a shape memory polymer to endow this foam with a shape memory effect (SME), and in-situ generated PA nanofibers are employed to reinforce the PLA cell walls and provide an additional permanent phase. Concentration of PA, 5 wt.%, is chosen to form an entangled nanofibrillar network. Foams of PLA/PA nanoblends with the same content of constituents are fabricated to reveal the effect of minor phase morphology on the cell structure and shape memory behavior of polymer foams. Profiting from the reinforcing effect of PA nanofibers, the PLA/PA nanocomposite foam exhibits smaller foam cells, a narrower cell size distribution and a comparable cell concentration than the PLA/PA nanoblend foam. In addition, PA nanofibers, unlike PA nanodroplets, favor the shape fixation ratio and recovery ratio and shorten the shape recovery time.

The evaluation of landslide hazard in seismic areas is based on a deterministic analysis to define the stability of the sites. However, deterministic analysis is not able to consider various uncertainties in the analysis process. This paper focuses on the results established by the probabilistic seismic hazard analysis and extends the probabilistic landslide hazard analysis to consider the uncertainties of opposing ground deformations and strength. The work studies the areas between Nibbiano and Sant'Erasmo inhabited centres in the Camerino municipality located in central Italy. The current state of the areas shows dangerous conditions for the building structures. In fact, all constructions present evidence of damage caused by both the 2016 earthquake and slope instability. The studies, carried out through careful geological survey and detailed geophysical investigations, are performed to define the geological, geomorphological and hydrogeological characteristics of the areas and also to run a new analysis aimed to define the material stress-strain behavior. Both inhabited Centers are located on debris deposits with highly variable thicknesses. The key point regards the definition of the depth of unstable mass, its position and its extension. Therefore, this contribution aims to summarize the knowledge of the landslide phenomenon that affected the area, trying to interpret its evolution with new field investigations. Indeed, during the first step, related to the geological and geomorphological detailed surveys, a remarkable geological model was obtained to better interpret the slope dynamics in relation to the structures of this section of the ridge. This model was also confirmed by the geophysical investigations. The study involves a more detailed definition of the landslide perimeter. Indeed, to analyze the phenomenon, it was necessary to study both the local seismic amplification with finite element 2D simulations and the slope stability conditions with finite 3D difference simulation.

Tsetlin machine (TM) is a novel machine learning approach that implements propositional logic to perform different tasks like classification and regression. The TM can not only achieves competitive accuracy in these tasks but it is also able to provide results that are explainable and easy to implement using simple hardware. The TM learns using clauses based off the features of the data. Final classification is done using a combination of the clauses. In this paper, we propose the novel idea of adding regularizers to TM, called as Regularized TM (RegTM), to improve generalization. Regularizers have been widely used in machine learning to improve accuracy. We explore different regularization strategies and how they would influence performance. We show the feasibility of our methodology by different experiments on benchmark datasets.

The insuperable obstacle between gravity and quantum mechanics is their spacetimes. Introduction of a (quantum) quadratic form on a state Hilbert space and a (quantum) metric tensor in (3+1)D quantum spacetime solves the problem by providing a common spacetime for both gravity and quantum mechanics which helps further to develop an accurate and generalized form of quantum gravity, i.e., General Quantum Gravity. This formalism yields a (3+1)D pseudo-gravity first order quantum equation, which is technically more accurate than Schr\"odinger equation. A (quantum) Lorentz-like transformation is also possible to be developed in (3+1)D quantum spacetime. Instead of one common Einstein field equation to explain gravitational effects, this formalism yields two individual (quantum) Einstein field equations, namely, one for bosons and another for fermions. In this work, (quantum) metric tensor as well as quantum Riemann tensor, quantum Ricci tensor and quantum Ricci scalar all are found to be the objects of quantization. Apparently, these quantum Riemann tensor, quantum Ricci tensor and quantum Ricci scalar are also found to be multiplicatively renormalizable while all divergences of their higher orders vanish in (3+1)D quantum spacetime. Ultimately, General Quantum Gravity yields that: Quantum Mechanics and Theory of Special and General Relativity are inseparable and naturally inter-expressible for the (quantum) quadratic form and the (quantum) metric tensor in (3+1)D quantum spacetime.

