Currently, obesity is a critical global public health burden. Numerous studies have demonstrated the regulation of the pathogenesis of obesity and metabolic abnormalities by the gut microbiota and microbial factors; however, their involvement in the various degrees of obesity is not yet understood well. Previously, obesity has been shown to be associated with decreased levels of vitamin B12. Considering exclusive microbial production of vitamin B12, we hypothesised that a decrease in cobalamin levels in obese individuals may be at least partially caused by its depleted production in the intestinal tract by the commensal microbiota. In the present study, our aim was to estimate the abundance of enzymes and metabolic pathways for vitamin B12 synthesis in the gut microbiota of mouse models of alimentary and genetically determined obesity, to evaluate the contribution of the obesogenic microbiome to vitamin B12 synthesis in the gut. We have defined a significantly lower representation of enzymes and metabolic pathways for vitamin B12 biosynthesis in obese mice compared to non-obese mice, wherein enzyme depletion was more pronounced in lepr(-/-) (db/db) mice, which developed severe obesity. The representation of enzymes involved in cobalamin synthesis is strongly correlated with representation of several microbes in high-fat diet fed mice, whereas there were almost no correlations in db/db mice. Therefore, the degree of obesity and the composition of the correspondent microbiota are the main contributors to the representation of genes and pathways for cobalamin biosynthesis in the mouse gut.

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

Cell-penetrating peptides (CPPs), a group of small peptides capable of promoting the transport of molecular cargo across the plasma membrane, have become important tools in promoting the cellular uptake of exogenously delivered macromolecules. GV1001, a peptide derived from a reverse-transcriptase subunit of telomerase (hTERT) and developed as a vaccine against various cancers, reportedly has unexpected CPP properties. Unlike typical CPPs, such as the HIV-1 TAT peptide, GV1001 enabled the cytosolic delivery of macromolecules such as proteins, DNA and siRNA via extracellular heat shock protein 90 (eHSP90) and 70 (eHSP70) complexes. The eHSP-GV1001 interaction may have biological effects in addition to its cytosolic delivery function. GV1001 was originally designed as a MHC class II-binding cancer epitope, but its CPP properties may contribute to its strong anti-cancer immune response relative to other telomerase peptide-based vaccines. Cell signaling via eHSP-GV1001 binding may lead to unexpected biological effects, such as direct anticancer or antiviral effects. In this review, we focus on the CPP effects of GV1001 bound to eHSP90 and ehsp70.

Utilizing ecological engineering to manage pests by enhancing the biological control function of natural predators through the selection of high-efficiency bank plants and the establishment of bank plant systems within agricultural ecosystems is an optimal strategy. While bank plant systems have found widespread use in greenhouse crop cultivation, their application in rice fields remains limited. This study aimed to assess the efficacy of Leersia sayanuka as a bank plant for controlling planthoppers and to explore the potential of sesame as a complementary plant to enhance the effectiveness of the bank plant system through field trials. The findings revealed that planting a strip of L. sayanuka, at least 50 cm wide and extending over 1 m in length, had a notable impact on planthopper control in paddy fields. Moreover, the incorporation of Sesamum indicum, a nectar plant, into the bank plant system consisting of L. sayanuka, Nilaparvata muiri, and Anagrus nilaparvatae, led to a significant increase in the population of Anagrus spp. in rice fields. This increase was accompanied by a rise in the egg parasitism rate targeting rice planthoppers, resulting in a marked improvement in overall rice planthopper control in paddy fields. Thus, the combined system demonstrated efficiency as an effective bank plant system for managing rice planthoppers.

Microsoft has implemented several measures to defend against macro viruses, including the use of Anti-malware Scan Interface (AMSI) and automatic macro blocking. Nevertheless, evidence shows that threat actors have found ways to bypass these mechanisms. As a result, phishing emails continue to apply malicious macros as their essential attack vector. In this paper, we analyze 77 obfuscation features from the attacker’s point of view and extract 46 suspicious keywords in macro. We first combine the above two types of features to train machine learning models on a public dataset. Then, we carry out the same experiment on self-constructed dataset, a collection of newly discovered samples, to see if our proposed method could discover the unseen malicious macros. Experimental results demonstrate that, comparing with the existing researches, our proposed method has a higher detection rate and better consistency. Furthermore, ensemble multi-classifiers with distinct feature selection further improve the detection performance.

Osteoarthritis (OA) is the most common degenerative joint disease that causes chronic pain and disability. Different innate immune components, including macrophages, T cells, and neutrophils, participate in osteoarthritis pathophysiology. Neutrophils are the most abundant circulating leukocytes with multiple specialized functions contributing to innate and adaptive immune functions. Although neutrophils produce proinflammatory cytokines and chemokines, reactive oxygen species (ROS), matrix-degrading enzymes, and neutrophil extracellular traps (NET) that promote joint degradation as the first recruit cells in an inflamed joint, these cells also play an important role in joint repair by regulating the immune response, releasing anti-inflammatory factors, and activating some protective genes. In this review, various aspects of neutrophil biology, their role in inflammation and its association with osteoarthritis, and possible therapeutic approaches to target neutrophils for the treatment of osteoarthritis are described. Understanding neutrophil heterogeneity and their mechanisms of action in joint inflammation, provides a potential strategy for OA management.

This article responds to an identified and significant gap in the existing scholarly canon to consider the extent to which cinematic representations construct heroin users—the ‘junkie’—as a criminalised ‘Other’ which confer legitimacy on the notion that such are criminogenic and deviant. Positioned within the disciplinary bounds of cultural criminology, this article focuses on five films - Sid and Nancy (1986); The Basketball Diaries (1995); Trainspotting (1996); Requiem for a Dream (2000); and, T2 Trainspotting (2017). Drawing together Hall’s (1997) theories of representation and Hjelm’s (2014) theories of social constructionism, the findings from a narrative analysis of each of the films—individually and comparatively—explores the following themes, junkies: as criminogenic; as dangerous underclass; as embodying decay and depravity; and in relation to female junkies, as junkie whores. In doing so, this article elucidates new thinking and ideas about cinematic representations of junkies and how this shapes and influences social norms and mores.

The endocannabinoid system (ECS) is a complex neuromodulatory network in the human body that have enormous impact in developing the central nervous system, as well as modulates various physiological processes. The ECS is made up of anandamide, 2-arachidonoylglycerol (2-AG), which are responsible for the synthesis and degradation of endocannabinoids, and enzymes such as FAAH and MAGL ensures the precise control over endocannabinoid levels. The ECS relies on CB1 receptors found in the central nervous system (CNS), and CB2 receptors found in the peripheral nervous system and immune cells. Dysregulation of the ECS has been associated with several health conditions, prompting investigations into its association with phytocannabinoids to harness their therapeutic potential. The ECS is intricately linked with the nervous system, and it is known to modulate neuronal activities in the CNS, influencing pain and mood modulation. The ECS is also associated with neuropathies-peripheral nerve damage leading to symptoms such as weakness, numbness and pain as evidenced in diabetic neuropathy, Guillain-Barré Syndrome, carpal tunnel syndrome, peripheral neuropathy, and postherpetic neuralgia. The focus of this review is on the interplay between 2-arachidonoylglycerol and anandamide amidohydrolase in neuropathy, elucidating their roles in pain regulation, inflammation, and specific neuropathies. The knowledge generated is expected to have broad implications, providing comprehensive insights into neuropathic conditions and how to improve on them. Ultimately, this understanding may lead to more effective treatments and improved quality of life for individuals affected by these conditions.

In eastern part of Ethiopia groundnut is very commonly intercropped with sorghum. Even though intercropping of sorghum with groundnut is practiced in the eastern part of Ethiopia the population density at what ratio should not be known. Therefore determining the population ratio is found to be crucial to draw management options. The study was conducted at two locations at Fadis (on station) and Babile (sub- station). Sorghum variety (Teshale) and groundnut (Fetene) were used as planting materials. All agronomic data were collected and analyzed by using GenStat software. Significant different were obtained among the treatments. The research result showed that, there were significance difference for both sorghum and ground nut yield per hectare in the years (2014 and 2015) among treatments. The result over time(in 2014 and 2015) at Fadis and Erer showed that the highest sorghum and groundnut yield per hectare were obtained/harvested from the intra row spacing of 25cmx20cm and 30cmx20cm intercropping sorghum with ground nut system with (1.27 and 1.31) respectively. The highest LER (1.31) and highest GMV (10218.00 ETBr/ha) were obtained from the intercropping of 30cmx20cm (Sorghum & groundnut). Clearly showed that, with intercropping of sorghum and Groundnut, it is possible to produce additional yield of sorghum without significant reduction in groundnut yield. As a result of this, the intra spacing of the main crop (sorghum) 25cm and 30cm and for the subsidiary crop (groundnut) 20cm was recommended for further production in the study areas of eastern Harerghe zone and similar agro-ecologies.

Abstract. Results of research into four-membered phosphorus(V)-heterocycles, 2-halogen-1,2λ⁵-oxaphosphetanes are presented. The preparation of 2-halo-1,2λ⁵-oxaphosphetanes by reaction of P-haloylides with carbonyl compounds is described. The mechanism of asynchronous [2+2]-сycloaddition of ylides to aldehydes was proposed on the base of low-temperature NMR investigations. 2-Halogen-1,2λ⁵-oxaphosphetanes were isolated as individual compounds and their structures were confirmed by ¹Н, ¹³C, ¹⁹F and ³¹Р NMR spectra. These compounds are convenient reagents for preparing of various organic and organophosphorus compounds hardly available by other methods. Chemical and physical properties of the 2-halo-1,2λ⁵-oxaphosphetanes are reviewed. The 2-chloro-1,2λ⁵-oxaphosphetanes depending on substituents at the α-carbon atom rearrange with formation of 2-chloroalkylphosphonates or convert into trans-phosphorylated alkenes. Prospective synthetic applications of 2-halo-1,2λ⁵-oxaphosphetanes are analyzed. The 2-halo-1,2λ⁵-oxaphosphetanes may be easily converted to various alkenylphosphonates: allyl- or vinylphosphonates, phosphorus ketenes, thioketenes, ketenimines.

The SK model proposed by Sherrington and Kirkpatrick is an important mean field model for spin glasses. The dependence structure of Hamiltonians of the SK model is considered in this paper. We have investigated the covariance matrix of Hamiltonians of the SK model, and establish a relationship between the rank of the corresponding covariance matrix and the dimension of the space spanned by the Hamiltonians. Finally, we also give an upper bound for the rank of the covariance matrix, which might be the best.

DNA vaccines are stable, safe, cost effective to produce and relatively quick and easy to manufacture. However, to date DNA vaccines have shown relatively poor immunogenicity in humans despite promising preclinical results. Consequently, a number of different approaches have been investigated to improve the immunogenicity of DNA vaccines. These include the use of improved delivery methods, adjuvants, stronger promoters and enhancer elements to increase antigen expression, and codon optimization of the gene of interest. This review describes the creation and use of a DNA vaccine vector containing a porcine circovirus (PCV-1) enhancer element that significantly increases recombinant antigen expression and immunogenicity and allows for dose sparing. A 172bp region containing the PCV-1 capsid protein promoter (Pcap) and a smaller element (PC; 70 bp) within this were found to be equally effective. DNA vaccines containing the Pcap region expressing various HIV-1 antigens were found to be highly immunogenic in mice, rabbits and macaques, at 4 to 10-fold lower doses than normally used and to be highly effective in heterologous prime-boost regimens. By lowering the amount of DNA used for immunization, safety concerns over injecting large amounts of DNA into humans can be overcome.

The human landscape transforming activity contributes to the global change of vegetation in different forms. Hunting is one of the most ancient human landscape-shaping activities. Feeders for hunting are particularly disruptive to vegetation. In the present study, we conducted a vegetation survey in these highly disturbed places. We are investigating the vegetation dynamics over several years in in the turkey oak–sessile oak zone, in two areas with different moisture and shade conditions (forest and clearing). One important background factor is changes in precipitation and temperature. The novelty of our present study lies in the fact that the observation takes place with continuous disturbance, where the human influence is very large. Our results confirm that the weed infestation be detectable at bait sites over a long period, and the interannual changes were typical in every year, according to the fact that in August, more weed species are usually present with a greater cover at the bait sites. The main meteorological factors played a role in the degree of weed infestation in each year. There was a higher proportion of vegetation regeneration in wetter years, while dry periods do not favor successional processes. With the drying of the climate, the disturbed areas are constantly losing their natural value, but wetter weather is not an automatic solution either. A more precise understanding of vulnerable systems and careful planning of treatment interventions is necessary.

