The objective is to study the local perceptions on the conservation of wild chimpanzees (Pan troglodytes verus) in the Réserve Naturelle Communautaire de Dindéfélo (RNCD), southeast of Senegal, to design specific actions to improve conservation management. We conducted 338 semi-structured interviews in three main villages of RNCD. Three-fourths of the population were farmers. Of those interviewed, 29% received elementary education. Two of the three villages participated in a project to plant trees as fences. On average, 66% of the respondents were animists. Of the respondents who were afraid to see a chimpanzee, 68% answered because they attack. Seventy-seven percent washed their clothes in the forest river because there was more water than in the village wells. Of the interviewees who threw the old clothes into the forest, 50% did it due to tradition. Ninety-six percent of respondents stated that chimpanzees do not feed from their crops. The main problems of the locals with the Reserve were lack of water and basic resources and not been allow to cut trees in protected areas. There were significant relationships between education (1 relationship), environmental project (4 relationships) and animism (11 relationships) with local perceptions. The 93% of the respondents who had the traditional belief that “if the old clothes were burnt, children would become sick” feel fear of chimpanzees, while those who did not have this animistic belief the 6% are afraid (χ² = 1.57, P < 0.02). These local perceptions allow us to design specific course of action to improve chimpanzee conservation and sustainable coexistence in this complex period of the Anthropocene.

Background: Open AI developed ChatGPT, a language model based on the GPT architecture, designed for text-based communication. Trained on diverse internet texts, ChatGPT generates contextually appropriate responses using machine learning. It understands input through analysis and context interpretation, generating coherent and contextually relevant responses. Interaction is possible through messaging platforms. Materials and Methods: In November 2023, we utilized ChatGPT 3.5, the default version at that time, to answer questions from the Italian National Residency Program Admission Tests (SSMs) of 2021, 2022, and 2023. The questions cover clinical, diagnostic, analytical, therapeutic, and epidemiological scenarios, sometimes accompanied by images. The study compared ChatGPT's answers to the official corrections on the Italian Ministry of University and Research (MUR) website. The scoring method used for evaluation was 1 point for correct answers, 0 points for unanswered questions, and -0.25 points for incorrect answers, reflecting the SSM test scoring system. Results: In summary, ChatGPT was tested with a total of 420 questions, 140 for each test. It achieved an overall accuracy of 80.48%, providing correct answers for 338 questions and incorrect answers for 82 questions. When faced with questions containing both text and images, it answered 55% correctly and 45% incorrectly. The model's performance varied over time, with an 82.14% accuracy rate in 2021 and 2022 (115 correct out of 140) and a 77.14% accuracy rate in 2023 (108 correct out of 140). Applying thisscoring method to the SSM test, ChatGPT would have scored 105 points in 2021 and 2022, and 100 points in 2023. Conclusions: ChatGPT has exhibited above-average performance in the last three SSM tests, highlighting its robust capability to interpret clinical scenarios and offer precise diagnostic and therapeutic guidance. Despite this, some limitations persist, notably the software's inability to interpret non-textual information.

Requirements are predominantly written in Natural Language (NL), which makes them accessible to stakeholders with varying degrees of experience, as compared to a model-based language which requires special training. However, despite its benefits, NL can introduce ambiguities and inconsistencies in requirements, which may eventually result in system quality degradation and system failure altogether. The system complexity that characterizes current systems warrants an integrated and comprehensive approach to system design and development. This need has brought about a paradigm shift towards Model-Based Systems Engineering (MBSE) approaches to system design and a departure from traditional document-centric methods. While, MBSE shows great promise, the ambiguities and inconsistencies present in NL requirements hinder their conversion to models directly. The field of Natural Language Processing (NLP) has demonstrated great potential in facilitating the conversion of NL requirements into a semi-machine-readable format that enables their standardization and use in a model-based environment. A first step towards standardizing requirements consists of classifying them according to the ``type'' (design, functional, performance, etc.) they represent. To that end, a language model capable of classifying requirements needs to be fine-tuned on labeled aerospace requirements. This paper presents the development of an annotated aerospace requirements corpus and its use to fine-tune BERT$_\text{BASE-UNCASED}$ to obtain aeroBERT-Classifier: a new language model for classifying aerospace requirements into design, functional, or performance requirements. A comparison between aeroBERT-Classifier and bart-large-mnli (zero-shot text classification) showcased the superior performance of aeroBERT-Classifier on classifying aerospace requirements despite being fine-tuned on a small labeled dataset.

The current emergency due to the worldwide spread of the COVID-19 caused by the new SARS-CoV-2 is a great concern for global public health. Already in the past, the outbreak of severe acute respiratory syndrome (SARS) in 2003 and Middle Eastern respiratory syndrome (MERS) in 2012 demonstrates the potential of coronaviruses to cross-species borders and further underlines the importance of identifying new-targeted drugs. An ideal antiviral agent should target essential proteins involved in the lifecycle of SARS-CoV. Currently, some HIV protease inhibitors (i.e., Lopinavir) are proposed for the treatment of COVID-19, although their effectiveness was not yet assessed. The main protease (Mpro) provides a highly validated pharmacological target for the discovery and design of inhibitors. We identified potent Mpro inhibitors employing computational techniques that entail the screening of a Marine Natural Product (MNP) library. MNP library was screened by hyphenated pharmacophore model, and molecular docking approaches. Molecular dynamics and re-docking further confirmed the results obtained by structure-based techniques and allowed to highlight some crucial aspects. Seventeen potential SARS-CoV-2 Mpro inhibitors have been identified among the natural substances of marine origin. As these compounds were extensively validated by a consensus approach and by molecular dynamics, the likelihood that at least one of these compounds could be bioactive is excellent.

The rhizosphere is a rich source of actinomycetes which can produce several potential biologically-active secondary metabolites. The principal goals for this research are to extract, purify and characterize the bioactive secondary metabolites produced by three different strains of actinomycetes isolated from the rhizosphere of rosemary, black locust and olive. The plant growth-promoting effect (PGPE) of the studied strains of actinomycetes on Ocimum basilicum L. (basil) and disease-control effect on necrotic stem lesions of “black leg” caused by Fusarium tabacinum on basil, were in silico evaluated. The cell-free culture filtrates from the studied actinomycetes isolates were evaluated in vitro for their antimicrobial activity against some common phytopathogens. The secondary metabolites obtained from the cell-free culture filtrates have been chemically characterized using high‐resolution electrospray ionization of liquid-chromatography/mass-spectrometric detection (ESI-(HR)Orbitrap-MS). Results of in silico trial showed that all studied isolates demonstrated PGPE on basil seedlings improving some Eco-physiological characteristics and reduced the disease incidence of F. tabacinum. The extracted metabolites from the studied actinomycetes demonstrated antimicrobial activity in Petri-plates assay. The chemical analysis revealed the presence of totally 20 different components. This research emphasizes how valuable the examined isolates are for producing bioactive compounds, indicating their putative antimicrobial activity and their potential employment as fungal-biocontrol agents. In particular, the obtained results revealed the possibility of green-synthesis of some important secondary metabolites such as N-Acetyl-L-histidinol, Rhizocticin A and Eponemycin from actinomycetes. The bioactive metabolites may be successively used to develop novel bio-formulations for both crop protection and/or PGPE.

The present state of the natural environment is significantly influenced by the construction sector. Due to the large amounts of consumed natural resources and materials as well as the high volume of emitted pollutants, the building industry is considered to have a key influence in achieving a global sustainable development. A considerable part of the ecological impact of the built environment is influenced by the energy consumed for creating appropriate indoor conditions as well as by the greenhouse gases that are emitted during the operation stage of a building’s life cycle. Therefore, the energy performances of existing buildings must be significantly improved in the near future. This paper analyzes the influence of the thermal insulation thickness of walls on the linear thermal transmittances and the heating demand of an existing building. The resulted values show that while increasing the insulation of the walls leads to a serious heat demand reduction, a deep renovation of the building cannot be achieved solely by using this solution.

The natural frequency of buildings decreases during a strong-motion earthquake, and the structure loses its stiffness. As a result, understanding the damaging process in the structure owing to changes in structural properties is critical during a seismic excitation. The time-frequency technique can detect the damaged building’s time-varying frequency contents. Wigner distributions (WD), Wigner-Ville distributions (WVD), pseudo-Wigner-Ville distributions (PWVD), smoothed pseudo-Wigner-Ville distributions (SPWVD), and synchrosqueezing transforms (SST) have all become popular in recent years for a variety of earthquake engineering applications, including building damage detection. This study proposes the local maximum synchrosqueezing transform (LMSST) for detecting frequency shifts in buildings during strong earthquakes. The data presented in the research show that the suggested method outperforms as compared to the conventional time-frequency methods for detecting frequency shifting in earthquake-damaged structures.

Depressive disorder is a disease that causes a decrease in daily function, and the prevalence of depressive disorder in Korea is the highest among OECD countries. However, due to lack of manpower and negative social awareness, timely treatment is not being performed properly. As a solution to this, AI counseling chatbots are emerging, and it is essential to use labeled datasets to create chatbots. Currently, many countries around the world, including Korea, use the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition, DSM-5, but there are no nine labeled datasets according to the DSM-5 depression disorder diagnosis criteria. This study collects sympathetic datasets, analyzes morphemes using Kind Korean Morpheme Analyzer and augments and builds a word dictionary using Word2Vec. As a result, we have labeled the dataset with nine criteria for diagnosing DSM-5 depressive disorder, which will enhance the performance of the counseling chatbot.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder in women, characterized by fluid-filled sacs in the ovaries and various symptoms including high androgen levels, endometrial irregularities, and cysts. While the cause of PCOS is unknown, it has been linked to genetic, endocrine, and metabolic factors, and there are several treatment options, including lifestyle modifications, medications, and surgery. Natural products, such as medicinal plants and fruits, are being explored as potential treatments for PCOS due to their bioactive compounds with pharmacological effects related to antioxidant, antimicrobial, anticancer, and antidiabetic properties. Some of these compounds improve insulin sensitivity, reduce inflammation, and enhance glucose metabolism, which is beneficial for women with PCOS. This review examined the effects of natural products on PCOS, including their effects on ovarian histological changes, blood glucose, sex hormones, and lipid profile, based on animal and human studies. This study suggests that the use of natural products as complementary medicine can be a promising resource for the development of effective therapeutics for PCOS, but that further research is needed to fully understand their benefits

The rapid expansion of China's A-share market, coupled with the burgeoning volume of financial research reports, presents a unique challenge to traditional stock price prediction methodologies. This study introduces FAST-SCAN, a novel framework designed to leverage natural language processing (NLP) and time series analysis for the efficient and accurate prediction of stock market trends. By integrating advanced sentiment analysis, primarily through the utilization of the RoBERTa model, with dynamic time series forecasting, FAST-SCAN aims to distill actionable insights from the vast corpus of financial research reports. This approach not only enhances the speed and efficiency of financial analysis but also addresses the time-sensitive nature of market-influencing factors, thereby providing a competitive edge in stock price prediction. The framework demonstrates a notable improvement in predictive performance, achieving a 20% annualized return and a 7.19% RankIC, marking a significant advancement over conventional statistical analysis methods. Through its innovative combination of NLP and predictive analytics, FAST-SCAN paves the way for a more informed and strategic approach to investment in the A-share market, emphasizing the importance of timely, data-driven decision-making in the financial industry.

Numerous innovative advancements in dressing technology for wound healing have emerged. Among the various types of wound dressings available, hydrogel dressings, structured with a three-dimensional network and composed of predominantly hydrophilic components, are widely used for wound care due to their remarkable capacity to absorb abundant wound exudate, maintain a moisture environment, provide soothing and cooling effects, and mimic the extracellular matrix. Composite hydrogel dressings, one of the evolved dressings, address the limitations of traditional hydrogel dressings by incorporating additional components, including particles, fibers, fabrics, or foams, within the hydrogels, effectively promoting wound treatment and healing. The added elements enhance the features or add specific functionalities of the dressings, such as sensitivity to external factors, adhesiveness, mechanical strength, control over the release of therapeutic agents, antioxidant and antimicrobial properties, and tissue regeneration behavior. They can be categorized as natural or synthetic based on the origin of the main components of the hydrogel network. This review focuses on recent research on developing natural polysaccharide-based composite hydrogel wound dressings. Their preparation and composition, the reinforcement materials integrated into hydrogels, and therapeutic agents are also explored. Furthermore, their features and the specific types of wounds where applied are discussed as well.

