Severe Threat Research Articles

An Overview of Tobacco Control Policies in China

The tobacco epidemic is a global public health concern as the toxic substances in tobacco pose a severe threat to human health. In response to this issue, countries around the world have adopted various strategies to control tobacco use. The Chinese government began to pay attention to the health hazards of smoking in the 1970s and joined the international community in tobacco control endeavors by becoming a signatory of the World Health Organization Framework Convention on Tobacco Control in the early 21st century. It has further intensified its tobacco control measures in recent years with its “Healthy China” strategy. This article gives an overview of tobacco control policies released by the Chinese government over the years, with a separate analysis of the provisions on smoking control stipulated in the Health China Action Plan (2019-2030). Also discussed are the outcomes of those policies and the challenges China faces in tobacco control, as well as the lessons China can learn from international experiences in this regard.

Organoids research progress in gynecological cancers: a bibliometric analysis

Background Gynecological cancers (GC) pose a severe threat to the health and safety of women’s lives, and organoids, as in-vitro research models, have demonstrated significant advantages in simulating tissue characteristics and drug screening. In recent years, there has been a rapid increase in research outcomes related to organoids in GC. However, there has been no bibliometric study concerning.Methods Publications related to GC and organoids from 2010-2023 were retrieved from the Web of Science Core Collection (WoSCC). We conducted a bibliometric analysis and visualization using CiteSpace, VOSviewer, and the Bibliometrix R Package. This analysis included the spatiotemporal distribution, author, sources, references, and keywords.Results A total of 333 publications were included. The number of annual publications indicated an explosive phase of development since 2019. The USA was the most important country in terms of cooperation, publication output, citation and centrality. University of California system ranked first in productivity among institutions, and HIPPO Y is the most relevant author in the research field. CANCERS published the most documents, and NATURE is the most cited sources. Analysis of Keywords and References, it is possible to establish the trend, and find the hotspots in the research field.Conclusion This bibliometric analysis delineated global landscapes and progress trends in GC organoids research. This study emphasized that organoids can effectively replicate the original tissue or tumors, providing a good in-vitro model for research on tumor-related mechanisms and showing significant advantages in drug screening and efficacy clinical prediction. Additionally, as preclinical models, they provide compelling evidence for personalized therapy and prediction of patient drug responses.

Distributed Photovoltaic Communication Anomaly Detection Based on Spatiotemporal Feature Collaborative Modeling

As distributed photovoltaic (PV) technology rapidly develops and is widely applied, the methods of cyberattacks are continuously evolving, posing increasingly severe threats to the communication networks of distributed PV systems. Recent studies have shown that the Transformer model, which effectively integrates global information and handles long-distance dependencies, has garnered significant attention. Based on this, our research proposes a model named STformer, which is applied to the task of attack detection in distributed PV communication. Specifically, we propose a temporal attention mechanism and a variable attention mechanism. The temporal attention mechanism focuses on capturing subtle changes and trends in data sequences over time, ensuring a highly sensitive recognition of patterns inherent in time-series data. In contrast, the variable attention mechanism analyzes the intrinsic relationships and interactions between different variables, uncovering critical correlations that may indicate abnormal behavior or potential attacks. Additionally, we incorporate the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique. This technique not only helps reduce computational complexity but, in certain cases, can enhance anomaly detection performance. Finally, compared to classical and advanced methods, STformer demonstrates satisfactory performance in simulation experiments.

Simple solution and paper-based fluorescent aptasensors for toxic metal ions, thallium(l) and lead(ll).

Heavy metal ions, such as thallium(I) and lead(II) are environmental toxicants known to cause a severe threat to human and ecosystem health. This work investigates aptamers and intercalating chromophore-based complexes for the detection of these toxic species. In one method, a selective label-free "turn-on" biosensor was developed using a G-quadruplex-intercalating agent, berberine. Fluorescence, melting temperature (Tm), and circular dichroism analysis confirmed the affinity and selectivity results, illustrating the potential of these aptasensor methods for improving detection limits. These fluorescence assays were found to perform with a detection limit of 3.4 μM for Tl(I) and 0.84 nM for Pb(II). Furthermore, the assays were challenged successfully with Tl(I) and Pb(II) spiked into river water samples. We next developed paper-based fluorescent assays for Tl(I) and Pb(II), where the aptamer/berberine complex was spotted onto the paper test zone. When Tl(I) or Pb(II) ions solutions were spotted onto the top of the test zone and the spot was illuminated with a portable UV light (365 nm), a strong green fluorescence could be easily visualized with the naked eye. The lowest detection limits achieved with these fluorescent paper-based assays for Tl(I) and Pb(II) were 1.1 nM and 1.6 nM, respectively. The two fluorescent approaches presented here have the potential to be the basis of rapid, fast, and cost-efficient screening assays for these toxic species.