COVID-19, a contagious disease caused by the novel coronavirus SARS-CoV-2, emerged in 2019 and quickly became a pandemic, infecting more than 700 million people worldwide. The disease incidence, morbidity and mortality rates have started to decline since the development of effective vaccines against the virus and the widespread immunization of the population. SARS-CoV-2 vaccines are associated with minor local or systemic adverse reactions, while serious adverse effects are rare. Thyroid-related disorders have been reported after vaccination for COVID-19, and Graves’ disease (GD) is the second most common amongst them. Thyroid eye disease (TED), an extrathyroidal manifestation of GD is rarely observed post COVID-19 vaccination. All TED cases followed mRNA-based vaccinations, but two new onset mild TED cases post viral vector vaccine (ChAdox1nCoV-19) have also been reported. We report the case of a 63-year-old woman who presented with new onset hyperthyroidism and moderate-to-severe and active TED ten days after she received the first dose of a viral vector vaccine against SARS-CoV-2. This is the first case of moderate-to-severe TED after such a vaccine. Our patient was initially treated with intravenous glucocorticoids and subsequently with intravenous rituximab, due to no response. The disease was rendered inactive after rituximab, but constant diplopia persisted, and the patient was referred for rehabilitative surgery.

Anxiety disorders significantly reduce patients' quality of life. Current pharmacological treatments, primarily benzodiazepines and antidepressants, are associated with numerous side effects. Consequently, there is a continual search for alternative methods to traditional therapies that are less burdensome for patients and broaden their therapeutic options. In this review, we examined recent evidence on alternative treatments for anxiety disorders, including physical activity, mindfulness, VR technology, biofeedback, herbal remedies, and transcranial magnetic stimulation (TMS), cryotherapy, hyperbaric therapy, vagus nerve stimulation, MDMA, electroconvulsive therapy (ECT), and eye movement desensitization and reprocessing (EMDR) therapy. Our objective was to determine the role of these selected alternative methods in the treatment of anxiety disorders. Alternative treatments significantly expand the options available to patients and clinicians, with many serving as adjuncts to traditional therapies. Among the methods presented, mindfulness has the most significant therapeutic potential.

Despite advances in understanding of the molecular pathogenesis of glioma, outcomes remain dismal. Developing successful treatments for glioma requires faithful in vivo disease modeling and rigorous preclinical testing. Murine models, including xenograft, syngeneic, and genetically engineered models, are used to study gliomagenesis, identify methods of tumor progression, and test novel treatment strategies. Since the discovery of highly recurrent isocitrate dehydrogenase (IDH) mutations in lower-grade gliomas, there is increasing emphasis on effective modeling of IDH mutant brain tumors. Improvements in preclinical models that capture the phenotypic and molecular heterogeneity of gliomas are critical for the development of effective new therapies. Herein, we explore the current status, advancements, and challenges with contemporary murine glioma models.

Background: This study aimed to evaluate whether measles vaccine uptake can be predicted directly or indirectly by parental perceptions about the availability of measles vaccine services with parental hesitancy towards the measles vaccine as a potential mediator. Methods: This was a community-based cross-sectional study conducted at Omdurman locality in Khartoum state, Sudan in February 2019. The study population included parents/ guardians having at least one child aged 2 -3 years old. Mediation analysis was conducted using two models, the ordinary least squares path analysis and multiple logistic regression. Results: a total of 495 responded and the mean age of the mothers who participated in the study was 31.1 (SD=5.73). A half of the respondents (50.1%) completed university education and nearly three-quarters of the respondents (74.7%) were housewives. After controlling for the other factors, including the mother’s age and the number of children, parental perception about the accessibility and availability of the measles vaccine influences the uptake of the measles vaccine indirectly through the mediation effect of measles vaccine hesitancy. Conclusions: We suggest that intervening in measles vaccine hesitancy in addition to measles vaccination access issues will have positive impact on the uptake and coverage of the measles vaccine in Sudan.

A dioxomolybdenum catalyst [(MoO2Cl2-O-MoO2Cl2)(L)] (1) (with L (1S,2S)-N,N´-bis(2-pyridinecarboxamide)-1,2-cyclohexane) was prepared and used as catalyst for the desulfurization of a multicomponent model fuel containing the most refractory sulfur compounds in real fuels. This complex showed to have a high efficiency to oxidize the aromatic benzothiophene derivative compounds present in fuels, mainly using a biphasic 1:1 model fuel/MeOH system. This process conciliates catalytic oxidative and extractive desulfurization, occurring the oxidation of the sulfur compounds in the polar organic solvent. The oxidative catalytic performance of (1) showed to be influenced by the presence of water in the system. Using 50% aq. H2O2 it was possible to reuse the catalyst and the extraction solvent MeOH during ten consecutive cycles without loss of desulfurization efficiency.

In order to study the key factors that induce green travel among urban residents under the carbon inclusive mechanism, analyze the formation mechanism of green travel intention and reduce urban transportation carbon emissions. First, an SEM model of urban residents’ green travel under the carbon inclusive mechanism was constructed (the 8 latent variables including Subjective Norms (SN), Attitude (ATT), Perceived Behavioral Control (PBC), Habits (HAB), Perceived Usefulness (PU), Perceived Ease of Use (PEU), Continuous Intention of Carbon Inclusive Platform application (CI), Behavioral Intention (BI)). Then, based on the data analysis and path analysis results of 354 valid sample data obtained through online surveys, the interrelationships and corresponding parameters between latent variables and manifest variables were obtained. The results show that SN, ATT, PBC, HAB, PU, PEU and CI all have varying degrees of effects on BI. Among them, in the direct influence relationship, the influence path coefficients of CI, PBC and ATT on BI are 0.529, 0.299 and 0.146 respectively; The influence coefficient of HAB on BI is 0.152, but P>0.05, it fails the test, so the influence of HAB on BI is Not significant; in the indirect impact relationship, the coefficient of HAB indirectly affecting BI through ATT is 0.102, and the coefficient of PU indirectly affecting BI through CI and SN is 0.450. It can be seen that under the carbon inclusive mechanism, useful methods such as increasing the value of carbon points and the breadth of application, improving perceived behavioral control, increasing publicity and social awareness, and cultivating green travel attitudes can promote the willingness of urban residents’ green travel behavioral intention.

Heavy metal accumulation in soils used for agricultural purposes is a major issue as it can affect the growth of plants and contain health risks as plants containing heavy metals are taken as food. This study provided the potential of three Bacillus pumilus strains (C-2PMW-8, C-1 SSK-8, and C-1PWK-7) for mitigation of the health factors related to heavy metals on lettuce (Lactuca sativa) grown in chromium contaminated soil from activities related to mining. Lettuce seeds inoculated with B. pumilus strains were grown in pots with garden soil amended with varying amount of chromite mine soil (2.27%, 4.65%, 7.14%). Growth indicators like days to germination, percentage of germination, leaf weight, leaf area and rate of survival decreased with incremental soil chromium levels but improved highly with inoculation of B. pumilus compared to uninoculated controls. When B. pumilus strains were provided, there was also a decreased level in the buildup of chromium in soil as well as lettuce shoots. The results explains that plant growth-promoting B. pumilus strains may be able to lower down the detrimental effects of chromium on lettuce development, hence increment in the effectiveness of phytoremediation.

Leveraging the open-world understanding capacity of large-scale visual-language pre-trained models has become a hot-spot in point cloud classification. Recent approaches rely on transferable visual-language pre-trained models, classifying point clouds by projecting them into 2D images and evaluating consistency with textual prompts. These methods benefit from the robust open-world understanding capabilities of visual-language pre-trained models and require no additional training. However, they face several challenges summarized as prompt ambiguity, image domain gap, view weights confusion, and feature deviation. In response to these challenges, we propose PointBLIP, a zero-training point cloud classification network based on the recently introduced BLIP-2 visual-language model. PointBLIP is adept at processing similarities between multi-images and multi-prompts. We separately introduce a novel method for point cloud zero-shot and few-shot classification, which involves comparing multiple features to achieve effective classification. Simultaneously, we enhance the input data quality for both the image and text sides of PointBLIP. In point cloud zero-shot classification tasks, we outperform state-of-the-art methods on three benchmark datasets. For few-shot classification tasks, to the best of our knowledge, we present the first zero-training few-shot point cloud method, surpassing previous works under the same conditions and showcasing comparable performance to full-training methods.

The rapidly evolving field of artificial intelligence (AI) continues to witness the introduction of innovative open-source pre-trained models, fostering advancements in various applications. One such model is Llama 2, an open-source pre-trained model released by Meta, which has garnered significant attention among early adopters. In addition to exploring the foundational elements of the Llama v2 model, this paper investigates how these early adopters leverage the capabilities of Llama 2 in their AI projects. Through a qualitative study, we delve into the perspectives, experiences, and strategies employed by early adopters to leverage Llama 2's capabilities. For the purpose of data analysis, the capabilities inherent in the Llama 2 model were employed to conduct keyword extraction from the context of the early adopters' case studies. The findings shed light on the model's strengths, weaknesses, and areas of improvement, offering valuable insights for the AI community and Meta to enhance future model iterations. Additionally, we discuss the implications of Llama 2's adoption on the broader open-source AI landscape, addressing challenges and opportunities for developers and researchers in the pursuit of cutting-edge AI solutions. The present study constitutes an early exploration of the Llama 2 pre-trained model, holding promise as a foundational basis for forthcoming research investigations.

COVID-19 is a contagious respiratory disease that is causing significant global morbidity and mortality. Understanding the impact of a SARS-CoV-2 infection on the host metabolism is still in its infancy but of great importance. Herein, we investigated the metabolic response during viral shedding and post-shedding in an asymptomatic SARS-CoV-2 ferret model (n=6) challenged with two SARS-CoV-2 isolates. Virological and metabolic analyses were performed on (minimally invasive) collected oral swabs, rectal swabs, and nasal washes. Fragments of SARS-CoV-2 RNA were only found in the nasal wash samples in four of the six ferrets, and in the samples collected 3 to 9 days post-infection (referred to as viral shedding). Central carbon metabolism metabolites were analyzed during viral shedding and post-shedding periods using a dynamic MRM (dMRM) database and method. Subsequent untargeted metabolomics and lipidomics of the same samples were performed using an LC-QToF-MS methodology, building upon the identified differentiated central carbon metabolism metabolites. Multivariate analysis of the acquired data identified 29 significant metabolites and three lipids that were subjected to pathway enrichment and impact analysis. The presence of viral shedding coincided with the challenge dose administered and significant changes in the citric acid cycle, purine metabolism, and pentose phosphate pathways, amongst others, in the host nasal wash samples. An elevated immune response in the host was also observed between the two isolates studied. These results support other reported metabolomic-based findings found in clinical observational studies and indicate the utility of metabolomics applied to ferrets for further COVID-19 research that advances early diagnosis of asymptomatic and mild clinical COVID-19 infections, in addition to assessing the effectiveness of new or re-purposed drug therapies.

In the present work we propose a dynamical mathematical model of the lung cells inflammation process in response to SARS-CoV-2 infection. In this scenario the main protease Nsp5 enhances the inflammatory process, increasing the levels of NF kB, IL-6, Cox2, and PGE2 with respect to a reference state without the virus. In presence of the virus the translation rates of NF kB and IkB arise to a high constant value, and when the translation rate of IL-6 also increases above the threshold value of 7 pg mL^-1 s^-1 the model predicts a persistent over stimulated immune state with high levels of the cytokine IL-6. Our model shows how such over stimulated immune state becomes autonomous of the signals from other immune cells such as macrophages and lymphocytes, and does not shut down by itself. We also show that in the context of the dynamical model presented here, Dexamethasone or Nimesulide have little effect on such inflammation state of the infected lung cell, and the only form to suppress it is with the inhibition of the activity of the viral protein Nsp5.To that end, our model suggest that drugs like Saquinavir may be useful. In this form, our model suggests that Nsp5 is effectively a central node underlying the severe acute lung inflammation during SARS-CoV-2 infection. The persistent production of IL-6 by lung cells can be one of the causes of the cytokine storm observed in critical patients with COVID19. Nsp5 seems to be the switch to start inflammation, the consequent overproduction of the ACE2 receptor, and an important underlying cause of the most severe cases of COVID19.

