Risk management of electric power transmission lines requires knowledge from different areas such as environment, land, investors, regulations, and engineering. Despite the widespread availability of databases for most of those areas, integrating them into a single database or model is a challenging problem. Instead, in this paper, we use a single source, the Brazilian National Electric Energy Agency’s (ANEEL) weekly reports, which contains decisions about the electrical grid, comprising most of the areas. Since the data is unstructured (text), we employed NLP techniques such as stemming and tokenization to identify keywords related to common causes of risks provided by an expert group on energy transmission. Then, we used models to estimate the probability of each risk. Our results show that we were able to estimate the probability of 97 risks out of 233.

Mucilage from Coccinia grandis was extracted, isolated by maceration technique and precipitated, accordingly. The mucilage was evaluated for its physicochemical, binding, and disintegrant properties in tablets using paracetamol as a model drug. The crucial physicochemical properties such as flow properties, solubility, swelling index, loss on drying, viscosity, pH, microbial load, cytotoxicity were evaluated and the compatibility was analysed using sophisticated instrumental methods (TGA, DTA, DSC, and FTIR). The binding properties of the mucilage were used at three different concentrations and compared with starch and PVP as standard binders. The disintegrant properties of mucilage were used at two different concentrations and compared with standard disintegrants MCCP, SSG, and CCS. The wet granulation technique was used for the preparation of granules and was evaluated for the flow properties. The tablets were punched and evaluated for their hardness, friability, assay, disintegration time, in vitro dissolution profiles. In vitro cytotoxicity study of the mucilage was performed in human embryonic kidney (HEK) cell line using cytotoxic assay by MTT method. The outcome of the study indicated that the mucilage had good performance when compared with starch and PVP. Further, the mucilage acts as a good disintegrant than MCCP, SSG and CCS to paracetamol tablets. Moreover, the in vitro cytotoxicity evaluation results demonstrated that the mucilage is non-cytotoxic to human cells and is safe.

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.

The Life Cycle Analysis (LCA) was used to assess the impact of biomethane plant of the “La Católica” in Pedregal-Majes-Arequipa farm, fed with cow manure and holding a production of 60 Nm3/day of purified biogas. Life cycle inventory, impact assessment and interpretation were performed. The functional unit established was 1 MJ of energy produced; the study was modeled with SimaPro software, Ecoinvent Database and ReCiPe Midpoint (H) impact assessment methodology, according to the impact categories of climate change and fossil resource depletion. The impact analysis was limited to the Well to Tank (WTT) approach, which involves feedstock transport, substrate mixed, anaerobic digestion, biogas purification, storage and injection of the fuel into transport vehicles. The digestion process generated the highest amount of CO2 emissions (1.79E-02 kg CO2 eq/MJ-biomethane) and the highest depletion of fossil resources (6.58E-03 kg oil eq/MJ-biomethane), compared to the other fuel production, due to energy consumption and transport infrastructure. Biomethane was then compared to fossil fuels, resulting in natural gas generating the least amount of CO2 emissions, followed by diesel and finally biomethane. For the fossil resource depletion category, biomethane presented the lowest amount of fossil fuel consumption (1.37E-02 kg oil eq/MJ-biomethane), followed by natural gas and diesel.

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.

The realization of the third-generation artificial intelligence (AI) requires the evolution from perceptual intelligence to cognitive intelligence, where knowledge graphs may not meet the practical needs anymore. Based on the dual channel theory, cognitive graphs are established and developed through coordinating the implicit extraction module and the explicit reasoning module as well as integrating knowledge graphs, cognitive reasoning and logical expressions, which have achieved successes in multi-hop question answering. It is desired for cognitive graphs to be widely used in advanced AI applications such as large-scale knowledge representations and intelligent responses, promoting the development of Al dramatically. This review discusses cognitive graphs systematically and elaborately, including basic concepts, generations, theories and technologies. Moreover, we try to predict the development of cognitive intelligence in the short-term future and further enlighten more researches and studies.

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.

Kandelia obovata (Ko) and Aegiceras corniculatum (Ac) are common and dominant plant species in mangrove wetlands in South China, and distribute in the similar tidal zones along the coastline. The present study aimed to determine the allelopathic effects of leaf litter leachates (LLLs) from Ko and their purified condensed tannins (PCTs) on the germination and growth of Ac by mangrove microcosms. Replicate pots containing five different levels of LLLs and PCTs were separately prepared and propagules of Ac were placed in each treatment. Both LLLs and PCTs significantly inhibited the germination and growth of Ac, especially in high levels. The final germination rates of roots, stems, and the number of fine roots declined continuously while other growth indicators, including the lengths of fine roots, nutritive roots, the biomasses of roots, stems, leaves, increased firstly and then decreased with increasing levels. These results indicated that LLLs from the leaf litter of Ko, in particular, their PCTs exerted an inhibition effect on propagule germination and seedling growth of Ac, and the inhibitory effects were concentration dependent. This study suggested that condensed tannins from leaf litter, acting as allelochemicals, could regulate the natural regeneration of a mangrove forest.

Live attenuated influenza virus (LAIV) vaccines elicit a combination of systemic and mucosal immunity by mimicking a natural infection. To further enhance protective mucosal responses, we incorporated the gene encoding the IgA-inducing protein (IGIP) into the LAIV genomes of the cold-adapted A/Leningrad/134/17/57 (H2N2) strain (caLen) and the experimental attenuated backbone A/turkey/Ohio/313053/04 (H3N2) (OH/04att). Incorporation of IGIP into the caLen background led to a virus that grew poorly in prototypical substrates. In contrast, IGIP in the OH/04att background (IGIP-H1att) virus grew to titers comparable to the isogenic backbone H1att (H1N1) without IGIP. IGIP-H1att- and H1caLen-vaccinated mice were protected against lethal challenge with a homologous virus. The IGIP-H1att vaccine generated robust serum HAI responses in naïve mice against the homologous virus, equal or better than those obtained with the H1caLen vaccine. Analyses of IgG and IgA responses using a protein microarray revealed qualitative differences in humoral and mucosal responses between vaccine groups. Overall, serum and bronchoalveolar lavage samples from the IGIP-H1att group showed trends towards increased stimulation of IgG and IgA responses compared to H1caLen samples. In summary, introduction of genes encoding immunomodulatory functions into a candidate LAIV that can serve as natural adjuvants to improve overall vaccine safety and efficacy.

Despite increased global interest in the impacts of natural disasters on tourism, less study executes exploring how tourism sensitivity is addressed at the destination level. Generating a link between tourism and natural disaster management is vital in places that rely heavily on tourism and are prone to natural hazards. Ranau, Sabah (Malaysia) is one of the disaster-prone tourists' destination area. Hence, this paper applies the case study of Ranau earthquake 2015 to explore tourism sensitivity towards natural disasters. A qualitative of in-depth interview is applied to acquire information needed from the Ranau tourism entrepreneurs and operators. To analyse the qualitative data, a thematic analysis is conducted. Overall findings show that tourism activity in Ranau are identified to be sensitive towards the 2015 earthquake with a significant percentage of sensitivity level on two elements. These elements are known as Source and Power. The Source element includes tourism products, size of business, development, and natural disasters management with a significant sensitivity compared to the Power element (social capital). This provides insight to the need of specific tourism system adaptation as response to the earthquake and considering the integration of natural disaster management into tourism development to enhance long term sustainability.

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.

Objective: The purpose of this study was to determine if Porphyra tenera extract (PTE) has immune-enhancing effects and is safe in healthy adults. Methods: Subjects (3x10³ ≤ peripheral blood leukocyte levels < 8x10³ cells/μl) who met the inclusion criteria were recruited for this study. Enrolled subjects (n=120) were randomly assigned to either the PTE group (n=60) who were given 2.5 g/day of PTE (as Porphyra tenera extract) in capsule form or the placebo group (n=60) who were given crystal cellulose capsules with the identical appearance, weight, and flavor as the PTE capsules for 8 weeks. Outcomes were assessed by measuring natural killer cell (NK-cell) activity, cytokines, and upper respiratory infection (URI), and safety parameters were assessed at baseline and 8 weeks. Results: Compared to baseline, NK cell activity (%) increased for all effector cell to target cell ratios in the PTE group after 8 weeks, but there were no changes in the placebo group (p<0.1). Subgroup analysis of 101 subjects without an URI revealed that NK-cell activity in the PTE group tended to be increased for all E:T ratios (E:T=12.5:1 p=0.068; E:T=25:1 p=0.036; E:T=50:1 p=0.081) compared to the placebo group. There was a significant difference between these two groups for the E:T=25:1 ratio, which increased from 20.3±12.0% at baseline to 23.2±12.4% after 8 weeks in the PTE group (p=0.036). There was no significant difference in levels of cytokines between these two groups. Conclusions: PTE supplementation appears to enhance immune function by improving NK-cell activity without adverse effects in healthy adults.

In this experiment, an efficient and accurate network of detecting automatically disseminated (bot) content on social platforms is devised. Through the utilisation of parallel convolutional neural network (CNN) which processes variable n-grams of text 15, 20, and 25 tokens in length encoded by Byte Pair Encoding (BPE), the complexities of linguistic content on social platforms are effectively captured and analysed. With validation on two sets of previously unexposed data, the model was able to achieve an accuracy of around 96.6% and 97.4% respectively — meeting or exceeding the performance of other comparable supervised ML solutions to this problem. Through testing, it is concluded that this method of text processing and analysis proves to be an effective way of classifying potentially artificially synthesized user data — aiding the security and integrity of social platforms.

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.

Natural products have been used for the treatment of human diseases since ancient history. Over time, due to the lack of precise tools and techniques for the separation, purification, and structural elucidation of active constituents in natural resources there has been a decline in financial support and efforts in characterization of natural products. Advances in the design of chemical compounds and the understanding of their functions is of pharmacological importance for the biomedical field. However, natural products regained attention as sources of novel drug candidates upon recent developments and progress in technology. Natural compounds were shown to bear an inherent ability to bind to biomacromolecules and cover an unparalleled chemical space in comparison to most libraries used for high-throughput screening. Thus, natural products hold a great potential for the drug discovery of new scaffolds for therapeutic targets such as Sirtuins. Sirtuins are Class III histone deacetylases that have been linked to many diseases such as Parkinson`s disease, Alzheimer’s disease, type II diabetes, and cancer linked to aging. In this review, we examine the revitalization of interest in natural products for drug discovery and discuss natural product modulators of Sirtuins that could serve as a starting point for the development of isoform selective and highly potent drug-like compounds.

The study utilizes the energy-flux-vector method to analyze the heat transfer characteristics of natural convection in a wavy-wall porous square cavity with a partially-heated bottom surface. The effects of the modified Darcy number and modified Rayleigh number on the energy-flux-vector distribution and mean Nusselt number are examined. The results show that when a low modified Darcy number with any value of modified Rayleigh number is given, the recirculation regions are not formed in the energy-flux-vector distribution within the porous cavity. Therefore, a low mean Nusselt number is obtained. The recirculation regions do still not form and thus the mean Nusselt number has a low value when a low modified Darcy number with a high modified Rayleigh number is given. However, when the values of the modified Darcy number and modified Rayleigh number are high, the energy flux vectors generate recirculation regions and thus a high mean Nusselt number is obtained.

