Transition to renewable energy has been recognized as a crucial step to addressing climate change and achieving greenhouse gas reduction targets, but it can also cause energy sprawl if not planned properly. Clean renewable energy communities (CREC) are emerging globally as an approach for decentralized energy systems and an alternative to traditional centralized energy systems. CREC aims to lower the energy carbon footprint, enhance local energy resilience, and improve the quality of life of residents. Through a comprehensive literature review, this study reviews metrics that can assess the impact of energy transition plans and support decision-making to select technologies that create efficient, reliable, and accessible energy systems. It classifies these metrics into a five-dimensional sustainability approach: environmental, technical, social, economic, political and institutional. The paper proposes a conceptual framework to guide decision-makers in recognizing the role of sustainable land development, sustainable energy planning, and resiliency as an integrated approach to energy transition planning. This framework stresses mapping the place-based potential for clean renewable energy at various scales, highlights the importance of resilience in energy planning, and addresses challenges associated with energy source selection, built environment efficiency, and energy trade. While the framework can serve as a starting point for evaluating energy transition plans, further work is needed to address the limitations of existing metrics and identify additional evaluations for energy mixed land use that are critical to managing energy sprawl on ecosystem services and other land uses.

Despite impressive progress in quantum biology, many questions about perception, cognition, memory, and their relation to processing of information remain unanswered. Here is an attempt to draw a unified picture starting with the basic quantum-mechanical phenomenon of nano-superconductivity in the microtubules and extending it to quantum processing of information, and generation of coherent Josephson radiation. A model for the generation in the axons of neurons is suggested. Like in a symphony, but on frequencies ten orders of magnitude higher, the radiation forms resonant standing electromagnetic waves, which are defined by the anatomy, synchronize all the functions in organism, and are behind the memory, perception, and cognition of a living being. It is true that we are made of atoms, but we are coherent electromagnetic waves.

To date, the patent foramen ovale (PFO) does not get the deserved medical attention. The PFO poses a serious threat to health and even life of mankind. The first respective case report in the medical literature dates back to the 19th century. It led to death. The fact that a PFO is present in roughly 25% of people underscores its overall potential to do harm. Yet at the same time by the sheer number, it discourages the medical community from screening for it and from treating it. About 5% of the population have particularly dangerous forms of PFOs. Such PFOs portray a high enough risk for clinical events, the likes of death, stroke, myocardial infarction, or ocular, visceral, and peripheral embolism, to justify screening for them. Highly significant health incidents being at stake, it appears obvious that PFO closure should be used for primary prevention. This is supported by the fact that closing a PFO is the simplest intervention in cardiology, with presumably the highest clinical yield. Being mainly a preventive measure, PFO closure represents a mechanical vaccination. When closing PFOs for one of the rarer therapeutic indications (migraine, platypnea orthodeoxia, etc.), patients automatically profit from the collateral benefit of getting at the same time mechanically vaccinated for life against paradoxical embolism. Vice versa, closing a PFO for prevention of paradoxical embolism not infrequently betters or cures migraine or exercise dyspnea, thereby improving quality of life as a collateral benefit.

Artificial intelligence (AI) has witnessed an exponential increase in its use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software engineering problems. However, a comprehensive and holistic understanding of the current status is missing. To close the above gap, synthetic knowledge synthesis was used to induce a research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and five themes, namely natural language processing in software engineering, use of artificial intelligence in the management of software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n=2042), followed by the United States (n=1193), India (n=934), Germany (n=445), and Canada (n=381). A high percentage (n=47.4%) of papers were funded, showing a strong interest in this research topic. The convergence of AI and software engineering can significantly reduce needed resources, improve quality, increase user experience, and improve the well-being of software developers.

Laboratory experiments and investigations into natural phenomena in this research series have unveiled the presence of a gravitational repulsion force that permeates our environment on both microscopic and macroscopic scales, contingent upon the thermal energy present. This paper presents a study exploring novel properties of gravitational forces among gas molecules, employing principles of thermodynamics. A model has been devised based on the interactions between pairs of gas molecules. While traditional models treat gravitational interaction as a singular force, our experimental approach validates it as the composite of two distinct forces: gravitational repulsion and attraction. By utilizing established experimental data on gas thermodynamics, our model demonstrates robust performance both analytically and experimentally. It verifies the coexistence of gravitational repulsion and attraction forces among gas molecules, showcases their Inverse-Cube relationship with distance, and elucidates the direct proportionality of the repulsion force to absolute temperature. This bridges a crucial gap between energy and fundamental forces. The order of magnitudes of gravitational repulsion and attraction forces are found to be considerably large, contrasting with the low values predicted by classical theory, which results from their amalgamation. Recognizing these forces as substantial in magnitude promises unprecedented outcomes and advancements.

While providing safe water for the rural poor is considered a basic human right, there are numerous issues associated with existing technologies with shortcomings. Performance issues have continued to fail to meet the needs of impoverished families due to issues including high cost, difficulties with performance, and continuing needs for maintenance. These issues have severely interfered the safe water accessibility. Key aspects of the Guelph Water Filter (GWF) system can avoid/minimize many of these issues. The GWF as described herein enables delivery of low cost, long-term performance at 3Log removal of E.coli and can deliver 1 to 3 L of treated water per hour. The GWF is simple to operate, has an ability to provide sufficient water for a family, maintains longevity of performance, is easy to maintain and has protection against breakage during the cleaning process, is repairable at village level, and operates using a sizable reservoir of water to supply raw water, meaning the technology does not need to be refilled frequently. Hence, the capability of the novel GWF technology is shown to bypass many of the troublesome features of alternative low-tech water treatment technologies. The potential for the GWF to function for 2 days continuously avoids the need for young girls to fetch raw water frequently during a day, thereby enabling them to attend school. Hence, the GWF enhances the potential to result in ‘safe water and full schools’, providing the opportunity for girls to receive education and capture socio-economic benefits for the community.

This study investigates the metabolic parallels between stimulated pancreatic beta cells and cancer cells, emphasizing glucose and glutamine metabolism. Addressing the significant public health challenges of Type 2 Diabetes Mellitus (T2DM) and cancer, we aim to deepen understanding of the mechanisms driving insulin secretion and cellular proliferation. Our analysis of anaplerotic cycles and NADPH's role in biosynthesis elucidates their vital functions in both processes. Additionally, we find that both cell types share an antioxidative response mediated by the Nrf2 signaling pathway, glutathione synthesis, and UCP2 upregulation. Notably, UCP2 facilitates the transfer of C4 metabolites, enhancing reductive TCA cycle metabolism. We also uncover that hypoxic responses are transient in beta cells post-stimulation but persistent in cancer cells. By synthesizing these insights, our research suggests novel therapeutic targets for T2DM, highlighting the shared metabolic strategies of stimulated beta cells and cancer cells. This comparative analysis not only illuminates the metabolic complexity of these conditions but also emphasizes the crucial role of metabolic pathways in cell function and survival, offering fresh perspectives for tackling T2DM and cancer challenges.

We show that Universe inflation occurs when Einstein's gravity couples to nonlinear electromagnetic fields with cosmic stochastic magnetic fields background. Nonlinear electrodynamics with two parameters is used. The strength of magnetic fields to have the Universe inflation is obtained. It is shown that singularities of the energy density and pressure are absent for any scale factors. At large scale factors one arrives at equation of state for ultra-relativistic case. It is demonstrated that the curvature invariants do not have singularities. By computing the deceleration parameter we show that graceful exit takes place with reasonable e-folding number. The duration of Universe inflation as a function of model parameters is obtained. The classical stability and causality are analyzed by calculating the speed of the sound.

Aim: To establish, through molecular modelling, safe and clinically acceptable putative antagonists of E571K-mutated exportin-1 among the bioactive compounds in various parts of Juglans mandshurica. Methods: The bioactive compounds were subjected to compendium of druglikeness and lead-likeness filter workflows prior to docking of the resultant compounds into E571K exportin-1 active site using PyRx AutoDock vina to establish their binding affinity and interaction profile. The evolutionary algorithm of Osiris property explorer DataWarrior software as well as lead-likeness filter were employed for generation of novel non-promiscuous analogues of the lead compound with better putative selectivity and clinical acceptability as E571K Exportin-1 antagonists.Results: The findings of this study present taxifolin as the putatively effective and lead-like E571K Exportin-1 inhibitor with high potential of qualifying for clinical evaluation but is associated with high promiscuity tendency in high throughput screening. The evolutionary derivation of novel analogues of the compound, however, results in the generation of putatively non-promiscuous, non-toxic, and lead-like E571K Exportin-1 antagonists with high synthetic accessibility and clinical developability for evaluation in the strategy for treatment of drug-resistant KRAS-mutant lung adenocarcinoma condition.

Staphylococcus aureus is a common pathogen in human. Methicillin resistant Staphylococcus aureus (MRSA) infection poses a big and perplexing difficulty in terms of therapy. The acquisition of the non-native gene PBP2a, which has a decreased tolerance for β-lactam antibiotics, frequently confers resistance. PBP2a has a less attraction for methicillin and it helps bacteria to continue peptidoglycan biosynthesis, and it is cell wall’s core component in bacteria. So, even in the presence of methicillin or any other antibiotic, bacteria develop resistance. Due to resistance-causing genes, S. aureus becomes MRSA. The main premise of the resistance mechanism is well understood. The current demand for novel antibiotics is legitimate in the face of therapeutic concerns posed by resistant micro-organisms. The emphasis of this review is on PBP2a scaffolds and the different screening approaches used to find PBP2a inhibitors. Penicillin, Cephalosporins, Pyrazole-Benzimidazole based derivatives, Oxadiazole containing derivatives, non-β-lactam allosteric inhibitors, 4-(3H)-Quinazolinones, Pyrrolylated chalcone, Bis-2-Oxoazetidinyl macrocycle (β-lactam antibiotics with 1, 3-Bridges), Macrocycle-embedded β-lactams as novel inhibitors, Pyridine-Coupled Pyrimidinones, novel Naphthalimide corbelled aminothiazoximes, non-covalent inhibitors, Investigational-β-lactam antibiotics Carbapenem, novel Benzoxazole derivatives, Pyrazolylpyridine analogues, and other miscellaneous classes of scaffolds for PBP2a are also represented as well as with their biological activity is discussed. The penicillin-binding protein is also discussed, which is the crucial target for the cell wall of MRSA. Various aspects of PBP2a, the cell wall of bacteria, peptidoglycans, different crystal structures of PBP2a, synthetic routes for PBP2a inhibitors, and future perspectives of MRSA inhibitors are also enumerated.

Introduction: The presence of multiple comorbidities increases the risk of all-cause mortality, but the effects of the comorbidity sequence before the baseline date on mortality remained unexplored. This study investigated the relationship between coronary heart disease (CHD), atrial fibrillation (AF) and heart failure (HF) sequence on all-cause mortality risk in type 2 diabetes mellitus. Methods: This study included patients with type 2 diabetes mellitus prescribed antidiabetic/cardiovascular medications in public hospitals of Hong Kong between January 1st, 2009 and December 31st, 2009 with follow-up until death or December 31st, 2019. Cox regression was used to identify comorbidity sequences predicting all-cause mortality in patients with different medication subgroups. Results: A total of 249291 patients (age: 66.0±12.4 years, 47.4% male) were included. At baseline, 7564, 10900 and 25589 patients had AF, HF and CHD, respectively. Over follow-up (3524±1218 days), 85870 patients died (mortality rate: 35.7 per 1000 person-years). Sulphonylurea users with CHD developed later, but insulin users with CHD developing earlier, in the disease course had lower mortality risks. Amongst insulin users with two of the three comorbidities, CHD with preceding AF (hazard ratio [HR]: 3.06, 95% CI: [2.60-3.61], p<0.001) or HF (HR: 3.84 [3.47- 4.24], p<0.001) had a higher mortality. In users of lipid-lowering agents with all three comorbidities, those with preceding AF had higher risk of mortality (AF-CHD-HF: HR: 3.22, [2.24-4.61], p<0.001; AF-HF-CHD: HR: 3.71, [2.66-5.16], p<0.001). Conclusion: The sequence of comorbidity development affects the risk of all-cause mortality to varying degrees in diabetic patients on different antidiabetic/cardiovascular medications.

Smoking is a risk factor for respiratory infections and there is reasonable concern that it may affect COVID-19 susceptibility and severity. Recent studies have focused on the interaction between smoking (and nicotine) and ACE2 expression, suggesting that ACE2 up-regulation could contribute to enhanced viral cell entry. However, case series have shown that there is an unexpectedly low prevalence of smoking among hospitalized COVID-19 cases. Since early April, we were the first to hypothesize that dysfunction of the nicotinic cholinergic system (NCS) may be implicated in the pathophysiology of severe COVID-19. We recently reported that many of the clinical manifestations of severe COVID-19 could be explained by dysregulation of the NCS. In this study, we present an amino acid sequence in the receptor binding domain of the SARS-CoV-2 Spike glycoprotein which is homologous to a sequence of a snake venom toxin. We present the 3D structural location of this “toxin-like” sequence on the Spike Glycoprotein. These findings suggest that SARS-CoV-2 could potentially interact with acetylcholine receptors causing dysregulation of the NCS and the cholinergic anti-inflammatory pathway.

A new coronavirus (SARS-CoV-2) have determined a pneumonia outbreak in China (Wuhan and Hubei) on December 2019. While pharmaceutical and non-pharmaceutical intervention strategies are strengthened worldwide, the scientific community has been studying the risk factors associated with SARS-Cov-2, to enrich epidemiological information. For a long time, before the industrialized era, air pollution has been a real and big health concern and it is today a very serious environmental risk for many diseases and anticipated deaths in the world. It has long been known that air pollutants increasing the invasiveness of pathogens for humans by acting as a carrier and making people more sensitive to pathogens through a negative influence on the immune system. Based on scientific evidences, the hypothesis that air pollution, resulting from a combination of factors such as meteorological data, level of industrialization as well as regional topography, can acts both as an infection carrier as a harmful factor of the health outcomes of COVID-19 disease has been raised recently. This hypothesis is turning in scientific evidence, thanks to the numerous studies that have been launched all over the world. With this review, we want to provide a first unique view of all the first epidemiological studies relating the association between air pollution and SARS-CoV-2. Major findings are consistent, highlighting the important contribution of air pollution on the COVID-19 spread and with a less extent also PM₁₀.