The term neuroinflammation defines the reactions of astrocytes and microglia to alterations in homeostasis in the diseased central nervous system (CNS), the exacerbation of which contributes to the neurodegenerative effects of Alzheimer's disease (AD). Local environmental conditions, such as the presence of proinflammatory molecules, mechanical properties of the extracellular matrix (ECM) and local cell-cell interactions, are determinants of glial cell phenotypes. In AD, the load of the cytotoxic/proinflammatory amyloid β peptide (Aβ) is a microenvironmental component increasingly growing in the CNS, imposing time-evolving challenges on resident cells. This study aimed at investigating the temporal and spatial variations of the effects produced by this process on astrocytes and microglia, either directly or by interfering in their interactions. Ex vivo confocal analyses of hippocampal sections from the mouse model TgCRND8 at different ages have shown that overproduction of Aβ peptide induced early and time persistent disassembly of functional astroglia syncytium and promoted a senile phenotype of reactive microglia, hindering Aβ clearance. In the late stages of disease, these patterns were altered in the presence of Aβ-plaques, surrounded by typically reactive astrocytes and microglia. Morphofunctional characterization of peri-plaque gliosis revealed a direct contribution of astrocytes in plaque buildup that might result in shielding Aβ-peptide cytotoxicity and, as a side effect, in exacerbating neuroinflammation.

The purpose of this study was to investigate the effect that Glycine max hydrolyzed with enzymes from Bacillus velezensis KMU01 has on dextran sulfate sodium (DSS)-induced colitis in mice. Hydrolysis improves functional health through the bioconversion of raw materials and increase in intestinal absorption rate and antioxidants. Therefore, G. max was hydrolyzed in this study using a food-derived microorganism, and its anti-inflammatory effect observed. Enzymatically hydrolyzed G. max (EHG) was orally administered once daily for four weeks before DSS treatment. Colitis was induced in mice through consumption of 5% (w/v) DSS in drinking water for eight days. Results showed that EHG treatment significantly alleviated DSS-induced body weight loss and decreased the disease activity index and colon length. In addition, EHG markedly reduced tumor necrosis factor-a, interleukin (IL)-1b, and IL-6 production, and increased that of IL-10. EHG improved DSS-induced histological changes and intestinal epithelial barrier integrity in mice. Moreover, we found that the abundance of 15 microorganisms changed significantly; that of Proteobacteria and Escherichia coli, which are upregulated in patients with Crohn’s disease and ulcerative colitis, decreased after EHG treatment. These results suggest that EHG has a protective effect against DSS-induced colitis and is a potential candidate for colitis treatment.

Understanding the optimal strategy for a real estate investment and how performance changes based on characteristics is crucial for optimising the achievable return. This is prominent in tourist areas such as Paphos where there is no clear distinction as to whether short or long-term approaches are optimal. The study aimed to develop a model for predicting the optimal rental strategy whilst assessing which model performed best and which property attributes impacted its return the greatest. Short-term data was collected from AirDNA and long-term data was manually collected from real estate agents websites. Furthermore, Random Forest, K-Nearest Neighbour and Multiple Linear Regression models were created to predict the highest and best use for each property. Model accuracy varied between data sets with the best performing model for short-term properties being Random Forest (R-Squared: 0.843), and Distance-Based Multiple Linear Regression for long-term approach (R-Squared: 0.843). The study demonstrated that accurate models could be created to predict the optimal rental strategy with the number of bedrooms being the main driver for rental income, followed by luxury finishes and the presence of a pool. It found that locational characteristics didn’t impact the returns significantly assuming that the property was located within a tourist area.

Climate change is affecting the planet, compromising survival and altering forest ecosystems. Investigating climatic variations in all regions is important. The aim of this research was to assess such variations in the W and Pendjari Biosphere Reserves in Benin from 1985 to 2015 and predict climate up to 2045. Drought frequency was assessed by using the Standardized Precipitation and Evapotranspiration Index (SPEI) and correlation was performed between climatic variables. Then a predictive analysis was computed. From 1985 to 2015, there was change in the climate with few severe dry and wet years while no extreme climatic condition was noticed. Most of the years showed nearly normal climatic conditions. The predicted climate indicates an absence of extreme conditions up to 2045 but an increase in the annual humidity is expected. A low decrease in annual mean temperature with high confidence is expected. Decision-makers can better organize adaptation programs and sensitize people in the reserves on their role in environmental conservation and mitigation of climate change using these outputs.

Global warming leads in general to a reduction in precipitations and non-rainfall water contributions such as dew. The present study, carried out in Gujarat (India) for 17 years between 2005 and 2021, shows however a rare increase of the rain falls and dew condensation, the latter related to an increase in relative humidity and a decrease in wind amplitudes. Rain was obtained from meteo stations. Dew was calculated from an energy model and meteo data from 15 meteo stations (14 in India and 1 in Pakistan) and compared to early and dedicated measurements in 13 sites. Maps of dew, rain and their relative contributions during the period are provided. They show that, while rain mainly occurs during the monsoon (June to September), dew forms during the dry season (October to April). This water resource was mapped over the Gujarat state of India by using a kriging process. According to the site, the dew/rain ratios can represent between 4.6% (Ahmedabad) to 37.2% (Jamnagar) on the whole period of 17 years. This positive evolution, which is observed since 2015-2017, is likely to continue in the future.

In agriculture, soil-borne fungal pathogens, especially Fusarium oxysporum strains, are posing a serious threat to efforts to achieve global food security. In the search for safer agrochemicals, silica nanoparticles (SiO2NPs) have recently been proposed as a new tool to alleviate pathogen damage including Fusarium wilt. Hollow mesoporous silica nanoparticles (HMSNs), a unique class of SiO2NPs, have been widely accepted as a desired pesticide carrier. However, their role in enhancing the disease resistance and the specific mechanism remain unknown. In this study, three sizes of HMSNs (19, 96 and 406 nm as HMSNs–19, HMSNs–96 and HMSNs–406, respectively) were synthesized and characterized to determine their effects on seed germination, seedling growth, and Fusarium oxysporum f. sp. phaseolus (FOP) suppression in cowpea roots by foliar spray using phenotypes, fresh biomass and disease progression as indicators. The results revealed that three HMSNs exhibited no adverse impacts on seed germination and tended to improve plant growth. Also, they exert their FOP suppression with a size- and concentration-dependent manner. HMSNs–406 possessed the best control effect at a concentration of 1000 mg/L showing an upto 40.00% decline in the disease severity. The Si(OH)4 control was also effective on FOP suppression at a lower concentration of 100 mg/L, whereas its higher concentrations exhibited obviously adverse impacts on FOP control, seed germination and plant growth. Moreover, we conformed that HMSNs posed their Fusarium wilt suppression in cowpea plant by activating SA (salicylic acid)-dependent SAR (systemic acquired resistance) responses rather than directly suppressing FOP. A higher level of SA content and elevated expression of its maker genes of PR-1 and PR-5 in HMSNs–406 treated cowpea roots provided substantial evidences of this mode of action. Other resistance-related genes, as well as defense-responsive enzymes, were also involved in the HMSNs-activated SAR pathway. Overall, for the first time, our results extended a new role of HMSNs as a potent elicitor to serve as a versatile alternative for plant disease protection of low cost, highly efficiency and sustainability.

Efficient textual data distributions (TDD) alignment and generation are open research problems in textual analytics and NLP. It is presently difficult to parsimoniously and methodologically confirm that two or more natural language datasets belong to similar distributions, and to identify the extent to which textual data possess alignment. This study focuses on addressing a segment of the broader problem described above by applying multiple supervised and unsupervised machine learning (ML) methods to explore the behavior of TDD by (i) topical alignment, and (ii) by sentiment alignment. Furthermore we use multiple text generation methods including fine-tuned GPT-2, to generate text by topic and by sentiment. Finally we develop a unique process driven variation of Kullback-Leibler divergence (KLD) application to TDD, named KL Textual Distributions Contrasts (KL-TDC) to identify the alignment of machine generated textual corpora with naturally occurring textual corpora. This study thus identifies a unique approach for generating and validating TDD by topic and sentiment, which can be used to help address sparse data problems and other research, practice and classroom situations in need of artificially generated topic or sentiment aligned textual data.

Geothermal energy, being a clean energy source, has immense potential, and accurate wellbore modeling is crucial for optimizing the drilling process and ensuring safety. This paper presents a novel geothermal wellbore model based on the drift-flux approach, tested under three different temperature and pressure well conditions. The proposed model integrates the conservation equations of mass, momentum, and energy, incorporating the gas-liquid two-phase flow drift-flux model and heat transfer model. Key features include handling heat transfer between the for-mation and the wellbore, addressing the slip relationship between gas and liquid phases, and accounting for wellbore friction. The nonlinear equations are discretized using the finite differ-ence method, and the highly nonlinear system is solved using the Newton-Raphson method. Numerical simulation, validation, and comparison with existing models demonstrate the en-hanced accuracy of this model. In our tests, the model achieved high accuracy in calculating bottom hole pressure and temperature, with Mean Relative Errors (MRE) significantly lower than those of other models. These results offer valuable insights for optimizing drilling parameters and ensuring drilling safety. Comparisons indicate that this approach significantly outperforms others in capturing the complex dynamics of geothermal wellbores, making it a superior tool for geo-thermal energy development.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

This short communication makes use of the principle of singular perturbation to approximate the ordinary differential equation (ODE) of prompt neutron (in the point kinetics model) as an algebraic equation. This approximation is shown to yield a large gain in computational efficiency without compromising any significant accuracy in the numerical simulation of primary coolant system dynamics in a PWR nuclear power plant. The approximate (i.e., singularly perturbed) model has been validated with a numerical solution of the original set of neutron point-kinetic and thermal–hydraulic equations. Both models use variable-step Runge–Kutta numerical integration.

Two monoclonal antibodies (mAbs) against human epidermal growth factor receptor 2 (HER2), trastuzumab and pertuzumab, were clinically approved. We previously developed a highly sensitive and specific anti-HER2 mAb, H2Mab-139 (mouse IgG1, kappa). In this study, we produced a defucosylated IgG2a version of anti‑HER2 mAb (H2Mab-139-mG2a-f) to enhance ADCC-mediated antitumor activity. H2Mab-139-mG2a-f exhibits a high binding affinity in flow cytometry with the dissociation constant (KD) determined to be 3.9 × 10‑9 M and 7.7 × 10‑9 M against HER2‑overexpressed Chinese hamster ovary (CHO)-K1 (CHO/HER2) and HER2-positive BT-474 cells, respectively. Moreover, we showed that H2Mab-139-mG2a-f exerted ADCC and complement-dependent cytotoxicity against CHO/HER2 and BT-474 cells in vitro and exhibited potent antitumor activities in the xenograft models. These results indicated that H2Mab-139-mG2a-f exerts antitumor effects against HER2-positive human breast cancers and could be useful for an antibody treatment regimen for HER2-positive human cancers.

This study evaluates the viability of n-octanol as an alternative fuel in a direct-injection diesel engine, aiming to enhance sustainability and efficiency. Experiments employed varying blends of n-octanol (10%, 30%, and 50% by volume) with pure diesel, analyzing impacts on engine performance and emissions. The methodology involved testing each blend in a single-cylinder engine, measuring engine performance parameters such as brake torque and brake power under full load conditions across an engine speed range from 1400 to 2500 rpm. Comparative assessments of performance and emission characteristics at a constant engine speed of 1700 rpm were also conducted with varying loads. Results indicated that while n-octanol blends consistently improved brake thermal efficiency, they also increased brake specific fuel consumption due to the lower energy content of n-octanol. Consequently, while all n-octanol blends reduced nitrogen oxides (NOx) emissions compared to pure diesel, they also significantly decreased carbon monoxide, hydrocarbons, and smoke opacity, presenting a comprehensive reduction in harmful emissions. However, the benefits came with nuanced trade-offs: notably, higher concentrations of n-octanol (especially the 50% blend) led to a relative increase in NOx emissions as the octanol ratio increased. The study concludes that n-octanol significantly improves engine efficiency and reduces diesel dependence, but optimizing the blend ratio is crucial to balance performance improvements with comprehensive emission reductions.

Global agriculture has been forced to increase food production to feed the growing human population, while confronting various environmental obstacles such as global warming, resistance to pathogens, and constraints on arable land caused by soil salinity, drought, rising sea level, saltwater intrusion, and urbanization. Regarding abiotic stresses, salinity is a worldwide problem for agricultural production. Many efforts, therefore, have been made to cope with the environmental challenges, however, the progress of salinization, which is mainly caused and accelerated by anthropogenic activities, is likely faster than our progress in finding ways to deal with this problem. In addition, drought represents a global threat to the production of major crops. In addition, pests and pathogens cause significant crop losses and diminish global food security. Among the various strategies that have been investigated and applied in plant science, antimicrobial peptides derived from plants have caught widespread attention from scientists since these peptides exhibit beneficial biological activities. In agricultural science, there have been reports on the roles of antimicrobial peptides with active properties against biotic and abiotic stresses. Non-specific plant lipid transfer proteins, thionins, systemins, defensins, cyclotides, and heveins-like antimicrobial peptides are common antimicrobial peptides that have been found to be involved in the defense system against fungi and insect pests. Based on their potential ability to protect crops from pests, bacteria, and pathogenic fungi, the use of antimicrobial peptide genes in creating transgenic plants has been largely conducted during the last decades, and these studies have obtained positive results against the growth of fungi and bacteria. This review will focus on the latest progress in studies of antimicrobial peptides related to biotic and abiotic stress tolerance in plants. We will also update the current progress in the development of antimicrobial peptide-based transgenic crops.