Integrated GIS-hydrologic-hydraulic modeling to assess combined flood drivers in coastal regions: a case study of Bonita Bay, Florida

Flooding poses a severe global threat, necessitating advanced methodologies to assess and manage its risks effectively. This study introduces a novel approach that integrates Geographic Information System (GIS) with hydrologic-hydraulic modeling to evaluate the combined drivers of current and future flood risks. The method is applied to the development Bonita Bay in southwest Florida. It occurs in a region highly susceptible to flooding due to its low elevation and proximity to tidal waters. The innovative integration of GIS with hydrologic-hydraulic models enables detailed assessment and visualization of flood inundation areas under multiple flood drivers including design storms, land use changes, groundwater rise, and sea-level rise. This allows for the seamless simulation of complex flood interactions with only minor adjustments to the model for the identified drivers. The results indicate significant increases in initial water storage caused by sea and groundwater level rise and amplified storm runoff from land use changes. A 2% increase in flooded areas is projected with stronger design storms, and a 5% increase by 2,100 compared to 2024. This approach provides a robust framework for developing tailored flood mitigation strategies and can be adapted to various coastal regions globally.

Goat Milk Protein-Derived ACE Inhibitory Peptide SLPQ Exerts Hypertension Alleviation Effects Partially by Regulating the Inflammatory Stress of Endothelial Cells

Hypertension has always posed a severe threat to people’s health. Food-derived angiotensin-converting enzyme (ACE)-inhibitory peptides have the potential to both prevent and treat hypertension. In the current investigation, two ACE-inhibitory peptides (SLPQ and PYVRYL) from goat milk were studied for their endothelial effects using EA.hy926 cells. PYVRYL outperformed SLPQ, yet neither impacted cell survival below 200 μg/mL. Investigation of SLPQ’s impact on EA.hy926 cell expression revealed 114 differentially expressed genes, with 65 downregulated and 49 upregulated. The genes were enriched in cytokine interactions, coagulation cascades, Hippo signaling, and ECM–receptor interaction. Decreased c-x-c motif chemokine ligand 2 (CXCL2), integrin subunit beta 2 (ITGB2), and fbj murine osteosarcoma viral oncogene homologue (FOS) expression and increased secreted phosphoprotein 1 (SPP1) expression may protect endothelial cells from inflammation. Our findings suggest that beyond ACE inhibition, SLPQ aids blood pressure control by influencing endothelial function, paving the way for its use as an antihypertensive food ingredient.

Informal settlements ablaze: decoding the combustible nature of building materials used in Bangladesh through experiments and numerical analysis

ABSTRACT Fire outbreaks in informal settlements pose severe threats to residents and their properties. Despite the frequent occurrence of fire events in Bangladesh, detailed studies on fire propagation behaviour remain limited. This study addresses this critical issue by comprehensive investigations into the ignitability and burning behaviour of different materials such as cardboard, polyurethane foam, plastic bags, wood, and structural timber that are commonly found in informal settlements. Additionally, an advanced numerical model based on the finite volume method was developed in Fire Dynamics Simulator and then employed to accurately predict the fire-reaction properties of materials under the cone calorimeter environment. The results revealed that cardboard ignites quickly, burns moderately, and does not produce flaming droplets or ignite the filter paper. PU foam burns vigorously, with a high burning rate, and emits dense black smoke and a toxic odour. Plastic bags ignite rapidly, burn intensely, and are greatly influenced by the presence of airflow. The numerical model successfully quantified fire-reaction properties such as ignition time, heat release rate (HRR), and flame spread characteristics. Notably, wood and structural timber demonstrate a distinct combustion pattern with a sharp HRR peak, while PU foam exhibits the highest peak HRR values followed by timber masonite.

