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

Bread wheat (Triticum aestivum L.) is staple of Pakistani people. However, its yield at farmer field is low as compared with its genetic potential. Integration of various crop and soil management strategies might be an option to enhance wheat productivity at farmer field. This 2-year experiment was conducted to check the influence of combine application of natural plant water extracts and biochar on the productvity of wheat during the winter season of 2015-16. The experiment consisted of seven treatment viz. (1) control (2) application of biochar (0.18 kg pot-1) alone, (3) application of sorghum water extract (SWE) alone, (4) application of moringa water extract (MWE) alone, (5) application of biochar + SWE, (6) application of biochar + MWE, (7) application of biochar + SWE+MWE. The results revealed that application of both crop water extracts in combination with biochar improved the growth and grain yield of wheat. Use of MWE in combination with biochar enhanced the grain weight, grain number and grain yield of wheat by 44, 14, and 24%, respectively than the control treatment. In crux, use of MWE in combination with biochar might be a viable option to improve the productivity of bread wheat.

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

The choice associated with words is a fundamental property of natural languages. It lies at the heart of quantitative linguistics, computational linguistics, and language sciences more generally. Information-theory gives us tools at hand to measure precisely the average amount of choice associated with words—the word entropy. Here we use three parallel corpora—encompassing ca. 450 million words in 1916 texts and 1259 languages—to tackle some of the major conceptual and practical problems of word entropy estimation: dependence on text size, register, style and estimation method, as well as non-independence of words in co-text. We present three main results: 1) a text size of 50K tokens is sufficient for word entropies to stabilize throughout the text, 2) across languages of the world, word entropies display a unimodal distribution that is skewed to the right. This suggests that there is a trade-off between the learnability and expressivity of words across languages of the world. 3) There is a strong linear relationship between unigram entropies and entropy rates, suggesting that they are inherently linked. We discuss the implications of these results for studying the diversity and evolution of languages from an information-theoretic point of view.

Natural gas hydrate, also known as combustible ice and mainly composed of methane, it is identified as the potential clean energy in the 21th century. Due to its large reserves, gas hydrate can ease problems caused by energy resource shortage and has gained attention around the world. In this paper, we focus on the exploration and development of gas hydrate as well as discussing its status and future development trend in China and abroad, then we analyze its opportunities and challenges in China from four aspects: resource, technology, economy and police with five forces model and PEST method. The results show, China has abundance gas hydrate resource; however the backward technologies and inadequate investment has seriously hindered the future development of gas hydrate, so China should establish relevant cooperation framework and intuitional arrangement to attract more investment as well as breaking through technical difficulties to make gas hydrate commercialization as soon as possible.

A Natural gas is increasingly being sought after as a vital source of energy, given that its production is very cheap and does not cause the same environmental harms that other resources, such as coal combustion, do. Understanding and characterizing the behavior of natural gas is essential in hydrocarbon reservoir engineering, natural gas transport, and process. Natural gas compressibility factor, as a critical parameter, defines the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial model based on the group method of data handling (GMDH) is presented to determine the compressibility factor of different natural gases at different conditions, using corresponding state principles. The accuracy of the model evaluated through graphical and statistical analyses. The results show that the model is capable of predicting natural gas compressibility with an average absolute error of only 2.88%, a root means square of 0.03, and a regression coefficient of 0.92. The performance of the developed model compared to widely known, previously published equations of state (EOSs) and correlations, and the precision of the results demonstrates its superiority over all other correlations and EOSs.

In recent years, online social networks have received important consideration in spatial modelling fields given the critical information that can be extracted from them for events in real time; one of the most latent issues is that regarding various natural disasters such as earthquakes. Although it is possible to retrieve data from these social networks with embedded geographic information provided by GPS, in many cases this is not possible. An alternative solution is to reconstruct specific locations using probabilistic language models, more specifically those based on Name Entity Recognition (NER), which extracts names from a user’s description about an event occurring in a specific place (e.g., a collapsed building on a specific avenue). In this work, we present a methodology to use twitter as a social sensor system for disasters. The methodology scores NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space is proposed. The proposed methodology is evaluated with tweets related to the 2017 earthquake in Mexico.

The generalized dynamic model of the rotor system, presented in the paper, is the first model that takes into account the interconnected oscillations of the “rotor - weakly conductive fluid – foundation” system under the action of such parameters as fluid and rotor motion, linear eccentricity, friction forces, foundation vibration and nonlinear characteristics of rolling bearings, as well as the action of a magnetic field on the fluid. Consistent equations of motion for the system “rotor - weakly conductive fluid – foundation” were derived and solved analytically. Forced and natural oscillations of the system were analyzed, and the distinctive features of the rotor system dynamics were revealed. The values of frequencies and amplitudes, which are one of the main factors determining the dynamic behavior of the system, were obtained and studied.

The pharmaceutical industry is one of the largest pollutants and generator of toxic solvent resi-dues for the environment and human beings. In the last decade, there has been a huge evolution in the design and development of new environmentally friendly products and processes, in re-sponse to the global environmental concern and the growing demand for products with quality ingredients and manufacturing processes aimed at achieving sustainability. Consumers are looking for products with the benefits of natural products, with sustainable and environmentally friendly formulations with few synthetic molecules as possible. Thus, based on the origins of the pharmaceutical industry, it is important to return to Nature and enjoy the benefits that natural products bring to us. Deep eutectic solvents appear as an alternative to toxic solvents commonly used to extract bioactive compounds. These solvents are considered green solvents because they are made up of natural compounds such as sugars, amino acids, and carboxylic acids. In addition, they are simple to prepare, with an atomic economy of 100%, with attractive physicochemical properties. This paper is a comprehensive review in which is described the main properties, characteristics, and production methods of deep eutectic solvents as well as its application to extract from natural sources bioactive compounds with pharmaceutical interest.