The presence and localization of plant metabolites are indicative of physiological processes, e.g., under biotic and abiotic stress conditions. Further, the chemical composition of plant parts is related to their quality as food or for medicinal applications. Mass spectrometry imaging (MSI) has become a popular analytical technique for exploring and visualizing the spatial distribution of plant molecules within a tissue. This review provides a summary of mass spectrometry methods used for mapping and identifying metabolites in plant tissues. We present the benefits and the disadvantages of both vacuum and ambient ionization methods, considering direct and indirect approaches. Finally, we discuss the current limitations in annotating and identifying molecules and perspectives for future investigations.

Pea shoots is a popular vegetable in certain regions of the world due to their unique taste and abundance of health-promoting phytochemicals. The perishable nature and susceptibility of root rot of pea shoots necessitate a new soilless production system located close to the market. This study compared the growth of pea shoots using various cultivation methods in LED plant factory. The results showed that early topping (4 days after transplanting, ET) promoted early harvest compared to later topping (20 days after transplanting, LT) and increased the number of harvested shoots by extending the harvest time to 2.8 times, ultimately resulting in a substantial yield improvement. Moreover, the yield of ET with lower planting density (72 plants m-2, ET-LD) was 8.7% higher than ET with higher planting density (126 plants m-2, ET-HD). Particularly, the average fresh weight of individual pea shoots under ET-LD exceeded that of ET-HD by 48.9%. It is advisable to consider adopting ET-LD for the cultivation of pea shoots in LED plant factory. Based on ET-LD, the yield, nutritional quality, and light use efficiency of pea shoots were further explored at different stages under three levels of light intensity (50, 100, 150 μmol m-2 s-1). Contrasted against the light intensity of 50 μmol m-2 s-1, the average fresh weight per pea shoots, number of harvested, and total fresh yield under light intensity of 150 μmol m-2 s-1 treatment increased by 60.2%, 62.8%, and 165.1%, respectively. Meanwhile, the fresh weight per pea shoots, photosynthetic capacity, soluble sugar and vitamin C levels in leaves, as well as light use efficiency and photon yield, initially increased and then decreased with the extension of the planting period. Among these, soluble sugar, LUE, and PY started to decrease after reaching the maximum value at 60-70 days after transplanting. In conclusion, light intensity of 150 μmol m-2 s-1 with a photoperiod of 16 h d-1 using LEDs, combined with early topping within a planting period of 60-70 days, proves to be suitable for the hydroponic production of pea shoots in LED plant factory.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of renal cancer and its treatment is hindered by resistance to targeted and immunotherapies and combinations ob both. We have reported that knockdown of the antisense noncoding mitochondrial RNAs (ASncmtRNAs) with chemically-modified antisense oligonucleotides induces proliferative arrest and apoptotic death in tumor cells from many human and mouse cancer types. These studies have been mostly performed in vitro and in vivo on commercially-available cancer cell lines and have shown that, in mouse models, tumor growth is stunted by the treatment. The present work was performed on cells derived from primary and metastatic ccRCC tumors. We established primary cultures fom primary and metastatic ccRCC tumors, which were subjected to knockdown of ASncmtRNAs in vitro and in vivo in an orthotopic xenograft model in NOD/SCID mice. We found that these primary ccRCC cells are affected in the same way as tumor cell lines and, in the orthotopic model, tumor growth was significantly reduced by the treatment. This study on patient-derived ccRCC tumor cells represents a model closer to actual patient ccRCC tumors and shows that knockdown of ASncmtRNAs poses a potential treatment option for these patients.

Exposure to particulate matter (PM) causes considerable breathing-related health risks. We assessed the inhibitory effect of Siraitia grosvenorii extract (SGE) on airway inflammation in mice exposed to a fine dust mixture of PM10 (PM diameter < 10 mm) and diesel exhaust particles (DEPs) known as PM10D. SGE attenuated neutrophil infiltration and the number of immune B and T cells in lung tissue and bronchoalveolar lavage fluid (BALF) of PM10D-exposed mice. SGE reduced the secretion of cytokines and chemokines, including interleukin (IL)-1a, tumor necrosis factor (TNF)- a, IL-17, chemokine (C-X-C motif) ligand (CXCL)-1, and macrophage inflammatory protein (MIP)-2 in BALF. Airway inflammation, accumulation of inflammatory cells, and collagen fibrosis in the lung after PM10D exposure were investigated by histopathological analysis, and SGE treatment ameliorated these symptoms. SGE decreased the mRNA expression of mucin 5AC (MUC5AC), CXCL-1, TNF-a, MIP-2, and transient receptor potential vanilloid and ankyrin 1 ion channels in the lung tissues of mice. Furthermore, SGE ameliorated mitogen-activated protein kinase (MAPK)/nuclear factor-kappa B (NF-κB) signaling activated by PM10D in mice. We conclude that SGE attenuated PM10D-induced neutrophilic airway inflammation via inhibiting MAPK/NF-κB activation. These results show that SGE may be a candidate for the treatment of inflammatory respiratory diseases.

GPR4, a member of proton activated GPCRs group. Previously we have reported that GPR4 is constitutively active at physiological pH and knockout of GPR4 has shown to protect dopaminergic neuronal cells from caspase-dependent mitochondrial apoptotic cell death. In this study we have investigated the role of GPR4 in 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) treated mice model of Parkinson’s disease. Subchronic administration of MPTP in mice produces oxidative stress induced apoptotic cell death of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and motor deficits. Treatment with NE52-QQ57, a selective antagonist of GPR4 reduced dopaminergic neuronal loss MPTP-intoxicated C57BL6/J mice and improved motor deficit and memory impairment. Co-treatment with NE52-QQ57 significantly decreases the protein level of proapoptotic marker (Bax), and increases the antiapoptotic marker (Bcl-2) in the SNpc and striatum tissue collected from the brain of MPTP inflicted mice. Further, MPTP induced activation of caspase 3 and cleavage of poly (ADP-ribose) polymerase (PARP) was significantly decreased in the SNpc and striatum tissue of NE52-QQ57 cotreated mice. Further mice receiving both MPTP and NE52-QQ57 mice showed significantly higher TH positive cells in the SNpc and striatum than MPTP treated mice alone. Moreover, NE52-QQ57 cotreatment improved the motor activity in the rotarod test and pole test and also improved spatial memory in Y maze test. Our findings suggest GPR4 as a potential therapeutic target for PD whereas the activation GPR4 is involved in the caspase mediated apoptotic cell death in SNpc and striatum of MPTP-intoxicated mice.

Objective: The aim of our study was to perform a retrospective analysis of the volume of cervical screening tests, the number of patients treated with an excision method, and the incidence of invasive and non-invasive cervical during a pandemic and pre-pandemic period of 24 months. Results: There was a statistically significant age difference between the two study periods, both by the average age of patients and by age group. The mean difference was 32 years before the pan-demic and 35 years during the pandemic (p-value >0.05). The majority of patients presenting for investigations before and during the pandemic were in the 30-39-year-old age group (31.95%, respectively; 34.2% (p-value = 0.003). The biggest patient loss ratio identified by age group was in the 50–59 years: 14,53% in the pre-pandemic period and 9,1% in the pandemic period. In the pan-demic period, patients from rural areas presented in the clinical trial with a lower rate of 39.52% (83 patients) vs. 60.47% (127 patients) in urban areas. A higher percentage of patients experiencing cervicorrhagia as a clinical manifestation in the pandemic period vs. the prepandemic period, with an increase in more severe lesions in the pandemic period, has a statistical significance of 8% more newly diagnosed compared to the pre-pandemic period. Conclusion: The addressability of the patients during the COVID period was not affected in a drastic way in our study. We encountered a decrease in appointments in the age group 50–59 years and a decrease in patients with rural residence. In our study, we found an increase in cervicorrhagia as a reason for consultation in the pandemic period with a higher lesion degree, both on a pap smear and on a cervical biopsy.

This study addresses the implementation of the International Financial Reporting Standard 9 (IFRS 9) in the European Union as of January 1, 2018, replacing the International Accounting Standard 39 (IAS 39) to introduce a new model for recognizing Loan Loss Provisions (LLP) based on Expected Credit Loss (ECL). This model responds to criticisms of the former Incurred Credit Loss (ICL) system for its inability to reflect credit losses in a timely manner, potentially exacerbating the effects of financial crises (Bushman & Williams, 2015; Gebhardt & Novotny-Farkas, 2011). The study focuses on the effects of adopting the ECL model on the level of Loan Loss Allowances (LLA) in loans, own equity, and the Common Equity Tier 1 (CET1) ratio across 13 Portuguese commercial banks. A mean comparison test is used to evaluate scenarios before and after the application of the ECL model, highlighting the importance of regulator actions and the adequacy of loss recognition policies, including the effects of Regulation (EU) 2017/2395 of December 12. The results obtained demonstrate significant negative impacts on the net values of loans, own equity, and the CET1 ratio upon adopting the IFRS 9 ECL model, due to the widespread increase in LLAs.

One of the main challenges of cultured meat is reducing the costs of production, often described as scaling. The main components of the cultivation process include: cells, media, scaffolds and bioreactors. This work presents an alternative to current stainless steel or glass bioreactors and uses food-grade plant-based scaffolds. Bioreactors must be sterile and leak-free to maintain a proper environment for cell growth. Thermoplastic films are a popular material for constructing these fluidic systems, but traditional methods of welding these film are expensive and do not allow rapid prototyping resulting in high research and development costs for companies. In this study, a laser welding method was developed to join thermoplastic films in a way that prevents contamination and leaks, affording effective macro-fluidics fabrication. This technique was tested using polyethylene (PET) films and a laser cutter operating with settings calibrated for the material to be welded or cut. The laser welding method was found to produce strong, leak-free seals in PET films with minimal heat-induced damage to the films. The ability to design fluidics, chambers and ports of various size. Using this method afforded the incorporation of plant-based scaffolds from food-grade plants (Rice). The laser welding method developed in this study provides a reliable, contamination-free method for the rapid fabrication of fluidic systems for cell cultivation. This technique has the potential to be compatible with a vast range of cell types and scaffolds and to be widely adopted in tissue engineering for regenerative medicine and food applications such as cultured meat.

Ectopic expression of the MYB transcription factor of AmROSEA1 from Antirrhinum majus has been reported to change anthocyanin and other metabolites in several species. In this study, we found that overexpression of AmRosea1 significantly improved the tolerance of transgenic rice to drought and salinity stress. Transcriptome analysis revealed that a considerable amount of stress-related genes were affected by exogenous AmRosea1 during both drought and salinity stress treatments. These affected genes are involved in stress signal transduction, the hormone signal pathway, ion homeostasis and the enzymes that remove peroxides. This work suggests that the AmRosea1 gene is a potential candidate for genetic engineering of crops.

In strawberry cultivation, precise disease management is crucial for maximizing yields and reducing unnecessary fungicide use. Traditional methods for measuring Leaf Wetness Duration (LWD), a critical factor in assessing the risk of fungal diseases such as botrytis fruit rot and anthracnose, have been reliant on sensors with known limitations in accuracy and reliability and difficulties with calibrating. To overcome these limitations, this study introduced an innovative algorithm for leaf wetness detection systems employing high-resolution imaging and deep learning technologies, including convolutional neural networks (CNNs). Implemented at the University of Florida's Plant Science Research and Education Unit (PSREU) in Citra, Florida, USA and expanded to three additional locations across Florida, USA, the system captured and analyzed images of a reference plate to accurately determine the wetness and, consequently, the LWD. The comparison of system outputs with manual observations across diverse environmental conditions demonstrated the enhanced accuracy and reliability of the artificial intelligence-driven approach. By integrating this system into the Strawberry Advisory System (SAS), this study provided an efficient solution to improve disease risk assessment and fungicide application strategies, promising significant economic benefits and sustainability advances in strawberry production.

