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Sociodemographic profile, functionality, depression, and frailty as determinants for the risk of abuse and violence against older people in the community: An observational study conducted in Brazil

This study aimed to analyze the relationship between sociodemographic factors, health, functionality, depression, and frailty with the risk of abuse and violence against younger and older adults individuals. A cross-sectional observational study with a quantitative approach was conducted among Brazilian older adults between April and July 2022. Participants aged 60 years and older were recruited from Brazilian Primary Health Care Units. The Hwalek-Sengstock Elder Abuse Screening Test, Lawton and Brody’s Instrumental Activities of Daily Living Scale, Edmonton Frail Scale, and the Geriatric Depression Scale (GDS-15) were used to assess the variables of interest. Odds Ratios (ORs) was calculated using binary logistic regression models to test the study hypothesis. The sample was divided into two groups: younger elderly individuals (aged 60–70 years) and older elderly individuals (> 70 years). A total of n = 200 individuals’ participants were included in the study (n = 132 younger and n = 68 older). Non-white skin color (n = 15/ 22.1%/ p = 0.016/ OR= 2.0) was identified as a risk factor for the older group, while illiteracy emerged as a risk factor for violence in both groups (OR> 1.0). The absence of depressive symptoms and frailty were protective factors against the risk of abuse and violence in both groups (OR>1.0). Logistic regression analysis indicated that depression was the variable most strongly associated with the risk of abuse and violence, particularly in the younger group (R² = 0.46/ p < 0.001/ ß = 0.56). Among the observed associations, non-white skin color was a risk factor for abuse and violence in the older group, whereas literacy, absence of depression, and absence of frailty were protective factors in both groups.

Applying qualitative methods to experimental designs: A tutorial for the behavioral sciences

Studies with experimental designs are almost invariably evaluated with quantitative outcomes and methods, both in behavioral sciences and other disciplines. We argue that there can be added value of using qualitative methods for the evaluation of (behavioral) experiments. Incorporating qualitative data can enhance the ecological validity of a study, by acquiring a more holistic understanding of the phenomenon of interest. There is, however, little methodological guidance on how to implement such an approach. In this paper we present the different steps and considerations for a qualitative evaluation of results in experimental designs. Methodological guidelines are offered for each stage of a study, from formulation of the research goals, through data collection and data analysis, to the interpretation of a potential effect of the intervention. In addition, there is ample attention for ensuring the rigor of the research. The presented guidelines are developed and illustrated using an empirical example, in which a constructivist grounded theory approach was applied to evaluate the effect of empathy prompts on the motivation to adhere to COVID-19 regulations.

Modeling of injury severity of distracted driving accident using statistical and machine learning models

Distracted Driving (DD) is one of the global causes of high mortality and fatality in road traffic accidents. The increase in the number of distracted driving accidents (DDAs) is one of the concerns among transportation communities. The present study aimed to examine the individual and interacted effects of the influential factors on the injury severity of the DDAs using the Binary Logistic Regression (BLR) method, and at the same, to select the best machine learning (ML) model in predicting the injury severity of the DDA. The selection of the best ML model was based on the optimum combination of accuracy, F1 score, and area under curve metrics. Ten years of DDA data (2011−2020) provided by the police department of Imphal, India, was used in the present study. The BLR model-without-interaction results revealed that out of twenty categorical variables, nine categorical variables (below 18, 18−24, 25−40, above 40 years age group, two-wheeler, heavy motor vehicle, 12AM-6AM, 6PM-12AM, and hit-object collision) were statistically significant to the injury severity of the DDAs. In interaction model results, there were 11, 1, and 1 significant combinations among categorical variables in two-way, three-way, and four-way interaction models, respectively. The ML model results showed that overall, the XGBoost model was reported as the best-performing model in the first hyperparameter set, and the Single Layer Perceptron model in the second set. These results may be useful for transportation policymakers while implementing any countermeasures to improve road safety in hilly areas.

Landscape to microhabitat: Uncovering the multiscale complexity of native and exotic forests on Terceira Island (Azores, Portugal)

This study aims to identify the structural and compositional differences between native and exotic woodlands on Terceira Island, Azores. Based on landscape, habitat, and microhabitat analyses, remnants of native forests appeared to be associated with less accessible terrains. A more homogeneous structural complexity is exhibited, derived from the numerous branching patterns of the endemic vascular plant species. In contrast, exotic forests exhibit structural heterogeneity driven by mixed non-indigenous vascular plant species as a result of human actions such as afforestation and latter invasion of exotic tree species, after abandonment of the agricultural use. The ground and canopy layers in exotic forests were more invaded by non-indigenous species, while the understory demonstrated greater resilience by being mostly composed of indigenous species. Our findings highlight the structural and ecological differences between native and exotic woodlands, reflecting the historical transformation of forest cover in the Azores. These insights emphasize the importance of long-term monitoring and structural assessments in informing conservation efforts aimed at preserving native forests and managing invasive species in exotic woodlands.

