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A new approach to MADM problem using interval-valued hesitant Fermatean fuzzy Hamacher operators and statistical variance
Populations of large-diameter trees are increasing across the United States
Large-diameter trees provide vital ecological functions in forested ecosystems. Old, large-diameter trees may also be vulnerable to climate-driven mortality events, but past work on large tree populations has been geographically limited. Here, we characterize the population of large-diameter trees from two size categories, 50 to 100 cm diameter at breast height (DBH) (medium) and >100 cm DBH (big), within the United States using Forest Inventory and Analysis data. Although populations of big trees are concentrated along the west coast, populations of medium trees are more evenly distributed across the nation. In the western United States, trees >50 cm DBH comprise ~75% of the total carbon stored in live trees, while in the eastern United States they comprise ~20%. Plot remeasurement data indicate that populations of big trees are increasing at an annual rate of 0.49% in the west and 2.9% in the east, and populations of medium trees are increasing at an annual rate of 0.5% in the west and 2.4% in the east. One exception is the Sierra Nevada region, where big trees are declining. Additionally, we observed declines for several individual species. While the overall population trend for large-diameter trees is positive, declines in these species could have localized impacts for the environments in which they occur.
Mowing management favors primary productivity and carbon sequestration without changing species diversity in a temperate hayfield in Central Interior British Columbia, Canada
We examined the effects of different mowing heights on the plant and soil characteristics of an irrigated and fertilized perennial cropping system in the central interior of British Columbia, Canada primarily composed of Medicago sativa, Phleum pratense, and Trifolium pratense. Mowing treatments included cutting heights of 0 cm, 5 cm, 10 cm, 15 cm, 20 cm, 25 cm, 30 cm, and an unmowed control treatment. Mowing treatments were applied three times throughout the study duration, followed by a final harvest. Data were collected on aboveground plant productivity, plant community diversity, and levels of soil carbon, nitrogen, and organic matter. Results showed plant productivity to be greatest at lower cutting heights, decreasing as cutting height increased. M0, M5, and M10 treatments produced over 300% more cumulative biomass than the control treatment. There were no differences across mowing treatments for measures of species diversity. The ten-centimetre treatment produced highest values of soil carbon, nitrogen, and organic matter than many other mowing treatments after three treatment applications (p < 0.05). Results indicate that lower cutting heights produced higher levels of aboveground biomass, did not alter crop species composition throughout the course of the study, and have potential to contribute towards the carbon pool. These results provide insight on the use of mowing within perennial cropping systems, and the effects on aboveground productivity and levels of soil carbon. The implications of this study allow agricultural producers to make informed decisions on how to manage their land for optimum productivity and environmental sustainability.
DNA color image encryption based on conservative chaotic system
Insulation between adjacent TADs is controlled by the width of their boundaries through distinct mechanisms
Topologically associating domains (TADs) are sub-Megabase regions in vertebrate genomes with enriched intradomain interactions that restrict enhancer–promoter contacts across their boundaries. However, the mechanisms that separate TADs remain incompletely understood. Most boundaries between TADs contain CTCF binding sites (CBSs), which individually contribute to the blocking of Cohesin-mediated loop extrusion. Using genome-wide classification, here we show that the width of TAD boundaries forms a continuum from narrow to highly extended and correlates with CBSs distribution, chromatin features, and gene regulatory elements. To investigate how these boundary widths emerge, we modified the random crosslinker polymer model to incorporate specific boundary configurations, enabling us to evaluate the differential impact of boundary composition on TAD insulation. Our analysis, using three generic boundary categories, identifies differential influence on TAD insulation, with varying local and distal effects on neighboring domains. Notably, we find that increasing boundary width reduces long-range inter-TAD contacts, as confirmed by Hi-C data. While blocking loop extrusion at boundaries indirectly promotes spurious intermingling of neighboring TADs, extended boundaries counteract this effect, emphasizing their role in establishing genome organization. In conclusion, TAD boundary width not only enhances the efficiency of loop extrusion blocking but may also modulate enhancer–promoter contacts over long distances across TAD boundaries, providing a further mechanism for transcriptional regulation.