Background In 2016, Global Polio Eradication Initiative (GPEI) recommended cessation of type 2 oral poliovirus vaccine (OPV) and OPV using countries switched from trivalent to bivalent OPV (bOPV) with addition of inactivated poliovirus vaccine (IPV) in the routine immunization schedule. The current GPEI strategy 2022-2026 includes bOPV cessation plan and switching to IPV standalone or the combination vaccine schedules in future. The focus of our study was to evaluate immunogenicity of monovalent OPV type 1 (mOPV1) with IPV and IPV only schedules. Methods This was a three-arm, multi-center randomized controlled trial conducted in 2016-2017 in India. Participants, at birth, were randomly assigned to bOPV-IPV (Arm A) or mOPV1-IPV (Arm B) or IPV (Arm C) schedules. Serum specimens collected at birth, 14-, 18- and 22- weeks age were analyzed with standard microneutralization assay for all three poliovirus serotypes. Results Results of 598 participants were analyzed. The type 1 cumulative seroconversion rates four weeks after completion of the schedule at 18 weeks were 99.5% (97.0-99.9), 100.0% (97.9-100.0) 96.0% (92.0-98.1) in Arms A (4bOPV+IPV), B (4mOPV1+IPV) and C (3IPV) respectively. Type 2 and type 3 seroconversion at 18 weeks were 80.0% (73.7-85.1), 76.9% (70.3-82.4), 93.2% (88.5-96.1) and 100.0% (98.0-100.0), 81.9% (75.6-86.8), 99.4% (96.9-99.9) respectively in the three arms. Conclusions This study shows high efficacy of different polio vaccines for serotype 1 in all three schedules. Type 1 seroconversion rate of mOPV1 is non-inferior to bOPV. All the vaccines provide high type specific immunogenicity. The program can adopt the use of different vaccines or schedules depending on epidemiology from time-to-time.

Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alter-ations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospi-talized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19 negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chro-matography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites al-tered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid seg-regated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify po-tential biomarkers for the diagnosis and prognosis of the disease.

In an age of market liberalization, globalization, and expanding agribusiness, there is a danger that small-scale farmers will find difficulty in fully participating in the market economy. One of the solutions to solve the above situation is to strengthen linkages between farmers and firms through contract farming. The purpose of this study is to assess the factors affecting the level of linkages between businesses and farmers in crop development in Thai Binh province, Vietnam. The two main research methods used were exploratory factor analysis (EFA) and regression analysis with survey data which was conducted from 300 farmers, 10 firms, and 15 factors related to contract-farming. The results indicated that there are 5 factors that significantly affect the level of participation of farmers in the linking process, the order of impacts from strong to weak is as follows: (1) Commitment and sharing of benefits and risks between farmers and firms in the linking process, (2) Issues related to management skills and enterprises capacity of link firms, (3) Price issues that farmers are interested in in the process of implementing links with firms, (4) Policy environment - Institutions related to farmer contracts between firms and farmers, (5) Household awareness about the benefits brought about by farmer contracts with firms. Based on results, a number of specific solutions have been proposed to enhance the efficiency of farming contracts between firms and farmers in cultivation production.

Neurological involvement following COronaVIrus Disease 19 (COVID-19) is thought to have a neuroinflammatory etiology. Co-ultraPEALut (an antiinflammatory molecule) and luteolin (an antioxidant) yielded promising results as neuroinflammation antagonists. This study aimed at describing cognitive impairment in post-COVID patient treated with co-ultraPEALut. Montreal Cognitive Assessment (MoCA), Prospective-Retrospective Memory Questionnaire (PRMQ), Fatigue Severity Scale (FSS) and a subjective evaluation were administered at baseline and after 10 months. Co-ultraPEALut-treated patients were retrospectively compared with controls. 26 co-ultraPEALut-treated patients showed significant improvement in PRMQ (T0: 51.94±10.55, T1: 39.67±13.02, p

We investigate, whereas the SU(4)/SU(2) model, is viable for describing strong interactions. The existing problems of confinement and asymptotic freedom, two phenomena that can not be described by the SU(3) model, might indicate that we need something "larger" than the SU(3) model. The considered coset SU(4)/SU(2) or the Stiefel manifold V^2(C_4) contains an su(3) algebra, plus additional degrees of freedom that resembes the Feddev-Poppov concept. The richer structure of this coset, give us enough room, to seek for new phenomena, as its dimensionality is 12. The consideration of the SU(4)/SU(2) model flavors firstly the unification of nuclear fields (strong and weak) in an SU(4) model.

The SARS (severe acute respiratory syndrome)-CoV (Coronavirus)-2 S(spike)-protein mRNA/cDNA currently being used as vaccines are antigenic but not antigens against SARS-CoV-2, that causes COVID (Coronavirus Disease) -19. Furthermore, the mRNA and cDNA antigenic vaccines also have potentials for homologous as well as heterologous recombination, primarily into the somatic cell DNA of the vaccine recipients. On the contrary, a SARS-CoV-2 RBD-protein antigen, a part of the S-protein, will directly stimulate antibody production against SARS-CoV-2. Hence, a vaccine composed of SARS-CoV-2 RBD-protein as a safer, fast acting, and effective vaccine against SARS-CoV-2 and thus against COVID-19. This is also useful for some immune compromised individuals.

The abundant use of lithium-ion batteries (LIBs) in a wide variety of electric devices and vehicles will generate a large number of depleted batteries, which contain several valuable metals such as Li, Co, Mn, and Ni present in the structure of the cathode material (LiMO2). The present work investigates chemical, technological, and environmental aspects in the treatment of such wastes, development of a methodology for the extraction of lithium, cobalt, nickel, manganese, and graphite by a carbochlorination pyrometallurgical process. Mixtures of cathode and anode materials (called black mass, mixed oxides of Li, Co, Ni, Mn, and graphite) from different LIBs, carbon black (as reducing agent), and CaCl2 (as chlorinating agent) were used. Non-isothermal thermogravimetric tests up to 850°C and isothermal tests at 700°C of the mixtures in an inert atmosphere were carried out. It was experimentally observed that the LiMO2-C-CaCl2 reaction takes place at 700°C. LiCl, Ni, and Co were obtained as final products, and to a lesser extent, CoO, NiO, and MnO2. CaCO3 was also obtained as a by-product. The obtained results show that carbochlorination is an efficient and effective alternative route for the extraction and recovery of metals from different LIBs, focused on the sustainability and circular economy

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

Tuberculosis (TB) remains a global health threat, necessitating innovative strategies for control and prevention. This comprehensive review explores the Mycobacterium tuberculosis Lysine Exporter (LysE) gene, unveiling its multifaceted roles and potential uses in controlling and preventing tuberculosis (TB). As a pivotal player in eliminating excess L-lysine and L-arginine, LysE contributes to the survival and virulence of M. tuberculosis. This review synthesizes findings from different electronic databases and includes 13 studies focused on the LysE of M. tuberculosis. The research unveils that LysE can be a potential drug target, a diagnostic marker for TB, and a promising candidate for vaccine development. The absence of LysE in the widely used BCG vaccine underscores its uniqueness and positions it as a novel area for TB prevention. In conclusion, this review underscores the significance of LysE in TB pathogenesis and its potential as a drug target, diagnostic marker, and vaccine candidate. The multifaceted nature of LysE positions it at the forefront of innovative approaches to combat TB, calling for sustained research efforts to harness its full potential in the global fight against this infectious disease.

Acoustic focusing of particle flow in microfluidics has been shown to be an efficient tool for particle separation for various chemical and biomedical applications. The mechanism behind the method is the selective effect of the acoustic radiation force on distinct particles. In this way, they can be selectively focused and separated. The technique can also be applied under stationary conditions, i.e., in the absence of fluid flows. In this study, the manipulation of self-propelled particles, such as Janus particles, in an acoustofluidic setup is investigated. In experiments with self-propelled Janus particles and passive beads, we explore the interplay between self-propulsion and the acoustic radiation force. Our results demonstrate unusual and potentially useful effects such as selective trapping, escape and assisted escape in binary mixtures of active and passive particles. We also analyze various aspects related to the behavior of Janus particles in acoustic traps in the presence and absence of flows.

The assortment of low-cost 3D printers for “in-practice” use, e.g., for clear aligner therapies, is ever increasing. To address concerns about the accuracy of orthodontic models produced on such printers when cost efficient modes of 3D printing are employed, this study examined the effect of print model body type and print angulation on accuracy. Six printing-configuration groups were included: 2 model types (solid or hollow shell) combined with 3 print angles (0°, 70°, or 90°) with 10 models/group; all models were printed with 100-µm layer thickness using a digital light processing-based 3-dimensional printer. Eleven selected structures and distances were measured on the printed models with a digital microscope and compared to the same measures on a digitized master model. The clinically-acceptability range was set at ±0.25 mm difference from the master model for single tooth measurements (intra-tooth) and ±0.5 mm for cross-arch measurements (inter-tooth). For individual measurements across all models, 98% fell within clinical acceptability. For mean measurements within each model group, only canine height for the shell-0° model had a mean difference (0.26 mm ± 0.03) outside the clinically acceptable range for intra-tooth measurements. Standard deviations for all intra-tooth measurements were within 0.07 mm. While none of the mean inter-tooth measurements exceeded the acceptability range, the standard deviations were larger (0.04 to 0.30 mm). Accuracy of orthodontic models for clear aligner therapies was not impacted beyond the clinically acceptable range when altering model body type and print angulation to improve efficiency of 3D printing. These findings suggest greater flexibility of the practitioner to alter print settings to address time and cost efficiency in various clinical scenarios and still maintain clinically acceptable model accuracy.

Objectives: The study aimed to develop a deep learning-based edge AI model deployed on electrocardiograph (ECG) devices for real-time detection of atrial fibrillation (AF)-risk during sinus rhythm (SR) using standard 10-second 12-lead electrocardiograms (ECGs). Methods: A novel approach was used to convert standard 12-lead ECGs into binary images for model input, and a lightweight convolutional neural network (CNN)-based model was trained using data collected by the Japan Agency for Medical and Research Development (AMED) between 2019 and 2022. Patients over 40 years old with digital, SR ECGs were retrospectively enrolled and divided into AF and non-AF groups. Data labeling was supervised by cardiologists. The dataset was randomly allocated into training, validation, and internal testing datasets. External testing was conducted on data collected from other hospitals.　Results: The best-trained model achieved an AUC of 0.82 and 0.80, sensitivity of 79.5% and 72.3%, specificity of 77.8% and 77.7%, precision of 78.2% and 76.4%, and overall accuracy of 78.6% and 75.0% in the internal and external testing datasets, respectively. The deployed model and app package utilized 2.5MB and 40MB of the available ROM and RAM capacity on the edge ECG device, correspondingly. Processing time for AF-risk detection was approximately 2 seconds.　Conclusion: The model maintains comparable performance and improves its suitability for deployment on resource-constrained ECG devices, thereby expanding its potential impact to a wide range of healthcare settings. Its successful deployment enables real-time AF-risk detection during SR, allowing for timely intervention to prevent AF-related serious consequences like stroke and premature death.

Considering the worldwide impact of heart failure, it is crucial to develop approaches that can help us comprehend its root cause and make accurate predictions about its outcome. This is essential for lowering the suffering and death rates connected with this widespread illness. Cardiomyopathies frequently result from genetic factors, and the study of heart failure genetics is advancing quickly. Dilated cardiomyopathy (DCM) is the most prevalent kind of cardiomyopathy, encompassing both hereditary and nongenetic abnormalities. It is distinguished by the enlargement of the left ventricle or both ventricles, accompanied by reduced contractility. The discovery of the molecular origins and subsequent awareness of the molecular mechanism is broadening our knowledge of DCM development. Additionally, it emphasizes the complicated nature of DCM and the necessity to formulate several different strategies to address the diverse underlying factors contributing to this disease. Genetic variations that can be transmitted from one generation to another can be a significant contributor to causing family or isolated DCM. Genetic variation also plays a significant role in determining susceptibility for acquired triggers for DCM. The genetic causes of DCM can have a diverse range of phenotypic expressions. It is crucial to select the most probable patients to have advantages from genetic testing. The purpose of this research is to emphasize the significance of identifying genetic DCM, the crucial relationships between genotype and phenotype, and the usefulness of genetic testing in the therapeutic care of both those affected and their relatives. This approach is expected to gain importance once treatment is guided by genotype-specific advice and disease-modifying medications.