Renewable energy is essential to environmental sustainability and ranks high among the United Nations strategic goals. In this context, as opposed to conventional biomass gasification, co-gasification emerges as a promising technological pathway to effectively combine the distinct benefits of various gasification feedstocks in order to generate hydrogen-rich syngas. The goal of achieving net-zero emissions has further raised the demand for biomass co-gasification. However, the complicated reactions involved in co-gasification process pose challenge in optimizing the process parameters for increased productivity and performance. To the author’s knowledge, there is no consensus regarding the most suitable machine learning (ML) based regression models for optimizing biomass-plastics co-gasification process, as no prior research has examined the effectiveness of different regression models for this process. Further, the practical application of ML models is adversely affected by their black box nature and lack of interpretability. To address these gaps, the objective of the currect research is two-fold: Firstly, to model the co-gasification process using seven different ML algorithms. Secondly, to develop an evaluation framework and assess model interpretability to select an ideal model for co-gasification interpretation and optimization. The support vector regression(SVR) model demonstrated the most reliable predictive performance. The study employed learning curve analysis and cross validation analysis to substantiate the model predictive performance and exhibit its potential to generalize. Beyond performance analysis, Shapely additive explanation, (SHAP) is first explored in the realm of cogasification to interpret the prediction of the studied models from global and local perspective. The research findings are expected to provide valuable insights to better understand and optimize biomass-plastics cogasification process for H2-rich syngas production.

In this paper we have investigated the driven quintic Duffing equa- tion. Using some analysis technique, we are able to predict the number of limit cycles around the equilibrium and to develop a theoretical ap- proach to chaos transition in damped driven systems.

A web application – IMERG Precipitation Extractor (IPE) was developed that relies on the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) data available at a global coverage. The IPE allows users to query, visualize, and download time series satellite precipitation data for various locations, including points, watersheds, country extents, and digitized areas of interest. It supports different temporal resolutions ranging from 30 minutes to 1 week. Additionally, the IPE facilitates advanced analyses such as storm tracking and anomaly detection, which can be used to monitor climate change through variations in precipitation frequency and intensity. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2,360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region (e.g., Nevada), the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of -22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash-Sutcliffe Efficiency (NSE) of 0.80, and a Kling-Gupta Efficiency (KGE) of 0.52. In the wet region (e.g., Louisiana), the average CC was 0.93, the PRB was 24.82%, the RMSE was 0.96, the MBR was 0.79, the NSE was 0.80, and the KGE was 0.18. Median RAI indices from both IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS, advocating for further global climate change studies.

Recently, the availability of many omics data source has given the rise of modelling biological networks for each individual or patient. Such networks are able to represent individual-specific characteristics, providing insights into the condition of each person. Given a set of networks of individuals, a network representing a particular condition (e.g., an individual with a specific disease) may be seen as an anomaly network. Consequently, the use of Graph Anomaly Detection techniques may support such analysis. Among the others, Generative Adversarial Networks present optimal per- formances in anomaly detection. This paper presents ADIN (Anomaly Detection in Individual Networks), a framework based on Generative Adversarial Attributed Networks (GAANs) for anomaly detection in convergence/divergence patients at- tributed networks. Preliminary results on networks generated from computational biology gene expression data demonstrate the effectiveness of our approach in detecting and explaining bladder cancer patients.

Although flow mixing and cooling can be greatly enhanced when considering the use of Fluidic Oscillators (FO), they are more commonly employed in Active Flow Control (AFC) applications where the injected pulsating flow interacts with the boundary layer in order to usually delay its separation. In fact, prior to any FO implementation in a given application, it is essential to study the range of frequencies and amplitudes it can generate as a function of the incoming mass flow and its dimensions. This point is what is being enlightened in the present manuscript for a rather novel FO configuration. We are numerically studding a standard FO configuration in three dimensions 3D and also using a two dimensional 2D approach. After comparing the 3D and the 2D results and analyzing the main differences, we modified some of the internal dimensions of the FO in order to evaluate the variation of its dynamic performance. The present results clarify which internal dimensional modifications are more effective in generating larger output frequencies and velocity field variations. Care is taken to analyze the origin of the self sustained oscillations. In fact, the present research needs to be seen as the continuation of a former one recently published , where the effect of several Reynolds numbers as well as some different internal dimensions was considered.

Numerical simulations of the sub-critical flow past a circular cylinder at the Reynolds number, Re=130,000 are performed using two numerical platforms, commercial, Ansys Fluent and open source, OpenFoam (finite volume method and large eddy simulation based on a differential equation for the sub-grid kinetic energy). An overview of the available experimental data and similar large eddy simulation studies is presented. A detailed analysis of all accumulated data demonstrates satisfactory agreement between them with a dispersion of the order of 10% (for the main integral flow parameters). A detailed comparison of the results obtained by single- and double-precision numerical methods using Ansys Fluent did not reveal any noticeable discrepancies for the integral and local flow parameters, as well as spectral characteristics. It is shown that the behavior of the dynamic system of the fluid dynamic equations computed with single precision is stable by Lyapunov and does not lead to any loss of accuracy. The reconstructed attractors of the dynamic systems in the phase space are limited by an ellipsoid.