Three new cembranoids (1–3) and a new casbanoid (4), along with three known analogues (5–7), have been isolated from the soft coral Sinularia nanolobata collected off Ximao Island. The structures including the absolute configurations of new compounds were established by extensive spectro-scopic data analysis, time-dependent density functional theory/electronic circular dichroism (TDDFT-ECD) calculations and the comparison with spectroscopic data of known compounds. In in vitro bioassay, compounds 1 and 5 exhibited moderate cytotoxic activities against human erythroleukemia (HEL) cells line with IC50 values of 37.1 and 42.4 uM, respectively.

This review uses the National Cancer Institute (NCI) COMPARE program to establish an extensive list of heterocyclic iminoquinones and quinones with similarities in differential growth inhibition across the 60-cell line panel of the NCI Developmental Therapeutic Program (DTP). Many natural products and synthetic analogues are revealed, as potential NAD(P)H:quinone oxidoreductase 1 (NQO1) substrates through correlations to dipyridoimidazo[5,4-f]benzimidazoleiminoquinone (DPIQ), and as potential thioredoxin reductase (TrxR) inhibitors, through correlations to benzo[1,2,4]triazin-7-ones and pleurotin. The strong correlation to NQO1 infers the enzyme has a major influence on the amount of active compound with benzo[e]perimidines, phenoxazinones, benz[f]pyrido[1,2-a]indole-6,11-quinones, seriniquinones, kalasinamide, indolequinones, and furano[2,3-b]naphthoquinones, hypothesized as prodrugs. Compounds with very strong correlations to known TrxR inhibitors had inverse correlations to the expression of both reductase enzymes, NQO1 and TrxR, including naphtho[2,3-b][1,4]oxazepane-6,11-diones, benzo[a]carbazole-1,4-diones, pyranonaphthoquinones (including kalafungin, nanomycin A, and analogues of griseusin A), and discorhabdin C. Quinoline-5,8-dione scaffolds based on streptonigrin and lavendamycin can correlate to either reductase. Inhibitors of TrxR are not necessarily (imino)quinones, e.g., parthenolides, while oxidizing moieties are essential for correlations to NQO1, as with the mitosenes. Herein, an overview of synthetic methods and biological activity of each family of heterocyclic imino(quinone) is provided.

In this study, we introduce a new type curve in 3-dimensional space which called B-lift curve and we obtain the Frenet operators of the B-lift curve. Moreover, we consider the correpondence of Frenet operators between the Blift curve and the natural lift curve. Finally, we investigate the B-lift curve according to the main curve is slant helix or darboux helix.

Our understanding and theoretical interpretation of observations in astrophysics and cosmology depends on our knowledge of the fundamental constants and their possible dependence on time and space. Atomic spectroscopy and radio astronomy give important information on the validity and stability of the fundamental constants. The possible dependence of the fine structure constant alpha on time and spatial direction is an active topic of research.Period doubling is a universal property of nonlinear dynamical systems, and the doubling is exact in principle. The value of the elementary charge squared can be calculated by the period doubling process from the Planck charge and thereby the value of alpha.If ‘old’ and ‘new’ electrons are identical, then the Planck charge, i.e. a set of natural constants, has remained constant over time. In this article we show that the value of alpha calculated from the Planck charge is 0.007 % smaller than the current accepted value of alpha.

The market of natural and organic cosmetics has been growing in last decades. The increase in interest in this type of product is a consequence of the concern that consumers have been presenting in relation to the environment and health. In addition to the appreciation the use of sustainable ingredients in cosmetic formulations, the consumers are also concerned about pollution caused by the use of plastics, which leads industries to reinvent themselves and rethink about the composition of packaging. The factor that most drives the purchase of natural and organic cosmetics is the fact that the consumer, in addition to contributing to the preservation of the environment, is also using a sustainable product. The growing demand for natural and organic cosmetics results in a concern of the brands with the organic issue, with the decreased use of animal derived ingredients and with the updating the parameters required for certification of a cosmetic as natural or organic. Due to the few studies available in this area, the importance of clarifying the definitions and concepts of natural and organic cosmetics is evident, in order to contribute with accurate information for the cosmetic sector.

We discuss further details on the concepts of “drug-likeness”, “lead-likeness”, and “natural product-likeness”. The discussion will first focus on natural products as drugs, then a discussion of previous studies in which the complexities of the scaffolds and chemical space of naturally occurring compounds have been compared with synthetic, semi-synthetic compounds and FDA-approved drugs. This is followed by guiding principles for designing “drug-like” natural product libraries for lead compound discovery purposes. We end up by presenting a tool for measuring “natural product-likeness” of compounds and a brief presentation of machine learning approaches and a binary quantitative structure-activity relationship (QSAR) for classifying drugs from non-drugs and natural compounds from non-natural ones, respectively.

For decades, sepsis research has been motivated by the idea of a dangerous overreaction of the immune system in sepsis. But is it true that the response to sepsis is dysregulated? This review surveys the history of sepsis trials and found that evidence for dysregulation does not exist in many of the physiologic mechanisms of sepsis. It is time to consider the alternative hypothesis, that sepsis traits are often functional, and do more harm than good. This review discusses the implications of this perspective for the future of sepsis research

Sourced so far mostly from beet root juice, betanin is a red-violet natural colorant increasingly used by the food, beverage and nutraceutical industries. We provide an updated bioeconomy perspective into a valued betacyanin whose supply and applications, we argue in this study, will rapidly expand.

The integral extraction via microwave-assisted hydrodiffusion and hydrodistillation of water-soluble bioproducts contained in the peel of Opuntia ficus-indica white and red cultivars harvested in Sicily, affords red and stable aqueous extracts mostly containing valued betanin, pectin and biophenols. Potentially useful as nutraceutical products, these aqueous extracts are a source of valued ingredients in high demand for a number of important food, cosmetic, beverage and nutraceutical applications.

Although evolutionary theory has yet to furnish a comprehensive rationale for the underlying mechanism of natural selection, it is pertinent to note that the phenomenon transcends the confines of biological systems, exhibiting analogous patterns in physical systems as well. Specifically, isolated and closed systems tend to evolve towards states of progressively increasing entropy. In scenarios where multiple paths for entropy increase are accessible, such systems tend to favor combinations of paths that exhibit the most rapid entropy increase rates. Among the various processes in nature, life activities constitute a salient means of achieving entropy increase. Genetic variation within organisms generates individuals that vary in their rates of entropy increase. As these organisms interact, compete and combine, they form diverse combinations of entropy-increasing pathways. Nature, in turn, selects among these combinations, favoring those that culminate in the fastest entropy increase, thereby propelling the evolution of life. Essentially, the evolution of life is an ongoing exploration of diverse combinations of pathways that maximize entropy increase across various energy pools. Amidst continuous genetic variation and the selective pressure imposed by nature for maximum entropy production rates, information accumulates, leading to a corresponding acceleration in the rate of entropy increase. This natural selection, which favors maximizing the entropy at the fastest possible rate, serves as the ultimate driving force for the origin and evolution of life.

The use of intelligent sensor-based packaging in food products provide quick evaluation of the quaility of the food. This study was done to develop bio-based color sensor from surimi-based color sensor incorporated with sappan wood extract (SWE) for smart food packaging. The SWE with different concentration (0.15%, 0.25%, and 0.35%) was incorporated to the myofibrillar protein-based edible film. The sappan wood-surimi edible film (SSEF) was subjected to physical properties analysis and the color changes at different pH values and soaking time at different condition were evaluated. Based on the results, different concentration of the SWE significantly (p&lt;0.05) affected the physical properties of the film. With the increasing of pH values, the darkness, redness, and blueness of the film was increased. Based on the evaluation of the SSEF with different soaking condition, the color changes of the film in acidic condition were more stable than in neutral and alkaline condition. The results from this study showed that SSEF have the possibilities to be used as a smart food packaging possessing the capabilities to act as color sensor due to its sensitivity to the changes in pH condition of the product.

During the last two years the COVID-19 pandemic has affected the world population in several ways. An important increase in mental health problems is a consequence of this pandemic that is ubiquitous worldwide. In this work we study the effect of the pandemic on the mental health of a population of teenagers and youth based on the analysis of natural language processing, machine learning algorithms and expert knowledge. The data analysed was obtained from a chat helpline called Safe time from theIt Get’s Better Foundation in Chile. The data consists of 10,986 conversations gathered from 2018 until 2020 between volunteers from the foundation and users of the platform. We compared the conversationsbefore and during the pandemic in terms of their thematic content. Our analysis found: a significantdecrease in self-image appreciation during the pandemic; a significant decrease in the quality of personalrelationships during the pandemic, and a significant increase of performance appreciation.

Streptomyces sp. HNS054, which was previously isolated from a marine sponge, demonstrates vigorous growth and facile genetic modification. This study explores its potential as a marine ac-tinomycete chassis through complete genome sequencing and genetic engineering. With a 7.5 Mb genome containing 6678 predicted genes, 21 secondary metabolic biosynthetic gene clusters (BGCs), and 8 common site-specific recombination (SSR) system attB loci, HNS054 shows a close phylogenetic relation to S. griseoincarnatus strain RB7AG. Utilizing multiplexed site-specific ge-nome engineering (MSGE) and the CRISPR/Cas9 method, engineered strains derived from HNS054 (with three copies of the target BGCs) produce higher amounts of aborycin and ac-tinorhodin than S. coelicolor M1346 (containing three copies of the same BGCs). HNS054 stands out for its remarkable characteristics, including salt tolerance, rapid growth, and compatibility with synthetic biology tools, and positions it as a promising candidate for developing marine ac-tinomycete hosts to enhance secondary metabolite production.

Abstract: One of the most significant medical advancements of the 20th century was the discovery of antibiotics, which continue to play a vital tool in the treatment and prevention of diseases in humans and animals. However, the imprudent use of antibiotics in all fields of One-Health and concerns about antibiotic resistance among bacterial pathogens have raised interest in antibiotic use restrictions on a global scale. Despite the failure of conventional antimicrobial agents, only about 15 new antibiotics have been introduced clinically since year 2000 to date. Moreover, there have been reports of resistance to some of these new antibiotics. This has necessitated a need to search for an alternative strategy to combat antimicrobial resistant pathogens. Thus, this review compiles and evaluates the approaches—natural compounds, phage treatment, and nanomaterials—that are being used and/or suggested as the potential substitutes for conventional antibiotics. These strategies include those that focus on the enzymes or proteins, as well as the physiology and metabolism of resistant bacteria

Genetic and environmental factors including lifestyle are thought to play an important role in the pathophysiology of rheumatoid arthritis (RA). There is evidence that diet can enhance the inflammatory response in genetically predisposed individuals. On the other hand, certain types of diets are thought to alleviate RA symptoms due to their anti-inflammatory and antioxidant activities. Also, natural compounds with potential effectiveness in RA management belong to different chemical classes such as flavonoids, polyphenols, carotenoids, and alkaloids with their antioxidant characteristics. In this paper, the nutritional approaches to prevent or extenuate the disease progress were examined in the light of current literature. Mediterranean and vegan diets equally have been shown to exhibit positive effects on RA as the consumption of dietary fiber, antioxidants and anti-inflammatory compounds from fruits, vegetables, grains, nuts, and seeds are high. Whereas Mediterranean diet additionally includes beneficial nutrients of animal origin such as omega-3 polyunsaturated fatty acids from fish and seafood, patients on vegan diet need to be monitored closely for intake of all critical nutrients. Certain calorie restrictions and intermittent fasting diets have been shown to benefit RA patients although there is an obvious need for further studies to establish solid evidence-based recommendations and guidelines. The research data available strongly suggest that dietary approaches may help delay the onset of RA and/or improve symptoms and thus nutrition should be routinely addressed to facilitate management of the disease.

Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT-3 as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein-protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.

Transient analysis of gas flow in pipeline was studied. Finite Element Method based on ANSYS FLOTRAN was used to account for changes in pressure, temperature and flow rate. Compressibility factor function of temperature and pressure was considered. For non-isothermal transient results, the pressure and the flowrate gave wave propagation as a result of slow transients created by demand condition at the outlet end of custody transfer. Results obtained were in agreement with the demand restrictions at the outlet end of custody transfer indicating that the predictions are accurate and reliable. The results demonstrated that the Finite Element Method gave accurate prediction of pressure, temperature and flowrate in transient gas studies. For steady state non-isothermal model, results showed that the magnitude of the average pressure drop was higher when pressure was predicted with constant compressibility factor, but the same average pressure drop was reduced when the pressure was predicted with variable compressibility factor, z. Since compressibility factor is a function of temperature and pressure, the above findings signifies that in the case when gas temperature does not stabilize, the prediction of pressure with isothermal model and constant compressibility factor will lead to significant errors.