Short-term energy consumption forecasting crucial for the operation of new electrical en-ergy grids, not only due to their own characteristics, but also due to the new elements pre-sent in the energy matrix. Many existing models are limited by their methodologies or by considering only a narrow set of factors influencing energy consumption. This study in-troduces a pipeline for developing an energy forecasting model and evaluates the signifi-cance of factors affecting energy consumption and, consequently, forecast accuracy. The study utilizes a dataset from ISO NE (Independent System Operator New England), span-ning the total electric load of various cities in New England from January 2017 to December 2019. This dataset comprises 23 independent variables, including weather data, economic indicators, and market information. The results outline the steps involved in constructing energy forecast models using time series analysis. By carefully selecting variables and rep-resenting external factors, the study demonstrates the feasibility of generating more accu-rate predictions with reduced computational resources.

Purpose: To evaluate prognostic factors for visual outcome in patients with diabetes who have undergone vitrectomy (PPV) for severe proliferative diabetic vitreo-retinopathy (PDVR) in at least one eye in the past 15 years. Methods: Medical records of 132 eyes of 66 patients were analyzed (median age 52ys 21-80; patients with type 1/2 diabetes 40/26, median follow-up 38 months 9-125). Correlation between final good visual outcome defined as 0.5≤ best corrected visual acuity (BCVA) and prognostic factors (age, sex, type and duration of diabetes, metabolic status, BCVA, diabetic retinopathy status, data of preoperative management, data of vitrectomy, postoperative complications) were analyzed. Results: BCVA improved significantly in the entire study cohort (from median 0.05 min-max 0.001-1 to 0.32 0.001-1, p <0.000). Visual stabilization was achieved in the majority of patients, and relatively good visual acuity (0.5≤BCVA) was maintained in more than one third of eyes. Multivariable GEE statistics showed that in addition to duration of diabetes and stable HbA1c values, only preoperative tractional macular detachment proved to be an independent significant predictor of visual outcome. Conclusions: Pars plana vitrectomy is a useful tool when performed early before tractional macular detachment. However, long-term visual stability can only be achieved with good metabolic control.

Keywords: liquid alloys, microstratification, dispersed particles, spontaneous dispersion, criteria for true (entropic) spontaneous dispersion, mechanisms of quasi-spontaneous dispersion, methods and results for assessing size of dispersed particles.

With increasing air and sea temperatures, the thermodynamic environments over the oceans become more favourable for the development of intense tropical cyclones (TCs) with rapid intensification (RI). In fact, the number of TCs undergoing RI is rising (Zhao, et al., 2018; Bhatia, et al., 2019; Song, et al., 2020; Hong & Wu, 2021; Liu, et al., 2022). RI over coastal sea areas before landfalling is particularly impactful and threatening to coastal lives and properties. There have been studies showing that offshore areas within 400 km of the coastline have experienced a significant increase in RI events with the count tripling from 1980 to 2020 (Li et al., 2023). The south China coastal region consists of highly densely populated cities especially the Pearl River Delta (PRD) region. Intense TCs maintaining its strength or RI of TCs close to the coastal region presents substantial forecasting challenges. This study investigates the effect of sea surface and subsurface temperature and salinity on the intensification of five TCs, namely Super Typhoons Hato in 2017 and Mangkhut in 2018, Typhoon Talim, Super Typhoon Saola and Severe Typhoon Koinu in 2023, which significantly affected the south China coastal region and triggered high TC warning signals in Hong Kong. The analysis utilized the Hong Kong Observatory (HKO) TC track and intensity data, along with the sea temperature and salinity profiles generated using the China Ocean ReAnalysis version 2 (CORA2) of the National Marine Data and Information Service (NMDIS) of China. While high sea surface temperature (SST) of 30°C or above, low surface sea surface salinity (SSS) of 34 psu or below, and strong salinity stratification of at least 0.8 psu per 100 m depth may offer useful hints for predicting RI, they are part of a broader set of contributing factors. This study represents the initial documentation of sub-surface salinity's impact on intense TCs over the South China Sea (SCS) in recent years and will encourage further research to refine forecasting RI of TCs before making landfall near PRD taking into account the effect of freshwater from the Pearl River.

A U.S. government health agency conducted epidemiological studies to evaluate whether exposures to drinking water contaminated with volatile organic compounds at U.S. Marine Corps Base Camp Lejeune, North Carolina, were associated with increased health risks to children and adults. These health studies required knowledge of contaminant concentrations in drinking water—at monthly intervals—delivered to family housing, barracks, and other facilities within the study area. Because concentration data were limited or unavailable during much of the period of contamination (1950s–1985), the historical reconstruction process was used to quantify estimates of monthly mean contaminant-specific concentrations. This paper integrates many efforts, reports, and papers into a synthesis of the overall approach to, and results from, a drinking-water historical reconstruction study. Results show that at the Tarawa Terrace water treatment plant (WTP) reconstructed (simulated) tetrachloroethylene (PCE) concentrations reached a maximum monthly average value of 183 micrograms per liter (ug/L) compared to a one-time maximum measured value of 215 ug/L and exceeded the U.S. Environmental Protection Agency’s current maximum contaminant level (MCL) of 5 ug/L during the period November 1957–February 1987. At the Hadnot Point WTP, reconstructed trichloroethylene (TCE) concentrations reached a maximum monthly average value of 783 ug/L compared to a one-time maximum measured value of 1,400 ug/L during the period August 1953–December 1984. The Hadnot Point WTP also provided contaminated drinking water to the Holcomb Boulevard housing area continuously prior to June 1972, when the Holcomb Boulevard WTP came on line (maximum reconstructed TCE concentration of 32 ug/L) and intermittently during the period June 1972–February 1985 (maximum reconstructed TCE concentration of 66 ug/L). Applying the historical reconstruction process to quantify contaminant-specific monthly drinking-water concentrations is advantageous for epidemiological studies when compared to using the classical exposed versus unexposed approach.

Larrea tridentata is an endemic plant from the Mexican highlands used in traditional medicine to treat hypertension and kidney stones, among many ailments. This study evaluated a vasorelaxant compound: 5- Hydroxy-2-(4-hydroxy-3-methoxyphenyl)-6,7,8-trimethoxy-4H-1-benzopyran-4-one (also known as 8-Methoxycirsilineol: 8-M) which we purified from an extract of this plant. Mesenteric artery rings were isolated from male guinea pigs for isometric force measurements. Freshly isolated smooth muscle cells from mesenteric artery were used for ionic current measurement for calcium and potassium. 10 μM 8-M completely relaxed vascular rings contracted with phenylephrine in a dose-dependent fashion with an EC50 = 4.26 ± 1.16 10-6 M and 4.56 ± 1.64 10-6 M for endothelium intact and endothelium-free tissue, respectively. The relaxant effect of 8-M was not affected by maximal doses of nifedipine, verapamil or iberiotoxin. Nevertheless, the relaxant effect was partially abolished by addition of 10 mM Tetraethylammonium (TEA). In accordance to this, calcium currents evoked by depolarization were not affected by 8-M, whereas TEA-sensitive potassium currents, were increased upon its addition. Vasorelaxation due to 8-M is partly due to activation of KV currents in an endothelium independent mode.

This paper experimentally reveals some of the resources offered by the evolution of the instantaneous active electric power in describing the state of three-phase AC induction asynchronous electric motors (with squirrel-cage rotor) operating under no-load conditions. A mechanical power is required to rotate the rotor at no-load and this mechanical power is satisfactorily reflected in the constant and variable part of instantaneous active electric power. The variable part of this electrical power should necessarily have a periodic component with the same period as the period of rotation of the rotor. The paper proposes a procedure for extracting this periodic component description (as a pattern, by means of a selective averaging of instantaneous active electrical power) and analysis. The time origin of this pattern is defined by the time of a selected first passage through the origin of an angular marker placed on the rotor, detectable by a proximity sensor (e.g. a laser sensor). The usefulness of the pattern in describing the state of the motor rotor has been demonstrated by several simple experiments which show that a slight change in the no-load running conditions of the motor (e.g. by placing a dynamically unbalanced mass on the rotor) has clear effects in changing the shape of the pattern.

Background: Acute kidney injury (AKI) and contrast-induced nephropathy (CIN) are common complications following percutaneous coronary intervention (PCI) or coronary angiography (CAG), presenting significant clinical challenges. Machine learning (ML) models offer promise for improving patient outcomes through early detection and intervention strategies. Methods: A comprehensive literature search following PRISMA guidelines was conducted in PubMed, Scopus, and Embase from inception to June 11, 2024. Study characteristics, ML Models, performance metrics (AUC, accuracy, sensitivity, specificity, precision), and risk of bias assessment using the PROBAST tool were extracted. Statistical analysis used a random-effects model to pool AUC values, with heterogeneity assessed via the I² statistic. Results: From 431 initial studies, 14 met the inclusion criteria. Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) models showed the highest pooled AUCs of 0.87 (95% CI: 0.82-0.92) and 0.85 (95% CI: 0.80-0.90), respectively, with low heterogeneity (I² < 30%). Random Forest (RF) had a similar AUC of 0.85 (95% CI: 0.78-0.92) but significant heterogeneity (I² > 90%). Multilayer Perceptron (MLP) and XGBoost models had moderate pooled AUCs of 0.79 (95% CI: 0.74-0.84) with high heterogeneity. RF showed strong accuracy (0.83, 95% CI: 0.70-0.96), while SVM had balanced sensitivity (0.69, 95% CI: 0.63-0.75) and specificity (0.73, 95% CI: 0.60-0.86). Age, serum creatinine, left ventricular ejection fraction, and hemoglobin consistently influenced model efficacy. Conclusions: GBM and SVM models, with robust AUCs and low heterogeneity, are effective in predicting AKI and CIN post-PCI/CAG. RF, MLP, and XGBoost, despite competitive AUCs, showed considerable heterogeneity, emphasizing the need for further validation.

Carbapenems-resistant enterobacteria are considered multidrug-resistant, making the control of their nosocomial infections a major challenge in healthcare settings. It is important to know the incubation site and source of the spread of these strains, especially in neonatal intensive care units (NICU). Colonization of the colon of preterm neonates per se by multidrug-resistant bacterial strains could be the source of dissemination of these multidrug-resistant strains and their site of incubation. In this study, colonization of the colon of preterm neonates with enterobacteria and their multidrug-resistant strains was investigated. The presence of enterobacteria in the stool of preterm neonates was investigated on EMB (eosin-methylene blue) agar, followed by morphological and biochemical characterization and phylogenetic analysis of the 16S rRNA-encoding gene sequences. Antibiotic susceptibility of all isolated strains was tested using VITEK 2 technology. All cases examined at two weeks of age and older showed bacterial colonization of the colon. Klebsiella pneumoniae colonized the colon in 56.2% of the preterm neonates and Klebsiella quasipneumoniae was found in 6.3%, corresponding to 62.5% in total. The colonizing Klebsiella pneumoniae was multidrug-resistant in 2 of 9 cases. One strain of Klebsiella pneumoniae was resistant to carbapenems and another showed intermediate resistance to these antibiotics. These Klebsiella pneumoniae strains showed resistance to most of the other antibiotics tested. Some cases contained more than one genus, with Escherichia coli found in 50% of cases in preterm neonates. In contrast to Klebsiella pneumoniae, none of the E. coli strains colonizing the colon of preterm neonates in this study showed resistance to carbapenems. The results of this study suggest that Klebsiella pneumoniae and its carbapenems multidrug-resistant strains are well marked in preterm neonates colonizing their colon, which is the incubation site and source of such multidrug-resistant bacterial strains in NICU. This study highlights the importance of careful management of preterm neonates in the NICU and possible contamination with multidrug-resistant Klebsiella pneumoniae incubated in their colon. Further studies would be of interest to avoid the possibility of fecal-oral transmission and infection by these multidrug-resistant bacteria and their treatment in the colon, where their incubation site is located.

The field of solar technology has experienced remarkable advancements, transforming from early photovoltaic discoveries to a diverse array of high-efficiency, cost-effective solutions. This review explores the evolution of solar technology, detailing its development from the initial discovery of the photovoltaic effect to contemporary innovations. The current generation of solar technologies, including silicon photovoltaic cells, thin-film solar cells, perovskite solar cells, bifacial panels, concentrated solar power, and building-integrated photovoltaics, is examined for their efficiencies, applications, and contributions to the renewable energy landscape. Future prospects highlight promising trends such as next-generation photovoltaics, advanced energy storage solutions, agrivoltaics, floating solar farms, and artificial photosynthesis. Despite the progress, challenges related to efficiency, stability, scalability, cost, grid integration, environmental impact, policy support, and public acceptance remain. Addressing these challenges through continued research, supportive policies, and collaborative efforts is crucial for realizing the full potential of solar technology. This review underscores the importance of solar energy in achieving a sustainable future and emphasizes the need for ongoing innovation and commitment to overcome existing barriers.

An easy and short-time TLC-densitometric method was developed for the separation of metronidazole, secnidazole, ornidazole, tinidazole, and 2-methyl-5-nitroimidazole and for the determination of metronidazole and tinidazole in Metronidazole Polpharma and Tinidazolum Polpharma tablets. Analyzes were performed on chromatographic plates precoated with silica gel 60F254 using the optimal mobile phase: chloroform + methanol + diethylamine in a volume ratio of 9:1:1. The method has been validated. The intraday and interday precision values for the three different concentrations ranged from 0.99% to 1.48% and 0.89% to 1.76%, and the precision values ranged from 1.13% to 2.48% and 0.95% to 2.49% for metronidazole and tinidazole, respectively. The limit of quantification (LOQ) was 0.036 and 0.066 µg/spot for metronidazole and tinidazole, respectively. The mean recovery was 103.1% and 100.6% for metronidazole and tinidazole, respectively. The content of metronidazole and tinidazole in tablets in relation to the content declared by the manufacturer was 101.3% and 99.8%, respectively. The obtained results were verified using the pharmacopoeial method. The presented method is fast, sensitive, precise, selective, accurate and robust. It allows for the analysis of several samples on one chromatography plate at the same time.