Recent advances in sampling and novel techniques in drug synthesis and isolation have promoted the discovery of anticancer agents from marine organisms to combat this major threat to public health worldwide. Bryozoans, filter-feeding, sessile aquatic invertebrates often characterized by a calcified skeleton, are an excellent source of pharmacologically interesting compounds including well-known chemical classes such as alkaloids and polyketides. This review covers the literature for secondary metabolites isolated from marine cheilostome and ctenostome bryozoans that have shown potential as cancer drugs. Moreover, we highlight examples such as bryostatins, the most known class of marine-derived compounds from this animal phylum, which is advancing through anticancer clinical trials due to their low toxicity and antineoplastic activity. The bryozoan antitumour compounds discovered until now show a wide range of chemical diversity and biological activities. Therefore, more research focusing on the isolation of secondary metabolites with potential anticancer properties from bryozoans and other overlooked taxa covering wider geographic areas is needed for an efficient bioprospecting of natural products.

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.

Thermophysical characterization of three paraffin waxes (RT27, RT21 and RT35HC) is carried out in this study using DSC, TGA and transient plane source technics. Then, a numerical study of their melting in a rectangular enclosure is examined. The enthalpy-porosity approach is used to formulate this problem in order to understand the heat transfer mechanism during the melting process. The analysis of the solid-liquid interface shape, the temperature field shows that the conduction is the dominant heat transfer mode in the beginning of the melting process. It is followed by a transition regime and the natural convection becomes the dominant heat transfer mode. The effects of the Rayleigh number and the aspect ratio of the enclosure on the melting phenomenon are studied and it is found that the intensity of the natural convection increases as the Rayleigh number is higher and the aspect ratio is smaller. In the second part of the numerical study, a comparison of the performance of paraffins waxes during the melting process is conducted. Results reveals that from a kinetically RT21 is the most performant but in term of heat storage capacity, it was inferred that RT35HC is the most efficient PCM.

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

Drug resistance has become an extremely serious problem worldwide. Antibiotic resistance genes (ARGs) entering the environment with wastewaters promote replenishment of the resistome of natural microbioms. Distribution of several clinically significant ARGs in wastewaters of Rostov-on-Don (Southern Russia), lower reaches of the Don River and natural waters of the neighboring region was investigated. Metagenomic DNA samples isolated from 250 ml of wastewaters or natural waters and 200 mg of surface sediments were used for the study. Identification of the ARGs was carried out with end-point detection PCR. Presence of NDM, OXA-48, CTX-M, VanA, VanB, ErmB, and TetM/TetO genes was detected in urban wastewaters. Samples of wastewater treatment plant (WWTP) sewage were enriched with ARGs in contrast to non-treated wastewaters from the sewage collector. NDM, VanA, ErmB, TetM/TetO genes were found only in wastewaters and were absent in samples of natural waters and surface sediments. Only OXA-48, VanB and CTXM genes were found in natural waters and surface sediments. The described ARGs are quite typical for urban and hospital wastewaters. The target ARGs were detected in the samples connected to the anthropogenous sources of pollution such as Rostov municipal WWTP or livestock enterprise effluents.

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.

Malaria, as a major global health problem, continues to affect a large number of people each year, especially those in the developing countries. Effective drug discovery is still one of the main efforts to control malaria. As natural products are still considered as a key source for discovery and development of therapeutic agents, we have evaluated more than 2000 plant extracts against Plasmodium falciparum. As a result, we discovered dozens of plant leads that displayed antimalarial activity. Our phytochemical study of some of these plant extracts led to identification of several potent antimalarial compounds. The prior comprehensive review article entitled “Antimalarial activity of plant metabolites” by Schwikkard and Van Heerden (2002) reported structures of plant-derived compounds with antiplasmodial activity and covered literature up to the year 2000. As a continuation of this effort, the present review covers the antimalarial compounds isolated from plants, including marine plants, reported in the literatures from 2001 to the end of 2017. During the span of the last 17 years, 175 antiplasmodial compounds were discovered from plants. These active compounds are organized in our review article according to their plant families. In addition, we also include ethnobotanical information of the antimalarial plants discussed.

Background: The efficacy of sunscreen is evaluated by SPF values, which are quantitatively determined in laboratories on the backs of human subjects according to a standardized procedure. However, SPF cannot be directly translated to sunburn protection under real-life situations because actual efficacy depends on various factors related to human behaviors and environmental conditions. This study clinically evaluated the efficacy of two sunscreen sprays (SPF 30 and SPF 70) under natural sunlight exposure on healthy subjects at the beach. Methods: Twenty subjects were divided into two cells for the two sunscreen sprays (SPF 70 and SPF 30) in a single-center, actual usage test. The primary endpoint of the study was sunburn protection on the dorsal arms and the secondary endpoint was protection on the face and neck. Subjects stayed at the beach for 4 h after application of the sunscreens with normal beach activities. Subjects’ behavior at the beach, the amounts of sunscreen applied and reapplied, and environmental conditions were all recorded. Results: There was no significant sunburn for a majority of the subjects in either cell. However, neither sunscreen completely blocked the sunburn, especially for the face/neck area. We found that the SPF 70 sunscreen was more effective than the SPF 30 sunscreen. Conclusion: Modern sunscreen sprays, applied liberally, are effective in providing sunburn protection for the body in a beach setting.

An abundance of natural resources is both an opportunity and a challenge for developing countries. Several resource-rich, low-income countries receive amounts of foreign aid that are similar to or larger than their actual or potential revenues from natural resources. In such countries, the donors may have an opportunity to help a government to use its resource revenues productively and minimize the magnitude of risks created by resource rents. Development of aid instruments tailored for such purposes might be helped by model-based analysis of the effects of foreign aid on resource-rich, low-income economies and its interactions with the flows of natural resource revenues. This paper develops a growth model à la Barro in which the government receives windfalls (from natural resources and foreign aid) and rent-seeking agents contest for public funds. The key conclusion is that making aid countercyclical helps to achieve higher economic growth, and so does conditioning disbursements on enhancement of public capital. Introducing elements of insurance in the design of both aid products financing investments in infrastructure and social services and supporting policy and institutional reforms may help to achieve both of these objectives.

Integrating voice assistant technology can potentially enhance accessibility and user interaction in web-based platforms for geospatial data visualisation and processing. This paper presents an open-source development incorporating a voice virtual assistant into the BStreams web application platform, designed specifically for visualising geospatial data. The study aimed to mitigate the observed limitations of virtual assistants in understanding the semantics and context of user commands in the geospatial domain. A discourse framework specialised for geospatial data was developed using a structured prototype methodology. In addition, a survey assisted in identifying commonly used English terminologies for geospatial data interaction. The questionnaire results were subsequently assessed using the ChatGPT natural language processing model. The application used open-source frameworks, including Web Speech API, Leaflet, Mapbox geocoding, and JavaScript. This investigation indicates that voice virtual assistants can potentially enhance human-computer interaction for geospatial data visualisation and processing. However, challenges related to natural language understanding, accents, and domain knowledge were raised. The paper advocates for further research to refine the integration of voice technology in this domain.

Periodontics is a complex field characterized by a constantly growing body of research, posing a challenge for researchers and stakeholders striving to stay abreast of its evolving literature. Tradi-tional bibliometric surveys, while accurate, are labor-intensive and not scalable to meet the demands of such rapidly expanding domains. The aim of this study was to explore the uses of a top-ic-modeling algorithm to investigate the structure of the periodontics research field. To this purpose, we employed BERTopic to automatically sift through a substantial corpus of periodontics manu-scripts published in MEDLINE from 2009 to 2024 and identify topics using two sets of settings, to get a broader and high detailed view of the field, respectively. BERTopic identified and categorized 31 topics, unveiling the internal structure and the relations between publication macroareas. When we used the high granularity settings, we identified 2428 topics, which revealed hotspots within the field. Specific regression highlighted novel research areas such as artificial intelligence and exo-somes. In conclusion, BERTopic offers a flexible tool to provide insights into periodontics, its most recent trends, and serve as a useful instrument to approach the field and direct future research.

Cancer remains a primary cause of mortality, with over 18.1 million new cases and 9.6 million deaths globally in 2018. Chemotherapy, which utilizes a spectrum of cytotoxic drugs targeting rapidly dividing cancer cells, is a predominant treatment modality. However, the tendency of chemotherapeutics to induce drug resistance and exhibit non-specific cytotoxicity necessitates the development of new anticancer agents with heightened efficacy and minimized toxicity. In recent years, the discovery of safe and effective antibacterial/antiviral agents has also been a hot spot in medicinal chemistry. This paper comprehensively reviews the synthesis, anticancer/ antibacterial/antiviral activity, and structure-activity relationships of natural 1,4-naphthoquinones and their derivatives. It highlights their potential as efficient and low-toxicity anti-tumor and anti-infectious drug candidates.

L-(+)-Furanomycin 1 is a miniature antibacterial natural product that contains an -amino acid core. This non-proteinogenic α-amino acid was first isolated in 1967 by Katagiri and co-workers from the fermentation broth of Streptomyces threomyceticus L-803 (ATCC 15795). It is a substrate of isoleucyl aminoacyl tRNA synthetase that replaces isoleucine in the protein translation process and exhibits antibacterial properties in vitro. It effectively acts as an antibacterial agent against M. tuberculosis, E. coli, B. subtilis, and some Shigella and Salmonella bacterial species at concentrations as low as the micromolar range. Consequently, synthetic chemists have garnered considerable interest from their specific structure-activity profile, distinctive chemical compositions, and distinct biological profile. This review comprehensively describes cutting-edge synthetic methodologies for synthesizing furanomycin and its analogs reported to date. Therefore, this review will offer an initial perspective on synthesizing furanomycin and its customized compounds.

Chronic wounds have a significant impact on patients' quality of life, necessitating the management of pain, infection, bleeding, and emotional challenges. Debridement, which involves the removal of nonviable tissue, is crucial for promoting wound healing. In addition to surgical methods, cost-effective alternatives such as local solutions and ointments with biological properties have been explored. The use of natural compounds with anti-inflammatory, antibacterial, and collagen-synthesizing abilities holds promise for wound healing. This study aimed to assess the safety and effectiveness of a wound-healing ointment containing bioactive ingredients derived from medicinal plants (extracts, essential oils, and vegetable oils). Pre-clinical tests were conducted following standardized protocols. The chemical composition of the ointment was characterized using Fourier transform infrared (FTIR) spectroscopy to gain insights into its synergistic action. FTIR analysis revealed similarities between the product's spectrum and that of bioactive compounds. The in vitro tests demonstrated that all formulations of the ointment induced no cell death, DNA damage, or acute toxicity in cell cultures. No lethal dose was observed, indicating the safety of the ointment at all concentrations. In vivo preclinical analyses demonstrated no adverse responses being effective in the healing process, similar to the gold standard (Silver Sulfadiazine). The regenerative ointment displayed excellent biocompatibility and bioactivity in vitro and in vivo studies, contributing to the development of innovative and sustainable wound management therapies.