Fire behavior of natural rubber latex foam under different thickness conditions(d=1,2and 5cm)were explored though a little of experiments by using the self-built small scale experimental platform. It can be shown that the flame spread law of thermally thin and thermally thickness are different. Natural rubber latex foam with thickness of 2cm show higher fire risk, which value of flame spread rate, maximum flame height, maximum mass loss rate and maximum temperature is 0.00293m/s, 851.875mm, 1.83g/s, 948K,repectively.That may because the thickness of residue formed of thermally thick materials is larger than the thin one, obstructing the contact of the natural rubber latex foam with fresh air .In addition, a special phenomenon is noticed that during the second stage, the bottom unburned zone located in the four edges(thermally thin material) and middle player(thermally thick material).

In the last decade and a half, the world has experienced the outbreak of a range of viruses such as COVID-19, H1N1, flu, Ebola, Zika Virus, Middle East Respiratory Syndrome (MERS), Measles, and West Nile Virus, just to name a few. During these virus outbreaks, the usage and effectiveness of social media platforms increased significantly as such platforms served as virtual communities, enabling their users to share and exchange information, news, perspectives, opinions, ideas, and comments related to the outbreaks. Analysis of this Big Data of conversations related to virus outbreaks using concepts of Natural Language Processing such as Topic Modeling has attracted the attention of researchers from different disciplines such as Healthcare, Epidemiology, Data Science, Medicine, and Computer Science. The recent outbreak of the MPox virus has resulted in a tremendous increase in the usage of Twitter. Prior works in this field have primarily focused on the sentiment analysis and content analysis of these Tweets, and the few works that have focused on topic modeling have multiple limitations. This paper aims to address this research gap and makes two scientific contributions to this field. First, it presents the results of performing Topic Modeling on 601,432 Tweets about the 2022 Mpox outbreak, which were posted on Twitter between May 7, 2022, and March 3, 2023. The results indicate that the conversations on Twitter related to Mpox during this time range may be broadly categorized into four distinct themes - Views and Perspectives about MPox, Updates on Cases and Investigations about Mpox, MPox and the LGBTQIA+ Community, and MPox and COVID-19. Second, the paper presents the findings from the analysis of these Tweets. The results show that the theme that was most popular on Twitter (in terms of the number of Tweets posted) during this time range was - Views and Perspectives about MPox. It is followed by the theme of MPox and the LGBTQIA+ Community, which is followed by the themes of MPox and COVID-19 and Updates on Cases and Investigations about Mpox, respectively. Finally, a comparison with prior works in this field is also presented to highlight the novelty and significance of this research work.

Since the exploitation of mineral resources results in the release of radionuclides, and consuming radionuclides affects public health in the short and long term. A case study of the environmental radiation impact from coal mining and germanium processing was carried out in southwest China. The coal mines contain germanium and uranium and have been exploited for more than 40 years. The farmlands around the site of coal mining and germanium processing have been contaminated by the solid waste and mine water in some extend since then. Samples of crops have been collected from contaminated farmlands in research area. The research area covers a radius of 5 km, in which there are 2 coal mines located. 210Pb and 210Po have been analyzed as the key radionuclides during monitoring program. The average activity concentrations of 210Pb and 210Po in the crops were 1.38 and 1.32 Bq/kg in cereals, 4.07 and 2.19 Bq/kg in leafy vegetables and 1.63 and 1.32 Bq/kg in root vegetables. The annual effective doses due to the ingestion of 210Pb and 210Po in consumed crops have been estimated for adult residents living in research area. The average annual effective dose was 0.336mSv/a, while the minimum was 0.171 mSv/a and the maximum was 0.948 mSv/a. The results show that crops grown on contaminated farmland contained an enhanced level of radioactivity concentration. Ingestion doses of local residents in research area were significantly higher than the China average level of 0.112 mSv/a, and the world average level of 0.042 mSv/a through 210Pb and 210Po in crops intake respectively.

Wound healing is rarely seen as a problem in healthy individuals; however, under certain pathophysiological conditions, this process can be impaired, leading to the emergence of chronic wounds, which are themselves a serious public health problem. This work aimed to review the most important and recent literature on the use of metallic nanoparticles, in this case those produced from plant extracts, and their application as healing agents. For this, we firstly provided an insight into the pathophysiology of the wound healing process and then the main routes for obtaining metallic nanoparticles. The methodology of synthesis, which is part of the so-called green synthesis, has been the subject of several studies reporting the use of medicinal plants as subtracts for the production of different nanoparticles (of silver, gold, and zinc). Additionally, their use as wound healing agents is close related with biological properties such as antimicrobial, anti-inflammatory, and healing. Finally, we addressed in vitro and in vivo studies reporting the efficiency of metallic nanoparticles synthesized with plant extracts and applied in different pharmaceutical forms on the wound healing process. Based on these satisfactory results, we verified that metallic nanoparticles are potential therapeutic alternatives when compared to the traditional synthetic healing agents, thus foreseeing the production of new drugs in the pharmaceutical market.

CONTEXT– Energy is widely involved in human activity and corresponding emissions of SOX, NOX and CO2 from energy generation processes affect global climate change. Clean fuels are desired by society because of their reduced greenhouse gas emissions. Hydrogen is once such candidate fuel. Much hydrogen is produced from fossil fuel, with biomass being an alternative process. OBJECTIVE– The project compared the environmental impact of hydrogen production by natural gas steam reforming vs. biomass gasification. METHOD–Environmental impact was calculated from the input and output data from life cycle inventory analysis. The impact assessment was focused on greenhouse gas emission, acidification, and eutrophication. Models of the two processes were developed and analysed in OpenLCA. The agribalyse database was used to connect inventory flow data to environmental impacts. FINDINGS– For all three metrics, biomass gasification had lower impacts than natural gas steam reforming, sometimes by large margins. For biomass gasification the silica sand production contributes most to all three impact categories, whereas for natural gas steam reforming it is the LPG extraction.

Abstract. Background: This paper investigates the propagation of behaviorally transmitted traits with negative effect on host fitness. Methods: We analyze equilibrium between genetically transmitted and behaviorally transmitted competing propagators and consider whether a behavioral propagator is linked to reproduction (e.g. vertical culture transmission), or not. We employ combined genetic and behavior-induced fitness components for hosts, while behavioral propagators have replication factors to distinguish from what’s good for the host (fitness). Results: A trait which spreads faster than its marginal host fitness contribution reduces population will establish itself. The often transient nature of laterally transmitted traits may be a defense against accumulation of deleterious traits. Laterally transmitted traits with high spreading rate often do not equalize with genetic traits, spreading outside natural selection of the hosts. Vertical transmission reduces replication rate and allows group selection against deleterious behaviorally transmitted traits. Competing mutually exclusive propagators contribute to inequality and altruism, but compete through adverse fitness since exclusivity assumes low conversion. Conclusion: Behaviorally transmitted traits, in some cases a tremendous advantage, may also be a significant problem in the development of societies.

Background: This year has seen the emergence of two major crises, a significant increase in frequency and severity of hurricanes and the COVID-19 pandemic. However, little is known as to how each of these two events have impacted the other. A rapid qualitative assessment was conducted to determine the impact of the pandemic on preparedness and response to natural disasters and the impact of past experiences with natural disasters in responding to the pandemic. Methods: Semi-structured interviews were conducted with 26 representatives of 24 different community-based programs in southern Louisiana. Data were analyzed using procedures embedded in the Rapid Assessment Procedure – Informed Community Ethnography methodology, using techniques of immersion and crystallization and focused thematic analysis. Results: The pandemic has impacted the form and function of disaster preparedness, making it harder to plan for evacuations in event of a hurricane. Specific concerns included being able to see people in-person, providing food and other resources to residents who shelter in place, finding volunteers to assist in food distribution and other forms of disaster response, competing for funds to support disaster-related activities, developing new support infrastructures, and focusing on equity in disaster preparedness. However, several strengths based on disaster preparedness experience and capabilities were identified, including providing a framework for how to respond and adapt to COVID and integration of COVID response with their normal disaster preparedness activities. Conclusions: Although prior experience has enabled community-based organizations to respond to the pandemic, the pandemic is also creating new challenges to preparing for and responding to natural disasters.

The assessment of the number of people exposed to natural hazards, especially in countries with strong urban growth, is difficult to be updated at the same rate as land use develops. This paper presents a remote sensing based procedure for quick updating the assessment of the population exposed to natural hazards. A relationship between satellite nightlights intensity and urbanization density from global available cartography is first assessed when all data are available. This can be used to extrapolate urbanization data at different time steps, updating exposure each time new nightlights intensity maps are available. As reliability test for the proposed methodology, the number of people exposed to riverine flood in Italy is assessed, deriving a probabilistic relationship between DMSP nightlights intensity and urbanization density from GUF database for the year 2011. People exposed to riverine flood are assessed crossing the population distributed on the derived urbanization density with flood hazard zones provided by ISPRA. The validation on reliable exposures derived from ISTAT data shows good agreement. The possibility to update exposure maps with higher refresh rate makes this approach particularly suitable for applications in developing countries, where exposure may change at sub-yearly scale.

Aim: Utilizing the subjective experience of nurse executives who have supervised nurses during a major disaster in a hospital setting, this study aims to describe the subjective experience of nurse executives (NE) who have supervised nurses’ response to major disaster. This paper will focus on strategies to support nursing response to disaster, specifically to strengthen resiliency and the ability to maintain function despite the shock of disaster, including those caused by climate changes. Methods: Semi-structured interviews were conducted with 11 hospital-based nurse executives who supervised nurses during some of the worst natural and human-made disasters in different regions of the United States. A phenomenological approach was used to analyze and describe emerging themes from qualitative data. Results: Three Nurse Executives demonstrated theme saturation for mitigation steps to augment traditional disaster readiness activities: 1.) Assessment of Human Infrastructure: Daily Skills Needed During Disaster Response, 2.) Identification and Study of Failure Points & Metrics, 3.) Strengthening Human Infrastructure: Rectifying Deficiencies), and 4.) Monitoring Metrics & Making Corrections During Conventional Periods. Conclusion: Mitigation steps may improve outcomes in hospital function during conventional times; therefore, may improve resiliency and the ability to maintain functions during major disasters, including climate change.

Natural circulation loop (NCL) systems are buoyancy-driven heat exchangers that are used in various industrial applications. The concept of passive heat exchange in NCL systems is attractive, because there is no need for an externally driven equipment (e.g., a pump) to maintain the fluid circulation. However, relying on buoyancy as the sole driving force may lead to several potential difficulties, one of which is generation of (possibly) time-varying nonlinearities in the dynamical system, where a difference in the time scales of heat transfer and fluid flow causes the flow to change from a steady-state regime to either an oscillatory regime or a flow-reversal regime, both of which are undesirable. In this paper, tools of symbolic time series analysis (e.g., probabilistic finite state automata (PFSA)) are proposed to identify selected regimes of operation in NCL systems, where the underlying principle is built upon the concept of pattern classification from measurements of fluid-flow dynamics. The proposed method is shown to be capable of identifying the current regime of operation from the initial time response under a given set of operational parameters. The efficacy of regime classification is demonstrated by testing on two data sets, generated from numerical simulation of a MATLAB SimuLink model that has previously been validated with experimental data. The results of the proposed PFSA-based classification are compared with those of a hidden Markov model (HMM) that serves as the baseline.

Aiming to improve the flame retardancy performance of natural rubber (NR), we developed a novel flame retardant synergistic agent through grafting of MCM-41 to graphene oxide (GO)，named as GO-NH-MCM-41，as an assistant of intumescent flame retardants (IFR). The structure of GO-NH-MCM-41 was characterized by FTIR, TEM and SEM tests, which confirmed that a fine grafting had been applied between GO and MCM-41. The flame retardancy of NR/IFR/GO-NH-MCM-41 composites was evaluated by limited oxygen index (LOI), UL-94 and cone calorimeter test. The LOI value of NR/IFR/GO-NH-MCM-41 reached to 26.3%; the UL-94 ratings improved to V-0 rating. Moreover, the addition of GO-NH-MCM-41 obviously decreased the peak heat release rate (PHRR) and the total heat release (THR) of the natural rubber composites. And the addition of GO-NH-MCM-41 increased the thickness of char residue. The images of SEM indicated the char residue were more compact and continuous. The degradation of GO-NH-MCM-41 based NR composites completed with a mass loss of 35.57% at 600 ℃. The tensile strength and the elongation at break of NR/IFR/GO-NH-MCM-41 composites were 13.9 MPa and 496.7%, respectively. The results of rubber process analyzer (RPA) reached the maximum value, probably due to a better network of the fillers in the matrix.