Cold plasma (CP) is an ionised gas containing excited molecules and ions, radicals, free electrons, and emits electric fields and UV radiation. CP is potently antimicrobial and can be applied safely to biological tissue, birthing the field of plasma medicine. Reactive oxygen and nitrogen species (RONS) produced by CP affect biological processes directly or indirectly via the modification of cellular lipids, proteins, DNA, and intracellular signalling pathways. CP can be applied at lower levels for oxidative eustress to activate cell proliferation, motility, migration, and antioxidant production in normal cells, mainly potentiated by the unfolded protein response, Nrf2-activated antioxidant response element and PI3K/Akt pathway, which also activates NFκB. At higher CP exposures, inactivation, apoptosis and autophagy of malignant cells can occur via degradation of PI3K/Akt and MAPK-dependent and -independent activation of the master tumour suppressor p53, leading to caspase-mediated cell death. These opposing responses validate a hormesis approach to plasma medicine. Clinical applications of CP are becoming increasingly realised in wound healing, while clinical effectiveness in tumours is currently coming to light. This review will outline advances in plasma medicine and compare the main redox and intracellular signalling responses to CP in wound healing and cancer.

The stimulus-secretion coupling of glucose-induced release is generally attributed to the metabolism of the hexose in the β-cells in the glycolytic pathway and the citric acid cycle. Glucose metabolism generates an increased cytosolic concentration of ATP and of the ATP/ADP ratio that closes the ATP-dependent K+-channel at the plasma membrane by the interaction of ATP with the regulatory Kir6.2 channel subunit The resultant depolarization of the β-cells opens voltage-dependent Ca2+-channels at the plasma membrane that allow an increase of the cytosolic cation concentration that triggers the exocytosis of insulin secretory granules. The resultant secretory response evolves in time as a biphasic secretion with a first and transient peak of approximately 10 minutes duration followed by a sustained phase of secretion lasting as long as the stimulus. Whereas the first transient peak can be reproduced by a simple depolarization of β-cells with high extracellular KCl maintaining the KATP-channels open with diazoxide, the sustained phase is agreed to depend on the participation of some metabolic signal that remains to be determined Our group has been investigating for several years the participation of β-cell GABA metabolism, together with that of glucose (the β-cell “specific” nutrient secretagogue) and some other “metabolic” secretagogues (branched-chain alpha-ketoacids and a mixture of L-Leucine + L-glutamine, at supraphysiological concentrations) in their mechanism of stimulation of insulin secretion. All three types of stimuli promote the flux in the GABA shunt of rat islets by different metabolic pathways that end in the production of α-ketoglutarate. This citric acid cycle intermediary is preferentially derived to the GABA shunt instead of its continuous oxidation in the citric acid cycle. Islet content of GABA is significantly suppressed by all the stimuli and blocking the GABA shunt with gabaculine, or ϒ-vinyl-GABA (GABAT inhibitors), diminish the insulin secretory responses as well as total ATP and the ATP/ADP ratio. It is concluded that GABA metabolism is increased in parallel to glucose metabolism and is significantly contributing to the magnitude of the insulin secretory response. Its possible implication in β-cells degradation in type-2 (perhaps also in type 1) diabetes is suggested.

Background: Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2. In Colombia, many commercial methods are now available to perform the RT-qPCR assays, and the laboratories must evaluate its diagnostic accuracy to ensure reliable results to suspected COVID-19 patients. The purpose of the study was to compare four commercial RT-qPCR assays for detection of SARS-CoV2 virus, from nasopharyngeal swab samples referred to Laboratorio Carvajal IPS, SAS of Tunja, Boyacá - Colombia. Methods: This prospective study was conducted on 152 samples of respiratory tract samples (Nasopharyngeal Swab) from patients with suspected SARS-CoV-2 infection. Diagnostic accuracy of GeneFinderTM COVID-19 Plus RealAmp (In Vitro diagnostic), One-Step Real-Time RT-PCR (Vitro Master diagnostica), Berlin modified protocol and gold standard Berlin protocol (Berlin Charite Probe One-Step RT-qPCR Kit, New England Biolabs) as reference was assessed. Operational characteristics were estimated in terms of sensitivity, specificity, agreement, and predictive values. Results: Using Berlin Charite Probe One-Step RT-qPCR Kit as reference, the sensitivity/specificity for the diagnostic tests were found to be GeneFinderTM COVID-19 Plus RealAmp Kit 100%/92.7%, One-Step Real-Time RT-PCR, One-Step Real-Time RT-PCR 92.75%/67.47%, and Berlin modified protocol 100%/96.39%. The results of four commercially available methods were found to be consistent with those obtained from Berlin Modified protocol analysis for % of the samples and showed good agreement (κ= 0.96). Concordant SARS-CoV2 negative and positive RT-qPCR results were reported for xxx and xxx samples, respectively. Summarize something about the Ct. Conclusion: Our data demonstrate that all commercially available methods are rapid and reliable for the identification of SARS-CoV-2 virus associated with COVID-19. One-Step RT-qPCR Kit and GeneFinderTM COVID-19 Plus RealAmp assay show optimal sensitivity compared with Belin modified protocol. In addition, there is no significant correlation between xxxxx

The COVID-19 pandemic has put a lot of pressure on health systems worldwide. Mass vaccination against SARS-CoV-2 has reduced morbidity and mortality worldwide. Despite their safety profiles, vaccines like any other medical product can cause adverse events. Yet, in countries with poor epidemiological surveillance and monitoring systems, reporting vaccine-related adverse events is scarce. The objective of this study was to describe self-reported vaccine adverse events after receiving one of the available COVID-19 vaccine schemes in Ecuador. A cross-sectional analysis based on an online self-reporting 32-questionnaire was conducted in Ecuador from April 1st to July 15th, 2021. Participants were invited by social media, radio, and TV to voluntarily participate in our study. A total of 6,654 participants were included in this study. A 38.2% of the participants reported having at least one comorbidity. Patients received AstraZeneca, Pfizer, and Sinovac vaccines, and these were distributed 38.4%, 31.1%, and 30.5%, respectively. Pain, inflammation at the injection site (20,01%), and headache (16,91%) were the most reported adverse events. Women addressed ESAVIs (64%), more often than men (36%). After receiving the first dose of any available COVID-19 vaccine, a total of 19,481 self-reported ESAVIs were informed (86.9% were mild, 11.6% moderate and 1.5% severe). In terms of vaccine type and brand, the most reactogenic vaccine was AstraZeneca with 57.8%, followed by Pfizer (24.9%) and Sinovac (17, 3 %). After the second dose, 6,757 self-reported ESAVIs were reported (87.0% mild, 10.9% moderate, and 2.1% severe). AstraZeneca vaccine users reported a higher proportion of ESAVIs (72.2%) in comparison to Pfizer/BioNTech (15.9%) and Sinovac Vaccine (11.9%). Swelling at the injection site, headache, muscle pain, and fatigue were the most common ESAVIs for the first as well as second dose. In conclusion, most ESAVIs were mild. AstraZeneca users were more likely to report adverse events. Participants without a history of COVID-19 infection, as well as those who receive the first dose, were more prone to report ESAVIs.

Primary ciliary dyskinesia (PCD) is caused by pathologic variants in over 50 different genes that affect the structure and function of motile cilia. Low nasal NO (nNO) is a biomarker for diagnosis in patients with features of PCD; but the reason why it is low is unclear. At the cellular level, a characteristic feature shared by most PCD patients is that antigens and other particles are not cleared from the epithelial surface. Poor antigen clearance results in pro-oxidant pathway activation and airway epithelial damage and may predispose PCD patients to DUOX1- and IL33- mediated asthma. Secondary ciliary dysfunction, such as that caused by viruses or by smoking, can also contribute to asthma development. Variants in genes underlying the function of cilia, can be associated with poor lung function, even in the absence of PCD. Single nucleotide polymorphisms in PCD – related genes can be associated with increased asthma severity. The role of cilia and antigen stasis is the pathophysiology of asthma is an important area for research, because specific airway clearance techniques and other therapeutic interventions, such as antioxidants, could be of value in preventing the development of asthma and asthma-like symptoms.

The intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance underscores the need for rigorous dataset evaluation and selection protocols to ensure the reliability and generalizability of classification outcomes. This study involved a thorough examination of selected publicly available plant diseases datasets, with an emphasis on how well they performed as measured by GLCM metrics. After first classifying the datasets according to their GLCM metrics, dataset_2 (D2) and dataset_5 (D5) were found, respectively, to be the best-performing dataset in all GLCM analyses. The same datasets were then used to train deep learning models, and their classification performances were assessed. A noteworthy association was observed between the results of training deep learning models and the performance ratings derived from GLCM studies. More specifically, dataset_2 (D2) performed best in both GLCM analysis and deep learning model performance, indicating a strong correlation between the accuracy of classification and the textural qualities that GLCM captured. In the context of plant disease identification, in particular, these results highlight the significance of clearly defined dataset selection criteria in deep learning applications. Scholars can improve the accuracy and dependability of deep learning models for diagnosing plant diseases by giving preference to datasets with favorable GLCM metrics. The research also emphasizes the importance of texture features being taken into account in addition to conventional image features, highlighting the necessity of transparency and rigor in dataset selection procedures.

This paper proposes a lightweight joint with bamboo culm cross connection, which is constructed by screwing one side of the screw into the other side of the wooden plug, its simple construction makes it easy to manufacture and cost-effective. This form connects adjacent bamboo culms to form corner joints, and bolt connects adjacent bamboo culm frames to form edge joints, to explore the mechanical properties of the combination joints formed by corner joints and edge joints, by combining experiments and numerical simulations, the method discovered and explained the relationship and mechanism between the number and position of bolts and the stiffness of edge joints. A calculation model for frame-unit bamboo culm members considering semi-stiffness of joints is proposed, and the conversion equations between linear stiffness of corner and edge joint and their rotational stiffnesses are derived. By establishing the series and parallel relationship between the corner and edge joints in the assembled members, a generalized relational equation between the total stiffness of the assembled joint and the stiffness of the corner and edge joints is derived. A numerical simulation method for frame-unit bamboo culm members with semi-rigid joints is proposed, which can obtain the stiffness calculation formula of edge joints and the average rotational stiffness value of semi-rigid corner joints. A finite element analysis method based on semi-rigid joints is proposed for frame-unit bamboo culm members, and the internal force sketches and calculation methods for bamboo culms and joints are presented, which can be used for the internal force analysis and design of frame-unit bamboo culm lattice structures.

This study analyzed the anaerobic co-digestion of Inoculum (I), Rubber Plant Effluent (RPE) and Gliricidia leaves (GLS) at different mixing ratios for the simultaneous production of methane and organic fertilizer. The results were analyzed for volatile fatty acids, pH, C/N ratio, and methane production. The organic quality of the substrate mixture before and after anaerobic digestion was analyzed for organic carbon, Kjeldahl nitrogen, potassium, and phosphorus content. The study concluded that the inoculum, Rubber Plant Effluent (RPE), and Gliricidia leaves (GLS) mixed in the ratio of 10:2:2 showed higher methane production than other experiments conducted at different mixing ratios. The nitrogen, Potassium and phosphorus content of the substrates are increased after anaerobic digestion. However, the organic carbon content in the substrate decreased in all experiments. This research recommends the planting of rubber trees and Gliricidia maculata to promote economic development in Mauritania. Keywords: Biogas production; Gliricidia leaves; Methane; Rubber Plant Effluent; Organic Manure

Spectral characteristics of CDOM in water column are a key parameter for bio-optical modeling. Knowledge of CDOM optical properties and spatial discrepancy based on the relationship between water quality and spectral parameters in Yinma River watershed with in situ data collected highly-polluted waters are exhibited in this study. Seasonal field data sets collected over a period of 2 months in 2015 in Yinma River Watershed. Based on the comprehensive index method, the riverine waters showed serious contamination, especially the COD, Fe, Mn, Hg and DO were out of range contamination warning. Dissolved organic carbon (DOC) and total suspended matter (TSM) with prominent non-homogenizing were significantly high in the riverine waters, but chlorophyll-a (Chl-a) was opposite. Ternary phase diagram showed that non-algal paritcles absorption played an important role in total non-water light absorption (>50%) in most sampling locations, and mean contribution of CDOM were 13% and 22% in summer and autumn respectively. Analysis of ratio of absorption at 250-365 nm (E_250:365) and spectral slope (S_275-295) indicated that CDOM had higher aromaticity and molecular weight in autumn than in summer, is consistent with the results of water quality and relative contribution. Redundancy analysis (RDA) indicated that the environmental variables OSM had a strong correlation with CDOM absorption, followed by heavy metal, e.g., Mn, Hg and Cr⁶⁺. However, for the specific UV absorbance (SUVA₂₅₄), the seasonal values showed opposite results compared with the reported literature. The potential reasons were the more UDOM (uncolored Dissolved Organic Matter) from human source (wastewater effluent) existed in waters. Terrigenous inputs simultaneously are in relation to the a_CDOM(440)-DOC relationship with the correlation coefficient was 0.90 in summer (2-tailed, p<0.01), and 0.58 in autumn (2-tailed, p<0.05). Spatial distribution of CDOM parameters exhibited that the downstream regions focused on dry land have high CDOM molecular weight and aromatic hydrocarbon. Partial sampling locations around the cities or countries generally showed abnormal values due to terrigenous inputs. As a bio-optical model parameter, spectral characteristic of CDOM is helpful in adjusting the derived algorithms in highly-polluted environments. The study on organic carbon and pollutants in highly-polluted waters had an important contribution to global carbon balance estimation and water environment protection.

A quantum theory of gravity is among the most pursued goals in physics. I present a definitive and direct experimental proof that refutes the widely believed hypothesis of quantum gravity. The detections of astrophysical gravitational waves (GW) are inferred from the differential oscillations of suspended mirrors of optical interferometers like the aLIGO detectors. If gravity is indeed quantized, then the average energy $\bar{E}$ in the minute oscillations of the mirrors, at a frequency $\nu$, corresponds to the absorption of an integer number $N$ gravitational quanta with total energy $E_{gw}=Nh\nu$. The coherent and coincident detections across large separations of detectors, and also the constraint of the equivalence principle, dictate that the average number of quanta $\bar{N}$ forcing the oscillations obeys $\bar{N}\gg 1$, or $1/\sqrt{\bar{N}}\ll 1$. However, the average energy $\bar{E}$ in the differential oscillations is smaller than the energy of a single quantum of GW radiation at the detected astrophysical GW frequencies. This startling finding implies that $\bar{N} <1$, which is physically impossible if gravity is quantized. This singular contradiction refutes the long-held hypothesis that gravity is quantized.