This study evaluates the impact of a Project-Based Learning (PBL) intervention on post-pandemic educational recovery in rural Colombia, focusing on student competencies in mathematics, language, science, and 21st-century skills. Conducted in rural schools, the intervention aimed to address significant learning gaps exacerbated by the COVID-19 pandemic by providing teacher training and direct student support. A pretest-posttest single-group design was used to assess the effectiveness of the intervention, with standardized tests measuring academic competencies and an analytical rubric evaluating 21st-century skills. The results indicate significant improvements in math, language, and science test scores, with notable gains in problem-solving, collaborative work, communication, and critical thinking. However, a decline in creativity scores highlights the need for a stronger emphasis on fostering creativity within the PBL framework. Gender differences were observed, with female students generally outperforming males, suggesting the need for tailored instructional approaches. The study's limitations, including the absence of a control group, non-random sampling, and the use of subjective assessment methods, are acknowledged, with recommendations for future research to address these issues. Despite these limitations, the findings underscore the potential of PBL to enhance student learning outcomes in rural settings, offering valuable insights for educators and policymakers aiming to support educational recovery and development in similar contexts. Further research is recommended to explore the long-term effects of PBL and to refine the intervention for broader implementation.

Introduction: Clostridium difficile is a gram-positive, spore-forming anaerobe and a leading cause of healthcare-associated diarrhea globally, primarily linked to antibiotic use. Its enzyme, Glutamate Dehydrogenase (GDH), plays a critical role in the metabolism of glutamate, and its detection has become a key diagnostic marker for C. difficile infection (CDI). This meta-analysis evaluates various diagnostic modalities used for GDH detection, focusing on their sensitivity, specificity, and clinical utility. Methods: A systematic search of PubMed, Google Scholar, and Cochrane Library was conducted. Studies comparing GDH testing with gold-standard methods (PCR, toxigenic culture) for CDI diagnosis were included. Data were extracted from eligible studies, and statistical analyses were performed to calculate pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Results: The pooled sensitivity and specificity of GDH testing were 93% and 92%, respectively. The DOR was calculated to be 30.98, and the AUC was 0.9794, indicating high diagnostic accuracy. GDH testing proved effective in identifying both infected and non-infected individuals, reducing false positives and negatives. Conclusion: GDH testing is a highly sensitive and specific diagnostic tool for CDI, supporting its role in clinical settings for early detection and intervention. Despite its strengths, variability in test performance suggests the need for further research to refine GDH-based protocols and ensure consistent outcomes across diverse healthcare environments.

Drug-resistant tuberculosis (DR-TB) and HIV-coinfection present a conundrum to public health globally, and the achievement of the global END TB strategy in 2035. A descriptive, retrospective review of medical records of patients diagnosed with DR-TB who received treatment was conducted. Student’s t-test was performed to assess differences between two means and ANOVA between groups. Either the Chi-square test with and without trend or Fischer’s exact test was used to test the degree of association of categorical variables. Logistic regression was used to determine predictors of DR-TB treatment outcomes. Also, a decision tree classifier, which is a supervised machine learning algorithm was used. Python version 3.8. and R version 4.1.1 software were used for data analysis. A p-value of 0.05 with a 95% confidence interval (CI) was used to determine statistical significance. A total of 456 DR-TB patients were included in the study with more male patients (n = 256, 56.1%) than female patients (n = 200, 43.9%). The overall treatment success rate was 61.4%. There was a significant decrease in the % of patients cured during the Covid-19 pandemic compared to the pre-pandemic period. Our findings showed that machine learning can be used to predict TB patients' treatment outcomes.

Pomegranate juice possesses therapeutic properties against metabolic syndrome. Microencapsulation of pomegranate juice (MPJ) may extend its shelf life; however, further research is necessary to ascertain its comparative efficacy with fresh juice. This work evaluated the effect of (MPJ) in an animal model with induced metabolic syndrome (MetS). Male Wistar rats were induced-MetS by administering 30% sucrose orally for 22 weeks. Once MetS was verified in the animals, 7 groups were formed as follows: control (Ctrl); MetS, MetS+Placebo; Ctrl+MPJ; MetS+MPJ1 600, MetS+MPJ2 1800, and MetS+MPJ3 2900 mg/kg. Systolic and diastolic blood pressure, glucose, triglycerides, and total HDL-c levels in the MetS+MPJ groups improved significantly (p &lt; 0.05) vs. the MetS group at the end of the study. Microencapsulation maintained the anti-metabolic syndrome properties of pomegranate juice and could be used to control metabolic syndrome.

The current practices of the technology transfer profession seem to reflect the belief that the chances for success are greatly improved when faculty inventors employ their social capital to facilitate the process. However, there is a gap in the university technology transfer literature regarding our understanding of faculty inventor social capital in the technology transfer process. The aim of this study was to understand whether the use of faculty inventor social capital is a causal condition for successful university technology transfer. This question was examined using a multiple case study approach and the qualitative comparative analysis (QCA) method. The results of the study suggest that the use of faculty inventor social capital is not a necessary, sufficient, or INUS condition for the occurrence of university technology transfer.