The effective removal of impurities from natural quartz is a very challenging subject, but there is no relevant study on the mesoscopic structure of quartz sand particles, and there is still a lack of direct evidence on the structure-activity relationship between mesoscopic structure and purification effect. In this paper, the effects of calcination temperature, calcination time, quenching frequency and grinding frequency on the formation of mesoscopic fractures in natural quartz sand were studied, and a linear regression model was established by fractal and differential methods. The results show that the cracked structure of quartz sand and its variation law have remarkable fractal characteristics, and thermal expansion and phase transformation are the main factors affecting the cracked structure and specific surface area of quartz sand. The non-phase change thermal expansion results in the formation of semi-closed wedge-shaped fractures in the open fractures of quartz sand, resulting in a significant decrease in the specific surface area of the cracked sand. On the contrary, the phase change expansion is conducive to the generation of more Me10 mesoporous fractures and the increase of the specific surface area of cracked sand. In addition, thermal stress and mechanical force are more likely to form Me50 and Me10 mesoporous cracks, where the average proportion of Me50 is higher than 75%. Based on this, the linear regression model between the fractal dimension and the pore volume distribution, SBET is further established, and the correlation coefficient R2 is mostly above 96%. In addition to offering insightful findings for the investigation of the structure-activity relationship between the purification effect and the mesoscopic structure of quartz sand, this also establishes the groundwork for the advancement of high purification technology for natural quartz sand.

This paper definitively settles the longstanding conjecture regarding odd perfect numbers. A perfect number is one whose sum of divisors equals itself doubled. While Euclid's method for constructing even perfect numbers is well-known, the existence of odd ones has remained elusive. By employing elementary techniques and analyzing the properties of the divisor sum function, we conclusively prove that no odd perfect numbers exist.

Oxidative stress, resulting from an imbalance between free radical production and antioxidant defenses, can negatively impact animal health, welfare, and productivity. In livestock, diseases are often correlated with reduced antioxidant status. To mitigate oxidative stress, natural antioxidants have gained attention as alternatives to synthetic compounds. The growing need for safe and natural animal products has led to the increased use of plant food additives (PFAs) as antioxidants in animal feed. These natural sources include plant extracts, essential oils, and bioactive compounds-rich by-products. Such antioxidants can enhance product quality, oxidative stability, and shelf life in ruminants, swine, and rabbits. Additionally, maintaining a good antioxidant status positively affects meat quality parameters, including vitamin E content and reduced lipid peroxidation. This review explores the properties of natural antioxidants, extraction methods, and their significance in promoting animal welfare, performance, and product quality.

We study the enhancement of the elastocaloric effect in natural rubber by using forced air convection in order to favour heat extraction during the elongation stage of an stretching-unstretching cycle. Elastocaloric performance is quantified by means of the adiabatic undercooling that occurs after a fast removal of the stress, measured by infrared thermography. To ensure accurate measurements, spatial averages on thermal maps of the sample surface are performed since undercooled samples display heterogeneities caused by various factors. The influence of the stretching velocity and the air velocity is analysed. The results show that, given the fact that the stretching velocities should be high enough to improve the cooling power, there is an optimal air velocity that provides maximum adiabatic undercooling. Our experiments have also allowed us to characterise the dependence of the Newton heat transfer coefficient on the air convection velocity. We have shown that this coefficient may show an enhancement up to 600% for air velocities around 4 m/s.

Context: The advent of new strains of resistant microbes and the concomitant growth in multidrug resistance have made antimicrobial resistance an urgent public health concern. New antimicrobials are desperately needed to boost the success rates of treating infectious diseases and save lives. There are many intriguing biomolecules with antibacterial action, which are mostly unexplored in microorganisms. Objectives: The objective of the present review article is to describe the importance of natural compounds against microorganisms using advanced techniques to protect people from diseases. Method: We have searched and incorporated many online literature to gather information from SCOPUS, SCI, PUBMED, ScienceDirect and Medline. We have discussed several microorganism sources for antimicrobials and antifungal drugs. In addition, describes microculturing techniques and applications of microbial-based microsystems. Results and Conclusion: Every kind of higher trophic life depends on microorganisms for sustenance. The unseen majority is essential to understanding how humans and other living forms can survive anthropogenic climate change. Antimicrobial substances and the latest methods, techniques, and strategies for developing effective treatments have been discussed in this article. Novel model systems and cutting-edge biomolecular and computational methodologies could help researchers enhance antimicrobial resistance by completely capitalizing on lead antimicrobials.

This study examines the impact of natural resource exploitation, renewable energy consumption, economic growth, biocapacity, and information and communication technologies (ICT) on the ecological footprint of BRICS+ nations using panel data from 1991 to 2019. Advanced econometric techniques, including FMOLS, DOLS, CCR, and GMM, reveal significant findings relevant to sustainability-focused policymakers. The results show that natural resource exploitation significantly contributes to environmental degradation, while renewable energy consumption mitigates these effects to varying degrees across the studied countries. Although economic growth is crucial, it worsens ecological footprints unless accompanied by sustainable practices. ICT and biocapacity emerge as key factors in promoting environmental sustainability, with ICT enhancing innovation and efficiency to reduce ecological impacts. The study emphasizes the need for tailored policy frameworks that align economic growth with environmental sustainability, advocating for the adoption of renewable energy, eco-innovations, and green finance initiatives suited to the unique conditions of BRICS+ nations. The findings aim to guide policymakers in advancing sustainable development and mitigating the adverse environmental impacts of rapid industrialization and urbanization, while contributing to the global discourse on achieving a balance between economic growth and ecological well-being in developing countries.

Indications of the passage of time are indirect reminders of our mortality. Humans have often responded to this discomforting thought by attempting, temporally at least, to escape its relentless march through the notion of a timeless present. This essay explores how architecture can assist us in that objective. It suggests that there are at least three different ways that buildings can offer us temporary psychological refuge from the progress of time. They can include the fascination of seemingly timeless natural movement patterns. They can recall archetypal environments, and they can isolate us from the external world of perpetual temporal progress.

The use of pesticides such as rotenone can lead to toxicity, dysfunction and neuronal death in humans. The Copaifera Reticulata Ducke Oil-Resin (CPOR) is widely used by the natives of the Amazon, however, studies on its potential neuroprotective effect are lacking. This study investigated the cytoprotective effect of CPOR on total cell cultures of the ventral midbrain (VMC), hippocampus (HC) and cerebral cortex (CCC) exposed to rotenone at different concentrations. The total cell cultures of VMC, HC and CCC were exposed for 72 or 24 hours to rotenone, CPOR and rotenone + CPOR. Cell viability was measured using the MTT test and compared using one-way ANOVA. Exposure to rotenone reduced the cell viability of CMV, HC and CCC. Exposure to CPOR reduced the cell viability of CMV. The cell viability of CMV exposed simultaneously to rotenone and CPOR was higher compared to rotenone alone. The cell viability of HC exposed simultaneously to rotenone and CPOR was higher than that exposed to rotenone. CMVs are more vulnerable to rotenone toxicity and more sensitive to CPOR. Considering simultaneous exposure, CPOR revealed a possible neuroprotective effect against rotenone damage only for VMC and HC.

In an era where the proliferation of large language models blurs the lines between human and machine-generated content, discerning text authenticity is paramount. This study investigates transformer-based language models—BERT, RoBERTa, and DistilBERT—in distinguishing human-written from machine-generated text. By leveraging a comprehensive corpus, including human-written text from sources such as Wikipedia, WikiHow, various news articles in different languages, and texts generated by OpenAI's GPT-2, we conduct rigorous comparative experiments. Our findings highlight the superior effectiveness of ensemble learning models over single classifiers in this critical task. This research underscores the versatility and efficacy of transformer-based methodologies for a wide range of natural language processing applications, significantly advancing text authenticity detection systems. The results demonstrate competitive performance, with the transformer-based method achieving an F-score score of 0.83 with RoBERTa-large (monolingual) and 0.70 with DistilBERT-base-uncased (multilingual).

: Based on fairness perspective, this paper combines the social vulnerability assessment of natural hazards with multi-objective site selection model to propose a new site selection planning model for central refuges. To begin with, the social vulnerability assessment system of regional natural disasters is founded through literature analysis, and the social vulnerability index of natural disasters is constructed; and based on the exponential differentiation of index to depict regional refuge demand, design vulnerability coverage function quantitive site selection fairness; then taking the shortest weighted evacuation distance, maximum vulnerability coverage and construction cost, the coverage population and refuge quantity as constraint conditions to construct multi-objective optimization model; finally, taking Zigong City, Sichuan Province as the object of empirical research, adapting NSGA-II algorithm to get the Pareto optimal solution set, and using TOPSIS method to get the optimal location scheme. The research results can provide a new method for the location layout of central refuge.

For centuries, humans have relied on natural products to prevent and treat numerous health issues. However, biologically active compounds from natural sources, such as polyphenols, face significant challenges, including low solubility, rapid metabolism, and instability, which hinder their effectiveness. Advances in science and nanotechnology have provided solutions to overcoming these problems through the use of porous silica materials. These materials possess unique properties, such as a high specific surface area, adjustable particle and pore sizes, and a surface that can be easily and selectively modified. In this review, we summarize and discuss findings on how the pore and particle size, structure, and surface modification of silica materials influence the preparation of efficient delivery systems for biologically active polyphenols from natural sources. The available data demonstrate how parameters such as adsorption capacity, release and antioxidant properties, bioavailability, solubility, stability, etc. of the studied delivery systems could be affected by the structural and chemical characteristics of the porous silica carriers.

Daylighting design is not only a stage of dimensioning glazed surfaces to provide sufficient natural light to an occupied space, but also a method of analysing how this matter can be achieved without unwanted effects, such as: gains and losses of heat, glare, and variations of the daylighting intensity, at various indoor distances and levels. According to the international regulations in force, glazed surfaces must be designed with a careful balance between the admission of natural light and thermal performance, such as heat loss in winter and heat gain in summer. In a novel project, it is essential to determine the optimal ratio of window-to-wall surface area. This necessity arises because the materials constituting the glazed surfaces of windows typically exhibit lower insulation properties compared to those of the building's walls. This methodology could be applied to building renovations when envelope modifications are possible and suitable. The case study presented in the paper highlights the energy consumed due to a group of windows with a large glazed area in an existing building, located in a temperate continental climate area. The energy consumption results from supplementary artificial lighting required to maintain adequate illumination for indoor activities and that to counterbalance heat loss during colder periods are evaluated. The analysis performed by modifying the glazed surface led to the identification of an optimum value of windows dimensions for an energy consumed minimum.

Oil spills upset the delicate balance of the environment. Remediation methods are employed to recover areas affected by contaminants. Green surfactants have considerable potential with regards to enhancing environmental remediation methods. The aim of the present study was to formulate a green natural detergent containing the biosurfactant produced by Starmerella bombicola ATCC 22214 grown in mineral medium containing 1.2% canola oil, 10% sucrose, and 0.5% corn steep liquor. The biosurfactant reduced surface tension to 31.84 mN/m and was produced at a yield of 22 g/L. Twelve formulations were initially proposed using cottonseed oil as the natural solvent and three different stabilisers (vegetable wax, hydroxyethyl cellulose, and sodium alginate). The natural detergent was assessed with regards to emulsifying capacity and stability over a 10-day period. Ecotoxicity of the formulated detergent was investigated using the marine recruitment test on metal plates covered with paint into which the biosurfactant was incorporated as well as tests with the microcrustacean Artemia salina and vegetable seeds. Dispersing capacity and the potential for removing oil from rock surfaces were also investigated. The formulation designated H, which was composed of 1% biosurfactant, 40% cottonseed oil and 2% hydroxyethyl cellulose, achieved the best results. The formulation exhibited both stability and emulsifying capacity (100% of petroleum). The ecotoxicity tests revealed no significant toxicity, demonstrating the safety of the natural detergent. The detergent achieved satisfactory oil dispersion and solubilised 98% of oil impregnated on rock. The results indicate the potential application of the natural detergent formulated in this study for the efficient remediation of environmental compartments contaminated with oil and petroleum derivatives.