Waste 4.0: transforming medical waste management through digitalization and automated segregation

Medical waste management is a crucial issue due to its potential health risks to humans and harm to the environment. The World Health Organization (WHO) advises separating medical waste into seven groups based on its type. However, despite the implementation of color-coded bin bags, cases of missegregation still occur frequently, leading to injuries and spreading diseases. Infectious waste such as sharps, human tissue, and body parts are often found mixed in waste bins, posing a severe threat to waste employees. To address this issue, this paper proposes exploring the potential of digitalization in waste segregation. The literature on current segregation methods and technology applications is analysed and compared, and a framework for utilizing barcode tagging and scanning to ensure waste is correctly categorized is presented. The barcodes and scanner will be connected through a monitoring system, which can notify waste generators and collectors of misplacing or mixed waste. This digitalization system is expected to serve as a monitoring agent for segregating waste before it is collected from any health facilities. Additionally, the exchanged data from waste generator bins can inform collectors and other waste stakeholders about the waste's condition, potentially opening up opportunities for recycling companies to purchase used plastics or metals from hospital wastes. By implementing digitalization in medical waste management, waste segregation can improve, reduce the spread of diseases and injuries, and promote the recycling of hospital waste materials.

Novel Phenoxyalkanoic Acid Derivatives as Free Fatty Acid Receptor 4 Agonists for Treating Type 2 Diabetes Mellitus

Diabetes mellitus (DM) is a common metabolic disease that poses a severe threat to human health. Despite a range of therapeutic approaches, there remains a lack of effective and safe therapies with the existing drugs. Therefore, there is an urgent need to develop novel, effective, and safe therapeutic strategies for DM. Free fatty acid receptor 4 (FFAR4), also known as GPR120, is a member of the G protein-coupled receptor family, which has received considerable attention as an attractive new therapeutic target for treating DM. In the present study, based on the structure of TUG-891, which has excellent activity and selectivity, a series of novel FFAR4 agonists was designed by replacing the phenylpropanoic acid β position carbon atom with an oxygen atom, while replacing the linking oxymethylene with an amide-linking group. The target compounds were evaluated for FFAR4 agonistic activity, and the preferred compounds were evaluated for selectivity, oral glucose tolerance in normal ICR mice, antidiabetic activity in diet-induced obese (DIO) mice, pharmacokinetic properties in ICR mice and molecular modeling studies. The results showed that compound 10f possessed excellent FFAR4 agonistic activity and selectivity, significantly improved glucose tolerance in normal ICR mice, lowered blood glucose and promoted insulin secretion in a dose-dependent manner in DIO mice, and showed favorable pharmacokinetic properties. These results indicate that compound 10f may be a promising compound that deserves further structure–activity relationship and pharmacological studies for the development of antidiabetic drugs.

Identification and characterization of linear B-cell epitopes on African swine fever virus H171R protein.

African swine fever virus (ASFV) poses a severe threat to the global swine industry. This study characterizes linear B-cell epitopes on the crucial ASFV H171R protein, facilitating the development of improved diagnostics and subunit vaccines. Four immunogenic epitopes were identified, offering valuable information for designing sensitive diagnostic assays and potential subunit vaccine candidates. By advancing the understanding of H171R's antigenic landscape, this research contributes to controlling ASFV's devastating impacts, safeguarding the swine industry, and ensuring food security.

Modeling and analysis using piecewise hybrid fractional operator in time scale measure for ebola virus epidemics under Mittag–Leffler kernel