Exposure to violence and associated factors among university students in Ethiopia: A cross-sectional study
Background Violence is a major public health concern with a significant impact on the health and well-being of individuals, families, and communities. Living in a new environment without parental control and experimenting with new lifestyles may increase the risk of violence among university students. Therefore, this study aimed to assess exposure to violence and its associated factors among university students in Ethiopia. Method A cross-sectional study was conducted among 2988 university students from six randomly selected universities in Ethiopia. A two-stage stratified sampling method was used to recruit the study participants. A self-administered questionnaire was utilized to collect information regarding exposure to emotional, physical, and sexual violence. Bivariable and multivariable logistic regression analyses were used to identify factors associated with violence exposure in the last 12 months. Results The prevalence of exposure to any type of violence in the last 12 months was 17.6% (n = 525) (17.9% among males, 16.5% among females). The adjusted odds ratio (AOR) of violence was 2.9 times higher (95% CI 1.6-5.0) among students older than 25 years than those aged 18-20 years. Those students who were in a relationship had 1.4 times higher odds of violence (95% CI 1.0-2.0) than those who were not in a relationship. In addition, those students who were from rural residences before coming to the university had 1.4 times higher odds of violence (95% CI 1.1-1.8) than those from urban residences. The odds of violence among those who consumed alcohol once a week or more in the past month were 2.2 times higher (95% CI 1.3-3.6) than those who did not consume alcohol. Furthermore, the likelihood of violence was 1.6 times higher (95% CI 1.0-2.4) among those who chewed khat and 2 times higher (95% CI 1.3-3.1) among those who used other drugs in the last 12 months. Conclusion Exposure to violence is a challenge for both male and female university students in Ethiopia. Several socio-demographic and behavioral factors were significantly associated with exposure to violence. Therefore, it is crucial for universities and stakeholders to raise awareness about contributing factors to minimize violence, regardless of gender.
Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors
Abstract The purpose of this paper is to suggest short-term Seasonal forecasting for hourly electricity demand in the New England Control Area (ISO-NE-CA). Precision improvements are also considered when creating a model. Where the whole database is split into four seasons based on demand patterns. This article’s integrated model is built on techniques for machine and deep learning methods: Adaptive Neural-based Fuzzy Inference System, Long Short-Term Memory, Gated Recurrent Units, and Artificial Neural Networks. The linear relationship between temperature and electricity consumption makes the relationship noteworthy. Comparing the temperature effect in a working day and a temperature effect on a weekend day where at night, the marginal effects of temperature on the demand in a working day for power are likewise at their highest. However, there are significant effects of temperature on the demand for a holiday, even a weekend or special holiday. Two scenarios are used to get the results by using machine and deep learning techniques in four seasons. The first scenario is to forecast a working day, and the second scenario is to forecast a holiday (weekend or special holiday) under the effect of the temperature in each of the four seasons and the cost of electricity. To clarify the four techniques’ performance and effectiveness, the results were compared using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Percentage Error (MAPE) values. The forecasting model shows that the four highlighted algorithms perform well with minimal inaccuracy. Where the highest and the lowest accuracy for the first scenario are (99.90%) in the winter by simulating an Adaptive Neural-based Fuzzy Inference System and (70.20%) in the autumn by simulating Artificial Neural Network. For the second scenario, the highest and the lowest accuracy are (96.50%) in the autumn by simulating Adaptive Neural-based Fuzzy Inference System and (68.40%) in the spring by simulating Long Short-Term Memory. In addition, the highest and the lowest values of Mean Absolute Error (MAE) for the first scenario are (46.6514, and 24.759 MWh) in the spring, and the summer by simulating Artificial Neural Networks. The highest and the lowest values of Mean Absolute Error (MAE) for the second scenario are (190.880, and 45.945 MWh) in the winter, and the autumn by simulating Long Short-Term Memory, and Adaptive Neural-based Fuzzy Inference System.
Quantifying compositional variability in microbial communities with FAVA
Microbial communities vary across space, time, and individual hosts, generating a need for statistical methods capable of quantifying variability across multiple microbiome samples at once. To understand heterogeneity across microbiome samples from different host individuals, sampling times, spatial locations, or experimental replicates, we present FAVA ( F ST -based Assessment of Variability across vectors of relative Abundances), a framework for characterizing compositional variability across two or more microbiome samples. FAVA quantifies variability across many samples of taxonomic or functional relative abundances in a single index ranging between 0 and 1, equaling 0 when all samples are identical and 1 when each sample is entirely composed of a single taxon (and at least two distinct taxa are present across samples). Its definition relies on the population-genetic statistic F ST , with samples playing the role of “populations” and taxa playing the role of “alleles.” Its mathematical properties allow users to compare datasets with different numbers of samples and taxonomic categories. We introduce extensions that incorporate phylogenetic similarity among taxa and spatial or temporal distances between samples. We demonstrate FAVA in two examples. First, we use FAVA to measure how the taxonomic and functional variability of gastrointestinal microbiomes across individuals from seven ruminant species changes along the gastrointestinal tract. Second, we use FAVA to quantify the increase in temporal variability of gut microbiomes in healthy humans following an antibiotic course and to measure the duration of the antibiotic’s influence on temporal microbiome variability. We have implemented this tool in an R package, FAVA , for use in pipelines for the analysis of microbial relative abundances.