Remotely sensed data play a crucial role in monitoring the El-Niño/La-Niña Southern Oscillation (ENSO), which is an oceanic-atmospheric phenomenon occurring quasi-periodically with several impacts worldwide, such as specific biological and global climate responses. Since 1980, Earth has witnessed three strong ENSO events (1982–1983, This study aims, 2015-2016). In September 2022, La-Niña entered its third year and was unlikely to continue through 2023. Instead, since 2022 forecasts pointed to a transition from La-Nina to a Neutral phase in summer or late 2023. The onset of the El-Niño occurred around April 2023, and it is anticipated by sophisticated models to be a strong event through the Northern Hemisphere winter (December 2023 - February 2024). The aim of this study is to demonstrate the ability of the combination of two new methods to improve the accuracy of the above claim because El-Niño apart from climate anomalies, significantly impacts Earth’s ecosystems and human societies, regulating the spread of diseases by insects (e.g., malaria and dengue fever), and influencing nutrients, phytoplankton biomass, and primary productivity. This is done by exploring first the previous major El-Niño events in the period January 1876-July 2023. Our calculations show that the ongoing 2023-2024 El-Niño will not be the strongest.

Context: Anomaly detection in a data center is a challenging task, having to consider different services on various resources. Current literature shows the application of artificial intelligence techniques to either log files or monitoring data: the former created by services at run time, while the latter produced by specific sensors directly on the physical or virtual machine. Objectives: We propose a model that exploits information both in log files and monitoring data to identify patterns and detect anomalies over time. Methods: The key idea is to use on one side natural language processing solutions to detect problems at service level, extracting words that represent anomalies. Clustering and topic modeling techniques have been used to identify patterns and group them with respect to topics. On the other side time series anomaly detection technique has been applied to sensors data in order to combine problems found in the log files with problems stored in the monitoring data. Results: We have tested our approach on a real data center equipped with log files and monitoring data that can characterize the behaviour of physical and virtual resources in production. We have observed a correspondence between anomalies in log files and monitoring data, e.g. an increase in memory usage or machine load. The results are extremely promising. Conclusion: Our model requires to integrate site administrators' expertise in order to consider all critical scenario in the data center and understand results properly.

Plants of the genus Monochoria have long been utilized in food, cosmetics, and traditional herbal treatment. Thailand has the highest species diversity of this genus and a new member, Monochoria angustifolia (G. X. Wang) Boonkerd & Tungmunnithum has been recently described. This plant is called “Siam Violet Pearl” as a common name or “ไข่มุกสีม่วงแห่งสยาม” as its vernacular name in the same meaning in Thai language. Despite their importance, few researches on Monochoria species have been conducted. This study, thus, provided the results to fill in this gap by: i) determining flavonoids phytochemical profiles of 25 natural populations of M. angustifolia covering the whole floristic regions in Thailand, and ii) determining antioxidant activity using various antioxidant assays to investigate the probable mechanism. The results revealed that M. angustifolia presented a higher flavonoid content than the outgroup, M. hastata. Our results also revealed that flavonoids might be used to investigate Monochoria evolutionary connections and for botanical authentication. The various antioxidant assays revealed that M. angustifolia extracts preferentially act through a hydrogen atom transfer antioxidant mechanism. Pearson correlation analysis indicated significant correlations emphasizing that the antioxidant capacity is most probably the result of a complex phytochemical combinations rather than of a single molecule. Altogether, these results showed that this new species provide an attractive alternative starting material with phytochemical variety and antioxidant potential for the phytopharmaceutical industry.

Non-melanoma skin cancers (NMSCs) are the leading cause of skin cancer-related morbidity and mortality. Effective strategies are needed to control NMSC occurrence and progression. Non-toxic, plant-derived extracts have been shown to exert multiple anti-cancer effects. Graviola (Annona muricata), a tropical fruit-bearing plant, has been used in traditional medicine against multiple human diseases including cancer. The current study investigated the effects of graviola leaf and stem extract (GLSE) and its solvent-extracted fractions on two human NMSC cell lines, UW-BCC1 and A431. GLSE was found to: i) dose-dependently suppress UW-BCC1 and A431 cell growth, motility, wound closure, and clonogenicity; ii) induce G₀/G₁ cell cycle arrest by downregulating cyclin/cdk factors while upregulating cdk inhibitors, and (iv) induce apoptosis as evidenced by cleavage of caspases-3, -8 and PARP. Further, GLSE suppressed levels of activated hedgehog (Hh) pathway components Smo, Gli 1/2, and Shh while inducing SuFu. GLSE also decreased the expression of pro-apoptotic protein Bax while decreasing the expression of the anti-apoptotic protein Bcl-2. We determined that these activities were concentrated in an acetogenin/alkaloid-rich dichloromethane subfraction of GLSE. Our data identify graviola extracts and their constituents as promising sources for new chemopreventive and therapeutic agent(s) to be further developed for the control of NMSCs