Noctuidae is known to have high species diversity, although the genomic diversity of Noctuidae species have not been studied extensively. Investigation of transposable elements (TEs) in this family can improve our understanding on the genomic diversity of Noctuidae. In this study, we annotated and characterized genome-wide TEs in ten noctuid species belonging to seven genera. With multiple annotation pipelines, we constructed a consensus sequence library containing 1,038 –2,826 TE consensus. The genome content of TEs showed high variation in the ten Noctuidae genomes, ranging from 11.3% to 45.0%. The relatedness analysis indicated that the TE content, especially the content of LINEs and DNA transposons, are positively correlated with the genome size (r=0.86, p-value=0.001). We identified SINE/B2 as a lineage-specific subfamily in Trichoplusia ni, a species-specific expansion of LTR/Gypsy subfamily in Spodoptera exigua, and a recent expansion of SINE/5S subfamily in Busseola fusca. We further revealed that of the four TE classes, only LINEs showed phylogenetic signal with high confidence. We also examined how the expansion of TEs contributed to the evolution of noctuid genomes. Moreover, we identified a total of 56 horizontal transfer TE (HTT) events among the ten noctuid species and at least three HTT events between the nine Noctuidae species and 11 non-noctuid arthropods. One of HTT events caused by a Gypsy transposon might have caused the recent expansion of Gypsy subfamily in the S.exigua genome. By determining the TE content, dynamics, and HTT events in the Noctuidae genomes, our study emphasized that TE activities and HTT events had substantial impacts on the Noctuidae genome evolution.

Laser Powder Bed Fusion (L-PBF)-based manufacturing is a growing area of research, with a particular focus on evaluating constraints in printed products. This research emphasises the importance of considering various factors—including mechanical properties and support structures—that influence the final product during the design phase. Specifically, the design of microchannel heat exchangers is highlighted, where all constraints are critical. This article will present a method for analysing channel pressure limitations and report findings about mechanical properties loss from examining tube porosity. This includes a summary of results and the establishment of an experimental device system.

Recently, environmental degradation has become an international issue due to the extensive economic activities around the globe. This aspect demands the policymakers' and new researchers’ emphasis. Hence, the present article examines the impact of corporate social responsibilities (CSR) to community and environment on the environmental performance (ENP) of the manufacturing industry in China. The article also investigates the mediating role of green innovation and environmental strategy among CSR to community, CSR to environment and ENP of manufacturing industry in China. The article gets the primary data from the selected employees of the manufacturing industry using questionnaires. The article also tests the data's validity and reliability, and association among variables using smart-PLS. The outcomes indicated that CSR to environment has a positive association with ENP. The outcomes also revealed that green innovation and environmental strategy significantly mediate among CSR to community, CSR to environment and ENP. The article guides the policymakers in making policies related to improve ENP using CSR to environment and green innovation.

Australian bat lyssavirus (ABLV) is a rhabdovirus that circulates in four species of pteropid bats (ABLVp) and the yellow-bellied sheath-tailed bat (ABLVs) in mainland Australia. In the three confirmed human cases of ABLV, rabies illness preceded fatality. As with rabies virus (RABV), post-exposure prophylaxis (PEP) for potential ABLV infections consists of wound cleansing, ad-ministration of the rabies vaccine and injection of rabies immunoglobulin (RIG) proximal to the wound. Despite the efficacy of PEP, the inaccessibility of human RIG (HRIG) in the developing world and the high immunogenicity of equine RIG (ERIG) has led to consideration of human monoclonal antibodies (hmAbs) as a passive immunization option that offers enhanced safety and specificity. Using a recombinant vesicular stomatitis virus (rVSV) expressing the glycoprotein (G) protein of ABLVs and phage display, we identified two hmAbs, A6 and F11, which completely neutralize ABLVs/ABLVp, and RABV at concentrations ranging from 0.19-3.12 µg/mL and 0.39-6.25 µg/mL respectively. A6 and F11 recognize overlapping epitopes in the lyssavirus G protein, ef-fectively neutralizing phylogroup 1 lyssaviruses, while having little effect on phylogroup 2 and non-grouped diverse lyssaviruses. These results suggest A6 and F11 could be effective therapeutic and diagnostic tools for phylogroup 1 lyssavirus infections.

Skin inflammation associated with chronic diseases has the direct action of keratinocytes in its immunopathogenesis, leading to a series of immune responses. However, there are still no highly targeted treatments, and the development of more specific therapies is highly desirable. In the present work, nanocapsules containing quinizarin (QZ/NC) were developed, evaluating their act in a model of the keratinocytes inflammatory process in vitro with the action of Photodynamic Therapy, analyzing the permeation in a 3D skin model. Physicochemical, stability, cytotoxicity and permeation analyses of the nanomaterials were carried out. The nanocapsules showed desirable physicochemical characteristics and were stable during the analysis period, without spectroscopic modifications. The cell viability test carried out showed no cytotoxicity at the lowest QZ/NC concentrations. The permeation and cellular uptake studies showed the permeation of QZ/NC in 3D skin models and the intracellular incorporation and internalization of the drug, enhancing its action in the drug delivery technique. Promising results were presented using the model developed to induce the inflammatory process in vitro and applying the synthesized nanomaterial in conjunction with PDT, where a decrease in the levels of cytokines analyzed was observed, representing a possible new therapeutic approach for inflammatory skin diseases.

Background/Objectives: The aim of this study was to investigate feasibility and safety of neuromuscular electrical stimulation (NMES) in patients on extracorporeal membrane oxygenation (ECMO) and assess thoroughly any potential adverse events. Methods: We conducted a safety and feasibility study including 16 ICU patients on ECMO support who were admitted to the cardiac surgery ICU from January 2022 to December 2023. Patients underwent a 45-minute NMES session, still on ECMO support, that included a warm up phase of 5 minutes, a main phase of 35 minutes and a recovery phase of 5 minutes. NMES was implemented on vastus lateralis, vastus medialis, gastrocnemius and peroneus longus muscle of both lower extremities. Two stimulators delivered biphasic, symmetric impulses of 75 Hz, with 400 μsec pulse duration, 5 sec on (1,6 sec ramp up and 0,8 sec ramp down) and 21 sec off. The intensity levels aimed to cause visible contractions and be well tolerated. Primary outcomes of the study were feasibility and safety, evaluated by whether the NMES sessions were successfully achieved, and by any adverse events and complications. Secondary outcomes included indices of rhabdomyolysis from biochemical blood test, 24 hours after the application of NMES. Results: All patients successfully completed their 45-minute NMES session, without presenting hemodynamic deterioration, adverse events or signs of rhabdomyolysis. The majority of patients achieved type 4 and 5 quality of muscle contraction. Conclusions: NMES is a safe and feasible exercise methodology in patients supported with ECMO.

In December 2019, a novel coronavirus (SARS-CoV-2) was identified in patients with pneumonia (called COVID-19) in Wuhan, Hubei Province, China. SARS-CoV-2 shares high sequence similarity and uses the same cell entry receptor, angiotensin-converting enzyme 2 (ACE2), as does severe acute respiratory syndrome coronavirus (SARS-CoV). Several studies have provided bioinformatic evidence of potential routes for SARS-CoV-2 infection in respiratory, cardiovascular, digestive and urinary systems. However, whether the reproductive system is a potential target of SARS-CoV-2 infection has not been determined. Here, we investigate the expression pattern of ACE2 in adult human testis at the level of single-cell transcriptomes. The results indicate that ACE2 is predominantly enriched in spermatogonia, Leydig and Sertoli cells. Gene ontology analyses indicate that GO categories associated with viral reproduction and transmission are highly enriched in ACE2-positive spermatogonia while male gamete generation related terms are down-regulated. Cell-cell junction and immunity related GO terms are increased in ACE2-positive Leydig and Sertoli cells, but mitochondria and reproduction related GO terms are decreased. These findings provide evidence that human testes are a potential target of SARS-CoV-2 infection which may have significant impact on our understanding of the pathophysiology of this rapidly spreading disease.

We introduce a new generalized inverse which is a natural generalization of (pseudo) right core inverse in a Banach *-algebra. We characterize this generalized inverse by using right core and quasi-nilpotent decomposition. We then present its polar-like characterization and investigate related algebraic properties. Finally, the right core-EP inverse is characterized by certain new ways.

The group delay response of chirped fiber Bragg gratings (CFBG) written in polarization-maintaining fiber can be measured using the beat spectrum of the reflections from the two polarization axis, with a resolution and reproducibility better than 50 fs. The method is theoretically described, and we analyze the effect of group delay ripples (GDR) on the performance of a stretcher-compressor pair used in chirped pulse amplification systems. Ripples with a spectral period of the order of the pulse bandwidth are the most deleterious. Those are readily captured with our method, despite the limit on resolution due to the fiber polarization beat length. As a rule of thumb, the GDR should not exceed half the pulse duration. The reconstructed phase of the CFBG is also used to predict the pulse compression performance for a given device. Measurement of GDR as low as 161 fs is experimentally demonstrated.

Natural language Processing (NLP) has significantly transformed in the last decade, especially in the field of Language Modeling. Large Language Models (LLMs) have achieved SOTA performances on Natural Language Understanding (NLU) and Natural Language Generation (NLG) tasks by learning language representation in self-supervised ways. This paper provides a comprehensive survey to capture the progression of advances in Language Models. In this paper, we examine the different aspects of Language Models, which started with a few million parameters but have reached the size of a trillion in a very short time. We also look at how these LLMs transitioned from task-specific to task-independent to task-and-language-independent architectures. This paper extensively discusses different pre-training objectives, benchmarks, and transfer learning methods used in LLMs. It also examines different fine-tuning and In-Context learning techniques used in downstream tasks. It also explores how LLMs can perform well across many domains and datasets if sufficiently trained on a large and diverse dataset. Next, it discusses how, over time, the availability of cheap computational power and large datasets have improved LLM’s capabilities and raised new challenges. As part of our study, we also inspect LLMs from the lens of scalability to see how their performance is affected by the model’s depth, width, and data size. Lastly, we provide an empirical comparison of existing trends and techniques and a comprehensive analysis of where the field of LLM currently stand.

Two experiments were conducted to develop a Nano green manganese and assess its effect on grapevines. The first experiment was microbiological, where several incorporation strategies were utilized to enrich the yeast with manganese, as follows: (1) adding manganese to the liquid medium (growth phase), (2) adding after 24 h of incubation (non-growth phase). As the results, growth phase reduced the possibility of medium contamination. Manganese concentration in the yeast cells was increased as that of manganese sulphate in the medium. Manganese incorporation in yeast cells was 99.93% higher than that of the medium at 0.0195 g/l of manganese. Although the concentration of manganese in the medium raised the OD of yeast cell biomass, manganese sulphate had no passive influence on it. The second experiment was horticultural, done on Flame Seedless grapevines, since both active fresh and frozen yeast enriched with manganese resulted from the first experiment were sprayed twice a year at 10 or 20 cm3/l comparing with manganese sulphate, and manganese chelate. The results demonstrated that yeast extracts in both forms increased the yield and improved fruit properties. The most effective treatment on vegetative growth, yield and fruit properties was the frozen yeast enriched with manganese at 20 cm3/l.

Dendritic cell (DC) cancer vaccines are a promising therapeutic approach, leveraging the immune system to fight tumors. These vaccines utilize DCs' ability to present tumor-associated antigens to T cells, triggering a robust immune response. DC vaccine development has progressed through three generations. The first generation involved priming DCs with tumor-associated antigens or messenger RNA outside the body, showing limited clinical success. The second generation improved efficacy by using cytokine mixtures and specialized DC subsets to enhance immunogenicity. The third generation uses blood-derived DCs to elicit a stronger immune response. Clinical trials indicate that cancer vaccines have lower toxicity than traditional cytotoxic treatments. However, achieving significant clinical responses with DC immunotherapy remains challenging. Combining DC vaccines with immune checkpoint inhibitors (ICIs) such as anti-cytotoxic T-lymphocyte Antigen 4 and anti-programmed Death-1 antibodies has shown promise by enhancing T cell responses and improving clinical outcomes. These combinations can transform non-inflamed tumors into inflamed ones, boosting ICIs' efficacy. Current research is exploring new checkpoint targets like LAG-3, TIM-3, and TIGIT, considering their potential with DC vaccines. Additionally, engineering T cells with chimeric antigen receptors or T cell receptors could further augment the anti-tumor response. This comprehensive strategy aims to enhance cancer immunotherapy, focusing on increased efficacy and improved patient survival rates.

The occurrence of ovarian dysfunction is often due to the imbalance between the formation of reactive oxygen species (ROS) and the (in)effectiveness of the antioxidative defense mechanisms. Primary sources of ROS are the respiratory electron transfer and the activity of NADPH oxidases (NOX), while Superoxide dismutases (SOD) are the main key reg-ulators that control the levels of ROS and reactive nitrogen species intra- and extracel-lularly. Because of their central role, SODs are the subject of research on human ovarian dysfunction, but the sample acquisition is low. The high degree of cellular and molecular similarity between Drosophila melanogaster ovaries and human ovaries provides this model organism with the best conditions for analyzing the role of ROS during ovarian function. In this study, we clarify the localization of the ROS producing enzyme dNox within ovaries of Drosophila melanogaster and by a tissue specific knockdown, we show that dNox derived ROS are involved in the chorion hardening process. Furthermore, we analyze the dSod3 localization and show that reduced activity of dSod3 impacts egg laying behavior, but not the chorion hardening process.

In 2013, the CMA at the İstanbul Stock Exchange increased the weight assigned to the Board of Directors component of its Corporate Governance Index to 35% from the previous 25%. Interpreting this as a recognition of the increasing vital role of the board, this study seeks to enhance the work of Abdıoğlu and Kılıç (2015) by putting more focus on the role of women in the boards and the effect of the busy chairman as well as the presence of outside directors on the effectivity of the Board. (The general business structure is associated with family owned groups and holdings which results into a network of intertwined board membership and cases of multiple directorship where, one board chairman can hold the same position or any directorship in as many as ten firmshence the busy chairman). I employ a different method of evaluating performance (EVA) together with the accounting measures of ROE and ROA (as opposed to the overused Tobin’s Q), which I regress against the Board Index to be created. The focus is on firms on the BIST 100 index (excluding financial) between 2009 and 2013. The results reveal that the BINDEX has a significant and positive relationship with firm performance as measured by EVA. A second model reveals no relationship between the BINDEX and firm ROA, similar to the results of Kiliç and Abdioğlu (2015). ROA however has a positive relationship with the proportion of female directors in the board, as earlier reported by LückerathRovers (2013). Another model using ROE as the proxy for performance registers a significant negative relationship with the index. The contradiction obtained in the results from these three models underscore the importance choosing the right methods when estimating the performance of a firm.