Maternal occupation and risk of adverse fetal outcomes in Tanzania: A hospital-based cross-sectional study
Background Women constitute a large proportion of the workforce in today’s world. Hazardous working environment conditions for these women pose threat to their reproductive health. Despite efforts to address maternal health in Tanzania, the impact of occupational risks during pregnancy remains unclear. We assessed whether maternal occupation during pregnancy is associated with adverse Foetal outcomes. Methods A cross-sectional study was conducted among 400 self-referred post-delivery women at a referral Hospital in Tanzania. Information on socio-demographic characteristics and maternal occupational characteristics was assessed through the use of a pre-tested questionnaire. Questions on physical demanding work and prolonged standing were obtained from the standardized Musculoskeletal Questionnaire. To assess occupational exposure to chemicals, job titles and task descriptions were linked to a job-exposure-matrix, an expert judgment on exposure to chemicals at the workplace. Information relating to obstetric characteristics and pregnancy outcomes was obtained from the medical files and clinic cards. Data was analyzed by using Statistical Package for Social Sciences (SPSS) version 23. Odds ratios > 1 was considered risk while Odds ratios < 1 was considered protective and P value < 0.05 was considered significant. Results The mean age was 28.0 ± 6.3. Out of 400 post-delivery women studied, 174 (43.5%) were engaged in various occupations. Agriculture (22.4%) was the most prevalent occupation followed by tailoring (19.0%). Relative to the referent group of other occupations, agriculture workers, had higher adjusted odds ratios of congenital malformation (AOR = 4.5, 95% CI; 1.6-12.8)preterm babies (AOR = 2.8, 95% CI; 1.3-7.9), low birth weight (AOR = 3.1, 95% CI; 1.4-8.4) and low Apgar score (AOR = 3.5, 95% CI; 1.3-9.5). Food vendors: low birth weight (AOR = 8.6, 95% CI; 2.7-24.8) and low Apgar score (AOR = 13.5, 95% CI; 4.5-39.4). Conclusion Understanding occupational characteristics and their relation to adverse Foetal outcomes is important to formulate appropriate strategies to promote and protect maternal and infant health at work.
Metagenomic analysis of the faecal microbiota and AMR in roe deer in Western Pomerania
Abstract As an integral part of the global wellbeing, the health of wild animals should be regarded just as important as that of humans and livestock. The investigation of wildlife health, however, is limited by the availability of samples. In an attempt to implement a method with little invasiveness and broad areas of application, shotgun metagenomics were utilised to investigate the faecal microbiome and its antimicrobial resistance genes (AMRG) in roe deer. These genes can facilitate antimicrobial resistances (AMR) in bacteria and are therefore of increasing importance in global health. Accordingly, the abundance in potential vectors like wildlife needs to be assessed. The samples were additionally investigated for ESBL-E. coli, an antibiotic resistant pathogen of global concern, via cultivation. Twenty-seven hunt-harvested animals in Western Pomerania were sampled. This study is the first to our knowledge to describe the faecal microbiome of the European roe deer (Capreolus capreolus), providing insights into the bacterial and archaeal composition. Among the animals, the microbiome was mostly similar and showed a comparable composition to what has been reported in related species, with a ratio of 1.76 between Bacillota and Bacteroidota. The normalised abundance of AMR genes was found to be 0.035 on average, which is similar to other investigations on wild ruminants. Selective cultivation found no ESBL-E. coli in the animals. The prevalence of AMRG in roe deer of Western Pomerania was found to be in line with previous results. The use of shotgun metagenomics allowed for the simultaneous investigation of composition and AMR genes in the faecal microbiome of roe deer, which suggests it as a promising method for the health monitoring of wildlife. This study is the first to describe the prokaryotic assemblage in the faeces of roe deer and its differences to the microbiomes published on other cervids were discussed.
Amplification-free, OR-gated CRISPR-Cascade reaction for pathogen detection in blood samples
Rapid and accurate detection of DNA from disease-causing pathogens is essential for controlling the spread of infections and administering timely treatments. While traditional molecular diagnostics techniques like PCR are highly sensitive, they include nucleic acid amplification and many need to be performed in centralized laboratories, limiting their utility in point-of-care settings. Recent advances in CRISPR-based diagnostics (CRISPR-Dx) have demonstrated the potential for highly specific molecular detection, but the sensitivity is often constrained by the slow trans-cleavage activity of Cas enzymes, necessitating preamplification of target nucleic acids. In this study, we present a CRISPR-Cascade assay that overcomes these limitations by integrating a positive feedback loop that enables nucleic acid amplification-free detection of pathogenic DNA at atto-molar levels and achieves a signal-to-noise ratio greater than 1.3 within just 10 min. The versatility of the assay is demonstrated through the detection of bloodstream infection pathogens, including Methicillin-Sensitive Staphylococcus aureus (MSSA), Methicillin-Resistant Staphylococcus aureus (MRSA), Escherichia coli , and Hepatitis B Virus (HBV) spiked in whole blood samples. Additionally, we introduce a multiplexing OR-function logic gate, further enhancing the potential of the CRISPR-Cascade assay for rapid and accurate diagnostics in clinical settings. Our findings highlight the ability of the CRISPR-Cascade assay to provide highly sensitive and specific molecular detection, paving the way for advanced applications in point-of-care diagnostics and beyond.