LLMs are a compressed repository of a vast corpus of valuable information on which they are trained. Therefore, this work hypothesizes that LLMs such as Llama, Orca, Falcon, and Mistral can be used for misinformation detection by making them cross-check new information with the repository on which they are trained. Accordingly, this paper describes the findings from the investigation of the abilities of LLMs in detecting misinformation on multiple datasets. The results are interpreted using explainable AI techniques such as LIME, SHAP, and Integrated Gradients. The LLMs themselves are also asked to explain their classification. These complementary approaches aid in better understanding the inner workings of misinformation detection using LLMs and lead to conclusions about their effectiveness at the task.

The global warming has caused poor grape skin coloration and reduced the production of high-quality berries. To overcome these problems, the effect of synephrine (Syn) on anthocyanin accumulation was investigated. Syn treatment above 1 mM increased anthocyanin content in cultured grape cells in a molecular-structure-specific and concentration-dependent manner, and increased only the anthocyanin without affecting the sugar/acid ratio in grape bunches. It increased the expression of genes linked to anthocyanin synthesis after 24 h of treatment, without affecting phytohormone levels. Syn treatment induced the expression of SOD3, elevating hydrogen peroxide (H2O2) production from 3 to 24 h after treatment in cultured cells. Subsequent increases in CAT and APX6 expression occurred, which encode H2O2-eliminating enzymes. In cultured grape cells, Syn and H2O2 treatments upregulated the H2O2-responsive gene Chit4 and the anthocyanin-biosynthesis-related genes mybA1 and UFGT 4 days after the treatments and promoted anthocyanin accumulation 7 days after the treatments. In berries, both treatments enhanced anthocyanin accumulation after 9 days. These results suggest that Syn promotes anthocyanin accumulation through the production of H2O2 without the upregulation of phytohormone biosynthesis. Syn treatment is expected to improve poor grape coloration and contribute to higher-quality fruit production.

This study delves into Personal Narratives (PN), which encompass both oral and written recounting of individual experiences, encompassing facts, events, people, and thoughts. Traditional emotion recognition and sentiment analysis primarily focus on broader categories like utterances or documents. Our research, however, centers on identifying Emotion Carriers (EC), which are specific segments within speech or text that elucidate the narrator's emotional state (e.g.,"losing a parent", "decision-making moments"). Extracting these ECs enriches the representation of a user's emotional state, thereby enhancing natural language understanding and the sophistication of dialogue models. While previous studies have utilized lexical attributes to identify ECs, we argue that incorporating spoken narratives offers a more nuanced view of the context and emotional state. This paper explores the integration of speech and textual embeddings at the word level, alongside both early and late fusion techniques, for improved EC detection in spoken narratives. We employ Residual Neural Networks (ResNet), initially pre-trained on diverse speech emotion datasets and subsequently fine-tuned for EC detection. Our experimental findings demonstrate that late fusion, in particular, significantly enhances EC detection capabilities.

Micro satellites must survive severe mechanical conditions during their launch phase. Therefore, structural design requirements are imposed on the micro satellites. These requirements are the strength requirement and the stiffness requirement. Usually, the structural design of a micro satellite is performed using the internal stress analysis and the natural frequency analysis, which are based on a Finite Element Method (FEM). The validity of this structural design is evaluated through vibration tests. In an early stage of development, which has a FEM model of a satellite in the process of creation, presumption of the natural frequency of a satellite may be difficult. In this study, a simple method for determining the longitudinal and lateral minimum natural frequencies of the micro satellites during the ascent phase was clarified. The structure of the micro satellites used in this research is made of aluminum alloy, and they have a monocoque structure. The Young's modulus and moment of inertia of area used to calculate the minimum natural frequencies were determined using the area ratio of the monocoque structure to the entire satellite. When this method is used, the estimated minimum natural frequencies of the longitudinal direction and the lateral direction are in agreement with the measured values acquired by the vibration tests. In order to shorten the process of micro satellites development, this paper describes a practical estimation method of the minimum natural frequency for micro satellites.

A very detailed water budget was conducted on Lake Trafford in southern Florida. The inflow was dominated by surface-water influx via five canals (61%) with groundwater influx constituting 12% and direct rainfall 27%. Lake discharge was dominated by sheet-flow (69%) and evapotranspiration (30.5%) with groundwater recharge of the hydraulically connected unconfined aquifer accounting for only 0.5%. Removal of 30 M tons (4.4 x 106 m3) of organic sediment impacted groundwater influx, causing enhanced groundwater flows into the deeper parts of the lake and mixed flows along the banks creating a rather unusual pattern. The large number of groundwater seepage meters used during the investigation led to a very reliable set of measurements with occasional failure of only a few meters. A distinctive relationship was found between the wet season lake stage, heavy rainfall events and pulses of exiting sheet-flow from the lake. Estimation of the evapotranspiration loss using data collected from a weather station on the lake allowed the use of three different models which, when averaged, produced results comparable to Lake Okeechobee (southern Florida). A limitation of the investigation was the inability to directly measure sheet-flow discharges, which had to be estimated as a residual within the calculated water budget.

Microencapsulation using polymer materials is a potent process to protect and prolong the survival of probiotics. Cissampelos pareira leaf contains natural gelling agents that possess solidifying properties. This study aimed to investigate the development of microencapsulation containing Lacticaseibacillus paracasei using C. pareira extract as a natural encapsulating material. The absorption bands near 1603 cm−1 and 1725 cm−1 detected by Infrared spectroscopy (FTIR) were identified as pectin in C. pareira structure. The L. paracasei-C. pareira microcapsules (LP-CP) showed high encapsulation efficiency by 90.5% which was confirmed by the evaluation of their survival rate. Under thermal conditions (85oC), bacterial viability detected in the microcapsules was 69% as opposed to non-encapsulated bacteria where viability was as low as 5%. Furthermore, the microcapsule presented 75% bacterial viability whereas the free cells showed 30% under acidic conditions (pH 2). During storage conditions, LP-CP viability remained at 50% when the storage time was extended to 90 days whereas, the survival rate of free cells significantly decreased by 100% after 90 days. This information suggests that C. pareira is a potent polymer which can be used as an eco-friendly material for the microencapsulation of probiotics relevant as dietary supplementation for humans or animals.

Silybin is a natural compound extensively studied for its hepatoprotective, neuroprotective and anti-cancer properties. Envisioning the enhancement of silybin potential by suitable modifications in its chemical structure, here, a series of new 7-O alkyl silybins derivatives were synthesized by Mitsunobu reaction starting from the silybins and tyrosol-based phenols, such as tyrosol (TYR, 3), 3-methoxytyrosol (MTYR, 4) and 3-hydroxytyrosol (HTYR, 5). This research sought to explore the antioxidant and anticancer properties of new eighteen derivatives and their mechanisms. In particular, the antioxidant properties of new derivatives outlined by DPPH assay, showed a very pronounced activity depending on the tyrosyl moiety (HTYR&gt;MTYR&gt;&gt;TYR). A significant contribution of the HTYR moiety was observed for silybins and 2,3-dehydro-silybin based derivatives. According to the very potent antioxidant activity, 2,3-dehydro-silybin derivatives 15ab, 15a and 15b exerted the most potent anticancer activity in human prostate cancer PC-3 cells. Furthermore, flow cytometric analysis for cell cycle and apoptosis revealed that 15ab, 15a and 15b induce strong G1 phase arrest and increase late apoptotic population in PC-3 cells. Additionally, western blotting for apoptotic marker cleaved caspase-3 confirmed apoptosis induction by these silybin derivatives in PC-3 cells. These findings hold significant importance in the investigation of anticancer properties of silybin derivatives and strongly encourage swift investigation in pre-clinical models and clinical trials.

Human language is a prime example of a complex system characterized by multiple scales of description. Understanding its origins and distinctiveness has sparked investigations with very different approaches, ranging from the Universal Grammar to statistical analyses of word usage, all of which highlight, from different angles, the potential existence of universal patterns shared by all languages. Yet, a cohesive perspective remains elusive. In this paper we address this challenge. First, we provide a basic structure of universality, and define recursion as a special case thereof. We cast generative grammars of formal languages, the Universal Grammar and the Greenberg Universals in our basic structure of universality, and compare their mathematical properties. We then define universality for writing systems and show that only those using the rebus principle are universal. Finally, we examine Zipf's law for the statistics of word usage, explain its role as a complexity attractor, and explore its relation to universal writing systems as well as its similarities with universal Turing machines. Overall, we find that there are two main kinds of universality, termed {\it mechanistic} and {\it emergent}, and unveil some connections between them.

In this review paper, we discuss the use of BERT, one of the most well-liked deep learning-based language models. The model's operation mechanism, key areas of applicability to text analytics tasks, comparisons with related models for each activity, and a description of certain proprietary models are all covered in this work. The data from several dozen original scientific studies that have been published in the last few years and have garnered the greatest interest from the scientific community were systematized in order to prepare this review. All researchers and students who wish to learn about the most recent developments in the field of natural language text analysis may find this survey helpful. Bidirectional Encoder Representations from Transformers, or BERT for short, is the subject of a thorough investigation that we present. The area of natural language processing holds significant importance in the creation of intelligent systems. To do a variety of activities, one must comprehend the sentence's correct meaning in order to produce the desired result. Computers have a hard time understanding languages because of how context constantly changes. The main challenge in natural language processing tasks is getting computers to understand text context, and BERT is seen as a revolution in this regard. It picks up the language and its meaning in a manner that is quite similar to how the human brain processes meaning from sentences. Its capacity to identify a word from both the left and right contexts of a sentence makes it special. A new era in the perception and comprehension of natural languages has been ushered in with the development of BERT, which could help computers understand natural languages more fully. This study aims to provide readers with a deeper knowledge of the BERT language model and how it is applied to different NLP tasks.

Overweight and Obesity are global health and economic concerns. According to the World Health Organization, the number of individuals with obesity has nearly tripled since 1975. This health issue is currently considered an epidemic, affecting over 1.9 billion adults and 340 million children and adolescents. It contributes to more than 5 million deaths each year and has a global economic burden that will reach $4.32 trillion annually by 2035, according to the World Obesity Federation. This disease can affect every system of the human body and can lead to complications such as metabolic syndrome, diabetes, cancer, dyslipidemia, cardiovascular diseases, and hypertension, among others. Treatment may sometimes include diet, exercise, drugs, and bariatric surgery. Nonetheless, not all people have access to these treatments, and public health strategies consider prevention the most important factor. In this regard, recent investigations are aiming to find alternatives and adjuvants for the treatment of obesity, its prevention, and the reversion of some of its complications, using natural sources of anti-obesogenic compounds like polyphenols, terpenes, alkaloids, saponins, among others. In this review, we gather the most current information using PubMed, Google Scholar, Scopus, Cochrane, and Web of Science. We present and discuss the current information about natural products that have shown anti-obesogenic effects at a molecular level. We also consider the impact of dietary habits and lifestyle in preventing overweight and obesity due to the evidence of the benefits of certain foods and compounds consumed regularly. We discuss mechanisms, pathways, and receptors involved in the modulation of obesity, especially those related to inflammation and oxidative stress linked to this disease, due to the relevance of these two aspects in developing complications.