The large energy band gap associated with titanium oxide nanoparticle is a limitation to its application as a photo catalyst within the solar spectrum. More than 45% of solar radiation can be utilized at a reduced energy band gap of 〖TiO〗_2. Various structural modifications in 〖TiO〗_2 targeted in optimized photo catalyst application include the enhancement of its support system. Activated carbon supported 〖TiO〗_2 nanoparticle may produce the desired reduction in the energy band gap, reduce electron recombination and consequently enhance its photocatalytic activity in the visible region of electromagnetic spectrum. In the current investigation, an activated carbon made from the leaves of an invasive plant (Acacia Saligna) was applied as a support in the synthesis (hydrothermal process) of 〖TiO〗_2 nanoparticle. The determination of energy band-gap of 〖TiO〗_2 supported activated carbon using UV-Vis spectrophotometer gave 2.11 eV. Likewise, Scanning Electron Microscopy (SEM-EDS), Fourier transformer infrared (FTIR), XRD and Brunauer- Emmett-Teller (BET) showed that 〖TiO〗_2was successfully immobilized on the activated carbon’s external surface. The BET results showed that the synthesized 〖TiO〗_2exhibits small hysteresis phenomenon which represent a typical type IV isotherm attributed to mesosoporous material with a low porosity. Meanwhile, these XRD results reveal the presence of mixture of rutile and anatase crystalline phase

In this paper, a novel deep learning approach based on conditional FLOW is proposed for sea clutter augmentation. Sea clutter data augmentation is important for testing detection algorithms for maritime remote sensing and surveillance due to the expensive and time-consuming nature of sea clutter data acquisition. While the conventional parametric methods face challenges in finding appropriate distributions and modeling time correlations of the sea clutter data, the proposed deep learning approach, GCC-FLOW , can learn the data distribution from the data without explicitly defining a mathematical model. Furthermore, unlike the existing generative deep learning approaches, the proposed GCC-FLOW is able to synthesize sea clutter data of arbitrary length with the stable autoregressive structure using conditional FLOW . In addition, the proposed algorithm generates sea clutter data not only with the same characteristics of the training data, but also with the interpolated characteristics of different training data by introducing a global condition variable corresponding to the target characteristic such as sea state. Experimental results demonstrate the effectiveness of the proposed GCC-FLOW in generating sea clutter data of arbitrary length under different sea state conditions.

Introduction: Guardians play a pivotal role in the management of childhood illness. Their responsibilities include home managing childhood illnesses and supporting continuity of care following healthcare facility treatment. Evidence has shown that guardians' negative experience in health care facilities when accessing under-five childcare services impacts their utilization of such facilities. Exploration of guardians' experiences is important in providing the evidence base necessary for quality childcare and reducing under-five morbidity and mortality. The study aimed to explore the experiences of guardians of children under five regarding the care received in Vhembe healthcare facilities. Methods: A qualitative approach using an interpretative phenomenology analysis (IPA) was used for this study. The study was conducted in eight primary healthcare facilities in the Vhembe district, with the highest headcount for children under five. Sixteen guardians were purposefully sampled for the study. Data analysis was done following the IPA steps of data analysis. Trustworthiness criteria were followed: credibility, confirmability, dependability, authenticity, and transferability. Ethical approval was obtained from the university and the province, followed by permission from the district and the operational managers before data collection. Results: The three emerging themes are associated with participants' positive experiences as they interfaced within the healthcare system: quality care, healthcare environment, support, and empowerment. The ten sub-themes were: timely service provision, availability of medicines, perceived nursing competence and professionalism, person-centred care, welcoming environment, effective communication, positive attitude of healthcare providers, guardians’ satisfaction with care received, guardians' involvement in childcare and community support. Conclusion: It is indeed not always bad. The participants in this study had positive care experiences, which is crucial in the optimal utilization of health care services, resulting in improved health outcomes.

Green investments help to create less harmful alternatives and adequate funds that contribute to economic growth, sustainable development, and social well-being. The applied methods are empirical and analytical based on the study of literature, multi-criteria modelling, determination of weights, and ranking of criteria in deciding green investments mapping of indicators, and monitoring of progress towards green growth through official OECD data. The analysis uses official sources on indicators related to the green economy and the environment. Multi-criteria decision-models (MCDM) were used to select adequate green investments, where instead of indicators from the official OECD-database, investors use the areas in which the indicators are distributed. According to the findings of AHP, half of the investments in the green economy come from public sources (0.51), and the other half are private (0.25), and institutional investors (0.24). While, BWM reveals that the best criterion for the decision to invest in the green economy is the environmental and resource productivity of the economy, and the worst is the base of natural assets. This paper aims to enable decision-makers to use these results as weights for the overall assessment of green investments in ESG and to simplify the decision-making approach in future analyses.

Graphene, a single layer of carbon atoms arranged in a two-dimensional honeycomb lattice, has attracted tremendous attention due to its exceptional properties and diverse applications. Synthesis methods play a crucial role in tailoring graphene's properties for specific applications. This review provides a comprehensive overview of recent advances in graphene synthesis techniques, including chemical vapor deposition (CVD), exfoliation, epitaxial growth, and chemical reduction. We discuss the principles, advantages, challenges, and applications associated with each method. Furthermore, emerging trends and future prospects in graphene synthesis are explored, with a focus on scalability, cost-effectiveness, and environmental sustainability.

The term "code smell" refers to an indication of a problem with the quality of source code. Numerous studies have been conducted to identify problematic features in source code. Initially, the focus was on utilizing metric-based and heuristic-based approaches. In recent years, however, there has been a shift towards using machine learning and deep learning (DL) techniques for smell detection. Nevertheless, the current algorithms are still considered to be in the early stages of development. Recognizing the challenges associated with identifying smells using DL methods, both academics and software developers have made efforts to address these obstacles. This work involves constructing and evaluating new DL models for code smell detection. Two models are built upon stacked autoencoders, employing a hybrid architecture that combines bidirectional long short-term memory and convolutional neural network components.

Today, Colebrook’s equation is mostly accepted as an informal standard for modeling of turbulent flow in hydraulically smooth and rough pipes including transient zone in between. The empirical Colebrook’s equation relates the unknown flow friction factor (λ) with the known Reynolds number (R) and the known relative roughness of inner pipe surface (ε/D). It is implicit in unknown friction factor (λ). Implicit Colebrook’s equation cannot be rearranged to derive friction factor (λ) directly and therefore it can be solved only iteratively [λ=f(λ, R, ε/D)] or using its explicit approximations [λ≈f(R, ε/D)]. Of course, approximations carry in certain error compared with the iterative solution where the highest level of accuracy can be reached after enough number of iterations. The explicit approximations give a relatively good prediction of the friction factor (λ) and can reproduce accurately Colebrook’s equation and its Moody’s plot. Usually, more complex models of approximations are more accurate and vice versa. In this paper, numerical values of parameters in various existing approximations are changed (optimized) using genetic algorithms to reduce maximal relative error. After this improvement computational burden stays unchanged while accuracy of approximations increases in some of the cases very significantly.

Food is one of the basic necessities for all living things in this globe to grow, maintain life and develop. It is a source of energy for almost all bodily functions and it directly affects our health status and how we feel each day and the future. Yet, there are millions of people around the globe who do not have the sumptuousness to provide enough food to themselves and their beloved ones. Ethiopia is one of the highly food insecure countries in the world, it's name has been illustrious for famine and drought for decades. The study's main aim is to assess the livelihood resources and strategies of the smallholding farmers in their strive to achieve food security at household level. For this particular study, from Tigray region, Kilte Awelalo was taken as a study area and three Tabias were selected; Ayenalem, Genfel and Tahetay Adikesanded. The total sample number of households from these three Tabias was 370 and a formal interview schedule was employed to collect relevant primary inputs for the study. The study found that farmland holding size and its fertility as major impacting factors behind the livelihood strategies and food security condition of the smallholding farmers. It was also found that about one third of the total sample population were chronically food insecure, whereas about half of the population were transitory food insecure and rest very little number of households were food secure.

C-reactive protein to lymphocyte ratio (CLR) has been shown to be associated with diseases characterized by chronic, low-grade inflammation. Since type 2 diabetes (T2DM) is also associated with inflammation, we aimed to study the association between CLR and T2DM. Patients with T2DM who presented to internal medicine outpatient clinics of our institution were divided into 2 groups according to their glycosylated hemoglobin (HbA1c) levels as well-controlled (HbA1c&lt;7%) and poorly controlled (HbA1c≥7%) T2DM groups. Subjects assigned as healthy in routine check-up were included as control group. CLR values of the well and poorly controlled diabetics and control cases were compared. CLR of T2DM group (3.51 (0.03-21.78)) was significantly higher than that of the controls (0.65 (0.02-2.92)) (p&lt;0.001). CLR was found to have a sensitivity of 63.2% and a specificity of 97.3% in predicting T2DM. The CLR value of patients with poor diabetic control was 4.76 (0.06-21.78), while the CLR value of patients with well controlled disease was 2.53 (0.03-12.07) (p&lt;0.001). The sensitivity and specificity of the CLR in demonstrating poor diabetic control was 41.2% and the 86.1%, respectively. In conclusion, elevated CLR in T2DM patients and even more increased levels in poorly controlled diabetics suggest that CLR could be a useful additional diagnostic tool in treatment follow-up of the T2DM population.

A model that predicts levels of coronavirus (CoV) respiratory/fecal-oral transmission potentials based on the outer shell hardness has been built using neural network (artificial intelligence, AI) analysis of the percentage of disorder (PID) in the nucleocapsid, N, and membrane, M, proteins of the inner and outer viral shells, respectively. Based mainly on the PID of N, SARS-CoV-2 is categorized as having intermediate levels of both respiratory and fecal oral transmission potential. Related to this, other studies have found strong positive correlations between virulence and inner shell disorder among numerous viruses, including Nipah, Ebola, and Dengue viruses. There is some evidence that this is also true for SARS-CoV-2 and SARS-CoV, which have N PIDs of 48% and 50%, and are characterized by case-fatality rates of 7.1% and 10.9%, respectively. The link between levels of respiratory transmission and virulence lies in viral load of body fluids and organ respectively. A virus can be infectious via respiratory modes only if the viral loads in saliva and mucus exceed certain minima. Likewise, a person may die, if the viral load is too high especially in viral organs. Inner shell proteins of viruses play important roles in the replication of viruses, and structural disorder enhances these roles by providing greater efficiency in protein-protein/DNA/RNA/lipid binding. This paper outlines a novel strategy in attenuating viruses involving comparison of disorder patterns of inner shells of related viruses to identify residues and regions that could be ideal for mutation. The M protein of SARS-CoV-2 has one of the lowest M PID values (6%) in its family, and therefore this virus has one of the hardest outer shells, which makes it resistant to antimicrobial enzymes in body fluid. While this is likely responsible for its contagiousness, the risks of creating an attenuated virus with a more disordered M are discussed.

Stroke represents one of the neurological diseases most responsible for death and permanent disability in the world. Different factors, such as thrombus, emboli and atherosclerosis, take part in the intricate pathophysiology of stroke. Comprehending the molecular processes involved in this mechanism is crucial to developing new, specific and efficient treatments. Some common mechanisms are excitotoxicity and calcium overload, oxidative stress, and neuroinflammation. Furthermore, non-coding RNAs (ncRNAs) are critical in pathophysiology and recovery after cerebral ischaemia. ncRNAs, particularly microRNAs, and the long non-coding RNAs (lncRNAs) are essential for angiogenesis and neuroprotection, and they have been suggested to be therapeutic, diagnostic and prognostic tools in cerebrovascular diseases, including stroke. This review summarizes the intricate molecular mechanisms underlying ischemic and hemorrhagic stroke and delves into the function of miRNAs in the development of brain damage. Furthermore, we will analyze new perspectives on treatment based on molecular mechanisms in addition to traditional stroke therapies.

Businesses now formally enter the phase of "think power differentiation and gap" in response to the new economic climate. While AI, big data, empowerment via the internet, and other aspects of the digital economy help to improve technical capabilities and save operating costs, they also push businesses to innovate their strategies at a faster rate. Enterprises are strategically choosing to de-enterprise, moving from focusing on a single business functioning benefit to building an ecosystem of platform services that will give them a competitive edge. Additionally, the organization's structure is evolving continually from a vertical model to a curved working style, in order to achieve this, businesses must recognize the importance of data analysis in day-to-day management and operations, encourage the development of an organizational atmosphere that is driven by data, fully utilize digital technology, modularize areas or production links that perform related tasks, and encourage the reform of organizational structures. By streamlining the output of the product or service to the external community while also enhancing the company's financial performance model, switching from process-to result-oriented evaluation, and putting in place a customized authority and accountability framework. Replace outcome-focused evaluation with process-focused evaluation, and create a customized incentive scheme that balances power, accountability, and rewards.

Convolutional neural networks (CNNs) have proven to be a very efficient class of machine learning (ML) architectures for handling multidimensional data by maintaining data locality, especially in the field of computer vision. Data pooling, a major component of CNNs, plays a crucial role for extracting important features of the input data and downsampling its dimensionality. Multidimensional pooling, however, is not efficiently implemented in existing ML algorithms. In particular, quantum machine learning (QML) algorithms have a tendency to ignore data locality for higher dimensions by representing/flattening multidimensional data as simple one-dimensional data. In this work, we propose using the quantum Haar transform (QHT) and quantum partial measurement for performing generalized pooling operations on multidimensional data. We present the corresponding decoherence-optimized quantum circuits for the proposed techniques along with their theoretical circuit depth analysis. Our experimental work was conducted using multidimensional data, ranging from 1-D audio data, to 2-D image data, to 3-D hyperspectral data, to demonstrate the scalability of the proposed methods. In our experiments, we utilized both noisy and noise-free quantum simulations on a state-of-the-art quantum simulator from IBM Quantum. We also show the efficiency of our proposed techniques for multidimensional data by reporting the fidelity of results.