In this study, we investigated the features of co-infection with SARS-CoV-2 and nonpathogenic strain LEV8 of coxsackievirus A7 or enterovirus A71 for Vero E6 cells and Syrian hamsters. The investigation of co-infection with SARS-СoV-2 and LEV-8 or EV-A71 in the cell model was shown that a competitive inhibitory effect for these viruses were especially significant against SARS-CoV-2. Pre-infection with enteroviruses in animals caused more than a 100-fold decrease in the levels of SARS-CoV-2 virus replication in the respiratory tracts and more rapid clearance of the lower respiratory tract from infectious SARS-CoV-2. Co-infection with SARS-CoV-2 and LEV-8 or EV-A71 also reduced the severity of clinical manifestations of the SARS-CoV-2 infection for animals. Additionally, the histological data illustrated that co-infection with nonpathogenic coxsackievirus decreased the level of pathological changes by SARS-CoV-2 in the lungs. Research into the chemokine/cytokine profile demonstrated that the studied enteroviruses efficiently triggered this part of antiviral immune response, which can be associated with significant inhibition of SARS-CoV-2 infection. These results demonstrate that there exists a strong viral interference between the studied nonpathogenic strain of coxsackievirus A7 or enteroviruses A71 and SARS-CoV-2 pathogenic for humans in vitro and in vivo.

Abrus precatorius is a medicinal herb originated from Southeast Asia and native to India. The Hindustani centre of origin was found to possess lot of variability especially with respect to seed color. However, no previous researchers have focused on studying the variability by including the entire variability of the region. A total of 99 accessions were analyzed for its biochemical components like moisture content, total phenols, antioxidants, ash content, protein content, total monomeric anthocyanin and flavonols. Principal component analysis (PCA) shown that first three components viz., seed color, phenols and antioxidants are major principal components that contribute to 80.86% of total variability in the germplasm. The dendrogram constructed based on the biochemical data and by using the software Darwin version 6.0. formed three main clusters i.e. cluster 1, cluster 2 and cluster 3 which contain 55 accessions, 40 accessions and 1 accession respectively. Seed color was found to be the main criteria based on which the Abrus accessions were grouped. Accessions from different clusters also differ for their mean phytochemical constituents. Germplasm curators / plant breeders can select such elite genotypes and utilize them in crop improvement programmes.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

The targeting-Her-2/neu therapy by passive application with trastuzumab is associated with acquired resistance and subsequent metastasis development, attributed to upregulation of tumoral PD-L1 expression and downregulation of Her-2/neu. We aimed to investigate this association, following active immunization with our recently constructed B cell–peptide based Her-2/neu vaccines in both preclinical and clinical settings. Immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and combined positive score (CPS), were applied to evaluate Her-2/neu and PD-L1 expression using a murine syngeneic tumor model for Her-2/neu lung metastases, and tumor biopsies from a gastric cancer patient with disease progression. A significant and concomitant reduction of Her-2/neu and upregulation of PD-L1 expression was observed in the vaccinated mice, after 7 but not after 4 weeks of metastases development. A significant increase of tumor-infiltrating B lymphocytes was observed at both time points. Downregulation of Her-2/neu and upregulation of PD-L1 were observed in a patient’s primary tumor at the disease progression time point but not prior to vaccination (Her-2/neu IHC: 3 to 0, FISH: 4.98 to 1.63; PD-L1 CPS: 0% to 5%). Our results further underline the need for combination therapy by targeting PD-L1 to prevent metastasis formation and immune evasion of Her-2/neu positive and PD-L1 negative tumor cells.

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.

Berberis vulgaris L. (Berberidaceae) is a shrub that has been widely used in European folk medicine as an anti-inflammatory and antimicrobial agent. The purpose of our study was to elucidate the mechanisms of antiproliferative action of the plant’s methanolic root extract (BVR) against colon cancer cells. Cytotoxicity and pro-apoptotic activity were determined in human colon adenocarcinoma cell lines (LS180 and HT-29) and control colon epithelial CCD841 CoN cells. According to the MTT assay, after 48 h of cell exposure, the IC50 values were as follows: 4.3; 46.1 and 50.2 µg/mL for LS180, HT-29, and CCD841 CoN cells, respectively, showing the greater sensitivity of cancer cells to BVR. The Cell Death Detection ELISAPLUS kit demonstrated that BVR induced programmed cell death only against HT-29 cells. RT-qPCR with relative quantification method showed significant changes in the expression of genes related to apoptosis in both colon cancer cell lines. The genes BCL2L1 (126.84-366.66%), BCL2L2 (277.08-330.12%), CASP3 (180.36-261.36%) and CASP9 (158.08-213.46%) had significantly elevated expression, while BCL2 (23.08-52.06%) had reduced expression. Furthermore, in a panel of antioxidant tests, BVR showed positive effects (63.93±0.01, 122.92±0.01, and 220.29±0.02 mg Trolox equivalents (TE)/g in the DPPH•, ABTS•+ and ORAC assays, respectively). In the lipoxygenase (LOX) inhibition test, BVR revealed 62,60±0.87% of enzyme inhibition. The chemical composition of BVR was determined using UHPLC-UV-CAD-MS/MS analysis and confirmed the presence of several known alkaloids, including berberine, as well as other alkaloids and two derivatives of hydroxycinnamic acid (ferulic and sinapic acid hexosides). This is the first such extensive assessment of the biological activity of BVR. The results are very promising and encourage for the use of BVR as a comprehensive chemopreventive agent (anti-inflammatory, antioxidant, and pro-apoptotic) in colorectal cancer.

This article explores the pressing need for a paradigm shift in economics, challenging the dominance of classical economic theories and models that have long held sway. We delve into the limitations of traditional economic paradigms, which often rely on unrealistic assumptions and fail to capture the complexities of human behavior and the modern global economy. Drawing from insights in behavioral economics, environmental economics, and complexity theory, we argue for a more nuanced and multidisciplinary approach to economic analysis. Our discussion addresses the challenges of breaking away from deeply entrenched academic norms and the resistance to change within the economics profession. We highlight strategies for individuals and groups to advocate for the incorporation of modern economic theories in education and research. By fostering interdisciplinary collaboration, encouraging critical thinking, and engaging with policymakers and the public, we aim to catalyze a shift toward economics that is better equipped to address the intricate challenges of the 21st century. This article serves as a call to action for economists and educators alike to embrace a more dynamic and responsive approach to understanding human behavior and decision-making in the modern world.

histology, the processing and staining of cells, tissues and organs to highlight contrast, visualize and analyze their microstructure under microscopy, encompasses a vast array of techniques used to confirm, refine, or refute the macroscopic findings in forensic investigations, for example in the case of autopsies. This review presents several applications of histological protocols, staining methods and techniques to case studies, with the aim of illustrating the established, current and potential applications of histological investigations to forensic sciences. The list of disciplines include anthropology, cytology, dendrochronology, diatomology, entomology, fiber analysis, palynology,human and veterinary pathology, taphonomy and thanatology.

Breast cancer represents the most common neoplasm worldwide. Viral infections are involved with carcinogenesis, especially those caused by oncogenic Human Papillomavirus (HPV) genotypes. The relationship between HPV and various types of cancer, such as breast and lung, still requires further studies. After infection with HPV, there is a remodeling of the tumor microenvironment, providing a post-infection alteration with immunosuppressive characteristics. This study evaluated the role of HPV in breast adenocarcinoma cell lineage (MDA-MB-231), transfected with the HPV16 E5, E6, and E7 oncogenes and co-cultured with PBMCs. Immunophenotyping was performed. There was an increase in CD4+ T cells number when compared with non-transfected and transfected MDA-MB-231, especially the Treg profile. Moreover, proinflammatory intracellular cytokines, such as IFN-γ, TNF-α, and IL-17 were impaired by transfected cells and a decrease in the cytolytic ac-tivity of the CD8+ and CD56+ lymphocytes was also observed in the presence of HPV oncogenes, mainly with E6 and E7 in NK and TCD8+ cells. In conclusion, MDA-MB-231 breast cell lineage transfected with HPV oncogenes can downregulate the number and function of lymphocytes and monocytes.

This research aims to examine the level of readiness of the community to carry out sustainable tourism development, especially community-based tourism on small islands, such as Lombok Island, Indonesia. More specifically, the objectives of this study are as follows: (1) Knowing the level of community readiness in carrying out sustainable tourism development. (2) Knowing the various dimensions that influence sustainable tourism development. (3) Knowing the role of stakeholders in sustainable tourism development. The method applied in this research is mixed methods, a combination of quantitative and qualitative approaches. Quantitative methods are used to determine community readiness for sustainable tourism development. A qualitative approach is used to determine the various factors that influence the development of community-based tourism and to determine the role of stakeholders in the development of community-based tourism. The level of community readiness for sustainable tourism development is high in terms of economic, social, cultural, environmental, and symbolic capital aspects. Aspects that influence the development of sustainable tourism are economic, social, and cultural. Stakeholders who play a role in developing sustainable tourism in the research area are the government, the private sector, universities, non-governmental organizations, and the media.

Background: Hypertension is a condition of a person's blood pressure being at a level above normal, hypertension treatment can be done herbally using avocado leaves. Avocado leaves can lower blood pressure because they contain active substances, namely flavonoids and quercetin. This research was conducted in the working area of the Tilango Health Center. Objective: This study aims to determine the effect of giving boiled water of avocado leaves on reducing blood pressure in the elderly with hypertension. Methods: Design This study used a two-group quasi-experimental pre-test - post-test design. The sample is the elderly who have hypertension, amounting to 125 people with purposive sampling technique. The research instrument used a questionnaire. Results: Statistical test using T-Test on blood pressure before and after the intervention was given a significance value of 0.000 (p-value < 0.05 ). Conclusion : This study concludes that there is a significant effect between giving avocado leaf boiled water on reducing blood pressure in the elderly with hypertension in the working area of the Tilango Health Center.

The shikimate pathway is crucial for the biosynthesis of aromatic amino acids in plants and represents a promising target for developing new herbicides. This work aimed to identify inhibitors of shikimate dehydrogenase (SDH), a key enzyme of the shikimate pathway that catalyzes the conversion of 3-dehydroshikimate to shikimate. Virtual screening and molecular docking were performed on the SDH active site of Arabidopsis thaliana (AtSDH), and 6-nitroquinazoline-2,4-diol (NQD) was identified as a potential inhibitor. In vitro assays showed that NQD decreased the activity of AtSDH by reducing Vmax while keeping KM unchanged, indicating noncompetitive inhibition. In vivo, hydroponic experiments revealed that NQD reduced the root length of soybean and maize. Additionally, NQD increased the total protein content and certain amino acids. Soybean roots uptake NQD more efficiently than maize roots. Furthermore, NQD reduced shikimate accumulation in glyphosate-treated soybean roots, suggesting its potential to restrict the flow of metabolites along the shikimate pathway in soybean. Simultaneous treatment of maize seedlings with glyphosate and NQD accumulated gallic acid in the roots, indicating that NQD inhibits SDH in vivo. Overall, the data indicate that NQD inhibits SDH both in vitro and in vivo, providing valuable insights into the potential development of herbicides targeting SDH.

Higher-risk myelodysplastic syndrome (HR-MDS) has a poor prognosis in the absence of efficient therapy. The evaluation of new therapies in animal models of HR-MDS is hampered by the absence of accurate in vivo biomarkers of the disease. In this study we compared [18F]Fluoro-desoxyglucose Positron Emission Tomography (FDG-PET) and [18F]Fluoro-thymidine (FLT)-PET imaging for disease follow-up in a triple transgenic MMTVtTA/TetoBCL-2/MRP8NRASD12 mouse model of HR-MDS. Normal control FVB/N mice (G1,n=9) and HR-MDS mice (G2,n=12) underwent both FDG- and FLT-PET procedures at 2-day intervals, on a dedicated small animal device. Blood cell counting, BCL-2 and Mac-1hi/Gr-1lo expression measurements in blood were performed before each PET procedure. Visually, PET images of G2 mice demonstrated homogeneous FDG uptake in the whole skeleton similar to that observed in G1 mice, and abnormal FLT hot spots in bone marrow not observed in G1 mice. The intensity of FLT hot spots in bone marrow was higher in 3-months old G2 mice than in 2-months old G2 mice, concordant with a higher percentage of cells expressing Mac-1hi/Gr-1lo and lower platelets counts. We conclude that FLT-PET/CT imaging is a more valuable surrogate non-invasive quantitative marker of HR-MDS bone marrow involvement than FDG-PET/CT in our mouse model of HR-MDS.

Pharmacists must acquire the necessary knowledge to provide better drug therapy to patients. In other words, educational programs need to facilitate behavioral changes among pharmacists. Therefore, this study aimed to evaluate how the behavior of pharmacists changed after attending a palliative care educational program. We conducted a web-based questionnaire survey examining the behavior of pharmacists regarding palliative care before participating in the program, two months after participating in the program, and eight months after participating in the program, to determine the behavior and changes over time. For all questions, scores were higher at two and eight months after attending the program than before attending the program (P &lt;0.05). In addition, no significant difference was observed between two and eight months after attending the program for all questions (P =0.504-1.000). The knowledge gained from the educational program was used to repeatedly intervene with patients with cancer, to address the various symptoms they experienced and maintain their behavior. The proven effectiveness of this program serves as a stepping stone for a nationwide rollout across Japan’s 47 prefectures.