This emerging infectious disease poses one of the most severe threats to public health in these locations, but there are not many reliable therapies yet. In this work, we developed the Ebola virus dynamics and control factors epidemic model with a piecewise hybrid fractional Operator in time scale measure insight of Mittag–Leffler kernel. Patterns and structures that repeat at various scales are the focus of fractal analysis, which has applications in complex systems such as biological ones. Both qualitatively and statistically, a proposed model with the Lipschitz criteria and linear growth is examined, considering positive solutions, boundedness, and uniqueness at equilibrium points with Leray-Schauder results under time scale ideas. The regulation for linear responses approach will be used by Chaos Control to stabilize the system after its equilibrium points. A fractional-order framework with a controlled design will be considered, where solutions are bounded in the feasible domain of relations of different compartments. Ulam–Hyers stability results in the solution are treated when function (constant or rising) for the component of qualitative inquiry in generalized form. The dynamical behaviors of the suggested model are discussed with the Newton polynomial approach used to implement on model in the sense of classical piecewise and Mittag–Leffler kernel at different fractional order values. The model shows that solutions are stable and confined within a feasible range, ensuring reliability. Through detailed simulations, it effectively captures how different interventions and infection rates influence Ebola spread. This fractional-order model enhances understanding of Ebola transmission, providing a strong basis for predicting outbreaks and planning effective control measures, with practical applications for analyzing real-world data.

Evaluation of management options for climate-change adaptation of threatened species: a case study of a restricted orchid

Context Global climate is changing rapidly, necessitating timely development of specific, actionable species conservation strategies that incorporate climate-change adaptation. Yet, detailed climate-change adaptation planning is noticeably absent from species management plans. This is problematic for restricted species, which often have greater extinction risk. Aims Focusing on the restricted and endangered Tarengo leek orchid (Prasophyllum petilum), we aimed to adapt and test a framework for producing strategies for its management under climate change. Methods We used expert elicitation to estimate the severity of threats and assess potential management actions to mitigate threat impacts. We created a conceptual model detailing ecology, threats and likely impacts of climate change on the species, including the interactions between components. Key results Although climate change-related threats will affect the species, the most severe threats were non-climate threats including grazing, weeds, and habitat degradation. Fire management was deemed highly beneficial but had low feasibility for some populations. Without management, experts estimated up to a 100% decrease in one P. petilum population, and up to 50% decrease if management remained unchanged. Conclusions Management actions with the highest benefit and feasibility addressed the non-climate threats, which, in turn, can give the species the best opportunity to withstand climate-change impacts. Experts highlighted the difficulty of addressing climate threats with such limited knowledge; therefore, further research was recommended. Implications This adapted framework enabled a structured analysis of threats, and informed selection of priority adaptation options. We recommend its use for other restricted species for efficient and robust decision-making in climate-change management.

Exploring El Nino effects on agricultural area using Landsat images analysis: A case study in Bondowoso Regency, Indonesia

El Nino, which hit Indonesia in 2023, poses severe food security threats due to the high dry season with no rainfall and minimal cloud cover and can trigger serious drought problems if it happen for a long time. This study aimed to explore the impact on agricultural land in Bondowoso Regency during El Nino events. The analysis in this study primarily uses land surface temperature (LST) and normalized difference vegetative index (NDVI) map distribution. The Landsat data from USGS are collected and processed to become LST and NDVI distribution maps. Data analysis focused on the agricultural area layers based on data from the Indonesia geospatial portal. Referring to the LST and NDVI map distribution, the notable rise of LST starts in August 2023, and the peak is in October 2023. Around 46% of areas in the Bondowoso regency are detected as hotspot areas, which had LST above 30<sup>o</sup>C in October 2023. El Nino affects the irrigated lands and rain-fed fields more than the plantations. The NDVI alteration data does not show that the Bondowoso Regency is experiencing extraordinary drought due to the short-term impact of El Nino. However, the emergence of numerous areas in the moderate NDVI category warns that stress affecting vegetation is starting to occur. Mitigation plans should be prepared for the long-term impact of El Nino, particularly in the hotspot areas. This study could be a comprehension tool for the government and farmers to prepare mitigation plans.

Sustainable watersheds management in Peru: Challenges and perspectives

Sustainable watershed management stands at the forefront of global efforts to tackle pressing environmental and socio-economic challenges. This study offers a pioneering assessment of the state of research on Peru's watersheds, integrating geographical, environmental, and socio-economic dimensions. By harnessing cutting-edge bibliometric tools, including Bibliometrix and VOSviewer, and adhering to PRISMA guidelines for a systematic review, this work maps the scientific landscape of watershed management in Peru. Utilizing comprehensive databases such as Scopus and Web of Science, the study identifies and analyses key thematic clusters, revealing an escalating focus on the urgent issues of climate change and urbanization, particularly within the critical Rimac River Basin. This basin, vital to the water supply of Lima, the world's second-largest desert city, faces severe threats from pollution and climate instability. Our findings not only chart the evolution of research in this domain but also spotlight emerging opportunities to modernize water management practices through the integration of climate change models, advanced data monitoring, and artificial intelligence. The study makes a compelling case for a collaborative approach, urging stronger alliances among local communities, research institutions, and international stakeholders to foster more resilient and sustainable watershed management strategies in Peru. This paper provides actionable insights, making it an indispensable resource for policymakers and practitioners aiming to secure Peru's water future.