Moving object detection plays a crucial role in various applications, particularly in traffic surveillance and collision warning systems. However, the high cost of training and slow detection pose significant challenges to the practicality and real-time performance of existing methods. These challenges hinder the practicality and real-time performance of existing methods. The need for a reliable and efficient moving object detection system prompts the search for cost-effective strategies. Enhancing detection speed without sacrificing accuracy is crucial for real-time applications. Overcoming these challenges is essential to advance the field of moving object detection and improve its applicability in various domains. This paper presents a comprehensive review of the literature on moving object detection, proposes novel strategies to overcome the challenges of high training costs and slow detection. The study emphasizes the importance of reliable speed monitoring systems and advancements in control systems to enhance driver assistance on urban highways. Each frame in the dataset undergoes YOLOv6 processing to detect and classify objects, generating bounding box predictions and class probabilities. Optical flow, specifically the Lucas-Kanade method, is then utilized to compute motion vectors between consecutive frames. These motion vectors facilitate object tracking across frames, enabling refined bounding box positions and improving overall object tracking accuracy. The results demonstrate the effectiveness of the proposed technique in reducing training costs, improving detection speed, and maintaining a high level of accuracy when compared to existing methods. The paper concludes by summarizing the findings and highlighting the potential of the proposed strategy for precise and reliable moving object recognition in real-world traffic scenarios

This research investigates the potential benefits of integrating machine learning algorithms and IoT sensors in modern agriculture. The focus is on optimizing crop production and reducing waste through informed decisions about planting, watering, and harvesting crops. The paper discusses the current state of machine learning and IoT in agriculture, highlighting key challenges and opportunities. It also presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, including real-time data from IoT sensors, farmers can make more informed verdicts about factors that affect crop growth. Eventually, the integration of these technologies can transform modern agriculture by increasing crop yields while minimizing waste. In our studies, we achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99. 46% using Naïve Bayes Classifier, and Hoeffding Tree algorithms. Our results indicate that we achieved high accuracy results in our experiments in order to increase crop growth.

Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.

Hyperspectral image (HSI) classification is a mechanism of analyzing differentiated land cover in remotely sensed hyperspectral images. In the last two decades, a number of different types of classification algorithms have been proposed for classifying hyperspectral data. These algorithms include supervised as well as unsupervised methods. Each of these algorithms has its own limitations. In this research, three different types of unsupervised classification methods are used to classify different datasets i-e Pavia Center, Pavia University, Cuprite, Moffett Field. The main objective is to assess the performance of all three classifiers K-Means, Spectral Matching, and Abundance Mapping, and observing their applicability on different datasets. This research also includes spectral feature extraction for hyperspectral datasets.

As the embedded device and internet of things (IoTs) concept prevalent in today's world, there is an increasing demand for the security and performance requirements on deploying these devices to private and public sectors. The crucial part of it is to protect privacy, confidentiality and integrity, meanwhile, maintain an adequate level of performance during transmission, storage and access of critical information. While the conventional cryptography methods, such as the Advanced Encryption Standard (AES), SHA$-$2 hashing method and RSA and Diffie Hellman for message authentication and identification, work well on systems which have reasonable processing power and memory capabilities. These do not scale well into a world with embedded systems and sensor networks, in conjunction with their nature of smaller size and lower cost. Thus, in the context of resource constrained device, lightweight cryptography methods are proposed to overcome many of the problems of conventional cryptography possessed. This includes constraints related to physical size, processing requirement, memory limitation, energy drain and production cost. This paper provides a survey of the architectures that are defined as replacements for conventional ciphers within an IoTs space and discuss some trends in the design of future lightweight algorithms. The performance metrics are carefully chosen to reflect and assess the suitability for embedded devices. The aim of this research is to identify the various performance metrics applied on different types of symmetric block ciphers especially lightweight ciphers and compare the results on versatile platforms with stream ciphers and public key methods. The comparative analysis on efficient LW cipher will be tested against other similar block ciphers on both MacBook Pro with Intel core and resource constrained device Raspberry Pi with ARM processor.

In the field of stochastic optimisation, the so-called structural bias constitutes an undesired behaviour of an algorithm that is unable to explore the search space to a uniform extent. In this paper, we investigate whether algorithms from a subclass of estimation of distribution algorithms, the compact algorithms, exhibit structural bias. Our approach, justified in our earlier publications, is based on conducting experiments on a test function whose values are uniformly distributed in its domain. For the experiment, 81 combinations of compact algorithms and strategies of dealing with infeasible solutions have been selected as test cases. We have applied two approaches for determining the presence and severity of structural bias, namely a visual and a statistical (Anderson-Darling) tests. Our results suggest that compact algorithms are more immune to structural bias than their counterparts maintaining explicit populations. Both tests indicate that strong structural bias is found only in one of the algorithms (cBFO) regardless of the choice of strategy of dealing with infeasible solutions and cPSO mirror. For other test cases, statistical and visual tests disagree on some cases classified as having mild or strong structural bias: the former one tends to make harsher decisions, thus needing further investigation.

Quantum computers are machines that are designed to use quantum mechanics in order to improve upon classical computers by running quantum algorithms. One of the main applications of quantum computing is solving optimization problems. For addressing optimization problems we can use linear programming. Linear programming is a method to obtain the best possible outcome in a special case of mathematical programming. Application areas of this problem consist of resource allocation, production scheduling, parameter estimation, etc. In our study, we looked at the duality of resource allocation problems. First, we chose a real world optimization problem and looked at its solution with linear programming. Then, we restudied this problem with a quantum algorithm in order to understand whether if there is a speedup of the solution. The improvement in computation is analysed and some interesting results are reported.

The sub-economic dynamics studies the relationship between people's economic impulses and market behavior. Using Bourbaki structuralism as a philosophical consideration and quantum chromodynamics as a conceptual and modeling method, sub-economic dynamics theory is constructed. In this theory, we discuss the deep structure of the free market and analyze the errors in Mises' anthropological arguments. Sub-economic dynamics is the psychological basis of market dynamics. Guided by the preface, this paper is divided into six sections: (1) The Free Market and Impulse Flavor Charge, including topics such as the fate of the free market, the food chain and psychology, and the flavor charge of economic impulses; (2) The Nature of Economic Impulses, including topics such as the flavor and generation of impulses and counter-impulses, and the bound state of impulses and fractional market charges; (3) Freud's Color Charge Space and Spin, including topics such as the Pauli exclusion rule and color charge, and Freud's personality theory; (4) Consciousness and Gluons; (5) Gauge Transformations and SU(3) Symmetry Group; and (6) General Discussion.

Multiple myeloma (MM) is characterized by the malignant proliferation of monoclonal plasma cells in the bone marrow with an elevation in monoclonal paraprotein, renal impairment, hypercalcemia, lytic bony lesions, and anemia. Immune cells and associated cytokines play a significant role in MM growth, progression, and dissemination. While some cytokines and their clinical significance are well described in MM biology, others remain relatively unknown. The aim of the present study was to assess the impact of pretreatment serum levels of 27 selected cytokines on progression-free survival (PFS) and overall survival (OS) in MM patients before first-line therapy with bortezomib-based regimens. Serum cytokine levels were assayed with a Bio-Rad Bio-Plex Pro Human Cytokine 27-Plex Assay on the MAGPIX Multiplex Reader and the Bio-Plex® 200 System (Bio-Rad) including IL-1β, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, Eotaxin, FGF, G-CSF, GM-CSF, IFN-γ, IP-10, MCP-1, MIP-1α, MIP-1β, PDGF-BB, RANTES, TNF-α, and VEGF. A total of 61 MM patients were examined. Most patients received a bortezomib, cyclophosphamide and dexamethasone (VCD) chemotherapy regimen. In the final multivariate model, IL-13 cytokine level (HR 0.1411, 95% CI: 0.0240-0.8291, p = 0.0302), and ASCT (HR 0.3722, 95% CI: 0.1826-0.7585, p=0.0065) significantly impacted PFS. Furthermore, ASCT (HR 0.142, 95% CI: 0.046-0.438, p=0.0007), presence of bone disease at diagnosis (HR 3.826, 95% CI: 1.471-9.949, p=0.0059) and two cytokine levels- IL-1Ra (HR 1.017, 95% CI: 1.004-1.030, p= 0.0091) and IL-4 (HR 0.161, 95% CI: 0.037-0.698, p = 0.0147) were independent predictors of OS. Three clusters of MM patients were identified with different cytokine profiles. In conclusion, serum pretreatment levels of IL-13 and IL-4 are predictors of better PFS and OS, respectively, whereas IL-1Ra pretreatment levels negatively impact OS in MM patients treated with bortezomib-based chemotherapy. Cytokine signature profile may have a potential influence on the outcome of patients treated with bortezomib.

In this preliminary investigation, a concise molecular docking study using the Pyrx program screened thousands of compounds against the active site of HIV-1 reverse transcriptase. The Virtual Screening library was employed to assess binding energetics scores, focusing on identifying candidates with high Vina Scores. Eight compounds, including Tetrophan, Imatinib, Amentoflavone, Alprazolam, Bilobetin, Bafetinib, Nilotinib, and Ginketin, emerged as promising candidates with significant binding energies.Recognizing compounds that strongly bind to HIV-1 reverse transcriptase holds potential for antiretroviral drug development in the fight against HIV infection. Subsequently, the study evaluated the toxicity properties of these candidates using pKCSM. Amentoflavone, a natural biflavonoid found in Ginkgo biloba and Nilotinib, a tyrosine kinase inhibitor, demonstrated lower overall toxicity profiles compared to other candidates. Further toxicity analysis using Pro-Tox-II revealed Amentoflavone's higher predictive LD50, lower toxicity class, and reduced probabilities of hepatotoxicity, carcinogenicity, mutagenicity, and moderate immunotoxicity compared to Nilotinib. While Nilotinib, a pharmaceutical drug, exhibited higher probabilities of adverse effects in these categories. In summary, Amentoflavone and Nilotinib present as promising candidates with lower toxicity concerns. Amentoflavone, as a natural substance, showcases a more favorable toxicity profile than Nilotinib, offering valuable insights for potential therapeutic applications.

The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent of deep learning necessitates a more nuanced evaluation, emphasizing the model's generalization ability, especially during market crises such as stock market crashes. This paper explores the critical aspects of evaluating deep learning models' risk control capabilities in finance, underscoring the importance of understanding both statistical metrics and generalization abilities, particularly in adverse market conditions. By examining deep learning models' performance in scenarios like stock market crashes and highlighting the significance of cross-validation techniques, this study aims to offer practitioners insights into constructing robust risk management systems. It advocates for a comprehensive approach integrating quantitative analysis with macroeconomic factors to enhance financial risk prediction and analysis in volatile markets. The experimentation reveals that different deep generative models excel in various aspects of financial time series analysis, with generative adversarial networks (GANs) demonstrating superior performance in predicting Value at Risk (VaR) and variational autoencoders (VAEs) excelling in return rate prediction. Moreover, integrating multiple models further enhances predictive performance, leveraging the strengths of each model to compensate for individual weaknesses. Overall, this paper underscores the potential and significance of deep generative models in financial time series analysis, offering a roadmap for improved risk management and decision-making in financial markets.

Most existing coal-fired power plants were designed for sustained operation at full load to maximize efficiency, reliability, and revenue, as well as to operate air pollution control devices at design conditions. Depending on plant type and design, these plants can adjust output within a fixed range in response to the plant operating or market conditions. The need for flexibility driven by increased penetration of variable and non-dispatchable power generation such as wind and solar is shifting the traditional mission profile of thermoelectric power plants in three ways: more frequent shutdowns when market or grid conditions warrant, more aggressive load ramp rates (rate of output change), and lower minimum sustainable load, which provides a wider operating range and helps avoid costly plant shutdowns. Recent studies have shown that flexibility of a coal-fired power plant can be improved by energy storage. The objective of this work was to analyze a set of energy storage options and determine their impact on flexibility and economics of a representative coal-fired power plant. The effect of three energy storage systems integrated with a coal power plant on plant flexibility and economics was investigated. The results obtained in this project show that Energy Storage Systems integrated with a thermal power plant improves plant flexibility and participation in the Energy and Ancillary Services markets which improves plant financial performance. The study was funded by the U.S. Department Office of Fossil Energy FE-1 under Award Number DE-FE0031903.

Dairy products are an important source of protein and other nutrients in the Mediterranean diet. In these countries the most common source of milk for producing dairies are cow, goat, sheep and buffalo. Andalusia is traditionally the largest producer of goat milk in Spain. Kefir is a fermented product made from bacteria and yeasts which has health benefits beyond its nutritional properties. There is a lack of knowledge about the molecular mechanisms and metabolites that bring about these benefits. In this work, the combination of analytical techniques (GC-FID, UHPLC-MS-QToF, GC-QqQ-MS, and GC-ToF-MS) resulted in the detection of 105 metabolites in kefir produced with goat milk from two different thermal treatments (raw and pasteurized) fermented at four times (12, 24, 36, and 48 h, using 0 h as control). Of these, 27 metabolites differed between kefir produced with raw and pasteurized milk. These changes could be possibly caused by the effect of pasteurization on the microbial population in the starting milk. Some interesting molecules were identified, as shikimic acid, dehydroabietic acid, GABA and tyramine that could be related to antibacterial properties, enhance of immune system and arterial pressure. Moreover, a viability assay of the NIRS technique was performed to evaluate its use on monitoring the fermentation and classification of samples, which had resulted in a 90 % of correct classified samples according to their fermentation time. This study represents the most comprehensive metabolomic analysis of goat milk kefir so far, revealing the intricate changes in metabolites during fermentation and the impact of milk treatment.

A new type of artificial block is proposed by modifying the traditional block. Based on the physical model experiment, the wave reflection and run-up characteristics of the new ecological blocks under regular waves were examined, and the influence of the height of the "planting trough" border on the hydrodynamic properties was analyzed. The results indicate that the wave reflection and run-up would decrease with a decrease in the breakwater slope. As wave height increases, wave run-up increases, and the reflection coefficient falls. Wave reflection and run-up increase with an increasing wave period. The relative depth of the water has a smaller impact on the wave reflection than the wave run-up. The reflection coefficient of the new ecological blocks is consistently lower than that of the conventional and continues to decrease as the border of the "planting trough" is raised. The raised borders prevent water loss in the trough, resulting in no significant improvement in the wave run-up performance of the new blocks. The reflection coefficient and the relative run-up R/H gradually increase with the average wave breaking parameter ξ increase. The crude permeability coefficients for the new blocks, New Heights 1 and New Heights 2, are 0.53, 0.53, and 0.54, respectively.