This study focused on evaluating the potential of natural fermentation of pea flour (F) to improve the release of antioxidant compounds. Two fermentation conditions of F were performed: 1: 36.4% w/w, 24 h, 30°C (FF1); 2: 14.3% w/w, 24 h, 37°C (FF2); pH values decreased to 4.4-4.7, with a predominance of lactic acid bacteria. An increment of the proteolysis degree (TNBS method) (greater for FF2), polypeptides aggregation and decrease of solubility, increase of &lt; 2 kDa peptides, and increase in the ORAC potency of PBS-soluble fractions after fermentation was demonstrated. After simulated gastrointestinal digestion (SGID, COST-INFOGEST), FF1D and FF2D showed greater proteolysis degrees (63 and 53%) than F1D and F2D (45%), with differences in the molecular composition of the different digests. ORAC and HORAC potencies increased in all cases. FF2D presented the greater ORAC value, with higher activities for &gt; 4 kDa, some in the range 2 to 0.3 kDa. and &lt; 0.10 kDa fractions. Fermentation also increased the 60 % ethanol extracted phenolic compounds, mainly flavonoids, and the ORAC activity. After SGID, the flavan-3-ols disappeared but some phenolic acids increased respect to F. Fermentation in condition 2 was considered the most appropriate to obtain a functional antioxidant ingredient.

Qinghai spruce (Picea crassifolia) is a ecologically important species in the forest ecosystem of the Qilian Mountains region in China. Natural regeneration of this species is critical to maintaining forest ecosystem function. We here analyzed several biological indicators among naturally re-generating Qinghai spruce across several elevations in the Pailugou watershed. Specifically, seedling density, basal diameter (BD), and plant height were measured, as were soil physico-chemical parameters, at 2700 m, 3000 m, and 3300 m above sea level. Differences in the biological indicators and correlations between the indicators and soil parameters were then assessed at each elevation. The results showed that soil stoichiometry was more sensitive to changes in el-evation than seedling indicators were. Furthermore, seedling density was positively correlated with soil pH, whereas BD was positively correlated with the carbon to nitrogen ratio (C/N), the carbon to phosphorus ratio, and soil organic carbon (SOC) contents. None of the analyzed soil parameters had a significant impact on elevation-specific differences in seedling density. However, soil pH, SOC, and C/N significantly affected variations in seedling diameter at each elevation. Finally, soil pH, SOC, C/N, and the carbon to phosphorus ratio significantly affected variations in seed-ling height at each elevation. This study provides a strong theoretical basis for further under-standing of the mechanisms associated with Qinghai spruce regeneration, ulti-mately contrib-uting to rational protection and management strategies for this important natural resource.

Tigrai region is one of the potential beekeeping areas in Ethiopia, which is endowed with diversified honeybee plant species. However, its honeybee plants are deforested and degraded due to natural and anthropogenic pressures. Area exclosures (AEs) has been practicing for the last three decades restore degraded habitats of the plant species in Ethiopia. Empirical data on the effectiveness of AE is still limited for restoration of honey bee plant species; this study was conducted to assess the roles of AE for species composition, density, and diversity and regeneration status of honeybee floras. Sixty quadrats were installed, 36 in AE and 24 in CGL (communal grazing lands) using a systematic sampling method. The study showed that a total of 42 honey bee plant species belonging to 23 families in AE and 33 species belonging to 17 families in CGL were recorded. The study also indicated a significant increase in the abundance, diversity, regeneration and the structure of the honey bee plant species was found in the AEs. The list presented herein provides a guide to stakeholders in establishing of AE for the foraging resource of honeybee in order to enhance the abundance, diversity and regeneration and structure. Thus, AE has made a significant contribution to solving the shortage of honeybee forages in the beekeeping industry. Therefore, the development of beekeeping is integrated with conservation of natural resources.

Antiviral drugs cannot treat dengue fever, a serious medical disease spread by the Aedes aegypti and Aedes albopictus mosquitoes. One important arbovirus disease is dengue fever. Safe, affordable and effective drugs that inhibit dengue virus (DENV) are needed to control dengue worldwide. Medicinal plants have long been considered an important alternative in the treatment of various diseases, including dengue fever. The aim of this study was to use databases PubMed, Google Scholar and Research Gate to identify plants of different tribes from different places that can be used for the treatment of DENV infection and open supportive care for dengue. Previous studies have shown antiviral activity of medicinal plants against DENV. The aim of the present study is to discover plants from different tribes from different places that can be used to treat DENV infection and provide open supportive care against dengue. The recommended reporting guidelines for systematic reviews and meta-analyses (PRISMA statement) were followed in the search and selection of articles using the PubMed database, Google Scholar and Research Gate. The final selection was made after careful consideration and up to 85 publications that met the inclusion criteria. It is said that different plants from different tribes and geographical locations can be used to treat DENV infection.

The critical enzyme Dihydrofolate Reductase-Thymidylate Synthase in Leishmania major (LmDHFR-TS) serves a dual-purpose role and is essential for DNA synthesis, a cornerstone of the parasite's reproductive processes. Consequently, the development of inhibitors against LmDHFR-TS is crucial for the creation of novel anti-Leishmania chemotherapies. In this study, we employed an in-house database containing 314 secondary metabolites derived from cinnamic acid that occurred in the Asteraceae family. We conducted a combined ligand/structure-based virtual screening to identify potential inhibitors against LmDHFR-TS. By consensus analysis of both approaches, we identified three compounds, i.e., lithospermic acid (237), diarctigenin (306), and isolappaol A (308), that exhibited a high probability of being inhibitors according to both approaches and were consequently classified as promising hits. Subsequently, we expanded the binding mode examination of these compounds within the active site of the test enzyme through molecular dynamics simulations, revealing a high degree of structural stability and minimal fluctuations in its tertiary structure. The in-silico predictions were then validated through in-vitro assays to examine the inhibitory capacity of the top-ranked naturally-occurring compounds against LmDHFR-TS recombinant protein. The test compounds effectively inhibited the enzyme with IC50 values ranging from 6.1 to 10.1 μM. In contrast, other common cinnamic acid derivatives (i.e., flavonoid glycosides) from the Asteraceae family, such as hesperidin, isovitexin 4′-O-glucoside, and rutin, exhibited low activity against this target. The selective index (SI) for all tested compounds was determined using HsDHFR, with moderate inhibitory effect. Among these hits, lignans 306 and 308 demonstrated the highest selectivity, displaying superior SI values compared to methotrexate, the reference inhibitor of DHFR-TS. Therefore, continued research into the anti-leishmanial potential of these C6C3-hybrid butyrolactone lignans may offer a brighter outlook for combating this neglected tropical disease.

Seaweeds have been the target of much attention due to the known bioactivities of some of their compounds. Although many studies about the application of seaweeds in various areas are available, these data are mostly about the raw extracts and not about specific compounds, such as seaweeds pigments. Moreover, the fungal infections of the skin, nails and hair caused by dermatophytes, are the most common fungal infections in humans. These pathologies require long periods of topical and/or systemic treatment that are associated with adverse effects and contraindications. This study had two objectives, the first was to evaluate the possibility of isolating and characterizing the pigments of Calliblepharis jubata and Fucus vesiculosus; the second was to assess their antifungal activity. The extraction of pigments was performed using a method of extraction by exhaustion; the purification was carried out by column chromatography. Three techniques were used to characterize the pigments, thin layer chromatography (TLC), UV-visible spectrophotometry and Fourier-transform infrared spectroscopy (FTIR). The antifungal activity was evaluated using the micro-dilution method, following the EUCAST international standards, on three dermatophytes, Trichophyton rubrum, Trichophyton mentagrophytes and Microsporum canis. It was possible to observe that the extract obtained from the seaweed C. jubata and F. vesiculosus, corresponding to the purified pigment phycobiliprotein, and the crude extract, an enriched extract, obtained from the seaweed C. jubata showed antifungal activity against the three fungal agents of human skin infection.

The spatial and temporal changes in social vulnerability to natural hazards in Mexico are analyzed. To this end, using census data from 2000, 2010, and 2020 and a statistical method, different indices were computed, and with a GIS-based approach, patterns of social vulnerability are examined. In addition, a risk assessment test for severe weather (thunderstorms, hailstorms, and tornadoes) is made out. The results show different common social vulnerability driving factors in the three analyzed years, with root causes that have not been addressed since the beginning of the century. Likewise, a wider gap between Mexico's most and least vulnerable populations is identified. The changes in spatial patterns respond to different historical situations, such as migration, urbanization, and increased population. Also, poverty, ethnicity, and marginalization factors located in very particular regions in Mexico have remained relatively the same in the last few years. These situations have strongly influenced the spatial-temporal distribution of vulnerability in the country. The role of social vulnerability in the disaster risk to extreme events such as thunderstorms, hailstorms, and tornadoes in Mexico is fundamental to understanding changes in disaster distribution at the national level, and it is the first step to generating improvements in integrated risk management.

We have particularly investigated the correlation law of the effect of different carbon black fillings on the hyper-elastic mechanical behavior of natural rubber by conducting uniaxial tensile tests over a wide range of deformations with different volume fractions of carbon black fillings (0%, 4.7%, 8.9%, 12.8%, 16.4%, 19.7%, 22.7%, 25.2%). The results show that the stress-strain curve for carbon black filled rubber increases with the amount of filling, meaning that the rubber gradually becomes "harder". We explore the correlation between the carbon black filling volume and the parameters of the Yeoh constitutive model by examining the Yeoh constitutive model to characterize the hyper-elastic mechanical behavior of rubber with different carbon black fillings. A quantitative relationship between the material parameters and the carbon black filling volume in the Yeoh constitutive model is presented. A method for calculating the material parameters of the Yeoh constitutive model is developed and it predicts the correlation between the hyper-elastic properties of rubber and the volume fraction of carbon black filling.

Multiple myeloma (MM) has witnessed improved patient outcomes through advancements in therapeutic approaches. Notably, allogeneic stem cell transplantation, proteasome inhibitors, immunomodulatory drugs, and monoclonal antibodies have contributed to enhanced quality of life. Recently, a promising avenue has emerged with chimeric antigen receptor (CAR) T cells targeting B-cell maturation antigen (BCMA), expressed widely on MM cells. To mitigate risks associated with allogenic T cells, we investigated the potential of BCMA CAR expression in natural killer cells (NKs), known for potent cytotoxicity and minimal side effects. Using the NK-92 cell line, we co-expressed BCMA CAR and soluble tumor necrosis factor-related apoptosis-inducing ligand (sTRAIL) employing the PiggyBac transposon system. Engineered NK cells (CAR-NK-92-TRAIL) demonstrated robust cytotoxicity against a panel of MM cell lines and primary patient samples, outperforming unmodified NK-92 cells with a mean difference in viability of 45,1% (± 26,1%, depending on the target cell line). Combination therapy was explored with the proteasome inhibitor bortezomib (BZ) and γ-secretase inhibitors (GSI), leading to a significant synergistic effect in combination with CAR-NK-92-TRAIL cells. This synergy was evident in cytotoxicity assays where a notable decrease in MM cell viability was observed in combinatorial therapy compared to single treatment. In summary, our study demonstrate the therapeutic potential of the CAR-NK-92-TRAIL cells for the treatment of MM. The synergistic impact of combining these engineered NK cells with BZ and GSI supports further development of allogeneic CAR-based products for effective MM therapy.