Based on screening in computational biology and biological in vitro assays, five natural products isolated from extracts of the herbal medicine toad skin, such as cinobufagin (CBFi), bufalin (BFi), arenobufagin (ABFi), telocinobufagin (TBFi), bufotalin (BFTi), were subjected to molecular docking calculations with the use of SARS-CoV-2 main protease (PDB 6LU7 and 7BTF) and top-scoring ligand-receptor complexes were obtained. The results showed that the binding energy of ABFi to the 3CL protein was -17.044kcal/mol, which was higher than CBFi and TBFi. However, the binding energy of ABFi to the RdRp protease was -23.250 kcal/mol, which was much lower than that of CBFi and TBFi, EVEN lower than that of ABFi to the 3CL protein. ABFi also has polar interactions with amino acids such as Glu811, Ser814, Ser681 and Thr680 of RdRp enzyme. The results revealed that ABFi had a moderate inhibitory effect on the cell proliferation of SARS-CoV-2 in vitro, with an inhibition rate of 61.12%, even weaker than Remdesivir. This new discovery provides us with new ideas for in-depth studies on the development of natural products with this class of structural generalizations as inhibitors of SARS-CoV-2, and provides an experimental basis for the next step of mechanistic studies.

Bovine coronavirus (BCoV) is one of the most common pathogens affecting cattle of all ages. BCoV possesses multiple tissue tropism in cattle, particularly in the respiratory and enteric tracts of the affected animals. Viral entry is a crucial step in the replication cycle of all viruses, including BCoV. The process of viral entry requires the orchestration between several proteins from the virus and the host side. The coronavirus spike glycoproteins contributed substantially to other viral infections and the molecular pathogenesis of coronaviruses, including BCoV. Meanwhile, among all corona-viruses, the hemagglutinin esterase enzyme is found in only BCoV and the human corona-virus-OC43 (HCoV-OC43). The viral receptors are among the key players in the viral entry and could explain the viral tropism, at least in part. The presence of some host cell proteases also en-hances the virus replication from the entry until the release of the virus from the host cells. Previous studies have shown that the 9- O-acetylated sialic acids could potentially act as receptors for the BCoV. However, little is known about the BCoV receptors and if other co/receptors might be needed to ensure viral entry to the host cells. In addition to that, the roles of other host cell proteases, such as Furin and TMPRSS2, in BCoV infection have not yet been well studied. The main objectives of the current study are to identify some novel receptors for BCoV and some host cell proteases that might enhance virus replication. We used some in silico docking tools to predict some new BCoV receptors out of some known other coronavirus receptors (ACE-2, NRP1, DPP4, APN, and CEACAM1). Our results postulated the potential actions of the host Furin and TMPRRS2 enzymes in the activation of BCoV-S glycoprotein, which potentially enhances BCoV replication. Our modeling confirms the high binding affinities of both the 9- O-acetylated sialic and the NRP1 with the BCoV-S and BCoV-HE compared to the ACE-2, DPP4, and CEACAM-1, suggesting their roles as receptors for BCoV. Our prediction models show the potential bindings of the Furin and TMPRSS2 to the BCoV-S polybasic cleavage site (RRSRR|A). To our knowledge, this is the first study to investigate the bovine coronavirus interaction with some other potential host receptors, particularly ACE-2, NRP1, DPP4, APN and CEACAM1, TMPRSS2, and the bovine Furin protease.

Tobacco mosaic virus (TMV) was the first virus to be studied in detail and for many years TMV and other tobamoviruses, particularly tomato mosaic virus (ToMV) and tobamoviruses infecting pepper (Capsicum spp.), were serious crop pathogens. By the end of the twentieth and for the first decade of the twenty-first century tobamoviruses were under some degree of control due to introgression of resistance genes into commercial tomato and pepper lines. However, tobamoviruses remained as important models for molecular biology, biotechnology and bio-nanotechnology. Recently, tobamoviruses have again become serious crop pathogens due to the advent of tomato brown rugose fruit virus, which overcomes tomato resistance against TMV and ToMV, and the slow but apparently inexorable worldwide spread of cucumber green mottle mosaic virus, which threatens all cucurbit crops. This review discusses a range of mainly molecular biology-based approaches for protecting crops against tobamoviruses. These include cross-protection (using mild tobamovirus strains to ‘immunize’ plants against severe strains), expressing viral gene products in transgenic plants to inhibit the viral infection cycle, inducing RNA silencing against tobamoviruses by expressing virus-derived RNA sequences in planta or by direct application of double-stranded RNA molecules to non-engineered plants, gene editing of host susceptibility factors, and the transfer and optimization of natural resistance genes.

Omicron SARS-CoV-2 variants have dominated the strains of COVID-19 circulating worldwide for the last two years. Although the mortality rate of the COVID-19 Omicron variants is only approximately half that of previous variants, vaccines effective against Omicron are needed. Demonstrating that a SARS-CoV-2 vaccine can protect against Omicron in an in vivo infection model, particularly in that of Syrian hamsters, is important for the preclinical characterization of SARS-CoV-2 vaccines. Here, we report on the evaluation of W-PreS-O for its ability to protect Syrian hamsters against infection by Omicron. W-PreS-O is a chimeric vaccine based on a single fusion protein (W-PreS-O), combining RBDs from Wuhan hu-1 wild-type and Omicron adsorbed to aluminum hydroxide. The Syrian hamsters were immunized three times at three-week intervals with W-PreS-O or with aluminum hydroxide (placebo), and then they were infected with Omicron BA.1. Non-infected and non-vaccinated animals served as controls. Neutralizing antibody (nAB) titers, weight, lung symptoms (i.e., edema and pneumonia index), and viral loads, as measured using RT-PCR in the upper and lower respiratory tracts, were determined. In addition, infectious virus titers from the lungs were measured using a plaque-forming assay. W-PreS-O-vaccinated hamsters developed robust nABs against Omicron, showed almost no development of pneumonia, and had significantly reduced infectious virus titers in the lungs. Importantly, the viral loads in the nasal cavities of W-PreS-O-vaccinated hamsters were close to or above the PCR cycle threshold considered to be non-infectious. Our data provide compelling evidence demonstrating that the W-PreS-O vaccine has protective effect against Omicron in a Syrian hamster in vivo infection model. W-PreS-O is therefore a highly promising candidate vaccine for Omicron infections, and it should be further evaluated in clinical studies.

We propose a pricing formula for a defaultable zero-coupon bond with imperfect information under a regime-switching model using a structural form of credit risk modelling. While the value of the firm’s equity is observed continuously, we assume that the total value of the firm is only observed at discrete times, such as the dates of the release of the firm’s annual reports, or quarterly reports. This uncertainty about the true value of the firm results in credit spreads that do not approach zero as the debt approaches maturity, which is a problem with many structural models. The firm’s value is typically defined as its equity and debt; however, we consider the asset-to-equity ratio, an accounting ratio used to examine a firm’s financial well-being. A dependent regime-switching process models the components of this ratio, and the Markov chain represents the states of the economy. The main contribution is to study this problem when the dynamics of a firm’s assets have different parameters with regime changes.

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.

Weight loss solutions are on the rise, and there is growing interest in natural alternatives to the pharmaceuticals in the market, which are not devoid of relevant side effects. To this end, the objective of the current study is to assess the effects of a botanical-based blend comprised of lemon verbena and hibiscus extracts in weight management, body fat, appetite, lipid, and glucose profiles, in an overweight population, for 90 days. As a result, the volunteers presented significant weight loss in absence of diet, which coincided with decreased body fat. Body fat was assessed by anthropometry, electrical bioimpedance and DEXA scan. Cholesterol levels were also significantly decreased. Satiety assessment revealed that the product contributed to increased feeling of fullness and satisfaction after a meal, compared to the placebo group, which could contribute to the weight loss observed. Blood glucose and blood pressure levels remained unchanged, which was expected as the volunteers were normoglycemic and their blood pressure was in the healthy range. In conclusion, daily supplementation of a botanical-based blend contributes to weight loss in overweight individuals, in absence of diet, while not affecting other parameters that are in the healthy range. This product could be a candidate alternative or add-on to other weight loss products, as part of a healthy lifestyle.

The critical systems, which the failure and malfunction may result in human, environment and financial severe damages, are essential components in various sectors and particularly in energy domains. Although unwished, integration error problems in supervision of critical systems occurs, incurring significant expenses because of operator’s subjective analysis and on hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in the critical systems supervision in terms of reliability. A real Hybrid Power Plant (HPP) case is considered as a requirement for a study case with simulated data. A method framework with informal approach (C4 diagram) and formal approach (Hierarchical Coloured Petri Nets) in radial spectrum is applied to the HPP supervision design. Using formal methods, a formulation and solution to this problem through structured, scalable and compact mathematical representations are possible. This malleable model intends to guarantee functional correctness and then reliability of the plant supervision system based on system software architecture. Outcomes suggest corroboration that the malleable model is appropriate for energy domain, and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied.

An increasing need for better agricultural practices that are sustainable to provide increased yields of crops for the ever increasing food demand to be met for the growing world population is a current necessity. It is estimated by the UN that in 2050 the world’s populace may be as much as 9.8 billion global, yet, farmers still face several obstacles, which includes soil degradation, extreme temperatures, and drought which as increasingly becoming more severe with climate changes. Plant health monitoring is a method that will help to increase awareness about plant enhancing by studying how to increase crop yields while decreasing the environmental impacts. The use of low-cost, controlled environments, soil quality, water levels, and pathogenic and pests attacks can be regularly monitored. Currently, several diagnostic technologies and portable point-of-use methods and technologies are employed for detection of diseases, although, the current available lab-based methods used for plant’s diagnostics test are inadequate for plant point-of-use monitoring. The promotion of use of these point of use methods will enable pathogen detection, poor plant growth, unsuitable environmental conditions and nutrient use efficiency allowing for immediate and timely corrective actions to ensure minimal damage to the plants with maximal yield. Increase in the use of sustainable agri practices are required for ensuring higher yields with minimal inputs and that are non-destructive to the land and the use of improved methodologies and technologies will be useful tools for achieving sustainable agriculture.

The most common type of head and neck cancer (HNC) is head and neck squamous cell carcinoma (HNSCC). It describes a heterogenous neoplastic disease that arises from the mucosal epithelium of the of the upper respiratory system and the gastrointestinal tract. It is characterized by high morbidity and mortality, being is the eighth most common cancer worldwide, with 931,931 newly diagnosed cases and 467,125 deaths according to the latest Global Cancer Statistics (GLOBOCAN) data. Despite the use of modern low-invasive surgical techniques and application of promising new chemoradiotherapies or combination treatment, the frequency of recurrences and the five-year survival of these patients remains unsatisfactory. The microenvironment of head and neck cancer comprises various cells that regulate neoplastic expansion. It is believed that the mesenchymal stromal cells (MSCs) present in the tumour milieu play a key role in the modulation of tumour initiation, development and progression, in the regulation of cancer treatment response, survival rates, patient outcomes and long-term prognosis; they also influence the resistance to cisplatin-based chemotherapy, the gold standard for advanced HNSCC. MSCs, are multipotent, heterogeneous, mobile cells, which meet specific stemness criteria defined by the International Society for Cellular Therapy (ISCT). Although no MSC-specific markers exist, they can be recognized based on several others, such as CD73, CD90 and CD105, while lacking the presence of CD45, CD34, CD14 or CD11b, CD79α, or CD19 and HLA-DR antigens; they share phenotypic similarity with stromal cells and their capacity to differentiate into other cell types. In the tumour niche, MSC populations are characterized by cell quiescence, self-renewal capacity, low reactive oxygen species production and the acquisition of epithelial-to-mesenchymal transition properties, they may play a key role in the process of acquiring drug resistance, and thus treatment failure. MSCs must also demonstrate plastic adherence in vitro and be able to differentiate into specific mesodermal cell types after stimulation i.e., adipocytes, chondrocytes, and osteoblasts when cultured under well-defined conditions. The present narrative review examines the links between mesenchymal stromal/stem cells and HNC, as well as the different mechanisms involved in the development of resistance to current chemo- and radiotherapies in HNSCC. It provides an overview of the current literature, including key opinion-forming systematic reviews, as well as cross-sectional, longitudinal, prospective and interventional studies based on cell culture models in vitro, animal or in vivo models of HNSCC; it also examines the possibilities of pharmacological targeting of stemness-related chemoresistance in HNSCC. It describes promising new strategies to optimize chemoradiotherapy with the potential to personalize patient treatment approaches, and highlights future therapeutic perspectives in HNC. The data concerning tumour-associated MSCs is arranged according to increasing clinical credibility.