The pre-chamber technologies can improve the ignition performance of IC engines by more than two orders in magnitude and thereby substantial economic benefits. Compared with the common pre-chamber, a plug-and-play passive scheme is suitable for quick retrofit, which is getting more attention from the automobile industry. Good scavenging is the precondition for improving turbulent jet ignition performance for a passive pre-chamber. Therefore, detailed evaluations of the scavenging process and turbulent jet ignition deserve investigations for new pre-chamber schemes. In this paper, the effects of design parameters on ignition processes of plug-and-play passive pre-chamber were numerically studied, allowing for the lateral angle, orifice diameter, and vertical angle design. Seven pre-chamber schemes were evaluated, and four optimal ones were selected for bench tests. The characteristics of the scavenging process, turbulent jet ignition, and main-chamber combustion were investigated and analyzed. The results show that allowing for the trade-off between ignition energy and scavenging efficiency, the volume ratio of the pre-chamber to clearance is recommended to be 0.2~0.7%, and the corresponding area-volume ratio is 0.003~0.006 mm-1. Compared with the original natural gas engine, the pre-chamber retrofit can save up to 13.2% fuel consumption, which presents a significant improvement in fuel economy.

Introduction: Around a million people are reported to die by suicide every year, and due to the stigma associated with the nature of the death, this figure is usually assumed to be an underestimate. Suicide may be prevented if prompt intervention is taken to mitigate risk. Machine learning and artificial intelligence-based modelling, such as natural language processing (NLP) and other text analytics approaches, has the potential to become a major technique for the detection, diagnosis, and treatment of people who are suffering from mental health issues. The primary aims of this research are to determine whether NLP techniques have been utilised in the field of suicide prevention, and if so, were they effective? What were their limitations? Methods: PubMed, EMBASE, MEDLINE, PsycInfo, and Global Health databases were searched for studies that reported use of NLP for suicide ideation or self-harm. Thematic analysis was used to synthesise and analyse the included studies. Findings were reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, and the Mixed Methods Appraisal Tool (MMAT) was used in assessing paper quality. Result: The preliminary search of five databases generated 387 results. Removal of duplicates resulted in 158 potentially suitable studies. Twenty papers were finally included in this review. Discussion: Studies show that combining structured and unstructured data in NLP data modelling yielded more accurate results than utilizing either alone. Also, to reduce suicides, people with mental problems must be continuously and passively monitored. Further, NLP and other machine learning/artificial intelligence technologies can be used to address health inequities and electronic health records provide valuable data for creating suicide risk tools. Finally, Online, social media, and smartphone applications can be leverage in detecting people with suicide ideation. Conclusion: The use of artificial intelligence and machine learning opens new avenues for considerably guiding risk prediction and advancing suicide prevention frameworks. The review's analysis of the included research revealed that the use of NLP may result in low-cost and effective alternatives to existing resource-intensive methods of suicide prevention. To summarise, there is substantial evidence that NLP is useful in identifying people who have suicide ideation.

Nowadays, an analysis of virtual media to predict society’s reaction to any events or processes is a task of great relevance. Especially it concerns meaningful information on healthcare problems. Internet sources contain a large amount of pharmacologically meaningful information useful for pharmacovigilance purposes and repurposing drug use. An analysis of such a scale of information demands developing the methods that require the creation of a corpus with labeled relations among entities. Before, there have been no such Russian language datasets. This paper considers the first Russian language dataset where labeled entity pairs are divided into multiple contexts within a single text (by used drugs, by different users, by the cases of use, etc.), and a method based on the XLM-RoBERTa language model, previously trained on medical texts to evaluate the state-of-the-art accuracy for the task of indication of the four types of relationships among entities: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. As shown based on the presented dataset from the Russian Drug Review Corpus, the developed method achieves the F1-score of 81.2% (obtained using cross-validation and averaged for the four types of relationships), which is 7.8% higher than the basic classifiers.

Arthrospira platensis is the widely available source of spirulina and contains distinctive natural pigments including carotenoids and C-phycocyanin (C-PC). In this study, the major carotenoid and C-PC contents were determined in seven commercially available spirulina powder products and laboratory-prepared A. platensis trichomes (AP-1) by an LC-DAD method and a UV-Visible spectrometry, respectively. The correlation of these two pigment content levels with Hunter color coordinates and antioxidant activity was also evaluated. The L* value failed to show a significant correlation with pigment content, but a positive correlation was observed between a* values and the contents of total carotenoid and C-PC. As b* values decreased, the total carotenoid and C-PC contents increased. AP-1 exhibited the highest content of total carotenoids, chlorophyll a and C-PC, and antioxidant activities among the samples. This observation could be related to degradation of these pigments during the mass production process. The carotenoid profiles suggested that the commercial spirulina powders originated from two different sources, A. platensis and A. maxima. Total carotenoid and C-PC content exhibited positive significant correlations with antioxidant activities measured by DPPH and ABTS assays. These results provide a strong scientific foundation for the establishment of standards for the commercial distribution of quality spirulina products.

Natural deep eutectic solvents (NADES) have been shown as selective and environmentally friendly solvents for the extraction of bioactive compounds. However, studies on the solubility of low molecular weight carbohydrates (LMWC) in NADES are scarce. In this work, new solubility data of LMWC in NADES are provided and a new approach based on the use of these solvents for the efficient fractionation of bioactive carbohydrates was explored for the first time. Several mono- and disaccharides and three NADES based on choline chloride (ChCl) and different donors [2-ethylene glycol (EtG), glycerol (Gly) and ethanedioic acid dihydrate (Eth)] were considered. While degradation of carbohydrates, mainly ketoses, was detected with ChCl:Eth due to its acidic nature, ChCl:EtG and ChCl:Gly were found to be useful alternatives for selectively separating bioactive ketoses and their corresponding aldoses (e.g. lactulose/lactose and tagatose/galactose) present in equimolar binary mixtures. In addition, the usefulness of ChCl:EtG for the selective enrichment of lactulose to be used as food ingredient or nutraceutical was proven (from a 25% in the reaction mixture to a 56% in the purified sample). NADES could be used for the selective fractionation of value-added carbohydrates from interfering sugars for several applications including food science, engineering or pharmaceuticals.

This research work focuses on the potential application of an organic compound, santalol obtained from santalum album in the inhibition of the enzyme tyrosinase which is actively involved in the biosynthesis of the melanin pigment. Over-production of melanin causes undesirable pigmentation in humans as well as other organisms that significantly downgrade their aesthetic value. The study is designed to explain the purification of tyrosinase from the mushroom Agaricus bisporus, followed by activity assay and enzyme kinetic to give insight into the santalol modulated tyrosinase inhibition in a dose dependent manner. The multi-spectroscopic techniques like UV-vis, fluorescence, and isothermal calorimetry are employed to deduce the efficiency of santalol as potential candidate against the tyrosinase enzyme activity. Experimental results are further verified by molecular docking. Santalol derived from the essential oils of santalum album, is widely used as remedy for skin disorders and potion for fair complexion since ancient times. Based on enzyme kinetics and biophysical characterization, this is the first scientific evidence where santalol inhibits tyrosinase, which may be employed in agriculture, food, and cosmetic industries by prevent excess melanin formation or browning.

The Abnormal increase in the mass of adipose tissue during adipogenesis results in obesity. Obesity is a predominant disorder and its widespread has become a critical concern worldwide. This is due to the burden of its associated co-morbidities like cancer, cardiovascular diseases and diabetes. Over-nutrition and the sedentary lifestyle are considered as the most significant causes of abnormal adipose tissue development. Appropriate lifestyle and behavioural interventions are the corner stones of successful weight loss, but to maintain such a lifestyle is highly challenging. Accordingly, natural products from plants either as crude extracts or purified phytochemicals have shown to have anti-obesogenic properties and are generally non-toxic and cost-effective. In this review, we discuss the comparative analysis of molecular mechanisms involved in adipogenesis and the recently reported anti-obesogenic effects of various plant products. We will provide a common platform for understanding obesity and adipogenesis, and a possible mechanism of action for anti-obesogenic plant products through AMP-activated protein kinase (AMPK), which will support the scientific development of traditional herbal medicine.

Petrosamine (1) – a colored pyridoacridine alkaloid from the Belizean sponge, Petrosia sp. that is also a potent inhibitor of acetylcholine esterase (AChE) – was investigated by spectroscopic and computational methods. Analysis of the petrosamine free energy landscapes, pKa and tautomerism revealed an accurate electronic depiction of the molecular structure of 1 as the di-keto form, with net charge of q = +1, rather than a dication (q = +2) under ambient conditions of isolation-purification. The pronounced solvatochromism (UV-vis) reported for 1, and related analogs, was investigated in detail and is best explained by charge delocalization and stabilization of the ground state (HOMO) of 1 rather than an equilibrium of competing tautomers. Refinement of the molecular structure of 1 by QM methods complements published computational docking studies to define the contact points in the enzyme active site that may improve design of new AChE inhibitors based on the pyridoacridine alkaloid molecular skeleton.

The classification of goods involved in international trade in Brazil is based on the Mercosur Common Nomenclature (NCM). The classification of these goods represents a real challenge due to the complexity involved in assigning the correct category codes especially considering the legal and fiscal implications of misclassification. This work focuses on the training of a classifier based on Bidirectional En-coder Representations from Transformers (BERT) for the tax classification of goods with NCM codes. In particular, this article presents results from using a specific Portuguese Language tuned BERT model as well results from using a Multilingual BERT. Experimental results justify the use of these models in the classification process and also that the language specific model has a slightly better performance.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have triggered a recent pandemic of respiratory disease and affected almost every country all over the world. A large amount of natural bioactive compounds is under clinical investigation for various diseas-es. Especially, marine natural compounds are gaining more attention in the new drug develop-ment process. The present study has aimed to identify potential marine-derived inhibitors against the target proteins of COVID-19 using a computational approach. Currently, 16 marine clinical-level compounds were selected for computational screening against the four SARS-CoV-2 main proteases. Computational screening resulted from the best drug candidates for each target based on the binding affinity scores and amino acid interactions. Among these, five marine-derived compounds namely Chrysophaentin A (-6.6 kcal/mol), Geodisterol sulfates (-6.6 kcal/mol), Hymenidin (-6.4 kcal/mol), Plinabulin (-6.4 kcal/mol) and Tetrodotoxin (-6.3 kcal/mol) expressed the minimized binding energy and molecular interactions such as covalent and hydrophobic interactions to the SARS CoV-2 Main Protease. Using Molecular dynamic stud-ies, the Root-Mean-Square Deviation (RMSD), Root-Mean-Square Fluctuation (RMSF), Radius of Gyration (ROG), and Hydrogen bonds (H-Bonds) values were calculated for SARS-CoV-2 Main Protease with Hymenidin docked complex. Additionally, in silico Druglikeness and pharmaco-kinetic property assessments of the compounds demonstrated favorable druggability. These re-sults suggested that marine natural compounds are capable of fighting SARS-CoV-2. Further, in vitro and in vivo studies need to be carried out to confirm their inhibitory potential.

Spatial approach based on the deformation measurement of volcanic dome and crater rim is key to evaluate the activity of a volcano, such as Merapi volcano where associated disaster risk is regularly taking lives. Within this framework, this study aime to detect localized deformation and change in the summit area that has occurred concomitantly with the dome growth and explosion reported. The methodology was focused on two sets of data, one LiDAR-based dataset of 2012 and one UAV-dataset of 2014. The results show that during the period 2012-2014, the crater walls are 100 m to 120 m high above the crater floor at its maximum (North to East-South-East sector), while the West and North sector presents a topographic range of 40 to 80 m. During the period 2012 – 2014, the evolution of the crater rim around the dome is generally stable (no large collapse). The opening of a new vent on the surface of the dome has displaced an equivalent volume of 2.04 E+04 m3 corresponding to a maximum -9 m (+/- 0.9 m) vertically. This concludes that during the period 2012 – 2014 when the dome of Merapi experienced phreatic or phreatomagmatic explosions, the topography around the dome rose. This rise does not seem to be related to large wall collapses, and it is likely that modification in the subsurface have triggered those changes.

In this investigation dyeing of alginate fiber was carried out after cationic treatment of EPTAC (2, 3-Epoxypropyl Trimethyl Ammonium Chloride). Optimum dyeing process and parameter show good dyeing performance when alginate fiber dyed with rhubarb dye. In addition to cationic treatment is taken into account for alginate fiber to improve fastness, color strength, breaking strength. Later in dyeing for fixation ferrous sulfate, copper sulphate was considered as mordents. Moreover using pre-mordanting methods conveyed the dyeing of alginate fiber with the stave of metallic mordant and without metallic salt mordents. The evaluation of each color dyed material was done through following two terms for instance CIELAB (L*, a*, and b*) and K/S values. According to AATCC test methods color fastness to washing of the dyed fiber was determined whereas according to the ASTM D3822M standard the breaking strength of alginate fiber was estimated and tested. When dyeing was carried out on alginate fiber through considering optimum parameter like 80◦C for 90 min, M: L 1:40 and at pH 7 which showed optimum results. In addition to mostly very good wash fastness was obtained while there was no fading of the color, whereas the moderate level of color fastness to crocking was achieved. Later after dyeing the evaluations of SEM of the cationized alginate fiber and FTIR of powder from the rhubarb dye were observed. The results suggested that the surface of cationized alginate fiber was smoother than the raw alginate fiber.