Abstract: Fertility tracking of menstrual cycles during perimenopause with a quantitative hormone monitor is a novel undertaking. Women in regular menstrual cycles have been tracking their fertility using different biomarkers since the 1960’s [1]. Presently, there are newer electronic hormonal devices used to track fertility that provide more exact and objective data to help delineate the fertile time frame of a woman’s cycle. These devices measure quantitative levels of estrogen, luteinizing hormone, progesterone, and follicle stimulating hormone, all of which occur at varying levels during the menstrual cycle. As women advance toward menopause, their cycles vary in length and their hormones fluctuate [2]. In this retrospective analysis, forty-two women aged 40 to 50 tracked their cycles over time and eight of the forty-two women used the quantitative hormonal device. With the use of this device, the perimenopausal period has revealed distinct hormonal cycle characteristics unique to this group of women. It is the purpose of this paper to discuss those cycle characteristics during perimenopause that were found with the use of the quantitative hormonal device.

Medicinal plants are being tested as options to prevent infectious diseases in many countries. Baccharis crispa Spreng. (Asteraceae), is a native medicinal species widely used throughout South America. Our objective was to evaluate the antiviral activity of four wild populations of B. crispa from the province of Córdoba (Argentina) and determine if there is variability between them. The cytotoxicity of the extracts (chlorofom, ethanol and aquose) was evaluated by the neutral red uptake method. Using the plaque-forming unit reduction assay, virucidal and antiviral activity against Herpes Simplex type 1 and Chikungunya viruses were tested. Phytochemical screening of each extract was also performed. Puesto Pedernera (PP) population showed the best antiviral and virucidal effect, standing out the ethanolic extract as the least toxic and with the best inhibitory activity against both viral models. Villa del Parque (VP) population was inactive, regardless of the extract and the virus tested. The results revealed that extracts bioactivities vary significantly depending on the geographic area of collection, with PP and VP populations showing the most significant variability, not only in activity but also in chemical composition. These findings support the potential use of extracts as a source of therapeutic agents against different viruses.

Introduction: The need for therapeutic regimens for non-critically ill patients during the COVID-19 pandemic remains unmet. Previous work has shown that a combination of three aromatic plants’ essential oil (CAPeo) (Thymbra capitata (L.) Cav., Origanum dictamnus L., Salvia fruticosa Mill.) has remarkable in vitro antiviral activity. Given its properties, it was urgent to explore its potential in treating mild COVID-19 patients in primary care. Methods: 69 adult patients were included in a clinical, Proof-of-Concept (PoC) intervention study. Family physicians implemented the observational study in two arms (intervention and control group) during three study periods (IG2020, n=13, IG2021/22, n=25 and CG2021/22, n=31). The SARS-CoV-2 infection was confirmed by real-time PCR. The CAPeo mixture, was administered daily for 14 days, per os in the intervention group, while the control group received usual care. Results: The PoC study, found that the number and frequency of general symptoms, including general fatigue, weakness, fever and myalgia, decreased following CAPeo administration. The average presence (number) of symptoms decreased in IG (4.7 to 1.4) as well as in CG (4.0 to 3.1) by Day 7 compared to Day 1, representing a significant decrease in cumulative presence in IC (-3.3 vs. -0.9, p<0.001; η2=0.20) on Day 7 and on Day 14 (-4.2 vs. -2.9, p=0.027; η2=0.08). Discussion/Conclusion: Our findings suggest that CAPeo, possesses potent antiviral activity, in addition tο the Influenza A and B and the human rhinovirus HRV14 strains against SARS-CoV-2. The early and effective impact in alleviating key symptoms of COVID-19 may suggest this mixture can act as a complementary natural agent for mild COVID-19 patients

To address the challenges of climate change and food security, the establishment of smart farm complexes is necessary. While there have been numerous studies on the productivity and environmental control of individual greenhouses, research on greenhouse complexes is considerably limited. Conducting environmental studies during the design phase of these complexes poses financial constraints and practical limitations in terms of on-site experiments. To identify potential issues that may arise when developing large-scale greenhouse complexes, it is possible to utilize modeling techniques using Computational Fluid Dynamics (CFD) to assess environmental concerns and location issues before constructing the facilities. Consequently, simulating large-scale CFD models that incorporate multiple greenhouses and atmospheric conditions simultaneously presents significant numerical challenges. The objective of this study was to develop a guideline for verifying CFD models for a large-scale Venlo greenhouse, where acquiring field data before construction is not feasible for designing a greenhouse complex. The verification processes of the CFD models were conducted using 2D and 3D iterative simulations of a 2-hectare greenhouse model, using the improved Grid Independence Test (GIT) and wall Y+ approaches. Subsequently, the aerodynamic characteristics were analyzed in a 3D greenhouse model to access its performance when the wind direction was 90° in summer season. The findings revealed that a grid resolution of 0.8 meters and a first layer height of 0.04 meters were suitable for developing large-scale greenhouse models, resulting in a low Root Mean Square Error (RMSE) of 3.9% and a high coefficient of determination (R2) of 0.968. This process led to a significant reduction of 38% in the number of grid cells. These results will serve as design standards for large-scale greenhouses.

News on the stock market contains positive or negative sentiments depending on whether the information provided is favorable or unfavorable to the stock market. This study aims to discover the news sentiment and classify news according to its sentiments with the application of PhoBERT, a Natural Language Processing model designed for the Vietnamese language. A collection of nearly 40,000 articles on financial and economic websites is used to train the model. After training, the model succeeds in assigning news to different classes of sentiments with an accuracy level of over 81%. The research also aims to investigate how investors concern about the daily news by testing the movements of the market before and after the news is released. The analysis results show that there is an insignificant difference in the stock price as a response to the news. However, investors tend to overreact to negative and positive news in the Vietnam stock market.

Radon (²²²Rn) is the second most common type of lung cancer after smoking. As radon poses a significant risk to human health, radon-affected areas should be to ensure people’s awareness of risk and remediation. The primary goal of this research was to investigate the local natural radioactivity (in soils, water and indoors) because of the presence of tuff outcrops (from middle lower Pleistocene volcanic activity) that naturally produce radioactive gas radon at Cerveteri (Rome, Central Italy). The study area is characterized by the presence of several funerary sites pertaining to the Etruscan Necropolis of Cerveteri, which extends over a volcanic rock plateau. The results of the radon survey highlighted moderate (>16000 Bq/m³) but localized anomalies in soils and medium low (< 200 Bq/m³) values of indoor radon excepting some cases exceeding the threshold (> 300 Bq/m³) recommended by 2013/59 Euratom Directive. Although for thoron (²²⁰Rn) effects on human health, there exist no clinical data, the study of ²²⁰Rn average activity concentration in the soil gas survey has revealed new insights for the interpretation of radon sources that can affect dwellings, even taking into account the considerable difference in the half-lives of the ²²²Rn and ²²⁰Rn.

Extracts of Cordyceps militaris which possess the bioactive capacity such as antimicrobial activity and DPPH-free radical scavenging capacity, are applied to the serum product. This finished serum product has been evaluated the in vitro bioactivity and the stability with expect to find out a good extract with a suitable percentage used for serum product as natural materials on preservatives and antioxidation.

Margarines are W/O emulsions with minimal fat and are heat treated when used for baking. Lipid oxi-dation has been identified as the major deterioration process of margarines and addition of antioxidants is necessary. Consumers are increasingly demanding that chemical antioxidants in foods be replaced by natural preservatives. A multivariate statistical study gave us oxidative stability depending on the com-position of 20 different commercial margarines and an analysis of the principal components allowed us to classify and select eight of them. This study analyzes the antioxidant capacity of six different natural ground herbs (Curcuma longa, Illicium verum, Rosmarinus officinalis, Taraxacum officinale, Thymus piperella and Thymus vulgaris) on the oxidative stability of eight selected margarines. In order to evalu-ate the oxidative stability, we determine the induction period (IP) with Rancimat assays at 120 ºC with 1.5 hours of suppression. As results, Rosmarinus officinalis increases the IP value by 73.17% with respect to its basal values, Curcuma longa, Thymus vulgaris and Thymus piperella increases it between 17% and 26% and Taraxacum officinale together with Illicium verum reduces it between 19% and 34% respec-tively. A multiple linear regression model approach shows that IP has a positive correlation with SFA, PUFA,  acids, MUFA/PUFA ratio and antioxidant activity.

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.

This article presents a state-of-the-art system to extract and synthesize causal statements from company reports into a directed causal graph. The extracted information is organized by the relevance to different stakeholder groups’ benefits (customers, employees, investors, and the community/environment). The presented method of synthesizing extracted data into a knowledge graph comprises a framework that can be used for similar tasks in other domains, e.g. medical information. The current work addresses the problem of finding, organizing, and synthesizing a view of the cause-and-effect relationships based textual data, in order to inform and even prescribe the best actions that may affect target business outcomes related to different stakeholders’ benefits (customers, employees, investors, and the community/environment).

Insect outbreaks are major drivers of natural disturbances in forest ecosystems. Outbreaks can have both direct and indirect effects on the composition of soil arthropod communities, through canopy opening, nutrient addition and predator-prey interactions. In this study, we aim to understand the effects of forest tent caterpillar (Malacosoma disstria; FTC) outbreaks on ant communities in both temperate and boreal forests in Canada. Using pitfall traps and Berlese funnels, we compared the ant communities as well as the surrounding arthropod communities between control and outbreak sites in boreal andboreal and temperate forests (in Québec, Canada). Using the Sørensen dissimilarity index, we determined the alpha and beta diversity of the ant community. Other arthropods collected in the traps were counted to evaluate the richness and abundance of potential prey for the ants and other potential predators of the FTC. We used an indicator species analysis to examine the species associated with sites defoliated by the outbreak. In the boreal forest, we found that FTC outbreaks caused decreases in species richness and increases in the evenness of ant communities in defoliated sites. In the boreal forest sites, species composition varied significantly between control and outbreak sites. This pattern was driven by the presence of other predators. We also saw no changes in beta diversity in the boreal forest but did see a significant decrease in the temperate forest between the outbreak sites and the control sites. A similar, but weaker pattern was observed in the temperate forest. Ant species in the boreal forest tended to exhibit a more marked preference for either control or previously defoliated sites than species in the temperate forest. Our study showed that disturbances like insect outbreaks can drive changes in the ant community. While we saw small effects of outbreaks, manipulation experiments using resource addition could help us validate the mechanisms behind these relationships.