The CACCC-box motif emerges as a pivotal cis-regulatory element implicated in diverse developmental processes and diseases, particularly cardiovascular diseases (CVDs). This study centers on the intricate interplay between the CACCC-box and its binding proteins such as: the Krüppel-like family (KLF) of transcription factors as primary effectors in the context of CVDs. Our analysis was through a bioinformatics approach, which reveal significant transcriptional activity among KLF subgroup 2, exhibiting the highest number of interactions focusing on the established roles: pluripotency, cancer, and cardiovascular development and diseases. Our analysis reveals KLF's interactions with GATA4, MEF2C, NKX2.5 and other ~ 90 potential genes that participate in the regulation of the hypertrophic environment (or CVD's Environment). Also, the GO analysis showed genes containing the motif CACCC were enriched for multiple CVD’s, in combination with STRING analysis these results pointed to a link between KLFs and these diseases. The analysis further identifies other potential CACCC-box binding factors, such as SP family members, WT1, VEZF1, and -SALL4, which are implicated in cardiac contraction, remodeling, and inflammation processes.

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

Tomato brown rugose fruit virus (ToBRFV) is an emerging tobamovirus infecting tomatoes and peppers. Due to its ability to trigger seed contamination, efficient mechanical transmission, and to break the popular resistance genes (Tm-1, Tm-2 and Tm-22), the virus has resulted in a global pandemic on greenhouse tomato production in recent years despite the implementation of strict hygiene and disinfection measures. The objective of this study was to evaluate whether ToBRFV can be transmissible through recirculating hydroponic systems and search for effective approach to contain its spread under such circumstances. We not only detected the presence of ToBRFV in the runoff water solutions collected from ToBRFV-infected tomato greenhouses, but also demonstrated the infectivity of detected virus through initiating ToBRFV infection on tomato plants using bioassay. We then conducted treatment using cold plasma-generated ozone on ToBRFV-contaminated water reservoir and further assessed the efficacy of the treatment to inac-tivate the virus. Results showed the effectiveness of the cold-plasma ozone treatment was de-pendent on the ozone concentrations (0.1 mg/L to 1.0 mg/L), the periods of exposure (24 min to 72 min), and the relative virus titers (1:100 to 1:10,000 - dilution of virus-infected tissue extract), demonstrating the cold-plasma ozone treatment could offer a promising solution to cope with the potential ToBRFV spread through a recirculating hydroponic system in a greenhouse.

Pyrazinacenes are linearly fused heteroaromatic rings, with N atoms replacing all apical CH moieties. Component rings may exist in a reduced state having NH groups instead of N, causing cross-conjugation. These compounds have interesting optical and electronic properties, including strong fluorescence in the near infrared region and photocatalytic properties, leading to diverse possible applications in bio-imaging, organic synthesis as well as obvious molecular electronic uses. In this study, we have investigated the behaviour of seven-ring pyrazinacene 2,3,11,12-tetraphenyl-7,16-dihydro-1,4,5,6,7,8,9,12,13,14,15,16,17,18-tetradecaazaheptacene (Ph4H2N14HEPT) with emphasis on protic processes including oxidation, tautomerism, deprotonation and protonation, and the species resulting from those processes. We have used computational methods to optimize the structures of the different species and generate/compare molecular orbital structures. Aromaticity of species generated by the different processes has been assessed using the nucleus independent chemical shifts, and trends in the values have been associated with the different transformations of the pyrazinacene core. The computational data was compared with experimental data obtained from synthetic samples of the molecule tBu8Ph4H2N14HEPT.

Keywords: Antibacterial activity, Antioxidant activity, Bioactive compounds, Elementary analysis, Nutritional and phytochemical composition, Seaweed.

The sustainability of ecotourism is the backbone of tourism development of a country. Ecotourism can contribute to both conservation and development in which involves dynamic relationship between tourism, biodiversity and communities, facilitate by great management. The purpose of this study is to analyze the dimensions of the socio-economic and environmental impacts of ecotourism in communities surrounding the Tasik Kenyir in Terengganu, Malaysia. This study provides a framework for the development and evaluation of ecotourism. The framework will determine if the relationships between indicators are positively correlated which will result in positive contribution to the other by using System Dynamic. Socio-cultural and economic data will be collected through interviews and group discussions in selected communities in Tasik Kenyir. Data on wildlife will be extracted from secondary data from Kenyir Research Institute, Universiti Malaysia Terengganu. The data analysis will explore the socio-cultural and economic differences between and within different communities due to ecotourism development, the contribution of ecotourism to conservation activities, local support for conservation and ecotourism as well as the influence of tourist activities on the distribution of wildlife species in Tasik Kenyir. This study aims to contribute toward understanding the natural resource community-ecotourism inter-relationship and help to bridge the knowledge gap that hinders biodiversity conservation initiatives. The findings will be used as a base for further development of ecotourism and will recommend alternative management options where necessary for the study area.

Various studies indicate that vaccination, especially with pneumococcal vaccines, protects against symptomatic cases of SARS-CoV-2 infection and death. This paper explores the possibility that pneumococcal vaccines in particular, but perhaps other vaccines as well, contain antigens that might be cross-reactive with SARS-CoV-2 antigens. Comparison of the glycosylation structures of SARS-CoV-2 with the polysaccharide structures of pneumococcal vaccines yielded no obvious similarities. However, while pneumococcal vaccines are primarily composed of capsular polysaccharides, some are conjugated to CRM197, a modified diphtheria toxin, and all contain about three percent protein contaminants, including the pneumococcal surface proteins PsaA, PspA and probably PspC. All of these proteins have very high degrees of similarity, using very stringent criteria, with several SARS-CoV-2 proteins including the spike protein, membrane protein and replicase 1a. CRM197 is also present in Hib and meningitis vaccines. Equivalent similarities were found at lower rates, or were completely absent, among the proteins in diphtheria, tetanus, pertussis, measles, mumps, rubella, and poliovirus vaccines. Notably, PspA and PspC are highly antigenic and new pneumococcal vaccines based on them are currently in human clinical trials so that their effectiveness against SARS-CoV-2 disease is easily testable.

Background: Coronavirus disease 2019 (COVID-19) has emerged as a global threat to human health and disease risk increases with advancing age. The regulation of the ACE2 gene that codes for COVID-19 host receptor ACE2 has been shown to be under epigenetic regulation. Here, we examined whether intensive DNA methylation profiling of the ACE2 gene differed by human host tissue and cell type, gender, and age. Results: Accessing four public datasets, we observed unique human cell-type-specific ACE2 DNA methylation patterns. In human lung tissues, gender differences in DNA methylation at 2 sites related to the ACE2 gene were identified. Further, in freshly isolated airway epithelial cells, DNA methylation near the transcription start site of the ACE2 gene associated with biological age. Conclusion: Epigenetic profiling of host tissue may permit discovery of age and gender-related potential risk factors for COVID-19. How perturbations in ACE2 methylation relate to clinical severity across the ages and gender needs to be determined to guide screening tools and potential epigenetic modification targeting to alleviate COVID-19 morbidity in the elderly.

To study the fermentation process of biocatalyzing decanoic acid to synthesize trans-2-decenoic acid, this study was based on the engineering E.coli constructed in the laboratory in the early stage and studied the effect of seed liquid culture time, culture temperature, inoculum amount, induction temperature, dissolution effects of substrate solvent, metal ions, and substrate loading on the yield of trans-2-decenoic acid. Based on single-factor experimental optimization, the Box–Behnken design was used to conduct response surface testing using the substrate feeding concentration, inducer concentration, and MnCl2 concentration as response variables and trans-2-decenoic acid production as the response value. The optimal fermentation process was determined through experiments: seed culture time 20 h, culture temperature 37°C, inoculation amount 1%, induction temperature 30°C, substrate flow amount 0.15 g/L, inducer concentration 5.60 g/L, MnCl2 concentration was 0.10 mM. Under these conditions, the average production of trans-2-decenoic acid was 1.982 ± 0.110 g/L. Compared with the basic LB medium, the production of trans-2-decenoic acid increased by 1.042 g/L; compared with the previous period, the yield of trans-2-decenoic acid studied increased by 1.501 ± 0.110 g/L, which provides a reliable basis for further research on the fermentation process of biocatalytic decanoic acid synthesis of trans-2-decenoic acid.

Background: This study investigated the biological effects on circulating monocytes after challenge with SARS-CoV-2 recombinant spike protein. Methods: Whole blood collected form seven ostensibly healthy healthcare workers was incubated for 15 min with 2 and 20 ng/mL recombinant spike protein of Ancestral, Alpha, Delta and Omicron variants. Samples were analyzed with Sysmex XN and DI-60 analyzers. Results: Cellular complexity (i.e., presence of granules, vacuoles and other cytoplasmic inclusions) increased in all samples challenged with the recombinant spike protein of Ancestral, Alpha and Delta variants, but not in those containing Omicron. The cellular content of nucleic acids was constantly decreased in most samples, achieving statistical significance in those containing 20 ng/mL of Alpha and Delta recombinant spike proteins. The heterogeneity of monocyte volumes significantly increased in all samples, achieving statistical significance in those containing 20 ng/mL of recombinant spike protein of Ancestral, Alpha and Delta variants. The monocyte morphological abnormalities after spike protein challenge included dysmorphia, granulation, intense vacuolization, platelet phagocytosis, development of aberrant nuclei and cytoplasmic extrusions. Conclusions: The SARS-CoV-2 spike protein triggers important monocyte morphological abnormalities, more evident in cells challenged with spike protein of the more clinically severe Alpha and Delta variants.

The repurposing of licenced drugs for use against COVID-19 is one of the most rapid ways to develop new and alternative therapeutic options to manage the ongoing pandemic. Given the approximately 8,000 licenced compounds available from Compounds Australia that can be screened, this paper demonstrates the utility of commercially-available ex vivo/3D airway and alveolar tissue models. These models are a closer representation of in vivo studies compared to in vitro models, but retain the benefits of rapid in vitro screening for drug efficacy. We demonstrate that several existing drugs appear to show anti-SARS-CoV-2 activity against both Delta and Omicron Variants of Concern in the airway model. In particular, fluvoxamine, as well as aprepitant, everolimus, and sirolimus have virus reduction efficacy comparable to the current standard of care (remdesivir, molnupiravir, nirmatrelvir). Whilst these results are encouraging, further testing and efficacy studies are required before clinical use can be considered.

The novel coronavirus (SARS-CoV-2) is a human pathogen recently emerged in China, causing a global pandemic of severe respiratory illness (COVID19). SARS-CoV-2 makes entry into human cells through its spike (S) protein that binds to cell surface receptors. Widespread of SARS-CoV-2 has been attributed to high affinity of S protein to its receptor. A homology model of the receptor binding domain of SARS-CoV-2 S protein (RBD) was built. RBD- receptor docking and published molecular dynamics data were used to map the key RBD-receptor interaction hotspot (RBDhp) on the RBD. Primary virtual screening was carried out against RBDhp using more than 3300 compounds approved by U.S Food and Drug Administration (FDA) and other authorities for human use. Compounds that bind to hpRBD with a binding energy ≤ - 6.5 kcal/mol were subjected to secondary screening using a recently published cryo EM (2.9 Å) structure of RBD. A cardiac glycoside (dgitoxin), two anthracyclines (zorubicin and aclarubicin), a tetracycline derivative (rolitetracycline), a cephalosporin (cefoperazone) and a food dye (E-155) were predicted to be most potent inhibitors of RBD – receptor interaction. An anti-asthmatic drug (zafirlukast) and several other drugs (itrazol, fazadinium, troglitazone, gliquidone, Idarubicin, Oxacillin) were found to be high affinity binders that may have a potential to inhibit RBD – receptor interaction.

Liver cancer is the fourth leading cause of cancer-related death worldwide. In Mexico, there is a high burden of liver cancer mortality in rural states, which affects both women and men equally. Thus, we aimed to describe the demographic and clinical characteristics of hepatocellular cancer (HCC) by sex in Mexico. Demographic and clinical information were extracted retrospectively from the medical records of patients with HCC initially treated (2015-2022) at participating institutions of a national survey across the country. The male-to-female ratio was calculated at the national and regional levels and the results were stratified by sex. Among 697 HCC patients, the age at diagnosis was 65.4±11.9y and 20% were diagnosed at ≥75y. The male-to-female ratio was 1.4:1, ranging from 1:1 in the northwestern and southwestern regions to 2.1:1 in the western region. The proportion of cirrhosis was similar between sexes; however, the etiologies for cirrhosis differed: cryptogenic cirrhosis was higher in women and alcohol consumption was higher in men. Men had a higher proportion of advanced HCC, poor/undifferentiated tumors, and ≥4 nodules than women. HCC in the Mexican population affects men and women at a 1.4:1 male-to-female ratio. This unique proportion by sex could be explained by the differences in the prevalence of risk factors across our heterogeneous country.

The paper describes schemes for the resource-efficient generation of twiddle factors for the fixed-radix version of the ubiquitous fast Fourier transform (FFT) algorithm. The schemes, which are targeted at a parallel implementation of the FFT, provide one with the facility for trading off arithmetic complexity, as expressed in terms of the required numbers of multiplications and additions (or subtractions), against the memory requirement, as expressed in terms of the amount of random access memory (RAM) required for constructing the look-up tables (LUTs) needed for the storage of the two twiddle factor components – one component being derived from the sine function and the other from the cosine function. Examples are provided which illustrate the advantages and disadvantages of each scheme – which are very much dependent upon the length of the FFT to be computed – for both the single-level and multi-level LUTs, highlighting those situations where their adoption might be most appropriate. More specifically, it is seen that the adoption of a multi‑level LUT scheme may be used to facilitate significant reductions in memory – namely, from an O(N) to an memory requirement, for the case of an N‑point FFT, where β ≥ 2 corresponds to the number of distinct angular resolutions used – at a relatively small cost in terms of increased FFT latency and arithmetic complexity.