Powder metallurgy is a preferred manufacturing method in various industries because it offers design flexibility, material efficiency and a cost-effective production. In this work, we study the effects of different compaction directions on the strength of parts produced by powder metallurgy. Al–4wt.%Cu alloys are used due to their recyclability, and three distinctive compaction pressures are applied, resulting in two sample groups. One group is air-cooled after sintering, while the other is water-quenched and naturally aged (T4 temper). Both the compressive and tensile strengths are measured and analyzed. The study reveals that both heat treatments and compaction directions significantly influence anisotropic strengths. The novelty of this research lies in the use of powders that can be reused from machining, turning, or foundry rejection. By omitting or reducing the melting stage and employing simple powder metallurgy, the cost-effective and environmentally friendly processes are achieved. The planning of compaction load, compaction direction, and heat treatments is found to play a crucial role in determining the final mechanical performance. Notably, this approach aligns with the environmental, social and governance (ESG) practices that have been increasingly adopted by industry.

The construction field uses up over one-third of the global energy consumption and contribute to 40% of CO2 emissions according to the International Energy Agency (IEA) and the 2020 annual reporter of United Nation, Goal 11 (Make cities inclusive, safe, resilient and sustainable) which discusses sustainable, safe and efficient buildings. Therefore, Morocco has a commitment to this program by publishing the law 47-09 of energy efficiency. This work aims to study the energy efficiency of two types of building, a conventional and a natural building. Conventional building is constructed using concrete, while the natural one uses sand clay and straws. As for the technique of making the natural building, it perpetually follows the same approach accustomed in rural zones of Atlas Mountains in Morocco. In this research we also simulate, temperature and humidity variation inside these buildings using TRNSYS software. Sketch Up software was employed to design these houses. The weather database is used for a typical meteorological year (TMY). In the case of natural building, many building configurations were simulated: roof insulation, floor insulation, different types of glazing and sun protection. What's more, the thermal comfort is revealed to be more conspicuous in the case of natural building.

Natural gas is an important energy source among various fossil fuels. However, natural gas typically contains a significant amount of water vapor, which can pose challenges in its usage. Therefore, the process of removing water vapor from natural gas, known as gas dehydration, is crucial in the gas industry. The presence of water vapor in the gas supply can lead to the formation of hydrates, which can cause issues such as blockages in pipelines. To tackle this problem effectively, gas dehydration makes use of Tri ethylene glycol (TEG) as a drying agent. TEG works by selectively absorbing water vapor from the natural gas flow, thereby significantly reducing its moisture content.In the gas dehydration process, wet gas is treated by using lean glycol in an absorber, where the water vapor is removed. The resulting rich glycol is then recovered and recycled for further use. In this study, we explore the possibility of replacing nitrogen with dry natural gas in the re-generator of the glycol dehydration system. The aim is to evaluate the feasibility and effectiveness of this alternative approach. To assess the performance of both techniques, we employed HYSYS modeling and simulation. Through this analysis, we compared the capital and utility expenses associated with each method while ensuring that the glycol purity requirements remained unchanged. By examining the results obtained from the simulations, we can gain insights into the economic viability and efficiency of utilizing dry natural gas in the re-generator stage of the glycol dehydration process.In addition to the primary purpose of removing water vapor, the wet gas obtained from the stripping mechanisms in the glycol dehydration process can serve additional functions. It can be utilized to power steam pumps and compressors, thereby maximizing energy efficiency within the system. Alternatively, the wet gas can be recycled back into the process for further treatment. To develop a comprehensive understanding of the entire mechanism, we constructed a model based on the actual flow diagram. This model takes into account the various components and processes involved in the glycol dehydration system, including the stripping mechanisms and their connection to other equipment. By analyzing the data and outcomes generated by this model, we can derive valuable insights. These findings can be utilized to optimize the heat and material balance within the plant, ensuring efficient operation and potentially leading to the design of an improved system in terms of energy consumption and overall performance

Agriculture has been experiencing a difficult situation because of limiting factors in its production, Natural biostimulants (NBs) have emerged as a novel alternative. This study reviews NBs produced through Solid-State Fermentation (SSF) from organic waste, focusing on the processes and production methods. The aim is to highlight their potential for improving agricultural productivity and promoting sustainable agriculture. Through a literature review, the effects of NBS on crops were examined, as well as the challenges associated with their production and application. The importance of standardizing production processes, optimizing fermentation conditions, and assessing their effects on different crops is emphasized. Furthermore, future research areas are identified, such as enhancing production efficiency and evaluating the effectiveness of SSF-produced NBS in different agricultural systems. In conclusion, SSF-produced NBS offers a promising alternative for sustainable agriculture, but further research and development are needed to maximize its efficacy and enable large-scale implementation.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a structurally diverse group of natural products. They feature a wide range of intriguing posttranslational modifications as exemplified by the biarylitides. These are a family of cyclic tripeptides found in Planomonospora, carrying a biaryl-linkage between two aromatic amino acids. Recent genomic analyses revealed the minimal biosynthetic prerequisite of biarylitide biosynthesis consisting of only one ribosomally synthesized pentapeptide precursor as substrate and a modifying cytochrome P450 dependent enzyme. In silico analyses revealed that the minimal biarylitide RiPP clusters are widespread among natural product producers across phylogenetic borders including myxobacteria. We report here the genome-guided discovery of the first myxobacterial biarylitide MeYLH termed Myxarylin from Pyxidicoccus fallax An d48. Myxarylin was found to be an N-methylated tripeptide surprisingly exhibiting a C–N biaryl crosslink. In contrast to Myxarylin, previously isolated biarylitides are N-acetylated tripeptides featuring a C–C biaryl crosslink. Furthermore, the formation of Myxarylin was confirmed by heterologous expression of the identified biosynthetic genes in Myxococcus xanthus DK1622. These findings expand the structural and biosynthetic scope of biarylitide type RiPPs and emphasize the distinct biochemistry found in the myxobacterial realm.

Cancer immunotherapies are gaining a large popularity and many of them have been approved as standard second-line or in some cases even as first-line treatment for a wide range of cancers. However, immunotherapy has not shown a clinically relevant success in glioblastoma (GBM), principally due to the brain’s “immune-privileged” status and the peculiar tumor microenvironment (TME) of GBM featured by lack of presence of tumor-infiltrating lymphocytes and the establishment of immunosuppressive mechanisms. Emerging evidence has highlighted the key role played by innate immune cells in immunosurveillance and in initiating and driving immune responses against GBM. Immunogenic cell death (ICD) is a promising approach to elicit direct activation of the innate immune system by inducing in target cancer cells the expression of molecular signatures recognized through a repertoire of innate immune cell pattern recognition receptors (PRRs) by effector innate immune cells. Herein, we explored local mild thermal treatment, generated by using ultrasmall (size ~ 17 nm) cubic-shaped iron oxide nanoparticles exposed to an external alternating magnetic field (AMF), to induce ICD in U87 glioblastoma cells. In accordance with what has been previously observed with other types of tumors, we found that mild hyperthermia modulates the immunological profile of U87 glioblastoma cells by inducing stress-associated signals leading to enhanced phagocytosis and killing of U87 cells by macrophages. Finally, we demonstrated that mild magnetic hyperthermia has a modulatory effect on the expression of inhibitory and activating NK cell ligands on target cells. Interestingly, alteration in the expression of NK ligands, caused by mild hyperthermia treatment, in U87 glioblastoma cells, increased their susceptibility to NK cell killing and NK cell functionality. The overall findings demonstrate that mild magnetic hyperthermia stimulates ICD and sensitizes GBM cells to NK-mediated killing by inducing the upregulation of specific stress ligands, providing a novel immunotherapeutic approach for GBM treatment, with potential to synergize with existing NK cell-based therapies thus improving their therapeutic outcomes.

(1) Background: To fight for COVID-19 pandemic, immunity against SARS-CoV-2 should be achieved not only through natural infection but also vaccination. Controversies exist about the effect of COVID-19 vaccination on previously infected persons; (2) Methods: A prospective cohort was undergone to collect sera from unvaccinated survivors and vaccinated persons--with and without COVID-19 pre-infection. The sera were analyzed for the anti-Receptor Binding Domain (RBD) titers by ELISA and for the capacity to neutralize the pseudovirus of the Wuhan-Hu-1 strain by luciferase assays; (3) Results; Neither the antibody titers nor the neutralization capacity was significantly different between the three groups. However, the correlation between the antibody titers and the percentage of viral neutralization derived from sera of unvaccinated survivors was higher than that from vaccinated persons with pre-infection (Spearman correlation coefficient (r) = -0.8558; 95% CI, -0.9259 to -0.7288), p &lt;0.0001 vs -0. 581; 95% CI, -0.7679 to -0.3028; p = 0.0002, respectively), indicating the capacity to neutralize the virus is better among the unvaccinated individuals. (4) Conclusions: Vaccines induced anti-RBD titers as high as the natural infection with lower neutralization capacity, and it did not boost the immunity in pre-infected persons.

Head and neck squamous cell carcinoma (HNSCC) is a type of cancer that arises from the epithelium lining of the oral cavity, hypopharynx, oropharynx, and larynx. Despite advancement of the current treatment to HNSCC, such as surgery, chemotherapy, and radiotherapy, the overall survival rate of HNSCC remains poor due to late diagnosis and acquired resistance to treatment. Natural products have been extensively explored as a safer and more acceptable alternative to current treatments, with numerous studies displaying their potential against HNSCC. This review highlights preclinical studies in the past 5 years involving natural products against HNSCC and explores the signalling pathways altered by these products. This review also addresses challenges and future directions of the use of natural products as chemotherapeutic and chemoprevention agents against HNSCC.

Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element for the CADD. This work reviews the progress toward developing compound databases of natural origin, particularly databases developed in Latin America, and their practical applications in the drug discovery area. We also survey the computational methods, emphasizing chemoinformatic approaches to profile natural product databases.

Pygeum africanum bark has been shown to inhibit the production of pro-inflammatory prostaglandins in the prostate and reduce the production of leukotrienes and other 5-lipoxygenase (5-LO) metabolites. It has been suggested that inflammation plays an important role in the pathophysiology of benign prostatic hyperplasia (BPH). In clinical trials, P. africanum improved the symptoms and objective measures of BPH. This in vitro study aimed to assess the anti-inflammatory potential of a proprietary Pygeum bark standardized extract (Prunera®) on cytokine release by lipopolysaccharide-simulated human peripheral blood mononuclear cells (PBMCs). PBMCs were obtained from four donors and a bead-based assay (ProcartaPlex™ panel) was used for the detection and quantitation of cytokines. Pygeum africanum bark standardized extract (PABE) showed the following effects: IL-2 was lowered in all donors in absence of a clear dose–response relationship; IL-4, IL-5, IL-6, IL-9, and IL-13 levels were decreased in most donors; IL-22 levels seemed to be suppressed only for donor 4 at lower and medium concentrations; and IL-27 and levels of TNF-α decreased at all PABE concentrations in all donors. The anti-inflammatory effect of PABE supports the potential use of this natural compound in the management of BPH and other conditions in which pro-inflammatory cytokines are involved in their underlying pathophysiological mechanisms.