Windthrows in the forestry fund, which have become more frequent due to the increase in extreme weather events, have had and continue to have negative economic and ecological effects, making them a pressing issue in forestry research. Their urgency has been amplified in light of the need to develop sustainable forest management systems. The main objectives of this study are to evaluate the effects of windthrows on some microbiological properties of forest soils and to monitor the evolution of the degraded tree regeneration, 4 years after the event, for three tree species: Norway spruce (Picea abies L.), sessile oak (Quercus petraea), and European beech (Fagus sylvatica L.). The experimental plot used is arranged in dispersed blocks and subdivided plots, with three repetitions, bifactorial, factor A representing the tree species, and factor B the windthrows: in two situations: affected (AW) and not affected by felling (WW). Three representative soil profiles were studied for each tree species, including dehydrogenase activity (DA) and the number of fungi (NF) in the organic horizon at the soil surface. Dehydrogenase activity was determined by the extraction of triphenylformazan, and NF was evaluated using the Plate Count Method (Petri plates) and the Sabouraud Agar culture medium. The values of Current Dehydrogenase Activity (CDA) and Potential Dehydrogenase Activity (PDA) did not show significant statistical differences in relation to the windthrow factor, but were distinctly significant (p &lt; 0.01) for the tree species factor. On the other hand, NF showed statistical significance for both tree species and windthrows factors, at a level of p &lt; 0.01. Correlations were highlighted between the differences in AW and WW of CDA, PDA, and NF with the number of naturally regenerated seedlings (NRS), and the type of soil. The regressions established between NRS and the differences in CDA and PDA, the dependence of the differences in PDA on the differences in NF, and the regression between the differences in NF and the type of soil demonstrate the importance of soil characteristics in the natural regeneration process of the main tree species - Norway spruce, sessile oak, and European beech. Stimulating NRS in AW forests and increasing the volume of terrestrial organic carbon (TOC) biomass is directly dependent on soil fertility, primarily determined by soil organic carbon (SOC), which accumulates in the soil as a result of organic matter, deposited on the surface. Sustainable forest management of AW plots should stimulate the accumulation of SOC, including the partial or total preservation of dead trees, provided that the attack of specific diseases and pests is avoided.

Rice bran arabinoxylan compound (RBAC) is a polysaccharide modified by Lentinus edodes mycelial enzyme widely used as a nutraceutical. To explore translational research on RBAC, a scoping review was conducted to synthesise research evidence from English, Japanese, Korean, and Chinese sources while combining bibliometrics and network analyses for data visualisation. Ninety-eight articles on RBAC and the biological activities related to human health or disease were included. Research progressed with linear growth (median=3/year) from 1998 to 2022, predominantly on Biobran MGN-3 (86.73%) and contributed by 289 authors from 100 institutions across 18 countries. Clinical studies constitute 61.1% of recent articles (2018 to 2022). A shifting focus from immuno-cellular activities to human translations over the years was shown via keyword visualisation. Beneficial effects of RBAC include immunomodulation, synergistic anticancer properties, hepatoprotection, antiinflammation, and antioxidation. Cancer patients reported reduced side effects from chemoradiotherapy and improved quality of life in human studies, indicating RBAC’s impact on the psycho-neuro-immune axis. RBAC has been studied in 17 conditions, including cancer, liver diseases, HIV, allergy, chronic fatigue, gastroenteritis, cold/flu, diabetes, and in healthy participants. Further translational research on the impact on patient and community health is required for the evidence-informed use of RBAC in health and disease.

A frequent problem in Geotechnics is the soils with inadequate physical-mechanical properties before the efforts to which they will be subjected by the constructions, incurring cost overruns caused by their engineering improvement. The need to improve the engineering properties of soils is not recent, currently observing that the most common alternatives are binders such as cement and lime. The climate change observed in recent decades and the uncontrolled emission of greenhouse gases have motivated Geotechnical and Geoenvironmental researchers to seek mechanisms for soil reinforcement from a more sustainable and environmentally friendly approach by proposing the use of recycled and waste materials. An alternative is natural fibers, which can be obtained as waste from many agro-industrial processes, implying their high availability and low cost. Sawdust as a by-product of wood processing has a rough texture that can generate high friction between the fiber and the matrix of the soils, leading to a significant increase in its shearing strength and bearing capacity. This concept of improving the properties of soils using natural fibers distributed randomly is inspired by the natural phenomenon of grass and/or plants that when growing on a slope can effectively stabilize a said slope.

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.

Cancer is one of the leading causes of death, and latest predictions indicate that cancer- related deaths will increase over the next few decades. Despite significant advances in conventional therapies, treatments remain far from ideal due to limitations such as lack of selectivity, non-specific distribution, and multidrug resistance. Current research is focusing on the development of several strategies to improve the efficiency of chemotherapeutic agents and, as a result, overcome the conventional therapies challenges. In this regard, combined therapy with natural compounds and other therapeutic agents, such as chemotherapeutics or nucleic acids, has recently emerged as a new strategy for tackling the drawbacks of conventional therapies. Taking this strategy into consideration, the co-delivery of the above-mentioned agents in lipid-based nanocarriers provide some advantages by improving the potential of the therapeutic agents carried. This review encompassed different strategies associated with combined therapy, at the same time highlighting the most recent advances and outstanding advantages of lipid-based nanocarriers for achieving a promising combination therapy that may overcome cancer treatment challenges.

Vagueira Beach on the Central Portuguese coast, is known as one of the places in Europe most affected by coastal erosion. The area has suffered more than 156 meters of coastline retreat over the period 1958 to 2001. With the aim to evaluate the influence of local factors on coastal erosion, this paper assesses the anthropogenic and natural factors that are related with the retreat of the coastline, by adopting statistical correlation and regression analyses. Through the Pearson's correlation coefficient (r) it was observed that local factors such as annual dredging at the Aveiro Port entrance (r = 0.93), the total length of groins in the Espinho-Vagueira section (r = 0.89), and storm events (r = 0.52) are directly related to coastline retreat in the area. A multiple linear regression model was developed in which coastline retreat is explained by these same factors over the period 1980 to 2006. With a coefficient of determination of R² = 0.91, it was observed that length of groins (significant at the 1% level), dredging of the port entrance (significant at the 5% level) and precipitation (as a proxy for storm events; significant at the 10% level) are significantly correlated with coastline retreat. Hence, it is shown that anthropogenic factors are the main drivers of coastline retreat in Vagueira Beach. This study provides an innovative approach for the assessment of coastal erosion, resulting in important information that can be used for decision-making related to coastal zone management as it allows to understand in greater detail the main drivers of coastal erosion.

There is a strong need and demand from the United Nations, public institutions, and private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by the community volunteers. Results demonstrate that especially RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

The experimental verification for the computational method sometimes varies due to numerous factors such as the manufacturing process and the materials' property change due to environmental aspects. In this work, we performed verification of experimental and computational evaluation of a hybrid composite moderate thick plate. The experiment was performed with simplistic approaches and without the advanced tools of preparing composite materials. This is due to the fact that most of the students in many developing countries around the world cannot have access to such equipment. As such, in this research, we are presenting cheap and easy preparation methods, with some details, for even equipment calibration and some tricks to attain a reliable composite structure for educational purposes. Moreover, the software and solvers used in this study are freely provided by the supplier for educational purposes. This study examined two methods for producing carbon and glass/polyester composite plates and discussed which one was best based on mechanical properties for different volume fractions, random stacking sequences, and ply angles (using OCTAVE's random estimation program). It also determined the three natural frequencies experimentally and with the aid of ANSYS. Less than 6% separated the experimentally determined natural frequencies from the calculated results.

Version Control and Source Code Management Systems, such as GitHub, contain large amount ofunstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrieve information from very large collection of unstructured data. In this study, we have extended our previous study by increasing our datasets and ML and clustering techniques. Method: We have followed a complex methodology made up of various steps. Starting from the raw commit messages we have employed NLP techniques to build a structured database. We have extracted their main features and used as input of different clustering algorithms. Once labelled each entry, we have applied supervised machine learning techniques to build a prediction and classification model. Results: We have developed a machine learning-based model to automatically classify commit messages of a software project. Our model exploits a ground-truth dataset which includes commit messages obtained from various GitHub projects belonging to the HEP context. Conclusions: The contribution of this paper is two-fold: it proposes a ground-truth database; it provides a machine learning prediction model. They automatically identify the more change-proneness areas of code. Our model has obtained a very high average precision, recall and F1-score.

Tree pruning is a management tool in agroforestry systems for reducing shade, enhancing nutrient cycling or providing fodder. However, little information is available on the effect of pruning management on plant growth, non-structural carbohydrate (NSC) levels in the roots and N2 fixation of Sesbania sesban. A glasshouse experiment was conducted to assess the effect of pruning frequency on biomass production, NSC levels and N2 fixation of Sesbania sesban. Treatments included pruning at (i) 3 months only, (ii) 3 and 6 months, and (iii) 3, 6 and 9 months, with each pruning removing shoot biomass above 50% of the initial height. Increased pruning frequency decreased above- and belowground dry matter (DM), and root NSC levels. The decrease in aboveground DM correlated with reduced levels of starch, sugar and total non-structural carbohydrates. Pruning more frequently significantly decreased nodulation, the percentage N derived from the atmosphere and N2 fixed. It could be concluded that more frequent prunings decreases biomass and NSC levels which could reduce the regeneration capacity of trees since they rely on NSCs to regrow. The suppressive effects of increased pruning frequency on N2 fixation could decrease the desirable benefits of legume trees on soil N fertility improvement.

Recent maritime legislations demand the transformation of the sector to greener and more energy efficient transportation. Liquified Natural Gas (LNG) seems a promising alternative fuel solution that could replace the conventional fuel sources. Various studies have been focused on the prediction of LNG price, however, no previous work has been made on the forecast of spot charter rate of LNG carrier ships. An important knowledge for the maritime industries and companies when it comes to decision-making. Therefore, this study is focused on the development of a machine learning pipeline to address the aforementioned problem by: (i) forming a dataset with variables relevant to LNG; (ii) identifying the variables that impact on the freight price of LNG carrier; (iii) developing and evaluating regression models for short and mid-term forecast. The results showed that the General Regression Neural Network presented a stable overall performance for 2, 4 and 6 months forecast.

Objective: Food security during public health emergencies relies on situational awareness of needs and resources. Artificial intelligence (AI) has revolutionized situational awareness during crises, allowing the allocation of resources to needs through machine learning algorithms. Limited research exists monitoring Twitter for changes in the food security-related public discourse during the COVID-19 pandemic. We aim to address that gap with AI by classifying food security topics on Twitter and showing topic frequency per day. Methods: Tweets were scraped from Twitter from January 2020 through December 2021 using food security keywords. Latent Dirichlet Allocation (LDA) topic modeling was performed, followed by time-series analyses on topic frequency per day.Results: 237,107 tweets were scraped and classified into topics, including food needs and resources, emergency preparedness and response, and mental/physical health. After the WHO’s pandemic declaration, there were relative increases in topic density per day regarding food pantries, food banks, economic and food security crises, essential services, and emergency preparedness advice. Threats to food security in Tigray emerged in 2021.Conclusions: AI is a powerful yet underused tool to monitor food insecurity on social media. Machine learning tools to improve emergency response should be prioritized, along with measurement of impact. Further food insecurity word patterns testing, as generated by this research, with supervised machine learning models can accelerate the uptake of these tools by policymakers and aid organizations.