The provision requirement of 10% openings of the total floor area stated in the Uniform Building by Law 1984 Malaysia has been practiced by designers for building plan submission approval. However, the effectiveness of thermal performance in landed residential buildings, despite the imposition by the by-law, has never been empirically measured and proven. Although terraced houses in Malaysia have dominated 40.9% of the total property transaction in 2019, such mass production with typical designs hardly provides its occupants with thermal comfort due to the static outdoor air condition and lack of external windows, where the conventional ventilation technique does not work well, even for houses with an air well system. Consequently, the occupants need to rely on mechanical cooling, which is a high energy-consuming component contributing to outdoor heat dissipation and therefore urban heat island effect. Thus, encouraging more effective natural ventilation to eliminate excessive heat from the indoor environment is critical. Since most of the research focuses on simulation modelling lacking sufficient empirical validation, this paper drawing on field measurement investigates natural ventilation performance in terraced housing with an air well system. More importantly, the key concern as to what extent the current air well system serving as a ventilator is effective to provide better thermal performance in the single storey terraced house is to be addressed. By adopting an existing single storey air welled terrace house, the existing indoor environmental conditions and thermal performance were monitored and measured using scientific equipment, namely HOBO U12 air temperature and air humidity, the HOBO U12 anemometer and the Delta Ohm HD32.3 Wet Bulb Globe Temperature meter for a six-month duration. The findings show that the air temperature of the air well ranged from 27.48°C to 30.92°C, while the mean relative humidity were from 72.67% to 79.25%. The mean air temperature for a test room (single sided ventilation room) ranged from 28.04°C to 30.92°C with a relative humidity of 70.16% to 76%. These empirical findings are of importance, offering novel policy insights and suggestions to potentially revising the existing building code standard and by laws; since the minimum provision of 10% openings has been revealed to be less effective to provide a desired thermal performance and comfort, mandatory compliance with, and the necessity for, the bylaw requirement should be revisited and further studied.

(1) Background: Sentinel lymph node (SLN) mapping represents an accurate and feasible technique for the surgical staging of endometrial and cervical cancer. This is commonly performed by conventional laparoscopy or robotic-assisted laparoscopy, but in recent years, a new retroperitoneal transvaginal natural orifice transluminal endoscopic surgery (vNOTES) approach has been described. This technique provides easy visualization of lymphatic afferent vessels and pelvic lymph nodes, early SLNs assessment, and a coherent mapping methodology following the lymphatic flow from caudal to cranial. However, only a few publications have reported it. Following the IDEAL (Idea Development Exploration Assessment Long-term follow-up) framework, research concerning this technique is at stage 2a, with only small case series as evidence of its feasibility. Its standardized description appears necessary to provide the surgical homogeneity required to move further. (2) Methods: description of a standardized approach for retroperitoneal pelvic SLN mapping by vNOTES. (3) Results: we described a 10-step approach to successfully perform retroperitoneal vNOTES SLN mapping, including pre-, intra-, and postoperative management. (4) Conclusions: This IDEAL stage 2a study could help other surgeons approach this new technique, and it proposes a common methodology necessary to evolve through future IDEAL stage 2b (multi-center studies) and stage 3 (randomized controlled trials) studies.

This paper introduces a new adaptation of the enthalpic lattice Boltzmann method (LBM) tailored to simulate the intricate dynamics of conjugate heat transfer in non-homogeneous media, showcasing considerable potential for enhancing heat exchanger designs. This approach expands upon the conventional enthalpic LBM framework by seamlessly incorporating tailored source terms, enabling precise representation of local and transient variations in the medium’s thermophysical properties. The resulting LBM model comprehensively captures the energy conservation equation, with exceptions made only for flow compressibility and viscous dissipation, thereby significantly augmenting its versatility in analyzing heat exchanger configurations. Verification tests, comprising assessments of both transient and steady-state solutions, unequivocally validate the accuracy of the proposed model, further bolstering its reliability for analyzing heat exchange processes within intricate heat exchanger geometries. To demonstrate its efficacy, simulations of conjugate heat transfer processes involving fluid natural convection within structured cavities were performed, imposing both Dirichlet and Neumann boundary conditions to emulate scenarios encountered in practical heat exchanger operations. The findings yield compelling insights, particularly in the transient regime, revealing the beneficial impact of structured cavities on enhancing heat transfer processes. These insights inform potential design optimizations for heat exchangers. The results emphasize the potential of the modified enthalpic LBM approach to elucidate complex heat transfer phenomena in non-homogeneous media and structured geometries, offering valuable implications for heat exchanger engineering and optimization.

The article justifies the need to involve the technogenic deposits (off-balance ore and wastes) into the economic circulation of mining enterprises when there is natural resources depletion. It could be considered as one of the tools of the circular economy. The authors analyze global trends in the copper deposits development, global demand for copper, and design recommendations for possible alternative options for the copper production. The authors use a case of Erdenet Mining Corporation SOE based in Mongolia to develop the approach for economic, social and environmental problem-solving. The millions of mining dumps are proposed to develop as technogenic resources for recycled materials, prolonging profitable activities of the mine. The Hierarchy analysis method is used to obtain optimum order of the mining dumps development to get economic, social and environmental effect.

Sentiment analysis has become an important tool for understanding public opinion across languages and domains. Recently, there has been an increase in the number of studies on sentiment analysis in low-resource languages such as Kazakh. This is important to ensure that modern text analysis technologies are accessible to all users, regardless of their language background. The aim of the study is to create a sentiment analysis model for analyzing texts in Kazakh. As part of this work, we aim to use fine-tuning techniques on our own dataset for already existing models, thus improving their accuracy and efficiency for analyzing Kazakh language texts. This paper presents a manually collected dataset "KazIntTelCom" from the city information service 2GIS, consisting of user reviews, manually annotated by the authors taking into account the polarity of sentiment (i.e. negative positive or neutral). This dataset was used to fine-tune two pre-trained multi-lingual Transformer-based sentiment analysis models taken from the HuggingFace platform. The distillBERT and XLM-RoBERTa models were used for tuning. Also, the models were tested on the dataset "KazSAnDRA". The results show that accurate tuning even on a relatively small dataset gives a significant increase in performance, which is confirmed by an increase in the accuracy index by 20%-30%. In addition, false misses and false detections are analyzed, which allows us to identify directions for further improvement of the models. The contribution of this work, in addition to the dataset, is the analysis of model errors, which will help future developers to make more accurate settings of hyperparameters of training for sentiment analysis in Kazakh. These results are important for natural language processing and their adaptation to low-resource languages, promoting more inclusive and equitable access to modern analytical tools. Thus, this study demonstrates the effectiveness of the Transformers architecture for sentiment analysis in Kazakh and opens new opportunities for further model improvement.