Studies have shown that vehicle trajectory data are effective for calibrating microsimulation mod-els. Light Detection and Ranging (LiDAR) technology offers high-resolution 3-D data, allowing for detailed mapping of the surrounding environment, including road geometry, roadside infrastruc-tures, and moving objects such as vehicles, cyclists, and pedestrians. This research presents a comprehensive framework for integrating LiDAR sensor data into simulation models and accu-rately calibrating for proactive safety analysis. Vehicle trajectory data were extracted from LiDAR point cloud collected at six urban signalized intersections in Lubbock, Texas in USA. Each study intersection was modelled with PTV VISSIM and calibrated to replicate the observed field scenarios. The Directed Brute Force method was used to calibrate two car-following and two lane-change parameters of Wiedemann 1999 model in VISSIM, resulting in an average accuracy of 92.7%. Rear-end conflicts extracted from the calibrated models combined with a ten-year historical crash dataset were fitted into a Negative Binomial (NB) model to estimate the model's parameters. In all the six intersections, rear-end conflict count is a statistically significant predictor (p-value < 0.05) of observed rear-end crash frequency. The outcome of this study provides a framework for the com-bined use of LiDAR-based vehicle trajectory data, microsimulation and surrogate safety assessment tools to transportation professionals for proactive safety evaluation.

We propose a Gauss model (GM), a map from time to the bell-shaped Gauss function to model the deaths per day and country, as a quick and simple model to make predictions on the coronavirus epidemic. Justified by the sigmoidal nature of a pandemic, i.e. initial exponential spread to eventual saturation, we apply the GM to existing data, as of April 2, 2020, from 25 countries during first corona pandemic wave and study the model's predictions. We find that logarithmic daily fatalities caused by Covid-19 are well described by a quadratic function in time. By fitting the data to second order polynomials from a statistical chi2-fit with 95\% confidence, we are able to obtain the characteristic parameters of the GM, i.e. a width, peak height and time of peak, for each country separately, with which we extrapolate to future times to make predictions. We provide evidence that this supposedly oversimplifying model might still have predictive power and use it to forecast the further course of the fatalities caused by Covid-19 per country, including peak number of deaths per day, date of peak, and duration within most deaths occur. While our main goal is to present the general idea of the simple modeling process using GMs, we also describe possible estimates for the number of required respiratory machines and the duration left until the number of infected will be significantly reduced.

Type 2 diabetes mellitus (T2DM), a multifactorial and complicated metabolic disorder, is a growing public health problems. Numerous studies indicated that bioactive compounds from herbal medicine have beneficial effects on T2DM prevention and treatment, owing to their numerous biological properties. Curcumin, the major curcuminoid of turmeric, is one of the most studied bioactive components of herbal supplements, which has a variety of biological activities. Clinical trials and preclinical research have recently produced compelling data to demonstrate the crucial functions of curcumin against T2DM via several routes. Accordingly, this review systematically summarizes the antidiabetic activity of curcumin, along with various mechanisms. Results showed that effectiveness of curcumin on T2DM is due to it being anti-inflammatory, anti-oxidant, anti-hyperglycemic, anti-apoptotic, anti-hyperlipidemia and other activities. In light of these results, curcumin may be a promising prevention/treatment choice for T2DM.

In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We have proposed a comprehensive framework to aid in this decision-making process, underpinned by a detailed comparative analysis of both types of libraries on one of the key IoT ML task; image classification. We have introduced a novel algorithm designed to calculate the green footprint of ML model training, factoring in execution time, memory utilization, power consumption, and CPU temperature, addressing the urgent need for sustainable ML practices. Through an empirical evaluation of popular libraries such as PyTorch for high-level and Libtorch for low-level development, we have assessed their performance, development efficiency, and hardware compatibility. The culmination of our research is a decision support system that synthesizes the experimental findings to guide developers toward choices that harmonize model performance with environmental sustainability.

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

Keywords: emerging pollutants; pharmaceuticals; plant uptake; soil behavior; soil remediation.

Plant conciousness is a scenario where people imbibe the culture of engaging plants as a constant part of their day to day activity. The earth supports both plants and animals, but , plants unlike animals are unable to express themselves by shouting, nagging or crying, e.t.c. They are only able to response saliently thus making them oblivious and constantly neglected. Unfortunately, plants play just as much major roles in sustaining the earth as much as people and animals do. The proper handling of plants is much reliant on the ‘’Plant consciousness or obliviousness’’ of people including both the young and the old. Are people well aware of plants, how important they are to the earth, the environment and it sustenance?, the answer is not enough. There is a need for plant knowledge bridges to create more and adequate awareness about plants, their importance, how they should be handled, how they should be cared for, and an understanding that they are partners for the sustaining our dying world. We seek to change this obvious lack of appreciation and understanding of plants, but how can this be done? We discuss methodologies that have and can been used for bridging this knowledge gap to ensure that our world is safe by protecting and caring for plants.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

Although global life expectancy has increased over the past 20 years due to improvements in managing infectious diseases, one in five people still succumbs to infections. Humanity is tirelessly working towards developing vaccines and antimicrobial agents in response to this ongoing threat. However, pathogens continue to acquire numerous resistance mechanisms and proliferate. Meanwhile, the COVID-19 pandemic has prompted us to recognize that focusing solely on the killing strategies of immunology and microbiology in complex and multisystemic infectious diseases is insufficient for understanding survival. In essence, there is a need for a treatment approach centered on disease tolerance, capable of alleviating pathology and/or promoting physiological functions in conjunction with the existing strategies of host immune response and pathogen destruction. Against this backdrop, this narrative review comprehends and introduces microbiome therapeutics, which recently garnered attention as next-generation treatments regarding two defense strategies against infectious diseases. First, we delineate the application of existing probiotics and then the microbiome in infectious diseases. Subsequently, we expand this concept to encompass host microbiome crosstalk and present related microbiome therapeutics' current status and direction. Ultimately, the review presents a fresh perspective on understanding microbiome therapeutics in infectious diseases based on a cooperative defense system.

The traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the network incorporates the Squeezed-and-Excitation (SE) attention mechanism to improve. Moreover, the YOLOv5 algorithm’s loss function has been optimized by substituting it with EIoU_loss, which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model , trained with this enhanced network, achieves a 99.4% mAP on a custom dataset, which is 6.3% higher than the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements.

CAMx-LPiG (Comprehensive Air Quality Model with Extensions – Linear Plume in Grid) is an online hybrid model, based on the Chemistry and Transport Model (CTM) CAMx, that includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions, called LPiG. LPiG is a plume in grid module specifically developed by extending the capabilities of the Lagrangian puff sub-grid model available in CAMx. The online integration of the local scale model within the Eulerian CTM allows for a multiscale simulation of air quality from the regional scale to the urban scale, preserving a coherent description of the chemical state of the atmosphere at all spatial scales and avoiding any double counting of the emissions simulated by the sub-grid module. In this work the model is presented and evaluated against measured NO2 concentrations for the city of Milan for the month of January 2017. The model is able to introduce road traffic induced gradient in NO2 concentration at sub-grid resolution. Moreover, CAMx-LPiG is shown to reduce the bias compared to CAMx stand-alone simulations.

The paper presents a new type of hydraulic clutch operating with magnetorheological (MR) fluids and the results achieved from both theoretical analysis and experimental measurement. The hy-draulic clutch system of its own design with MR working fluid and a rotating magnetic field lo-cated is designed. The principle of this clutch is to use a rotating magnetic field created by an al-ternating current electromagnet to set the MR fluid in motion. In the tests of the hydraulic clutch with a rotating magnetic field, MR fluid produced on their own, consisting of solid iron particles of various diameters mixed with a silicone oil, are used as working fluids. The rheological properties of MR fluids are assessed on the basis of tests carried out with a Brookfield DV2T rheometer equipped with a magnetic device for generating the magnetic field of its own design. The char-acteristics of the hydraulic clutch with MR working fluid and the rotating magnetic field are tested on a specially built test stand. As a result of the research, it has been found that the torque transmitted by the clutch is the greater, the higher the rotational speed of the magnetic field and the lower the rotational speed of the beaker in which the working fluid is placed, and that the greatest torque occurs for the working fluid with the highest iron content. Based on the analysis of the structure and characteristics of the clutch in which the magnetic field is used, it has been shown that the design of the developed clutch is similar to that of an induction clutch, and its characteristics correspond to the characteristics of the eddy current clutch. Therefore, the proposed new clutch working with MR fluid and the rotating magnetic field can be applied to stationary power transmission system similarly to an eddy current clutch.

To address the problems of insufficient semantic fusion between text and questions and the lack of consideration of global semantic information encountered in machine reading comprehension models, we proposed a machine reading comprehension model called BERT_hybrid based on BERT and hybrid attention mechanism. In this model, BERT is utilized to separately map the text and questions into the feature space. Through the integration of Bi-LSTM, attention mechanism, and self-attention mechanism, the proposed model achieves comprehensive semantic fusion between text and questions. The probabilities distribution of answers is computed using Softmax. Experimental results on the public dataset DuReader demonstrate that the proposed model achieves improvements in BLEU-4 and ROUGE-L scores compared to existing models. Furthermore, to validate the effectiveness of the proposed model design, we analyze the factors influencing the model’s performance.

Melatonin is an important hormone with a methoxy-indole structure. It is secreted regularly by the pineal gland of mammals. It takes part in many chemical, biological, and physiological activities of the body. Melatonin, acts as a basic hormone, and is responsible for circadian rhythm regulation and sleep control. It has been shown to occur in the highest concentration in organisms during the night times. In living organisms, many activities that have physiological, biological, hormonal, and psychological characteristics follows a certain rhythm. Body temperature, hormone levels, sleep-wakefulness, and some other cognitive functions in humans also change with the circadian rhythm. Its synthesis and release begin with darkness and end with light. This review aims to explore the functions of melatonin such as its stimulating effects on antioxidant enzymes, reducing lipid peroxidation, and protecting brain tissue from oxidative damage. It also has properties that stop the proliferation of cancer cells and prevent tumor growth. Melatonin, which was previously known only as an animal hormone, has been found to also exist in plants. Research has shown that melatonin is present in almost all plant species analyzed to date and that it performs functions similar to those in animals. In plants, it plays a vital role in dealing with various abiotic and biotic stresses. Melatonin acts as an important antioxidant in post-harvest technology and extends the post-harvest life of fruits and vegetables. It also takes part in the regulation of stress-specific genes and the activation of pathogenesis-related proteins and antioxidant enzymes. The most prominent roles of melatonin in plants include regulating circadian rhythm, growth, and development, and increasing tolerance to a wide range of environmental stress factors. The review reveals the effects of melatonin as an extremely important secondary metabolite, on human, animal, and plant metabolism. The use of PRISMA guidelines was used for reporting, search, selection, and synthesis. Information up to 2024 was searched from search engines for the occurrence, nature and function of melatonin. Key words such as structural function, hormonal function, secretion and metabolism were used in the search of publications.

The sustainable management of Land - Water - Energy - Food (LWEF) nexus requires an envi-ronmental characterization that allows the comparison of complex interlinkages between nexus resources and livelihoods. This complexity makes this characterization difficult coupled with limited study in quantifying sustainability of LWEF nexus and its linkage with livelihood. Therefore, the present study aimed to investigate the link between sustainable LWEF nexus and livelihoods. We used analytical hierarchy process and pairwise comparison matrix in combina-tion with weighting model. The result of composite LWEF nexus index was 0.083 representing, low sustainability. This could be linked with nexus resources consumption, use, and manage-ment. From the analysis of the weight of land, water, energy and food nexus resources, the highest weight was observed for food. The focus of on food production only shows no clear synergy on provisioning, supporting or regulating nexus resources to address livelihoods. The result further showed that LWEF nexus resources have strong correlation with livelihoods. This was evidenced by social (r>0.8, P<0.01), natural (r>0.3, P<0.05) and physical (r>0.6, P<0.01) liveli-hood indicators showed strong positive correlation with LWEF nexus resources. From this re-sults, it was observed that managing nexus resources not only provide a significant contribution to achieve sustainable LWEF nexus, but also be effective for enhancing livelihood through food security. This could be attained by strong evidence based policy to ensure sustainable use of nexus resources. The results provided by this study would serve as the foundation for future study, policy formulation and implementation.