Natural convection is intensively explored, especially in a valley-shaped trapezoidal enclosure, because of its broad presence in both technical settings and nature. This study deals with a trapezoidal cavity, which is initially filled with linearly stratified air. Though the side walls remain adiabatic, the bottom wall is heated, and the top wall is cooled. For the stratified fluid (air), the temperature of the fluid adjacent to the top and the bottom walls is the same as that of the walls. Natural convection in the trapezoidal cavity is simulated in two dimensions using numerical simulations, by varying Rayleigh numbers (Ra) from 100 to 108 with constant Prandtl number, Pr = 0.71, and aspect ratio, A = 0.5. According to numerical results, the development of transient flow within the enclosure owing to the predefined conditions for boundary may be categorized into three distinct stages: early, transitional, and steady or unsteady. The flow characteristics at each of the three phases and the impact of the Rayleigh number on the flow’s growth are stated in this study. In addition, heat transfer through the bottom and the top surfaces is described in this study.

Fungal infections impact the lives of at least 12 million people every year, killing over 1.5 million. Wide-spread use of fungicides and prophylactic antifungal therapy have driven resistance in many serious fungal pathogens, and there is an urgent need to expand the current antifungal ar-senal. Recent research has focused on improving azoles, our most successful class of antifungals, by looking for synergistic interactions with secondary compounds. Synergists can co-operate with azoles by targeting steps in related pathways, or they may act on mechanisms related to re-sistance like active efflux or on totally disparate pathways or processes. A variety of sources of potential synergists have been explored, including pre-existing antimicrobials, pharmaceuticals approved for other uses, bioactive natural compounds and phytochemicals, and novel synthetic compounds. Synergy can successfully widen the antifungal spectrum, decrease inhibitory dosag-es, reduce toxicity, and prevent the development of resistance. This review highlights the diversity of mechanisms that have been exploited for the purposes of azole synergy and demonstrates that synergy remains a promising approach for meeting the urgent need for novel antifungal strate-gies.

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.

This work focuses on analyzing acoustic emission (AE) signals as a means to predict failure in structures. Two main approaches are considered: (i) long-range correlation analysis using both the Hurst (H) and the Detrended Fluctuation Analysis (DFA) exponents, and (ii) natural time domain (NT) analysis. These methodologies are applied to the data collected from two application examples: a glass fiber reinforced polymeric plate and a spaghetti bridge model, where both structures were subjected to increasing loads until collapse. A traditional (AE) signal analysis is also performed to reference the study of the other methods. Results indicate that the proposed methods yield a reliable indication of failure in the studied structures.

At the end of 2018, a high school student asked a question in Zhihu community, claiming that he had proved Goldbach's conjecture. The problem caused an explosive reaction and a large number of users participated in the discussion. And has caused the widespread influence. On January 1, 2019, the questioner issued his "proof". His proof was soon proved wrong. The heated discussion caused by the incident contains a lot of information of social science analysis value. Therefore, we follow up the event in the first time and build a time series dataset for the event. Taking the questioner's "proof" as the dividing line, all the answers, comments, sub comments and user information of writing these texts before and after two days were recorded. This series of temporal information can reflect the dynamic features of the interaction between user opinions, and the impact of exogenous shocks (proof release) on community opinions. The dataset can be used not only for the demonstration of various social network analysis algorithms, but also for a series of natural language processing tasks such as fine-grained sentiment analysis for long texts, as well as multimodal tasks combining natural language processing and social network analysis. This paper introduces the characteristics and structure of the dataset, shows the visualization effect of social network, and uses the dataset train the benchmark model of emotion analysis.

The ketogenic diet and walking exercise training activity are two key public health lifestyle factors. The potential of combined lifestyle factors interventions focused on getting to compliance in forced exercise. A balanced ketogenic diet and regular exercise activity is a key modifiable factor to the prevention and management of chronic diseases. Influence health across the lifespan and reduction of the risk of premature death through several biological mechanisms. Community older group’s lifestyle factors interventions contribute identity in their natural living environment. While the older health benefits of walking exercise training strategies are commonly to study, combined ketogenic diet and walking exercise interventions have induced greater benefits in community older groups.

Men are more likely to develop cancer than women. In fact, male predominance is one of the most consistent cancer epidemiology findings. Additionally, men have a poorer prognosis and an increased risk of secondary malignancies compared to women. These differences have been investigated in order to better understand cancer and to better treat both men and women. In this review, we discuss factors that may cause this gender difference, focusing on urothelial bladder cancer (UBC) pathogenesis. We consider physiological factors that may cause higher male cancer rates, including differences in X chromosome gene expression. We discuss how androgens may promote bladder cancer development directly by stimulating bladder urothelium and indirectly by suppressing immunity. We are particularly interested in natural killer (NK) cells because they are important, but often overlooked anti-cancer lymphocytes.

Most Acinetobacter baumannii strains are naturally competent. Although some information is available about factors that enhance or reduce the frequency of transformation of this bacterium, the regulatory elements and mechanisms are barely understood. In this article, we describe studies on the role of H-NS in the regulation of expression of genes related to natural competency and the ability to uptake foreign DNA. The expression levels of the natural transformation-related genes pilA, pilT, pilQ, comEA, comEC, comF, and drpA were significantly increased in a Δhns derivative of Acinetobacter baumannii A118. Complementation of the mutant with a recombinant plasmid harboring hns restored expression levels of six of these genes (pilT remained expressed at high levels) to those of the wild-type strain. The transformation frequency of the A. baumannii A118 Δhns strain was significantly higher than that of the wild-type. Similar, albeit not identical, effects occurred when hns was deleted from the hypervirulent A. baumannii AB5075 strain. Reduction of gene expression in a few cases was not as pronounced as to reach wild-type levels, and expression of comEA was enhanced further. In conclusion, the expression of all seven transformation-related genes was enhanced after deleting hns in A. baumannii A118 and AB5075, and these modifications are accompanied by an increase in the cells’ transformability. The results demonstrate a role of H-NS in A. baumannii’s natural competence.

Gastric cancer, also known as stomach cancer, is a cancer which develops from the lining of the stomach. Accumulated evidences and epidemiological studies have been indicated that natural products play an important role in gastric cancer prevention and treatment, although its mechanism of action did not elucidate yet. Particularly, experimental studies have been showed that natural products displayed a protective effect against gastric cancer via numerous molecular mechanisms such as suppression of cell metastasis, anti-angiogenesis, inhibition of cell proliferation, induction of apoptosis, and modulation of autophagy. Although chemotherapy remains the standard treatment for advanced gastric cancer along with surgery, radiation therapy, hormone therapy and immunotherapy, but its adverse side effects including neutropenia, stomatitis, mucositis, diarrhea, nausea, and emesis are well documented. Additionally, intake of naturally occurring phytochemicals could increase the efficacy of gastric chemotherapy and chemotherapeutics resistance. However, natural product structural stability and powerful bioactivity are important to develop novel treatments for gastric cancer that may minimize such adverse effects. Therefore, the purpose of this review is to summarize the potential therapeutic effects of natural products on prevention and treatment of gastric cancer with intensive molecular mechanisms of action, bioavailability, and safety efficacy.

The search for new chemical compounds with antitumor pharmacological activity is a necessary process for creating more effective drugs for each specific malignancy type. This review presents the outcomes of screening studies of natural compounds with high anti-glioma activity. Despite significant advances in cancer therapy, there are still some tumors currently considered completely incurable including brain gliomas. This review covers the main problems of the glioma chemotherapy including drug resistance, side effects of common anti-glioma drugs, and genetic diversity of brain tumors. The main emphasis is made on the characterization of natural compounds isolated from marine organisms because taxonomic diversity of organisms in seawaters significantly exceeds that of terrestrial species. Thus, we should expect greater chemical diversity of marine compounds and greater likelihood of finding effective molecules with antiglioma activity. The review covers at least 15 classes of organic compounds with their chemical formulas provided as well as semi-inhibitory concentrations, mechanisms of action, and pharmacokinetic profiles. In conclusion, the analysis of the taxonomic diversity of marine species containing bioactives with antiglioma activity is performed noting cytotoxicity indicators and to the tumor cells in comparison with similar indicators of antitumor agents approved for clinical use as antiglioblastoma chemotherapeutics.

Phosphodiesterases (PDEs) hydrolyze cyclic nucleotides to modulate multiple signaling events in cells. PDEs are recognized to actively associate with cyclic nucleotide receptors (Protein Kinases, PK) in larger macromolecular assemblies referred to as signalosomes. Complexation of PDEs with PK generates an expanded active site which enhances PDE activity. This facilitates signalosome-associated PDEs to preferentially catalyze active hydrolysis of cyclic nucleotides bound to PK, and aid in signal termination. PDEs are important drug targets and current strategies for inhibitor discovery are based entirely on targeting conserved PDE catalytic domains. This often results in inhibitors with cross-reactivity amongst closely related PDEs and attendant unwanted side effects. Here, our approach targets PDE-PK complexes as they would occur in signalosomes, thereby offering greater specificity. Our developed fluorescence polarization assay has been adapted to identify inhibitors that block cyclic nucleotide pockets in PDE-PK complexes in one mode, and disrupt protein-protein interactions between PDEs and cyclic nucleotide activating protein kinases in a second mode. We tested this approach with three different systems: cAMP-specific PDE8-PKAR, cGMP-specific PDE5-PKG and dual-specificity RegA-RD complexes and ranked inhibitors according to their inhibition potency. Targeting PDE-PK complexes offers biochemical tools for describing the exquisite specificity of cyclic nucleotide signaling networks in cells.

Climate information can help vulnerable populations anticipate disasters before they happen and enable communities prepare for and cope with them. A complementary log-log regression model based on quantitative study in coastal communities in Tanzania was used to assess the likelihood of the rural poor, rural non poor, urban poor, and urban non poor to receive early warning information about flood, drought and other disasters. Individuals in the urban poor category were 35% less likely to receive early warning information about flood, drought and other disasters whereas urban non-poor residents were 49% more likely to receive same. This disadvantage to the urban poor persists even when adaptive capacity, geographical location and socio-environmental factors are accounted. Nuclear households consisting of a male head and female partner who have children or not had higher likelihood of receiving early warning information on flood, drought and other disasters (OR = 1.47, p < 0.001) compared with their counterparts in female-centred households (no husband or male partner). Residents with tertiary education were more likely (OR = 1.91, p < 0.001) to report that they had received early warning information about flood, drought and other disasters compared with their counterparts without any formal education. This zero-order relationship was completed mediated by geographical location and socio-environmental factors in the multivariate model. The results underline the key role access to climate- and disaster-related information play in enhancing adaptive capacity to reduce vulnerability to natural hazards, as well as the importance of targeting the most vulnerable households in policy interventions to improve resilience in the face of a changing climate.

The incidence of lung cancer has increased in recent years and causes major mortalities across the globe. Besides, the availability of the several chemotherapeutics modalities in the management, there is still a challenge to find out an efficient remedy with lesser or no toxic effects. Hence, there is a necessity to employ complementary research to establish effective management for lung cancer. In this study, we have implemented a novel herbal informatics model to find out the alternative remedy in the treatment of lung cancer. This model utilizes five major steps of the bioprospection process based on the classical surge followed by the binary index and rationale-based selection of herbal products targeting the cancer-causing factors which are explained in detail in the methodology section of this model. This study revealed 07 herbals such as Withania somnifera (Ws), Berberis vulgaris(Bv), Glycyrrhiza glabra(Gg), Andrographis paniculate(Ap), Azadirachta indica(Ai), Cinnamomum Verum(Cv), Piper longum(Pl) based on the fuzzy set optimization scoring(0.6-1) that could be further studied in vitro and in vivo level for utilization in the management of lung cancer.