Senna rugosa is a species found in the Cerrado and used in folk medicine as a vermifuge and in the treatment of poisonous snakebites accidents. In this work, we identified the main secondary metabolites present in ethanolic extracts of the leaves (ELSR) and roots (ERSR) of S. rugosa and evaluated the potential cytoprotective effect against cellular macromolecular damage, as well as the cytotoxic properties of the extracts on the K562 and Jurkat leukemic cell lines. The identification of metabolites was done by liquid chromatography coupled with mass spectrometry. The antioxidant activities were investigated by direct ABTS•+ and DPPH• radical scavenging methods, protection against oxidative damage in proteins and DNA. The cytotoxic properties were investigated against healthy cells, isolated from human peripheral blood (PBMC) and leukemic cell lines. The leaf extract contains catechin, rutin, epigallocatechin derivatives, kaempferol glycosides, luteolin, and dimeric and trimeric procyanidins, while the root extract profile shows obtusichromoneside derivatives, 2-methoxystypandrone, stilbene derivatives, naphthopyranones, and flavanone derivatives. The extracts showed antioxidant activity, with IC50 of 4.86 ± 0.51 µg/mL and 8.33 ± 0.90 µg/mL in the ABTS assay for ELSR and ERSR, respectively. And in the DPPH• assay, the IC50 was 19.98 ± 1.96 µg/mL for ELSR and 13.37 ± 1.05 µg/mL for ERSR. The extracts protected macromolecules against oxidative damage at concentrations of 5 µg/mL. The cytotoxicity test against leukemic strains was observed after 24 and 48 hours of treatment. After 48 h, results against the K562 cell line demonstrate IC50 of 242.54 ± 2.38 µg/mL and 223.00 ± 2.34 µg/mL for ELSR and ERSR , respectively. While against the Jurkat cell line, these extracts showed IC50 of 171.45 ± 2.25 µg/mL and 189.30 ± 2.27 µg/mL, respectively Results on PBMC viability, demonstrate that the extracts showed selectivity for the leukemic cell lines. Together, our results reveal that the leaves and roots of S. rugosa have completely distinct and complex chemical compositions. This study expands the repertoire of identified chemical compounds and underscores their significant pharmacological potential in conditions related to oxidative stress and leukemia.

This study investigates the impact of climate change on the efficiency of organic honey production in Türkiye using a machine learning approach. The eXtreme Gradient Boosting (XGBoost) algorithm is employed for the Machine Learning (ML) modelling. The analysis includes data on organic honey production performance from 2004 to 2023, spanning a period of 20 years, and 8 different climate variables (16 variables depending on daytime and nighttime values). The dataset created to investigate the impact of climate change on organic honey yield comprises 28 columns and 120,960 rows. The study aimed to determine the significance of different climatic variables on the efficiency of organic honey production. The study found that, in addition to daytime solar radiation and temperature, wind speed and direction were also important factors. The sensitivity analysis has shown that minor alterations in the variables have a significant impact on the model's predictions. Additionally, non-linear relationships between the variables and honey production were identified. These findings suggest that it may be beneficial to consider implementing climate change adaptation and mitigation strategies in agricultural policies and beekeeping practices. The study suggests that machine learning has the potential to provide valuable insights into the complex relationship between climate change and agricultural production. This could aid in the sustainability and economic optimization of the sector.

Background: The COVID-19 pandemic has affected Madagascar, Cameroon, and the Central African Republic (CAR), with each experiencing multiple waves by mid-2022. This study aimed to evaluate immunity against SARS-CoV-2 strains Wuhan (W) and BA.2 (BA.2) among healthcare workers (HCWs) in these countries, focusing on vaccination and natural infection effects. Methods: HCWs' serum samples were analyzed for neutralizing antibodies (nAbs) against W and BA.2 variants, with statistical analyses comparing responses between countries and vaccination statuses. Results: Madagascar showed significantly higher nAb titers against both strains compared to CAR and Cameroon. Vaccination notably increased nAb levels against W by 2.6-fold in CAR and 1.8-fold in Madagascar, and against BA.2 by 1.6-fold in Madagascar and 1.5-fold in CAR. However, in Cameroon, there was no significant difference in nAb levels between vaccinated and unvaccinated groups. Conclusion: The study highlights the complex relationship between natural and vaccine-induced immunity, emphasizing the importance of assessing immunity in regions with varied epidemic experiences and low vaccination rates.

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

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

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

In this paper, the main characteristics of the natural bitumen of the Munaily-Mola oil sands were studied with comparison crude oil of Karazhanbas oil field. During the experiment, the density, heating value, ash content and elemental composition of natural bitumen were determined, and these parameters were compared with the properties of Karazhanbas crude oil. As well as oil, resin and asphaltene fractions of both hydrocarbons were separated and each of them were analyzed at a Fourier transform IR-spectroscopy Spectrum-65 with 450-4000 cm−1 range. According to these indicators, it is concluded that natural bitumen is a heavy hydrocarbon raw material with highly resin-asphaltene compounds. Like Karazhanbas oil, natural bitumen can be used as a raw material for various petrochemical products. By finding ways of effective processing of natural bitumen, it can be used in useful areas of the national economy.