The case of naturalization of the zebra mussel, Dreissena polymorpha (Pallas), in coun-tries lying beyond its native Ponto-Caspian range is remarkable as one of the first instances, when the scientific community as early as the mid-19th century was fully aware of the non-indigenous status of a particular species as well as of the need for the study and monitoring of this process. Based on a study of contemporary sources, I reconstruct the early response of the European naturalists (including those that today would be called ‘citizen scientist’) to the invasion of Dreissena and describe their attitudes to the problem, including the divergence of opinion about the origin and the means of dispersal of this bivalve species. An analysis of pa-pers of the English, French, German, and Russian authors published between 1774 and 1920 has shown that the invasion of D. polymorpha was by no means ‘silent’; quite opposite, it provoked an immediate reaction of naturalists, and the scientific agenda for the study of the new invader was proposed in England as early as 1838

The 2030 Agenda for Sustainable Development, the SDGs, are high on the agenda for most countries of the world. In its publication of the SDGs, the UN has provided the goals and target descriptions that, if implemented at a country level, would lead towards a sustainable future. The IAEG (InterAgency Expert Group of the SDGs) was tasked with disseminating indicators and methods to countries that can be used to gather data describing the global progress towards sustainability. However 2030 Agenda leaves it to countries to adopt the targets with each government setting its own national targets guided by the global level of ambition but taking into account national circumstances. At present, guidance on how to go about this is scant, but it is clear that the responsibility is with countries to implement and that it is actions at a country level that will determine the success of the SDGs. SDG reporting by countries takes on two forms 1) global reporting using prescribed indicator methods and data; 2) National Voluntary Reviews where a country reports on its own progress in more detail but is also able to present data that are more appropriate for the country. For the latter, countries need to be able to adapt the global indicators to fit national priorities and context, thus the global description of an indicator could be reduced to describe only what is relevant to the country. Countries may also, for the National Voluntary Review, use indicators that are unique to the country but nevertheless contribute to measurement of progress towards the global SDG target. Importantly, for those indicators that relate to the security of natural resources security (e.g. water) indicators, there are no prescribed numerical targets/standards or benchmarks. Rather countries will need to set their own benchmarks or standards against which performance can be evaluated. This paper presents a procedure that would enable a country to describe national targets with associated benchmarks that are appropriate for the country. The procedure focusses on those SDG targets that are natural resource-security focussed e.g. extent of water-related ecosystems (6.6), desertification (15.3) etc., because the selection of indicator methods and benchmarks is based on the location of natural resources, their use and present state and how they fit into national strategies.

Chiral hydroxamic acid (HA) and bis-hydroxamic acid (BHA) ligands have made significant contributions to the field of asymmetric synthesis, particularly in the synthesis of natural products. These ligands possess unique molecular structures that allow for exceptional stereochemical control, leading to their widespread use in catalytic systems. This review highlights the advancements made in asymmetric synthesis using chiral hydroxamic acid and bis-hydroxamic acid ligands and their impact on the synthesis of complex natural products. The discussion encompasses their role in enantioselective C-C bond formation, the functionalization of C-H bonds, asymmetric transformations involving heteroatoms, and their application in the total synthesis of natural products. The versatility and efficiency of chiral hydroxamic acid ligands and bis-hydroxamic acid ligands make them invaluable tools for synthetic chemists working towards the efficient and selective synthesis of natural products. This review provides a comprehensive overview of their contributions, showcasing their potential to expand the boundaries of chemical synthesis and access the diverse array of natural product scaffolds.

With widespread resistance to a limited number of insecticides available for medical and veterinary pests, new insecticides and insecticide synergists are desperately needed in this market space. We assessed the topical toxicity of carvone, menthone, and fenchone compared to permethrin and methomyl against the yellow fever mosquito, Aedes aegypti, and the house fly, Musca domestica. We also evaluated the synergistic potential of the monoterpenoids with permethrin and methomyl. Additionally, we assessed the acetylcholinesterase inhibitory potential of each monoterpenoid compared to methomyl. While all three monoterpenoids performed relatively poorly as topical insecticides (LD50 > 4000 ng/mg on M. domestica; > 6000 ng/mg on Ae. aegypti), they synergized both permethrin and methomyl as well as or better than piperonyl butoxide (PBO). Carvone and menthone yielded synergistic co-toxicity factors (23 and 29, respectively), which were each higher than PBO at 24 h. Acetylcholinesterase inhibition did not appear to explain the toxic or synergistic effects of the three monoterpenoids with IC50 values greater than 1 mM for all, compared to the 2.5 and 1.7 µM for methomyl on Aedes aegypti and Musca domestica, respectively. This study provides valuable monoterpenoid toxicity and synergism data on two pestiferous insects and highlights potential for these chemistries in future pest control formulations.

In the second half of the 19th century new methods for quarrying and processing natural stone are developed. In the Nordic countries Sweden, Norway and Finland this technological pro­gress goes hand in hand with a systematic geological mapping and large-scale exploitation of natural stone deposits. As a result, new constructions are developed—changing the building practice in these countries. With the end of historicism a new architecture arises that particularly in Norway and Finland acquires a national-romantic character. This paper examines the interaction between geological exploration, commercial development, technical inventions and the development of a national-romantic architecture.

Biosynthetic gene clusters are specific genomic regions in microorganisms, like bacteria and fungi, responsible for producing bioactive compounds. Identifying these clusters is complex due to their diverse nature. This research presents a comprehensive approach to effective BGC identification. The study focuses on five classes of Natural Products: PKS , NRPS, RiPP, Terpenes, and Hybrid PKS-NRPS. Data was gathered from the MiBIG database in GBK format. Protein sequences from each file were extracted, and sequences under the same BGC ID were combined. Physicochemical properties were calculated, and sequence embeddings were generated using NLP techniques like CountVec, TFIDF, and Word2Vec specific to each NP class. An integrated feature matrix was created by merging physicochemical properties and generated embeddings. This matrix was used for training and testing of nine ML models such SVM, RF and many more. The study explored data balancing techniques with and without SMOTE and employed Grid Search for parameter optimization. This led to six datasets and 54 models. The LR model, using TFIDF with SMOTE, emerged as the most effective, achieving an accuracy of 0.96, AUC of 0.9912, and other strong metrics. This method enhances BGC identification for drug development, offering a broader understanding of their applications in medicine and biotechnology.

Sustainable urban design or planning is not a LEGO-like assembly of prefabricated elements, but an embryo-like growth with persistent differentiation and adaptation towards a coherent whole. The coherent whole has a striking character – called living structure – that consists of far more small substructures than large ones. To detect the living structure, natural streets or axial lines have been previously adopted to be topologically represent an urban environment as a coherent whole. This paper develops a new approach to detecting the underlying living structure of urban environments. The approach takes an urban environment as a whole and recursively decomposes it into meaningful subwholes at different levels of hierarchy or scale ranging from the largest to the smallest. We compared the new approach to natural street and axial line approaches and demonstrated, through four case studies, that the new approach is better and more powerful. Based on the study, we further discuss how the new approach can be used not only for understanding, but also for effectively designing or planning the living structure of an urban environment to be more living or more livable.

Incidence of autoimmune diseases increases. Antinuclear antibodies (ANA) testing is a critical tool for their diagnosis. However, ANA prevalence in health increased over last decades, especially among young people. ANA in health occur in low concentrations, with prevalence up to 50% in some populations, which demands a cutoff revision. The review deals with origin and probable physiological or compensatory function of ANA in health, according to the concept of immunological clearance, theory of autoimmune regulation of cell functions and the concept of functional autoantibodies. Considering ANA titers ≤1:320 as a serological marker of autoimmune diseases seems inappropriate. The role of anti-DFS70/LEDGFp75 autoantibodies is highlighted as possible anti-risk biomarker for autoimmune rheumatic disorders. ANA prevalence in health is different in various regions due to several underlying causes discussed in the review, all influencing in additive combinations according to the concept of the mosaic of autoimmunity. Not only titer, but the HEp-2 IFA staining patterns, like AC-2, is also important. Accepting autoantibodies as a kind of bioregulators, not only upper, but also lower borders of their normal range should be determined. Not only their excess, but also lack of them or “autoimmunodeficiency” could be a reason of disorders.

The enantioselective alpha-amination of 1,3-dicarbonyl compounds has been performed using a catalytic system based on deep eutectic solvents (DES) and chiral 2-amino benzimidazole-derived organocatalysts. This procedure avoids the use of toxic volatile organic compounds (VOCs) as a reaction medium, providing access to highly functionalized chiral molecules, which are important intermediates for the natural product synthesis, in a selective and efficient manner. Furthermore, the reaction can be performed on a large scale and recycling the catalytic system is possible for at least five times, leading to a clean, cheap, simple, and scalable procedure that meets most of the criteria required to be a green and sustainable process.

Solid tumor cells have an altered metabolism that can protect them from cytotoxic lymphocytes. The antidiabetic drug metformin modifies tumor cell metabolism and several clinical trials are testing its effectiveness for the treatment of solid cancers. The use of metformin in hematologic cancers has received much less attention, although allogeneic cytotoxic lymphocytes are very effective against these tumors. We show here that metformin induces expression of Natural Killer G2-D (NKG2D) ligands (NKG2DL) and intercellular adhesion molecule-1 (ICAM-1), a ligand of the lymphocyte function-associated antigen 1 (LFA-1). This leads to enhance sensitivity to cytotoxic lymphocytes. Overexpression of antiapoptotic Bcl-2 family members decrease both metformin effects. The sensitization to activated cytotoxic lymphocytes is mainly mediated by the increase on ICAM-1 levels, which favors cytotoxic lymphocytes binding to tumor cells. Finally, metformin decreases the growth of human hematological tumor cells in xenograft models, mainly in presence of monoclonal antibodies that recognize tumor antigens. Our results suggest that metformin could improve cytotoxic lymphocyte-mediated therapy.

Securing funding is a critical yet complex task for organizations and individuals. This study presents an innovative chatbot designed to streamline the process using advanced Natural Language Processing (NLP) techniques and, specifically, a Retrieval-Augmented Generation (RAG) pipeline optimized for real-world applications. Our chatbot assists users in identifying suitable public tenders for financial support through natural language queries and a comprehensive public data database. The chatbot operates in a two-stage interaction model, initially providing summarized tender information for exploratory brainstorming, followed by detailed data upon user selection. A custom filtering mechanism ensures that the user interface elements responsible for swapping the interaction stages are consistent with the responses generated by the conversational agent. Human-evaluation tests demonstrated an average accuracy of 90.4% in document retrieval, with an average of 2.11 interactions required to find a specific tender. User satisfaction, rated on a scale of 1 to 5, averaged 3.14 (±1.73), indicating generally positive user experience with room for improvement. This approach addresses challenges of relevance, accuracy, and conversational flow, resulting in a reliable chatbot that simplifies the process of finding funding opportunities.

Synthesis of natural products with complicated architecture often requires segments having the functional groups that can be structurally transformed with the desired stereogenic centers. Bicyclic ?-lactones have a great potential for a suitable segment for natural product synthesis. However, the stereoselective construction of such functionalized bicyclic ?-lactones is not as straightforward as one might expect. On the other hand, the Lewis acid template-mediated Diels-Alder reaction is one of the most powerful and versatile methods for providing bicyclic ?-lactones with high regioselectivity and stereoselectivity. In this reaction, the Lewis acid template activates the dienophile as well as linking the diene and dienophile by a temporary tether. Therefore, Diels-Alder reaction smoothly proceeds in a stereocontrolled manner, allowing a simple access to bicyclic ?-lactone as a valuable building block for the synthesis of complicated natural products. In this review, some highlights of the Lewis acid-mediated Diels-Alder reaction, along with its application to the synthesis of biologically active compounds are discussed.

Artificial intelligence (AI) tools, such as OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), have great potential in various fields, such as healthcare, education, and industry. This review highlights the applications and challenges of the ChatGPT versions 3.5 and 4.0 in radiology. ChatGPT can process medical texts, analyze medical images, and generate reports and summaries for different audiences. ChatGPT can also answer patients' questions and translate complex medical information into a plain language. ChatGPT has shown impressive results in medical tests and quizzes, indicating its potential to enhance diagnostic accuracy and medical education. These models show promise for reducing diagnosis times and healthcare costs. However, ChatGPT also faces challenges and limitations, such as a lack of visual content, ethical and legal issues, data quality, and error detection. This review highlights the benefits and future directions of ChatGPT and artificial intelligence (AI) in radiology.

It is essential to deal with data scattered among heterogeneous information sources, which can be structured, semi-structured, or unstructured, to give consumers a cohesive perspective of the data. Information gathering is challenging as a result, and one of the major causes of this is that data sources are developed to support specific applications. A method to simplify this procedure is to use ontology representation as an intermediate step. An ontology represents a knowledge structure that reasonably reflects the real world's complexity and is ideally used in many industries today. This research aims to enhance the functionality of a global ontology system by implementing and integrating a natural language query system. Leveraging NLP-based approaches, we constructed SPARQL queries for our ontologies to facilitate natural language translation into a well-structured query format. A dedicated query component was developed to transform natural language questions into SPARQL queries, with the selection of the optimal query graph to generate the final queries. In the subsequent phase, we explored the integration of distinct sentence encoders to improve latent sentence representations during query construction. The generated SPARQL queries were executed on the global ontology output and then translated into source-specific questions. This innovative approach enables unified access to heterogeneous data sources through a user-friendly natural language querying interface.