Research on Software Vulnerability Detection Methods Based on Deep Learning

This paper aims to investigate software vulnerability detection methods based on deep learning to address the ever-growing challenges in software security. With the rapid development of information technology, software vulnerabilities have become the primary targets of cyberattacks, posing severe threats to economic, military, and social security. By analyzing the limitations of existing software vulnerability detection methods, this paper explores the potential applications of deep learning technology in this field. Through a literature review, relevant theories of software vulnerabilities are introduced, including the concept, types, and impacts of vulnerabilities. Subsequently, detailed descriptions of deep learning-based vulnerability detection methods are provided, encompassing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and their variant LSTM, as well as the application of program slicing techniques in vulnerability detection. By evaluating and improving existing deep learning models, a novel deep learning-based vulnerability detection framework is proposed, with its workflow and technical principles elaborated. The proposed deep learning-based vulnerability detection method can effectively enhance the accuracy and efficiency of software vulnerability detection, reduce reliance on expert knowledge, and lower human resource costs. Experimental results demonstrate that this method performs exceptionally well on multiple datasets, indicating broad application prospects and significant research value. The deep learning-based software vulnerability detection method offers new insights and tools for addressing current software security challenges. In the future, with the continuous development and improvement of deep learning technology, this method will play an even more crucial role in the field of software vulnerability detection.

Chemical strategies for antisense antibiotics.

Antibacterial resistance is a severe threat to modern medicine and human health. To stay ahead of constantly-evolving bacteria we need to expand our arsenal of effective antibiotics. As such, antisense therapy is an attractive approach. The programmability allows to in principle target any RNA sequence within bacteria, enabling tremendous selectivity. In this Tutorial Review we provide guidelines for devising effective antibacterial antisense agents and offer a concise perspective for future research. We will review the chemical architectures of antibacterial antisense agents with a special focus on the delivery and target selection for successful antisense design. This Tutorial Review will strive to serve as an essential guide for antibacterial antisense technology development.

Fungal community characteristics of the last remaining habitat of three paphiopedilum species in China.

Paphiopedilum armeniacum, Paphiopedilum wenshanense and Paphiopedilum emersonii are critically endangered wild orchids. Their populations are under severe threat, with a dramatic decline in the number of their natural distribution sites. Ex situ conservation and artificial breeding are the keys to maintaining the population to ensure the success of ex situ conservation and field return in the future. The habitat characteristics and soil nutrient information of the last remaining wild distribution sites of the three species were studied. ITS high-throughput sequencing was used to reveal the composition and structure of the soil fungal community, analyze its diversity and functional characteristics, and reveal its relationship with soil nutrients. The three species preferred to grow on low-lying, ventilated and shaded declivities with good water drainage. There were significant differences in soil alkali-hydrolyzed nitrogen and available phosphorus among the three species. There were 336 fungal species detected in the samples. On average, there were different dominant groups in the soil fungal communities of the three species. The functional groups of soil fungi within their habitats were dominated by saprophytic fungi and ectomycorrhizae, with significant differences in diversity and structure. The co-occurrence network of habitat soil fungi was mainly positive. Soil pH significantly affected soil fungal diversity within their habitats of the three paphiopedilum species. The study confirmed that the dominant groups of soil fungi were significantly correlated with soil nutrients. The three species exhibit comparable habitat inclinations, yet they display substantial variations in the composition, structure, and diversity of soil fungi. The fungal functional group is characterized by a rich presence of saprophytic fungi, a proliferation of ectomycorrhizae, and a modest occurrence of orchid mycorrhizae. The symbiotic interactions among the soil fungi associated with these three species are well-coordinated, enhancing their resilience against challenging environmental conditions. There is a significant correlation between soil environmental factors and the composition of soil fungal communities, with pH emerging as a pivotal factor regulating fungal diversity. Our research into the habitat traits and soil fungal ecosystems of the three wild Paphiopedilum species has established a cornerstone for prospective ex situ conservation measures and the eventual reestablishment of these species in their native landscapes.