Using machine learning to predict depression among middle-aged and elderly population in China and conducting empirical analysis
Objective To develop a predictive model for evaluating depression among middle-aged and elderly individuals in China. Methods Participants aged ≥ 45 from the 2020 China Health and Retirement Survey (CHARLS) cross-sectional study were enrolled. Depressive mood was defined as a score of 10 or higher on the CESD-10 scale, which has a maximum score of 30. A predictive model was developed using five selected machine learning algorithms. The model was trained and validated on the 2020 database cohort and externally validated through a questionnaire survey of middle-aged and elderly individuals in Shaanxi Province, China, following the same criteria. SHapley Additive Interpretation (SHAP) was employed to assess the importance of predictive factors. Results The stacked ensemble model demonstrated an AUC of 0.8021 in the test set of the training cohort for predicting depressive symptoms; the corresponding AUC in the external validation cohort was 0.7448, outperforming all base models. Conclusion The stacked ensemble approach serves as an effective tool for identifying depression in a large population of middle-aged and elderly individuals in China. For depression prediction, factors such as life satisfaction, self-reported health, pain, sleep duration, and cognitive function are identified as highly significant predictive factors.
Development of a UVC application machine for managing plant diseases in soilless greenhouse crop production
The <i>Arabidopsis</i> demethylase REF6 physically interacts with phyB to promote hypocotyl elongation under red light
The plant photoreceptor phytochrome B (phyB) mediates the responses of plants to red (R) light. Trimethylation of histone H3 at Lys27 (H3K27me3) plays a crucial role in governing gene expression and controlling the response of plants to environmental changes. However, how dynamic H3K27me3 mediates plant response to R light is poorly understood. Here, we report that RELATIVE OF EARLY FLOWERING 6 (REF6), an H3K27me3 demethylase, promotes hypocotyl elongation under R light in Arabidopsis . Upon exposure to R light, REF6 preferentially interacts with the active Pfr form of phyB. Consequently, phyB enhances REF6 accumulation and its binding ability, which are necessary for inducing cell-elongation-related genes from open chromatin, ensuring normal plant growth under prolonged light exposure. Moreover, REF6 acts together with the phyB-PIF4 module to mediate light regulation of hypocotyl growth. These findings provide insights into the understanding of how phytochromes, epigenetic factors, and transcription factors coordinately control plant growth in response to changing light environment.
Retraction: SE-stacking: Improving user purchase behavior prediction by information fusion and ensemble learning
Relations between suggestibility, working memory and response inhibition in middle childhood
eDNA confirms lower trophic interactions help to modulate population outbreaks of the notorious crown-of-thorns sea star
Variability in predator–prey interactions can modulate population dynamics with impacts scalable to entire ecosystems. As notorious corallivores, crown-of-thorns sea stars (CoTS; Acanthaster spp.) have caused extensive losses of coral habitat during unexplained population outbreaks across the Indo-Pacific. While predation of adult CoTS may help to suppress their outbreaks, it does not sufficiently explain their profound boom-bust dynamics and so remains equivocal. Factors influencing early postsettlement mortality are generally more impactful on population size, thus lower trophic interactions involving juvenile CoTS may better contribute to outbreak prevention. We evaluated the impact of key predatory decapods that interact with juvenile CoTS in their coral rubble nursery before they emerge as destructive corallivores. Decapod density was influenced by habitat complexity and varied regionally, inverse to spatial trends in CoTS outbreaks on the Great Barrier Reef. Using eDNA gut content analysis, we confirmed seven species (~12% of individuals) of wild-caught decapod, collected from two reefs separated by >1,000 km, as CoTS predators. Owing to spatial variation in predator abundance and community structure, we estimated potential (previous aquarium experiments) and realized (eDNA results here) rates of CoTS consumption were ~3-fold and ~1.6-fold lower, respectively, in outbreak hotspots. Through combination of field and molecular techniques, we demonstrated the appreciable impact of cryptic predators on early population success of this nuisance species, which expands our knowledge of CoTS outbreaks, pest species management, and reef conservation. Resolving predator–prey interactions at lower levels of the ecosystem can be crucial to understanding broader ecological outcomes.