Human beings naturally move from one location to another in search of basic necessities. Road transportation is a low-cost, versatile, and widely accessible mode of transportation worldwide. However, using fossil-based (FB) fuels in transportation vehicles is inefficient, generates hazardous gases, and worsens environmental degradation. The current work assesses the application of clean fuels such as biofuels, natural gas, electricity, propane, and other emerging fuels to replace FB fuels in transport vehicles. Unlike FB fuels, the use of biohydrogen, biomethane, biodiesel, and bioethanol in transport vehicles resulted in emission reduction by 70 %, 63 %, 41 %, and 54 %, respectively, while electric vehicles lower maintenance costs, ensuring better engine performance, and better engine efficiency. The adoption of natural gas and propane as alternative fuels for transport vehicles is cost-effective, and promotes environmental sustainability. There is a need for targeted financial, infrastructural, and technical investments to further promote the application of these clean fuels for road transport application. The outcome of the study provides updated information and sensitizes automobile engineers, environmentalists, transport fleet operators, governments, and other stakeholders on the benefits derivable in the adoption of clean fuels as alternatives to FB fuels as transport vehicles fuel.

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

Tomato brown rugose fruit virus (ToBRFV) is a causal agent of severe emergent diseases in Solanaceae hosts with agronomic relevance, such as tomato and pepper. Herein, we analyzed the seed transmission rate and efficacy of different seed disinfection treatments and performed a comparison of molecular biology techniques seeking rapid and sensitive detection in seed lots. We evaluated several total RNA extraction and RT-PCR protocols using a distinct combination of PCR primers to test for the presence of the ToBRFV Mexican strain in tobacco host. Our results showed that the percentage of seed and seedlings transmission was less than 1% and that a 3% sodium hypochlorite solution was effective as a seed disinfection treatment. Finally, the most sensitive molecular method identified for virus detection consisted of a CTAB-Trizol RNA extraction followed by nested PCR using primers reported by Dovas et al. (2004). Additionally, we tested potential natural hosts in selected Cucurbitaceae and Solanaceae species. Our results showed that the ToBRFV Mexican strain is capable of experimentally infecting eggplant (Solanum melongena), tomatillo (Physalis ixocarpa), and tobacco (Nicotiana rustica).

In the food industry, Listeria is a standard control microorganism in ready-to-eat food since it may be pathogenic and cause a disease called listeriosis. The objective of the present study was to carry out a challenge test to verify the efficiency of different combinations of natural antimicrobial ingredients, against Listeria monocytogenes, to be used in ready-to-eat foods. Six different formulations of cooked ham were elaborated: a control, and five different formulations. An initial inoculation of 2 log cycles was used in the different products and the growth of Listeria was monitored at different temperatures and times (4° C for 17 w; 7ºC for 12 w). Control samples showed a progressive growth, reaching 5-6 log after 3 or 4 weeks. The rest of samples showed constant counts of Listeria during the all study. Only samples containing: 100 ppm nitrite + 250 PPM ascorbic acid+0,7% PRS DV-5 did not control the growth of Listeria at 7 ° C after 7 w of storage. The results obtained allowed to classified the cooked ham elaborated using any natural ingredients combinations as a “Ready-to-eat food unable to support the growth of L. monocytogenes other than those intended for infants and for special medical purposes”.

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

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

Breast cancer is one of the most common types of malignant tumors worldwide and the leading cause of cancer-related deaths in women. According to the latest report from the World Health Organization, breast cancer has surpassed lung cancer, becoming the most diagnosed cancer globally. Herein, a systematic, quantitative, and visual analysis of the scientific achievements in natural products research and development for breast cancer over the past 30 years is presented. The study utilizes bibliometric and knowledge graph visualization techniques to analyze the trends, co-occurrence of keywords, cooperative networks, cited literature, and research hotspots in the field. The analysis shows that breast cancer natural drug development involves mutual cooperation between multiple countries, with China, the United States, and India being the most active players. Pharmacology is the discipline with the largest number of articles published in nearly 30 years. The study also reveals that the extraction of drugs from organisms and their metabolites is an important direction in the current development of anticancer drugs. The development of natural medicines for breast cancer together with the mining of genetic knowledge play an important role in treating human breast cancer. The research presented here provides insights into the current state of natural product research and development for breast cancer, and highlights the need for strengthening mutual communication and cooperation between countries and scientific research institutions to enhance the development of breast cancer treatment technologies and natural medicines.

Titica vine fibers (TVFs) extracted from aerial roots of Heteropsis flexuosa, from the Amazon region, were 10, 20, 30 and 40 vol% incorporated into an epoxy matrix for applications in ballistic multilayered armor systems (MASs) and stand-alone tests for personal protection against high-velocity 7.62 mm ammunition. The back-face signature (BFS) depth measured for composites with 20 and 40 vol% TVFs used as an intermediate layer in MASs was 25.6 and 32.5 mm, respectively, below the maximum limit set by the international standard. Fracture mechanisms found by scanning electron microscopy (SEM) attested the relevance of increasing the fiber fraction for applications in MASs. The results of stand-alone tests showed that the control (0 vol%) and samples with 20 vol% TVFs absorbed the highest impact energy (Eabs) (212 – 176 J), and consequently limit velocity (VL) values (213 – 194 m/s), when compared with 40 vol% fiber fractions. However, the macroscopic evaluation found that the plain epoxy, referring to control samples, shattered completely. In addition, for 10 and 20 vol% TVFs, the composites were fragmented or exhibited delamination fracture, which contributed to their physical integrity. On the other hand, the composite with 30 and 40 vol% TVFs, whose Eabs and VL varied between 166 – 130 J and 189 – 167 m/s, respectively, showed the best dimensional stability. The SEM images indicated that for composites with 10 and 20 vol% TVFs the fracture mode was predominantly brittle, due to the greater performance of the epoxy resin and the discrete action of the fibers. While for composites with 30 and 40 vol% TVFs, there was the activation of more complex mechanisms such as pullout, shearing and fiber rupture.