Elliptic curve cryptography (ECC) over prime fields relies on scalar point multiplication realized by point addition and point doubling. Point addition and point doubling operations consist of many modular multiplications of large operands (256 bits for example), especially in projective and Jacobian coordinates which eliminate the modular inversion required in affine coordinates for every point addition or point doubling operation. Accelerating modular multiplication is therefore important for high-performance ECC. This paper presents the hardware implementations of modular multiplication algorithms, including 1) Interleaved modular multiplication (IMM), 2) Montgomery modular multiplication (MMM), 3) Shift-sub modular multiplication (SSMM), 4) SSMM with advance preparation (SSMMPRE), and 5) SSMM with CSAs and sign detection (SSMMCSA) algorithms, and evaluates their execution time (the number of clock cycles and clock frequency) and required hardware resources (ALMs and registers). Experimental results show that SSMM is 1.76 times faster than IMM, and SSMMCSA is 3.21 times faster than IMM. We also present the ECC hardware implementations based on the Secp256k1 protocol in affine, projective, and Jacobian coordinates using the IMM, SSMM, SSMMPRE, and SSMMCSA algorithms, and investigate their cost and performance. Our ECC implementations can be applied to the design of hardware security module systems.

This work deals with aerofoil aerodynamic features optimization, not only to improve flight features, but also to improve economy, ecology and safety of parameters of flight technique. In cruise mission, occupying the most flight time, the most important parameter is aerodynamic drag, which directly influences the aeroplane operational economy of transportation. Drag reduction is adequately reflected in the fuel consumption reduction. Consumption reduction is also adequately reflected in the flight ecology. In take-off and landing mission, the safety is priority and directly influences the aerofoil geometry. For cruise mission the new modified evolutionary algorithms (EA) are used to parameters incoming to Bezier-PARSEC 3434 parametrization. Such aerofoil is processed and evaluated by the Xfoil program. The change of model parameters results to optimal aerofoil shape. The DCAG (Direct Control Aerofoil Geometry) is unique developed mechanical device, makes possible the change of curvature of aerofoil, and also aerofoil geometry. DCAG is based on the rotary principle, which makes it possible to define the curvature of aerofoil for every roll as well as defining the geometry in the variable parts of aerofoil. For take-off and landing mission the best combination of slots and flaps is choosed. To improve of laminarity and reduce turbulent flow the DCAG is used. The work results to optimization, which is 50 times faster in comparison to ordinary optimization, with minimum of input parameters (flight speed, chord length, range of angles of attack and fitness function). The optimized aerofoil can achieve savings in fuel consumption up to 44% in comparison with unoptimized aerofoil, the aerodynamic drag reduction up to 44%. The output was checked by ANSYS Fluent simulation.

AbstractThis work deals with aerofoil aerodynamic features optimization, not only to improve flight features, but also to improve economy, ecology and safety of parameters of flight technique.In cruise mission, occupying the most flight time, the most important parameter is aerodynamic drag, which directly influences the aeroplane operational economy of transportation. Drag reduction is adequately reflected in the fuel consumption reduction. Consumption reduction is also adequately reflected in the flight ecology. In take-off and landing mission, the safety is priority and directly influences the aerofoil geometry.For cruise mission the new modified evolutionary algorithms (EA) are used to parameters incoming to Bezier-PARSEC 3434 parametrization. Such aerofoil is processed and evaluated by the Xfoil program. The change of model parameters results to optimal aerofoil shape.The DCAG (Direct Control Aerofoil Geometry) is unique developed mechanical device, makes possible the change of curvature of aerofoil, and also aerofoil geometry. DCAG is based on the rotary principle, which makes it possible to define the curvature of aerofoil for every roll as well as defining the geometry in the variable parts of aerofoil.For take-off and landing mission the best combination of slots and flaps is choosed. To improve of laminarity and reduce turbulent flow the DCAG is used.The work results to optimization, which is 50 times faster in comparison to ordinary optimization, with minimum of input parameters (flight speed, chord length, range of angles of attack and fitness function).The optimized aerofoil can achieve savings in fuel consumption up to 44% in comparison with unoptimized aerofoil, the aerodynamic drag reduction up to 44%.The output was checked by ANSYS Fluent simulation.

With the emerging technology connected with the internet, there is one constant issue related to that is data security. The only solution with which this issue can be resolved at a limit and can be used to protect the data is various algorithms for encryption. Though different approaches were used for the same, Cryptography seems to be efficiently protecting the data while transmitting in network from sender to receiver. Firstly the data is encrypted before sending to receiver using the most secure and reliable encryption algorithm. Secondly, at the receiver end it can be decrypted using the same decryption algorithm. Only receiver will have the key with which the data can be decrypted. In this paper, AES, DSS and RSA algorithms were implemented. These algorithms are encryption algorithms which perform encoding and decoding of data, to be sent from sender to receiver, using the keys. Each have different criteria for encryption and are then compared based on different parameters viz. delay, throughput, PDR is an acronym for packet delivery ratio, PLR represents packet loss ratio and RPC denotes Received Packet Count. The results in the form of graphs are given to analyze the security provided by each algorithm.

With the evolution of technology over the years, it has become possible to develop intelligent environments based on the concept of the Internet of Things, distributed systems, and machine learning. Such environments are incorporated with various solutions to solve user demands from services. One of these solutions is UBIPRI middleware, whose central concept is to maintain privacy in smart environments and to receive notifications as one of its services. However, this service is freely performed, disregarding the privacy that the environment employs. Another consideration is that based on the researched related works, it was possible to identify that the authors do not use statistical hypothesis tests in their solutions developed in the presented context. This work proposes an architecture for notification management in smart environments, composed by a notification manager named PRIPRO to assign it to UBIPRI and to aim to perform tests and comparisons between classification algorithms to delimit which one is most feasible to implement in the PRINM decision-making mechanism. The experiments showed that the J48 algorithm obtained the best results compared to the other algorithms tested and compared.

The importance of cognitive health for academic success is well-recognized. However, longitudinal data regarding the associations of body fatness, physical fitness and cognition are relatively scarce and mixed. The purpose of this study was to investigate whether body fatness, cardiorespiratory fitness (CRF) and muscular fitness (MF) in preschool are associated with cognitive skills in first grade at school. 133 South Estonian children aged 6-7 years were recruited from 13 kindergartens and again at 7-8 years when they had entered school. Body fat percentage (BF%), CRF, MF as the mean of z-scores of relative handgrip strength and standing long jump tests, and cognitive skills (verbal, conceptual, perceptual) were studied. There were no associations between BF% and CRF in pre-school with perceptual, conceptual or verbal skills in school in boys and girls. In boys, higher MF in preschool predicted higher verbal skills (β = 0.293, p = 0.021) in school after adjustment for con-founders. Cognitive skills at baseline seemed predominantly predict later cognitive performance in school. In conclusion, higher muscular fitness in preschool predicted better verbal skills in first grade at school in boys but not in girls. Body fatness and cardiorespiratory fitness in preschool were not associated with cognitive skills in school.

The ability of recombinant, SARS-CoV-2 Spike (S) protein to modulate the production of two COVID-19 relevant, pro-inflammatory cytokines (IL-6 and IFN-γ) in PBMC cultures of healthy, pre-Covid-19 subjects was investigated. We observed that cytokine production was largely and diversely modulated by the S protein depending on antigen or mitogen stimulation, as well as on the protein source, insect (S-in) or human (S-hu) cells. While both proteins co-stimulated cytokine production by polyclonally CD3-activated T cells, PBMC activation by the mitogenic lectin Concanavalin A (Con A) was up modulated by S-hu protein and down-modulated by S-in protein. These modulatory effects were likely mediated by the S glycans as demonstrated by direct Con A-S binding experiments and use of yeast mannan as Con A binder. While being ineffective in modulating memory antigenic T cell responses, the S proteins and mannan were able to induce IL-6 production in unstimulated PBMC cultures and upregulate the expression of Mannose Receptor (CD206), a marker of anti-inflammatory M2 macrophage. Our data point to a relevant role of N-glycans, particularly N-mannosidic chains, decorating the S protein in the immunomodulatory effects here reported. These novel biological activities of the S glycan ectodomain may add to the comprehension of Covid-19 pathology and immunity to SARS-CoV-2.

Both SARS-CoV-2 (COVID-19) and SARS coronaviruses (CoVs) are members of the subgenus Sarbecovirus. To understand the origin of SARS-CoV-2, protein sequences from sarbecoviruses were analyzed to identify highly-specific molecular markers consisting of conserved inserts or deletions (termed CSIs) in the spike (S) and nucleocapsid (N) proteins that are specific for either particular clusters/lineages of these viruses or are commonly shared by specific lineages. Three novel CSIs in the N-terminal domain of the spike protein S1-subunit (S1-NTD) are uniquely shared by the SARS-CoV-2, BatCoV-RaTG13 and most pangolin CoVs, distinguishing this cluster of viruses (SARS-CoV-2r) from all others. In the same positions, where these CSIs are found, related CSIs are also present in two other sarbecoviruses (viz. CoVZXC21 and CoVZC45 forming CoVZC cluster), which form an out group of the SARS-CoV-2r cluster. These three CSIs are not found in the SARS-CoVs. However, both SARS and SARS-CoV-2r CoVs contain two large CSIs in the C-terminal domain of S1 (S1-CTD), which binds the human ACE-2 receptor, that are absent in the CoVZC cluster of CoVs. These results indicate that while the S1-NTD of the SARS-CoV-2r viruses possesses the sequence characteristics of the CoVZC cluster of CoVs, their S1-CTD resembles the SARS viruses. Thus, the spike protein of SARS-CoV-2r viruses has likely originated from a recombination event between the S1-NTD of the CoVZC viruses and the S1-CTD of SARS viruses. This inference is also supported by the amino acid sequence similarity of the S1-NTD and S1-CTD from SARS-CoV-2 compared to the CoVZC and SARS CoVs. We also present evidence that one of the pangolin-CoV_MP789, whose receptor-binding domain is most similar to the SARS-CoV-2, is also derived by a recent recombination between the S1-NTD of the CoVZC CoVs and the S1-CTD of a SARS-CoV-2 related virus. Several other identified CSIs are specific for others clusters of sarbecoviruses including a clade consisting of bat SARS-CoVs (BM48-31/BGR/2008 and SARS_BtKY72). Structural mappings studies show that the identified CSIs are located within surface-exposed loops and form distinct patches on the surface of the spike protein. These surface loops/patches are predicted to interact with other host components and play important role in the biology/pathology of SARS-CoV-2 virus. Lastly, the CSIs specific for the SARS-CoV-2r clade provide novel means for development of new diagnostic and therapeutic targets for these viruses.

Cerebral ischemic stroke is one of the leading causes of death and disability worldwide. 2’-fucosyllactose (2’-FL), a human milk oligosaccharide, has anti-inflammatory effects and plays a protective role in arterial thrombosis, but its role in ischemic stroke remains unclear. This study aimed to investigate the neuroprotective effects of 2’-FL and its potential mechanisms in a mouse model of ischemic stroke. The administration of 2’-FL led to a reduction in the size of cerebral infarct and reactive oxygen species (ROS)-related products in the brain of MCAO mice. Further, 2’-FL promotes the recovery of neurological deficits and motor function in neurological scores and behavior tests. Biochemical studies showed that 2’-FL reduced upregulated IL-10 and downregu-lated TNF-α. Additionally,2’-FL enhanced M2-type microglial polar2’-FL upregulated IL-10 and downregulated TNF-α ization at 7 days after MCAO, with an increase in IL-4 levels and STAT6 activation at 3 days after MCAO. Our data showed that 2’-FL reduced the neurological symptoms of ischemic stroke and ROS accumulation in the brain through IL-4/STAT6-dependent M2-type mi-croglial polarization in MCAO mice. These results demonstrate that 2’-FL is a potentially effective therapeutic agent for ischemic stroke.

Recessive dystrophic epidermolysis bullosa (RDEB) is a rare severe hereditary skin disease characterized by skin and mucosa fragility, resulting in blister formation. The most severe complication in RDEB patients is the development of cutaneous squamous cell carcinoma (SCC) leading to premature death. Multiple evidences suggest a permissive tumor micro-environment (TME) as driver of SCC development in RDEB patients. In a cohort of RDEB patients, we characterized the immune profiles of RDEB-SCCs and compared them with clinical, histopathological, and prognostic features. RDEB-SCCs were subdivided into 4 groups based on their occurrence (first onset or recurrences) and grading according to their clinical-histopathological parameters of aggressiveness. Thirty-eight SCCs from 20 RDEB patients were analysed. Five RDEB patients experienced an unfavorable course af-ter the diagnosis of the first SCC, with early recurrence or metastasis, whereas 15 pa-tients developed multiple SCCs without metastasis. High-risk primary RDEB-SCCs showed a higher neutrophil-to-lymphocyte ratio in the tumor microenvironment, and an increased proportion of neutrophil extracellular traps (NET). Additionally, citrullinated histone H3, a marker of NETs, was increased in the serum of RDEB patients with high-risk primary SCC, suggesting that this modified form of histone H3 may serve as a potential blood marker of unfavorable prognosis in RDEB-SCCs.

Main pathogens affecting carob (Ceratonia siliqua) tree in the Mediterranean basin are described in this overview. The most widespread diseases periodically occurring on carob orchards are powdery mildew (Pseudoidium ceratoniae) and cercospora leaf spot (Pseudocercospora ceratoniae). The causal agents of so-called “black leaf spots” (e.g. Pestaliotiopsis, Phyllosticta and Septoria spp.) are responsible for similar symptoms to above mentioned foliar diseases but are reported on carob orchards with a negligible frequency. Likewise, canker and branch diebacks caused by fungal species belonging to Botryosphaeriaceae are almost never recorded. Among wood diseases that may compromise old or previously damaged carob specimens, “brown cubical heart rot” caused by Laetiporus sulphureus is the most widespread and recurrent issue; this pathogen is also well-known for edible fruit bodies that are also appreciated for pharmaceutical and industrial purposes. On the other hand, “white decays” caused by Fomes and Ganoderma species are less common and reported for the first time in this review. Like-gall protuberances on twigs at uncertain aetiology or tumours on branches associated to Rhyzobium radiobacter are described although these symptoms are seldom detected as it happens also for necrotic leaf spots caused by Pseudomonas syringae pv. ciccaronei. A list of worldwide pathogens not yet recorded but at high-risk potential of introduction in Italian carob producing area is also provided. Finally, some concerns are addressed to new phytopathogenic fungi vectored by invasive Xylosandrus compactus ambrosia beetle. Comprehensively, all described pathogens could potentially in near future become limiting factors for carob production, because they could be favoured by high-density orchards, increasing global network of trade exchanges and high frequency through which extreme events of climate changes globally occur. Thus, symptoms and signs, causal agents, epidemiology and whenever applicable recommendations for disease prevention and management are provided in this review.