Variable retention harvesting (VRH) was designed for timber purposes and biodiversity conservation in natural forests. This system was globally tested, but few studies are related to soil microbial components. The objective was to evaluate different retention types (aggregated and dispersed retention) considering different years-after-harvesting (6, 9, 16 YAH) on soil microbial community attributes compared with unmanaged primary forests (PF) in Nothofagus pumilio forests of Tierra del Fuego (Argentina). We also evaluated the influence of climate, soil and understory vegetation. Results showed that aggregated retention increased microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) and soil basal respiration (SBR) compared to dispersed retention, but with similar values than PF. However, harvested areas decreased MBC:MBN values compared with PF. We found an overall decreasing of microbial biomass and activity in 9 YAH stands, with a positive recovery at 16 YAH. Soil pH, mean annual temperature and understory vegetation cover showed a positive relationship with MBC, MBN, and SBR. The recovery after 16 YAH reaching to a different microbial community. Therefore, maintenance of retention components in managed stands for longer periods is needed. Our results highlight some advantages of VRH as a tool for conservation of forest-dwelling soil microorganisms (microbial biomass and activity).

Estimating Forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly, however integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and Airborne Laser Scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia's geographical regions, RF consistently delivered the best performance. In the northwest (NW), RF achieved an R² of 0.63 and an RMSE of 125.39 m³/plot. In the southwest (SW), it achieved an R² of 0.73 and an RMSE of 128.86 m³/plot. In the northeast (NE), RF achieved an R² of 0.64 and an RMSE of 133.77 m³/plot, and an R² of 0.70 and an RMSE of 120.72 m³/plot was achieved in the southeast (SE). These results underscore RF's precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy.

In the evolving digital landscape, the pervasive influence of Artificial Intelligence (AI) on social media platforms reveals a compelling paradox: the capability to provide personalized experiences juxtaposed with inherent biases reminiscent of human imperfections. Such biases prompt rigorous contemplation on matters of fairness, equity, and societal ramifications, and penetrate the foundational essence of AI. Within this intricate context, the research ventures into novel domains by examining the potential of quantum computing as a viable remedy for bias in artificial intelligence. The conceptual framework of the Quantum Sentinel is presented - an innovative approach that employs quantum principles for the detection and scrutiny of biases in AI algorithms. Furthermore, the study poses and investigates the question of whether the integration of advanced quantum computing to address AI bias is seen as an excessive measure or a requisite advancement commensurate with the intricacy of the issue. By intertwining quantum mechanics, AI bias, and the philosophical considerations they induce, this research fosters a discourse on the journey toward ethical AI, thus establishing a foundation for an ethically conscious and balanced digital environment.

This paper investigates the flexural behavior of unfired and fired one-way slabs constructed from reinforced ferronickel slag-based alkali-activated concrete. The study aims to expand the limited knowledge of large-scale testing of alkali-activated concrete structural elements, with a particular emphasis on their post-fire performance. Four slabs, each measuring 2.1 × 0.9 × 0.18 m³, were produced—two made of alkali-activated concrete and two of conventional concrete incorporating ordinary Portland cement. Both types of concrete exhibited similar compressive strengths. In each group, one slab served as a reference (control specimen—tested under monotonic four-point bending in its original state), while the other was subjected to a standard fire curve on the tensioned side before mechanical testing. The fired slabs were then tested in a cooled condition under ambient conditions. The effect of fire on the alkali-activated concrete was further examined using mercury intrusion porosity and scanning electron microscopy. The results reveal that the load capacity of structural members cast with alkali-activated concrete is only slightly lower than that of their conventional concrete counterparts (6% lower). However, the alkaliactivated slabs retain more of their stiffness and ductility after fire exposure.

Background: To prevent excessive compression of the cortical layer, which can lead to marginal bone loss, various companies have introduced specialized drills. However, these drills often lack the necessary precision, as the operator’s hand may not be stable enough to prevent ovalization and over-widening, nor precise enough to maintain coaxial alignment. Therefore, the aim of this study was to develop a device capable of achieving calibrated cortical preparation in terms of both dimension and coaxiality. Methods: A machining technology based on drilling principles was employed to create the device. Results: Nine blades were incorporated between the transmucosal neck and the implant threads, enabling the blades to cut the cortical bone coaxially during the implant insertion process. Conclusions: The implant with integrated blades was engineered to facilitate precise cortical bone preparation, ensuring both accurate dimensions and coaxial alignment. This ensures that the radial compressive stresses transmitted from the implant to the peri-implant bone during surgery can be precisely controlled. The experimental study outcomes discussed in this article validate the effectiveness of the implant design.