This paper sheds light on how crisis management affects post-disaster re-covery. Based on the study of the management of a major environmental disaster, hurricane IRMA, we consider how post-disaster recovery is impacted by crisis management strategies. The perspectives of local stakeholders from the islands of St Martin and St Barthélémy will be shared. We analyze how territories exposed to the same natural phenomena have different outcomes and consider some of the possible reasons. This study reveals underlying risk drivers, to address governance reorganization with a view to better managing socio-economic recovery.

Acute myeloid leukemia (AML) is the most common and incurable leukemia subtype. Despite extensive research into the disease's intricate molecular mechanisms, AML have not yet identified effective treatments or expanded diagnostic or prognostic markers. The morphological, immunophenotypic, cytogenetic, biomolecular, and clinical characteristics of AML patients are extensive and complex. Leukemia stem cells (LSC) consist of hematopoietic stem cells (HSC) and cancer cells transformed by the complex, finely-tuned interaction that causes the complexity of AML. Microenvironmental regulation of LSC dormancy, its diagnostic and therapeutic implications for identifying and targeting LSCs due to their significance in the pathogenesis of AML were discussed in this review. It is essential to perceive the relationship between the niche for LSCs and HSC, which together cause the progression of AML. Notably, methylation is a well-known epigenetic change that is significant in AML, and our data also reveals that microRNAs are a unique factor for LSC. Furthermore, multiple-targeted approaches to reduce the risk of epigenetic factors, such as the administration of natural compounds for the elimination of local LSC, may prevent potentially fatal relapses. Furthermore, the survival analysis of overlapping genes revealed that specific targets had significant effects on the survival and prognosis of patients. We predicted that the multiple-targeted effects of herbal products on epigenetic modification are governed by different mechanisms in AML and prevent potentially fatal relapses. Thus, these strategies can facilitate the incorporation of herbal medicine and natural compounds into the advanced drug discovery and development processes enabled achievable by Network Pharmacology research.

3D bioprinting is the process of manipulating cell-laden bioinks to fabricate living structures. 3D bioprinting techniques have brought considerable innovation in biomedicine, especially in the field of tissue engineering, allowing the production of 3D organ and tissue models for in vivo transplantation purposes or for in-depth and precise in vitro analyses. Natural-derived hydrogels, especially those obtained from the decellularization of biological tissues, are promising bioinks for 3D printing purposes, as they present the best biocompatibility characteristics. Despite this, many natural hydrogels do not possess the necessary mechanical properties to allow a simple and immediate application in the 3D printing process. In this review, we focus on the bioactive and mechanical characteristics that natural hydrogels may possess to allow efficient production of organs and tissues for biomedical applications, emphasizing the reinforcement techniques to improve their biomechanical properties.

Ground penetrating radar (georadar, GPR), widely used in geology, archeology and road construction, is an efficient tool for searching and studying subsurface objects and structures. When planning GPR missions, it is necessary to predict the resolution of the device and the achievable probing depth. The article discusses the methods and results of assessing these characteristics of the Loza-V and Loza-N radars obtained in the course of archaeological and geographical expedition works.

Atopic dermatitis (AD) is a chronic eczematous inflammatory disease that may arise from environmental, genetic and immunological factors. Despite the efficacy of current treatment options like corticosteroids, such approaches are mainly focused on symptoms relief, and may present certain undesirable side effects. In recent years, investigation regarding isolated natural compounds, oils, mixtures and/or extracts, have gained scientific attention because of their high efficiency and moderate to low toxicity. Despite their promising therapeutic effects, the applicability of such natural health care solutions is somehow limited by their instability, poor solubility, and low bioavailability. Therefore, novel nanoformulation-based systems have been designed to overpass these limitations, thus enhancing the therapeutic potential, by promoting the capacity of these natural drugs to properly exert their action in AD-like skin lesions. For the best of our knowledge, this is the first literature review that focused on summarizing the last nanoformulation-based solutions loaded with natural ingredients, and specifically for the management of AD. We suggest that future studies should focus on robust clinical trials that may confirm the security and effectiveness of such natural-based nanosystems, thus paving the way for more reliable AD treatments.

The objective of this study was to analyze the in vitro stability and toxicity of liposomes containing guarana in skin cell lines. The liposomes were produced by the reverse phase evaporation method containing 1 mg/mL guarana. The stability of the liposomes was evaluated by physical-chemical parameters for up to 90 d using three different storage conditions. The cytotoxicity of guarana (GL), liposomes (B-Lip), and guarana-loaded liposomes (G-Lip) was evaluated on spontaneously immortalized human keratinocyte cell lines (HaCaT), murine swiss albino fibroblasts (3T3), and human fibroblast (1BR.3.G). The evaluation was performed using cellular viability analysis. The techniques used were 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and neutral red capturing (NRU) and the analyses were conducted after 24, 48, and 72 h exposure of these cells to the different treatments. The G-Lip exhibited physical-chemical stability for 60 d when the samples were stored in a refrigerator. The GL, B-Lip, and G-Lip demonstrated low cytotoxicity in the three cell cultures tested since a low reduction in cell viability was only observed at the highest concentrations. In addition, greater cell damage was observed for B-Lip; however, guarana protects cells from this damage. Thus, G-Lip structures can be considered as promising systems for topical applications.