Wounds are breaks in the continuity of the skin and the underlying tissues, due to external causes, such as cuts, blows and impacts, or even, during surgical interventions. Millions of people suffer minor or serious injuries, sometimes ending in death. Today, there are many commercially available products that promote healing from wounds. The high demand from the healthcare industry has created a huge market that continues to grow, fueling research and development of new wound healing products. In the present article, we review recent advances in wound healing products based on natural biopolymers, stem cells, and microRNAs. We analyze the advances of both, the commercial products and the products that are still in research phase, and we also include an overview of the opportunities, barriers, and regulatory issues for the commercialization of these products in the European, American, and Asian markets.

This article explains how resistance alleles have become rare or have disappeared from honey bee populations in Europe (and probably also in North America). Honeybees have the highest recombination frequency of all animals, indicating that pathogenic bacteria, viruses, fungi and microsporidia are an important source of selection. To respond to new virulent strains of pathogens, honeybees need to have access to rare alleles that could foster immunity against a new pathogen. By mating in a large panmictic population, new rare alleles can be recruited, which can then be combined into new genotypes through recombination with useful alleles of other genes. Bee breeders, by selecting small samples from a large population tend to select common alleles and lose the rare ones. Rare alleles can become important when a new virulent pathogen arrives. The loss of rare alleles makes bees vulnerable to disease and parasites and explains, together with the problems caused by modern agriculture, why managed bees have high mortality rates.

Hydrogels prepared from natural polymer have attracted extensive attentions in biomedical fields such as drug delivery, wound healing, and regenerative medicine due to their good biocompatibility, degradability and flexibility. This review outlines the commonly used natural polymer in hydrogel preparation, including cellulose, chitosan, collagen/gelatin, alginate, hyaluronic acid and starch. The polymeric structure and process/synthesis of natural polymers are illustrated, and natural polymer-based hydrogels including the hydrogel formation and properties are elaborated. Subsequently, the biomedical application of hydrogels based on natural polymer in drug delivery, tissue regeneration, wound healing and other biomedical field is summarized. Finally, the future perspectives of natural polymers and hydrogels based on them are discussed. For natural polymer, novel technologies such as enzymatic and biological methods are developed to improve the structural properties and the development of new natural based polymers or natural polymer derivatives with high performance is still very important and challenging. For natural polymer-based hydrogels, novel hydrogel materials, like double-network hydrogel, multifunctional composite hydrogels and hydrogel microrobots are designed to meet the advanced requirements in biomedical application, and new strategies such as dual-crosslinking, microfluidic chip, micropatterning and 3D/4D bioprinting, have been explored to fabricate advanced hydrogel materials with designed properties for biomedical application. Overall, natural polymeric hydrogels have attracted increasing interests in biomedical application, and the development of novel natural polymer-based materials and new strategies/methods for hydrogel fabrication is badly desirable and still challenging.

In this study, various percentage combinations of waste aggregates such as PET and crumb rubber were used to replace coarse and fine aggregates in natural rubber latex (NRL) modified concretes. Engineering properties such as compressive and flexural strengths, modulus of elasticity, and toughness obtained from compressive- and flexural stress-strain curves were investigated. Scanning electron microscopy (SEM) analysis was performed to examine the microstructural properties and to study the strength development of the studied concretes. The results indicated that both the compressive and flexural strengths of NRL-modified concretes with PET and crumb rubber aggregate replacements decreased with increasing replacement ratios. SEM analysis indicated that PET and crumb rubber (hydrophobic and non-polar materials) can affect the microstructure of the studied concrete by creating a weak interface between the aggregate and cement pastes, leading to reducing strength development. With the addition of the NRL additive, the film formation was found to act as a bridge and improved the bond strength of aggregates and hydration products of NRL-modified concrete. Furthermore, the integration of PET and crumb rubber aggregate can enhance the ability of the concrete to absorb energy and improve ductility. It was found that 10% of PET and crumb rubber aggregate replacement can be used for NRL-modified concrete as a rigid pavement, as its compressive and flexural strengths met the minimum requirement specified by the Department of Highways, Thailand. This research helps repurpose waste materials and reduce the environmental footprint of concrete production.

Diabetes and its complications are a global health burden, necessitating novel therapeutic approaches. Natural products and derivatives are gaining attention as potential agents for managing diabetes due to their diverse constituents and pharmacological activities. Algae, in particular, offer promising bioactive compounds with antidiabetic properties. This review provides an overview of natural products derived from algae in diabetes management, including advancements and future prospects. It highlights the significance of natural products in diabetes, explores algae's diversity, bioactive compounds, and mechanisms of action. The challenges, limitations, and strategies for optimizing algae-based interventions are discussed, alongside future research directions. Algae's multifaceted pharmacological activities and chemical constituents make them attractive for developing novel antidiabetic therapeutics. Further research and optimization of algae-based interventions have the potential to address the global burden of diabetes and improve patient outcomes.

This article should examine ideas of how a chatbot can support decision making processes by col-lecting general opinion about the certain topics. The ChatGPT is tested in this article in respect of natural gas security of supply of the Republic of Serbia in the context of the current political situation in the world. The author first describes its own stance on the topic and then the ChatGPT is asked for its opinion (which most probably collects and compiles phrases from English speaking sources to build a text in a smart way). The topic is ort only about security of gas supply of Serbia but also political and highly sensitive, so it is examined if the ChatGPT can be useful tool to reach a balance between less on more favorable options in the process of policy making.

The purpose of this study is to examine the potential of the sacred groves and natural sites for tourism in local communities in Nigeria. Three communities in Inyi town were chosen as case studies: Umuome, Enugwu-Inyi, and Alum, using a purposive sampling technique. An ethnographic data collection method was adopted, using in-depth interviews and direct observation. Grove locations were located and mapped using a geographic information system (GIS) throughout the research area. The secondary data was obtained from scholarly journals. The findings showed that the sacred groves and natural sites had exceptional value for cultural and eco-tourism. There are not many works that have studied the tourism dimension of the sacred groves in the study domain. The implication of the study is that tourism aids in the preservation of the sites' integrity. The research helps the government and policymakers to adopt a policy that promotes tourism in the sacred groves. This research is important to researchers at the universities and research institutions that are seeking to carry out a related research. Conclusively, utilizing the area for tourism is a superior conservation alternative.

In the field of industrial process monitoring, more and more interest is being shown in specific process categories. These include time-varying processes, that is, those processes whereby the response one receives as output from the system depends on when the input signal is sent into it. There are many reasons for this process variability and such contexts are not always analyzed with this operational characteristic at their core. At the same time, interest in certain categories of techniques is also becoming more prominent, to meet certain application needs. Among these, clustering and unsupervised techniques in general are gaining ground. This is largely due to the difficulty of finding fault data with which to train, for example, supervised models. On the other hand, the clustering technique, on which this contribution focuses, also makes it possible to compensate for the lack of complete knowledge of the structure of the process itself. With these two considerations in mind, this contribution proposes a literature review on the topic of clustering applied in time-varying contexts, in the maintenance field. The aim is to present an overview of the main fields of study, the role of clustering in this context and the main clustering techniques used.

Personality is the most critical feature that tells us about an individual. It is the collection of the individual’s thoughts, opinions, emotions and more. Personality detection is an emerging field in research and Deep Learning models have only recently started being developed. There is a need for a larger dataset that is unbiased as the current dataset that is used is in the form of questionnaires that the individuals themselves answer, hence increasing the chance of unconscious bias. We have used the famous stream-of-consciousness essays collated by James Pennbaker and Laura King. We have used the Big Five Model often known as the five-factor model or OCEAN model. Document-level feature extraction has been performed using Google’s word2vec embeddings and Mairesse features. The processed data has been fed into a deep convolutional network and a binary classifier has been used to classify the presence or absence of the personality trait. Hold- out method has been used to evaluate the model, and the F1 score has been used as the performance metric.

Communication is indispensable for today's lifestyle, and thanks to technology, millions of people can communicate as quickly as possible. The effect of this breakthrough has transformed organizations to the degree that they generate billions of emails daily to facilitate their operations. There is implicit information behind this vast corpus of human-generated content that can be mined and used for their benefit. This paper tries to address the opportunity that email logs can bring to organizations and propose an approach to discover process models by combining supervised text classification and process mining. This framework consists of two main steps, text classification, and process mining. First, Emails will be classified with supervised machine learning, and to mine, the processes fuzzy Miner is used. To further investigate the application of this framework, we also applied this framework over a real-life dataset from a case study organization.

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

Social media, traditionally reserved for social exchanges on the net, has been increasingly used by researchers to gain insight into different facets of human life. Unemployment is an area that has gained attention by researchers in various fields. Medical practitioners especially in the area of mental health have traditionally monitored the effects of involuntary unemployment with great interest. In this work, we compare the feedback gathered from social media using crowdsourcing techniques to results obtained prior to the advent of Big Data. We find that the results are consistent in terms of 1) financial strain is the biggest stressor and concern, 2) onslaught of depression is typical and 3) possible interventions including reemployment and support from friends and family is crucial in minimizing the effects of involuntary unemployment. Lastly, we could not find enough evidence to study effects on physical health and somatization in this work.

The coronavirus COVID-19 epidemic has wreaked havoc on inhabitants of earth killing thousands of humans from more than 150 countries. The epidemic has put a number of countries under complete lockdown and the deadly situation is still prevailing around the globe. Vaccines have been long known as the most effective means of preventing viral infections. However, the lack of vaccines against COVID-19 has further worsened the situation. In this time of health crisis, it is the duty of scientific research community to provide alternative, effective and affordable strategies to vaccinate human bodies against viral infections-COVID-19 based on focused experimental approaches. Growing evidence suggests that certain natural foods and lifestyle changes have potential to optimize immune functions against viral infections including improving defense function, resistance towards invading pathogens, while maintaining self-tolerance. Boosting immune system gives an edge in fending off viruses and staying healthy. This review presents the six smart steps to add to your to-do list which let the inner work of immunity take place against viral infections-COVID-19 by dissolving the powers of disease and illness. Many of these factors are associated in their functions to improve or properly maintain the immune function such as promoting anti-inflammatory functions, inhibiting pro-inflammatory mediators, modulating cell-mediated immunity, altering the antigen-presenting cellular functions as well as promoting communication between the innate and adaptive immune responses. Thus, a scientific illustration of boosting the immune system by proper sleep, moderate exercise, avoiding stress, utilizing vitamins enriched foods, intake of more water and use of fruits and vegetables will hopefully help the community to deal with the coronavirus by vaccinating the human systems naturally.

In a bandsaw machine the blade guides provide additional stiffness and help to align the blade near the cutting region. Typically these are either in form of blocks made of carbide or ceramics or as sealed bearings. Abrasive particles, generated while cutting hard and brittle materials like natural stones, settle between the contact surfaces of the guides and the blade causing wear and premature failure. The hydrostatic guide system as presented in this work, is a contactless blade guiding method that uses force of several pressurized water jets to align the blade to the direction of the cut. For this investigation, cutting tests were performed on a marble block using a galvanic diamond coated bandsaw blade with the upper roller guides replaced by hydrostatic guides. The results show that the hydrostatic guides help to reduce the passive force while cutting to a constant near zero in contrast to the traditional guides. This also resulted in reduced surface roughness of the stone plates that were cut indicating a reduction in lateral vibration of the band. Additionally, it has also been shown that using hydrostatic guides the bandsaw blade can be tilted to counter the bandsaw drift opening opportunities for further research in active alignment control. This original research work has shown that the hydrostatic guide systems are capable of replacing and in fact perform better than state of the art bearing or block guides particularly for stone cutting applications.

Cyanobacteria are photosynthetic microorganisms that colonize diverse environments worldwide, ranging from ocean to freshwaters, soils, and extreme environments. Their adaptation capacities and the diversity of natural products (molecules, metabolites, or compounds) that they synthesize support the cyanobacterial success for the colonization of their respective ecological niches. Although cyanobacteria are well-known for their toxin production and their relative deleterious consequences, they also produce a large variety of molecules that exhibit beneficial properties with high potential for various fields of application (e.g., synthetic analog of the dolastatin 10 used against Hodgkin lymphoma). The present review specially focuses on the beneficial activities of cyanobacterial molecules described so far. Based on an analysis of 670 papers, it appears that more than 90 genera of cyanobacteria have been found to produce compounds with potential beneficial activities, most of them belonging to the orders Oscillatoriales, Nostocales Chroococcales, and Synechococcales. The rest of the cyanobacterial orders (i.e., Pleurocapsales, Chroococcidiopsales, and Gloeobacterales) remain poorly explored in terms of their molecular diversity and relative bioactivity. The diverse cyanobacterial molecules presenting beneficial bioactivities belong to 10 different chemical classes (alkaloids, depsipeptides, lipopeptides, macrolides/lactones, peptides, terpenes, polysaccharides, lipids, polyketides, and others) that exhibit 14 major kinds of bioactivity. However, no direct relation between the chemical class and the bioactivity of these molecules has been demonstrated. We further selected and specifically described 50 molecule families according to their specific bioactivities and their potential uses in pharmacology, cosmetology, agriculture, or other specific fields of interest. This up-to-date review takes advantage of the recent progresses in genome sequencing and biosynthetic pathway elucidation, and presents new perspectives for the rational discovery of new cyanobacterial metabolites with beneficial bioactivity.