The Fatal Mushroom Neurotoxin Muscarine is Released from a Harmless Phosphorylated Precursor upon Cellular Injury

l‐(+)‐Muscarine (1)‐producing mushrooms pose a severe threat to human health as ingestion can result in circulatory collapse or even death. However, their metabolic profile is surprisingly poorly understood, including knowledge of poison release and potentially toxic congeners. In the mycelium of the 1‐producing fool’s funnel mushroom Clitocybe rivulosa, we identified 4’‐phosphomuscarine (2) as the major natural product. Its structure was elucidated by high‐resolution mass spectrometry, nuclear magnetic resonance spectroscopy and by comparison with a synthesized reference. We also detected this previously overlooked phosphorylated compound in the fiber cap mushrooms Pseudosperma spectrale and Inocybe nitidiuscula. Studies on the activation of the muscarinic acetylcholine receptor M3 indicate only weak affinity of 2 to this target. Furthermore, we present biological evidence that muscaridine (3), a quaternary amine congener related to and co‐occurring with 1, does not activate the muscarinic acetylcholine receptor M3 on human embryonic kidney cells. Our work provides important insight into the metabolic profile and the pharmacology of some of the most poisonous mushrooms. As the harmless 2 can liberate the potentially fatal 1 by unspecific enzymatic ester cleavage, these results are highly relevant for emergency medicine to estimate the true toxicity of these mushrooms.

Applying Multi-Task Deep Learning Methods in Electricity Load Forecasting Using Meteorological Factors

The steady rise in carbon emissions has significantly exacerbated the global climate crisis, posing a severe threat to ecosystems due to the greenhouse gas effect. As one of the most pressing challenges of our time, the need for an immediate transition to renewable energy is imperative to meet the carbon reduction targets set by the Paris Agreement. Buildings, as major contributors to global energy consumption, play a pivotal role in climate change. This study diverges from previous research by employing multi-task deep learning techniques to develop a predictive model for electricity load in commercial buildings, incorporating auxiliary tasks such as temperature and cloud coverage. Using real data from a commercial building in Taiwan, this study explores the effects of varying batch sizes (100, 125, 150, and 200) on the model’s performance. The findings reveal that the multi-task deep learning model consistently surpasses single-task models in predicting electricity load, demonstrating superior accuracy and stability. These insights are crucial for companies aiming to enhance energy efficiency and formulate effective renewable energy procurement strategies, contributing to broader sustainability efforts and aligning with global climate action goals.

The Fatal Mushroom Neurotoxin Muscarine is Released from a Harmless Phosphorylated Precursor upon Cellular Injury.

l-(+)-Muscarine (1)-producing mushrooms pose a severe threat to human health as ingestion can result in circulatory collapse or even death. However, their metabolic profile is surprisingly poorly understood, including knowledge of poison release and potentially toxic congeners. In the mycelium of the 1-producing fool's funnel mushroom Clitocybe rivulosa, we identified 4'-phosphomuscarine (2) as the major natural product. Its structure was elucidated by high-resolution mass spectrometry, nuclear magnetic resonance spectroscopy and by comparison with a synthesized reference. We also detected this previously overlooked phosphorylated compound in the fiber cap mushrooms Pseudosperma spectrale and Inocybe nitidiuscula. Studies on the activation of the muscarinic acetylcholine receptor M3 indicate only weak affinity of 2 to this target. Furthermore, we present biological evidence that muscaridine (3), a quaternary amine congener related to and co-occurring with 1, does not activate the muscarinic acetylcholine receptor M3 on human embryonic kidney cells. Our work provides important insight into the metabolic profile and the pharmacology of some of the most poisonous mushrooms. As the harmless 2 can liberate the potentially fatal 1 by unspecific enzymatic ester cleavage, these results are highly relevant for emergency medicine to estimate the true toxicity of these mushrooms.