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

Emergent physics education research (PER) literatures have been disseminated through academic publications within the community. The growing body of literatures over years challenge Indonesian PER scholars to understand how the research community has been progressed and what possible future work that should be emphasized. Nevertheless, previous traditional method of thematic analysis performed serious limitation when the number of PER literatures exponentially increased. Dealing with this large volume of publications, one of the machine learning studies namely natural language processing (NLP) was employed in this study to automate our thematic analysis among Indonesian PER literatures that are still limited to be explored. One of the well-known NLP algorithm, latent Dirichlet allocation (LDA), has been performed to extract Indonesian PER topics and their associated development between 2014 and 2021. A total of 852 papers (~ 4 to 8 pages each) were collectively downloaded from five international conference proceedings organized by Indonesian PER researchers. Before their topics were modeled through LDA algorithm, our data corpus should be previously preprocessed through several common procedures of established NLP studies. Findings revealed that LDA has thematically quantified Indonesian PER topics and described their distinct development over certain period. The identified topics from this study demonstrated that Indonesian PER community has established robust development in eight distinctive topics to the present. They begin with initial interest in focusing research on physics laboratory and following the research based instruction in the late 2015. Indonesian PER scholars sustained to study continuous topic on 21st century skill until 2019 which gave way to a focus on developing relevant educational technology to address several forms of students’ performance including scientific literacy and problem solving. There is still lack of Indonesian PER literatures that have been attempted to address qualitative aspects of physics teaching and learning.

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.

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

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

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

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.

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.

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.

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.

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.

Textile industry is one of the major industries responsible for environmental pollution globally. This is mainly due to the use of synthetic dyes, toxic ingredients and solvents for dy-ing/treatments/protocols of textiles. Replacement of those non ecofriendly ingredients would be a very challenging target to develop novel sustainable textile dying strategies. Using biobased and bioactive pigments, antibacterial ingredients and especially natural solvents are recently pointed out as substances to included in the related formulations. In this study the dyeing ability of the crude gel prodigiosin produced by non-pathogenic bacteria Serratia plymuthica as a natural pigment was investigated on various multifiber fabrics at different condition (temperature and pH) and by using salts (NaOH and Na2S2O4) and alternative mordants (a conventional: FeSO4 and a new bio-mordant: L-Cysteine (L-Cys)) in the recipes with different compositions and especially replacing of organic solvent with a new type of gel-based Choline chloride (ChCl) : Lactic acid (LA) (1 : 2) deep eutectic solvents (DES) as a dyeing medium. For determination of the dying process performances, the color strength (K/S value) of dyed fabric and the dyeing equalization (dE) parameters were evaluated. The fastness properties of the dyed fabrics which displayed as a higher K/S value and good dE were evaluated. The nylon fabrics dyed with 3.0% owf L-Cys at pH = 8.3 improved the color fastness to washing, while the gel-based ChCl : LA (1 : 2) DES dyebath provided a better color fastness to light. Moreover, the nylon fabrics under these conditions exhibited remarkable anti-microbial activity against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa). As conclusion, the use of the crude gel prodigiosin pigment revealed an advantageous ability to dye the textile materials, responding to consumers' demand for more ecological and sustainable products. Furthermore, the natural reducing agent L-Cys, never tested before as a bio-mordant, and the use of gel-based DES as a dyeing medium demonstrated enhanced colorimetric and antibacterial properties on dyed nylon with crude gel prodigiosin pigment.

Man-made shallow fishponds in the Czech Republic have been facing a high eutrophication since 1950s. Anthropogenic eutrophication and feeding of fish have strongly affected the physico-chemical properties of water and its aquatic community composition leading to harmful algal bloom formation. In our current study, we have characterised the phytoplankton community across three hypertrophic ponds to assess the phytoplankton dynamics during the vegetation season. We microscopically identified and quantified 29 cyanobacterial taxa comprised of non-toxigenic and toxigenic species. Further, a detailed cyanopeptides (CNPs) profiling was performed using molecular networking analysis of liquid chromatography tandem mass spectrometry (LC–MS/MS) data coupled with dereplication strategy. This MS networking approach coupled with dereplication on online global natural product social networking (GNPS) web platform led us to putatively identify forty CNPs: fourteen anabaenopeptins, ten microcystins, five cyanopeptolins, six microginins, two cyanobactins, a dipeptide radiosumin, a cyclooctapeptide planktocyclin and epidolastatin12. We have applied the binary logistic regression to estimate the CNPs producer by correlating the GNPS data with the species abundance. Usage of The combination of molecular networking and dereplication on online global natural product social networking (GNPS) web platform has proved as a valuable approach for rapid and simultaneous detection of high number of peptides, and rapidly assessing the risk for harmful bloom.