Computational Fluid Dynamics (CFD) is utilized to study entropy generation for the rarefied steady state laminar 2-D flow of air-Al2O3 nanofluid in square cavity equipped with two solid fins at the hot wall. Such flows are of great importance in industrial applications, such cooling of the electronic equipment’s and nuclear reactors. In the current study, effects of Knudsen number (Kn), Rayleigh number (Ra) and the nano solid particles volume fraction (ϕ) on the entropy generation are investigated. The values of parameters considered in this work are as follows: 0≤Kn≤0.1, 〖10〗^3≤Ra≤〖10〗^6,0≤ϕ≤0.2. Length of the fins (LF) is considered to be fixed and equals to 0.5 m, whereas the location of the fins with respect to the lower wall (HF) is set to 0.25 and 0.75 m. Simulations demonstrate that there is an inverse direct effect of Kn on the entropy generation. Moreover, it is found that when Ra is less than 104, the entropy generation, due to the flow, increases as ϕ increases. In addition, the entropy generation due to the flow will decrease at Ra greater than 104 as ϕ increases. Moreover, the entropy generation due to heat will increase as both the ϕ and Ra increase. In addition, a correlation model of the total entropy generation as a function of all of the investigated parameters in this study is proposed. Finally, an optimization technique is adapted to find out the conditions at which the total entropy generation is minimized

A global revolution in information and communication technologies (ICT) has occurred over the past few decades, emerging first in industrialized countries and then in developing countries. While researchers have examined many facets of the ICT revolution, relatively little work has systematically examined the degree to which ICT has reduced natural disaster vulnerability. In this article we use cross-country data over the 1980-2013 period to estimate the relationship between newly-emerging cell phone access/use and disaster-induced fatalities. Our estimates suggest that a one-standard-deviation increase in cell phone usage reduces disaster fatalities by nearly one half. The estimated effect increases to almost three quarters for geologic events where people are typically afflicted without warning. The largest marginal benefit from cell phones in terms of saving lives come from events where there is typically no warning, thus many are caught by surprise; cell phones are used to call for help and coordinate assistance.

The modification of cellulose woven fabrics and viscose nonwovens was carried out with the aim of preparing sustainable coatings from biodegradable natural polymers. The modification of fabrics with biodegradable natural polymers represents an ecological alternative to other textile modifications such as the sol-gel process. Coatings were prepared from erythritol, gelatin and collagen in various formulations with the addition of propolis and alginate fibers and a natural plasticizer (glycerin). The morphology of the materials was determined before and after modification with Dino-lite, the pH value, the drop test method, the angle recovery angle, the thickness and the mass per unit area. The modifications have no significant effect on the thickness and mass per unit area, in a larger proportion they show hydrophilic properties, which favours the application for medical purposes - for example the absorption of exudates, wound dressings etc. Due to the neutral and slightly alkaline pH value of the modified samples, they are suitable for external application on the skin. The recovery angle of the modified samples proves that the samples do not tend to crease and that they retain their elasticity after modification and have a pleasant textile feel (fabric hand).

The new Common Agricultural Policy of the European Union (EU) (2023-2027) envisages a stronger Agricultural Knowledge and Innovation System (AKIS) that will unite all relevant ac-tors in a co-creation process to ensure knowledge flows within and across Member States. A sig-nificant and increasing body of literature focuses on the strategy and processes of strengthening AKIS. However, there is a relative gap in research related to the role of AKIS in achieving sus-tainability in the farming sector. The purpose of this paper is to provide an overview of the advancements so far of AKIS across the EU when it comes to facilitating the sustainable management of natural resources (SMNR). The paper builds upon a comprehensive review of rel-evant literature during the last 15 years covering all 28 EU countries (including the United King-dom until 2019). We conclude that AKIS architecture in most EU countries lacks a comprehensive structure promoting SMNR. Based on the results we identify countries with relatively high inten-sity of research and development projects linking AKIS to SMNR and where SMNR is better in-tegrated to AKIS. Our results can be of interest to the design of stronger AKIS during the new programming period.

The purpose of this article is to conduct a bibliometric review of literary production in terms of market interdependence. We investigate the effect of contagion on agricultural commodities and identify the commodities and methods used in the most cited publications over the decades. For this, we used the SCOPUS database, sorting with Rayyan, Excel, and finally, the Bibliometrix/R-project. The results showed that corn, wheat, soybeans, cotton, and sugar are the most studied commodities. There is no uniqueness concerning the methods used to identify interdependence and the contagion effect. Also, it is possible to perceive the growing scientific interest concerning the subject, with a diversity of authors and magazines. However, there is a direction of the and magazines most cited articles for analyzing agricultural commodities and energy, demonstrated a worldwide trend of studies relating natural resources, biofuels, clean energy, and efficiency. In addition, countries like China and the United States stand out in this scientific scenario as the countries that produce the most articles.

Calandrinia compressa Schrad. ex DC (Prodr. 3: 359. 1828) is the name currently widely applied to a polymorphic annual species of Calandrinia sect. Calandrinia endemic to the Chilean Floristic Region. A total of ten validly published heterotypic names plus six horticultural designations (and orthographic variants thereof) have been considered at some point as taxonomically the same as C. compressa. Two additional heterotypic taxa and their synonyms and two other designations possibly refer to this species. Two other taxa sometimes referred to this species are excluded. I treat here a total of 77 distinct nomenclaturally valid names and invalid designations and variants thereof. The type of one of the valid and legitimate names, Calandrinia pilosiuscula DC (Rev. Portulac. 9. 1827), is judged here to be taxonomically the same as the type of C. compressa. Because of its chronological priority, Calandrinia pilosiuscula DC must be accepted as the correct name for Calandrinia compressa Schrad. ex DC when the two are considered taxonomic synonyms. Here, the taxonomic history of this taxon is articulated and, where possible, types are identified and/or designated. The natural and anthropogenic history of the species is summarized critically in a theoretical context.

As a holistic medical school, Iranian traditional medicine (ITM) considers the human body as a dynamic and intricate network of interconnecting processes. Currently, systems biology and more precisely systems medicine and pharmacology can be an aid in providing rationalizations for many traditional medications and treatments and elucidating a great deal of knowledge they can offer to guide future research in medicine. Therefore, re-organization and standardization of traditional medicine data are requested more than ever before. To address this issue, we have constructed UNaProd, a Universal Natural Product database for materia medica of ITM. Primarily based on Makhzan al-Advieh, which is the most recent encyclopedia of materia medica in ITM with the largest number of monographs, this database was created using both text mining methods and manual editing. UNaProd is currently hosting to 2696 monographs from herbal to animal to mineral compounds in 16 diverse attributes such as origin and scientific name. In the current version, UNaProd is hyperlinked to IrGO and CMAUP databases for Mizaj and molecular features respectively and it is freely available at http://jafarilab.com/unaprod/.

Repeated exposure to pathogens leads to evolutionary selection of adaptive traits. Many species transfer immunological memory to their offspring to counteract future immune challenges. Transfer factor such as that found in colostrum is among the many mechanisms where transfer of immunologic memory from one generation to the next can be achieved for enhanced immune response. Here, a library of 100 plants with high protein contents were screened to find plant-based proteins that behave like a transfer factor moiety to boost human immunity. Aqueous extracts from candidate plants were tested on a human peripheral blood mononuclear cell (PBMC) cytotoxicity assay using human cancerous lymphoblast cells - K562 cells as a target and Natural Killer cells as an effector. Plant extracts that caused PBMCs to exhibit enhanced killing beyond the capability of the colostrum-based transfer factor were considered hits. Primary screening yielded an 11% hit rate. Protein content of these hits were tested by Bradford Assay and Coomassie stained-SDS-PAGE, where three extracts were confirmed to have high protein contents. Plants with high protein contents underwent C18 column fractionation using methanol gradients followed by membrane ultrafiltration to isolate proteins with molecular weight &lt;3 kDa, 3-30 kDa, and &gt;30 kDa fractions. It was found that the 3-30 kDa and &gt;30 kDa fractions had high activity in the PBMC cytotoxicity assay. The 3-30 kDa ultrafiltrates from the top two hits, seeds from Raphanus sativus and Brassica juncea, were then selected for protein identification by mass spectrometry. The majority of the proteins in the fractions were found to be seed storage proteins, with low abundance of proteins involved in plant defense and stress response. These findings suggest that Raphanus sativus or Brassica juncea extracts could be considered for further characterization and immune functional exploration with a possibility of supplemental use to bolster recipients’ immune response.

There is a growing interest in a potential use of essential oils (EOs) as a possible alternative for chemical pesticides. The formulation between different EOs could maximize the single biological efficacy by synergic mechanism. The aim of the current study was to evaluate the biological activity of three prepared blended oil formulations (BOFs) derived from Origanum vulgare L. (oregano) and Salvia officinalis L. (sage) compared to each parent EO. The chemical composition of each single studied EO has been revealed using GC-MS analysis. The three BOFs have been prepared as following: i) 25% oregano EO + 25% sage EO (BOF-I); ii) 25% oregano EO + 5% sage EO (BOF-II); iii) 5% oregano EO + 25% sage EO (BOF-III). The antibacterial activity of the new BOFs has been evaluated against Bacillus megaterium, B. cereus and Xanthomonas campestris, whereas, the antifungal activity has been carried out against Botrytis cinerea, Penicillium italicum and Fusarium oxysporum. The possible phytotoxic effect of the new BOFs has been evaluated on Lepidium sativum (garden cress), Solanum lycopersicum (tomato) and Lactuca sativa (lettuce) following the seed germination and radicle elongation assay. GC-MS analysis revealed that the oregano EO composed mainly from thymol, p-cymene and carvacrol with 76.0, 5.7 and 3.2 %, respectively. Whereas, salvia EO, the GC-MS analysis showed that the relevant predominant constituents were trans-thujone and camphor with 37,9 and 13,9 %, respectively. Results demonstrated that the tested BOFs possess antimicrobial effect higher than each parent EO. In particular, BOF-II showed the highest effect against all tested bacteria. Regarding the antifungal activity, BOF-II showed the highest significant antifungal effect against all tested fungi. Results showed also that both BOF-I and BOF-III explicated a moderate effect against B. cereus and all tested fungi compared to single EOs. In addition, the three BOFs showed notable phytotoxic effect against all three tested plants, particularly BOF-I. Whereas, the single sage EO at 25% showed the lowest significant phototoxic effect indicating its possible using as natural herbicides.

Enterococcus faecalis is a bacterium that can develop a multidrug resistance profile associated with the community as well as nosocomial-acquired infections. Among the treatment options for these infections are aminoglycosides combined with bacterial cell wall inhibitors such as beta-lactams, since E. faecalis is intrinsically resistant to aminoglycosides. One of its most representative resistance mechanisms is the expression of aminoglycoside-modifying enzymes, such as the aminoglycoside phosphotransferase type IIIa of E. faecalis (EfAPH(3')-IIIa). This enzyme acts by phosphorylating aminoglycosides in an ATP-dependent reaction, modifying the 3' position of hydroxyl groups of these antibiotics. Considering this scenario, 3,092 natural products obtained from the ZINC22 database were analyzed to select molecules with the highest affinity for the nucleotide-binding pocket of EfAPH(3')-IIIa, which could be potential aminoglycoside adjuvants. The molecules that showed the best-score results obtained from ensemble docking-based virtual screening were ZINC000000952700 (BS-1), ZINC000014793040 (BS-2) and ZINC000015498603 (BS-3). The most promising results were for BS-2, a flavone derivative, due to its improved stability profile in molecular dynamics simulation (average values of RMSD of 0.23 nm, and Rg of 1.94 nm), binding free energy calculations (average ΔG total of -35.3 nm), as well as better toxicological profile (lower probability of hepatotoxicity, carcinogenic, immunotoxicity, mutagenicity, and cytotoxicity effects), compared to BS-1 and BS-3. These results allow us to propose that a flavone derivative may act as an adjuvant to aminoglycosides in the treatment of E. faecalis infections, acting as an inhibitor in the nucleotide-binding pocket of EfAPH(3')-IIIa.