We report on the occurrence of natural Magnesium Alumina Silicate Hydrate (M-(A)-S-H) cement at Feragen and Leka in eastern and western Trøndelag Norway, respectively. Both occurrences are located in the sub-arctic climate zone and form in glacial till and moraine material deposited on ultramafic rock during the Weichselian glaciation. Weathering of serpentinized peridotite dissolve brucite and result in a relative high pH alkaline fluid that subsequently reacts with the felsic minerals of the till (quartz, plagioclase, K-feldspar) to form a cement consisting of an amorphous material or a mixture of nanocrystalline Mg-rich phyllosilicates, including illite. The presence of plagioclase in the till results in the enrichment of alumina in the cement, i.e., forms M-A-S-H instead of the M-S-H cement. Dissolution of quartz results in numerous etch-pits and negative quartz crystals filled with M-A-S-H cement. Where the quartz dissolution is faster that the cement precipitation, a honeycomb-like texture is formed. Compositionally the cemented till (tillite) contains more MgO and has a higher loss of ignition than the till, suggesting that the cement is formed by a MgO fluid that previously reacted with the peridotite. The M-(A)-S-H cemented till represent a new type of duricrust, coined magsilcrete. The study of natural Mg-cement provides information on peridotites as a Mg-source for Mg-cement and as a feedstock for CO2 sequestration.

The aim of this study was to build a tool to analyze, using artificial intelligence, the sentiment perception of users who answered two questions from the CSQ – 8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satis-faction level of the participants involved, with a view to establishing strategies to improve fu-ture experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks and transfer learning, so as to classify the inputs into the following 3 categories: negative, neutral and positive. Due to the lim-ited amount of data available - 86 registers for the first and 68 for the second - transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02 % and 90.53 % respectively based on ground truth labeled by 3 experts. Finally, we proposed a complementary analysis, using com-puter graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages

Course-based Undergraduate Research Experiences (CUREs) in high-enrollment, introductory classes are a 37 potentially transformative approach to retaining more students in STEM majors. We developed and piloted a CURE 38 in the introductory biology courses at the University of Washington. This CURE focuses on analyzing experimental 39 evolution of antibiotic resistance in Escherichia coli and generates data on two topics relevant to clinical practice: 40 compensatory mutations and cross-drug effects. By studying mutations in central cellular machinery that confer drug 41 resistance, students not only gain insight into fundamental cellular phenomena, but also recognize the molecular 42 basis of a medically important form of evolutionary change, connecting genetics, microbiology, and evolution.

This data descriptor summarizes the process applied and data gathered from 50 publications/papers reporting on the use of photography generated by tourists, tour operators and members of the public, with a particular focus on the crowdsourcing of photographs through online platforms and social networking sites (SNSs) as a method of research for wildlife conservation and ecotourism. The papers were collected in a systematic literature review to inform a pilot study of the feasibility of using SNSs to crowdsource georeferenced photographs of endangered Bornean Pygmy Elephants (Elephas maximus borneensis) taken by ecotourists along the Lower Kinabatangan River region of Sabah, Malaysia. Papers were sourced using the Murdoch University Findit online-search tool to search over 100 databases, including Proquest, Scopus and Web of Science. The criteria for a paper to be included in the review (and shared via the dataset attached to this this data descriptor) were that it was peer-reviewed, published in English, between 1997 and the 31 December 2017, had the full text accessible online and reported on a study or studies that utilized photographs that tourists, tour operators and/or members of the public generated and shared via SNSs or online platforms.

This note makes available five years of data gathered in a measurement site equipped with a micrometeorological station and two monitoring wells. Series of data of hydrological and atmospheric variables allow to estimate the flux of water across the atmosphere-land interface and to calculate the water budget, that are crucial topics in climate and environmental sciences. The measures of water-table began during 2017, one of the driest year of the whole instrumental period of climate history for the Central Mediterranean. Data from the micrometeorological station have been used to construct two more datasets of daily and monthly cumulative values of different terms of the surface water budget, from which the groundwater infiltration has been estimated. A significant decreasing trend characterizes both the data time series of piezometric heights and estimated infiltration in the considered period. All the data are collected in 2017-2022 datasets that are available in the supplementary material.

The establishment of a well characterized non-human primate model of Zika virus (ZIKV) infection is critical for the development of medical interventions. In this study, challenging Indian rhesus macaques (IRMs) with ZIKV strains of the Asian lineage resulted in dose dependent peak viral loads between days 2 and 5 post infection; and a robust immune response which protected the animals from homologous and heterologous re-challenge. In contrast, viremia in IRMs challenged with an African lineage strain was below the assays lower limit of quantitation and the immune response was insufficient to protect from re-challenge. These results corroborate previous observations but are contrary to reports using other African strains obviating the need for additional studies to elucidate the variables contributing to the disparities. Nonetheless, the utility of an Asian lineage ZIKV IRM model for countermeasures development was verified by vaccinating animals with a formalin inactivated reference vaccine and demonstrating sterilizing immunity against a subsequent subcutaneous challenge.

The excessive use of synthetic insecticides in modern agriculture has led to the contamination of the environment and the development of insect resistance. In this study, we evaluated the potential of tangerine (Citrus reticulata L.) peel essential oil (EO) as a natural insecticide against greenhouse whitefly (Trialeurodes vaporariorum W. (Homoptera: Aleyrodidae)), a common pest in greenhouse production. Petroleum ether (PET) and n-hexane (HEX) were used as solvents to extract essential oil (EO) from tangerine peels. The yield of EO was 1.59 % and 2.00 % (m/m) for PET and HEX, respectively. The insecticidal activity of EO was evaluated by measuring the mortality rate (MR) of greenhouse whiteflies at different time intervals. The results showed that PET and HEX extracts of tangerine EO effectively controlled the greenhouse whitefly. With both solvents, it was observed that with doses of 12.5% (v/v), similar results were achieved as with the positive control (corresponding to the commercial insecticide imidacloprid). Additionally, the FTIR analysis found that the EO contained d-limonene, which may be the source of its insecticidal properties. Therefore, tangerine peel essential oil is an excellent natural insecticide candidate for controlling greenhouse whiteflies effectively and sustainably.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.