Mushroom poisoning deaths and prevention practices in Hunan, 2014–2023

Background Wild mushroom poisoning is a major cause of foodborne illness-related deaths in Hunan Province. By analyzing the epidemiological characteristics of fatal incidents due to wild mushroom poisoning in Hunan Province over the past decade and preventive measures implemented in recent years, in this study, we aimed to provide insights for nationwide prevention and control of wild mushroom poisoning, focusing on reducing mortality. Methods Data from the “Foodborne Disease Outbreak Surveillance System” in Hunan Province were used to describe the characteristics of wild mushroom poisoning. Kernel density analysis was performed using ArcMap 10.8.1 to identify spatial clustering. Results It showed that from 2014 to 2023, 80 fatal wild mushroom poisoning incidents and 111 deaths, with a case fatality rate of 1.5%, were reported in Hunan Province. June (40 events, 50.0%) and August (17 events, 21.3%) are the peak periods for wild mushroom poisoning. The species most frequently associated with fatal poisoning were Russula subnigricans (20 events, 25.0%), Amanita fuliginea (18 events, 22.5%), and Amanita rimosa (11 events, 13.5%). Kernel density analysis indicated that the fatalities were primarily concentrated in the central and eastern regions of Hunan Province. All (100%) fatal incidence collected from households with poisoning caused by accidental harvesting and consumption. Rural areas accounted for 93.8% of the fatal incidents. The highest proportion of deaths (48.7%) occurred in the age group of 60 years and above. Conclusion Fatalities due to wild mushroom poisoning incidents exhibit seasonal and regional variations. We call for multi-sectoral collaboration in prevention and control efforts, focusing on key populations, regions, and time to conduct public education and promotion and ensure smooth access to medical care, which are crucial strategies for preventing wild mushroom poisoning incidents and reducing mortality.

Correction: Beyond words: From jaguar population trends to conservation and public policy in Mexico

Study on the aging performance of SBS modified asphalt in humid and hot environment

This study focused on the aging issues of asphalt pavement in high-temperature and high-humidity climatic zones, using SBS-modified asphalt with different additive contents as research objects to systematically investigate the aging evolution mechanisms under coupled hygrothermal conditions. A dual-temperature control system (60°C and 70°C) was constructed to conduct thermal-oxidative aging and wet-dry cycle aging tests. Multidimensional indicators including penetration ratio, softening point ratio, and ductility ratio were utilized to characterize the time-dependent aging characteristics of materials. Additionally, dynamic shear rheology (DSR) tests and rutting tests were integrated to comprehensively evaluate the high-temperature performance degradation of asphalt and its mixtures with varying modifier contents under cyclic aging. The research aimed to reveal the hygrothermal-coupled aging mechanisms and provide theoretical foundations for durable pavement design in humid tropical regions. Experimental results demonstrated that after thermal-oxidative and wet-dry cycle aging at 60°C and 70°C, the performance indices of SBS-modified asphalt with different additive contents deteriorated significantly. Specifically, the penetration ratio decreased by an average of 29.1%, the ductility ratio declined by 14.6%, and the softening point ratio increased by 60.3%. Concurrently, high-temperature rheological performance exhibited progressive degradation. Notably, increased SBS modifier content was observed to mitigate the aging-induced performance attenuation of modified asphalt. Analytical findings suggested that while higher SBS content enhanced structural stability to some extent, the presence of elevated temperature and moisture accelerated the degradation of SBS modifiers and the aging of base asphalt, thereby exacerbating the overall aging of modified asphalt and leading to performance deterioration.

stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs

Abstract Spatial multi-slice multi-omics (SMSMO) integration has transformed our understanding of cellular niches, particularly in tumors. However, challenges like data scale and diversity, disease heterogeneity, and limited sample population size, impede the derivation of clinical insights. Here, we propose stClinic, a dynamic graph model that integrates SMSMO and phenotype data to uncover clinically relevant niches. stClinic aggregates information from evolving neighboring nodes with similar-profiles across slices, aided by a Mixture-of-Gaussians prior on latent features. Furthermore, stClinic directly links niches to clinical manifestations by characterizing each slice with attention-based geometric statistical measures, relative to the population. In cancer studies, stClinic uses survival time to assess niche malignancy, identifying aggressive niches enriched with tumor-associated macrophages, alongside favorable prognostic niches abundant in B and plasma cells. Additionally, stClinic identifies a niche abundant in SPP1+ MTRNR2L12+ myeloid cells and cancer-associated fibroblasts driving colorectal cancer cell adaptation and invasion in healthy liver tissue. These findings are supported by independent functional and clinical data. Notably, stClinic excels in label annotation through zero-shot learning and facilitates multi-omics integration by relying on other tools for latent feature initialization.

Somatic gene delivery faithfully recapitulates a molecular spectrum of high-risk sarcomas

Abstract A major challenge hampering therapeutic advancements for high-risk sarcoma patients is the broad spectrum of molecularly distinct sarcoma types and the corresponding lack of suitable model systems. Here we describe the development of a genetically-controlled, yet versatile mouse modeling platform allowing delivery of different genetic lesions by muscle electroporation (EPO) in wildtype mice. This EPO-GEMM (EPO-based genetically engineered mouse model) platform allows the generation of ten genetically distinct sarcomas on an isogenic background, including the first model of ETV6::NTRK3-driven sarcoma. Comprehensive histological and molecular profiling reveals that this mouse sarcoma cohort recapitulates a spectrum of molecularly diverse sarcomas with gene fusions acting as major determinants of sarcoma biology. Integrative cross-species analyses show faithful recapitulation of human sarcoma subtypes, including expression of relevant immunotherapy targets. Comparison of syngeneic allografting methods enables reliable preservation and scalability of sarcoma-EPO-GEMMs for preclinical treatment trials, such as NTRK inhibitor therapy in an immunocompetent background.

Global warming may increase the burden of obstructive sleep apnea

Abstract High ambient temperatures are associated with reduced sleep duration and quality, but effects on obstructive sleep apnea (OSA) severity are unknown. Here we quantify the effect of 24 h ambient temperature on nightly OSA severity in 116,620 users of a Food and Drug Administration-cleared nearable over 3.5 years. Wellbeing and productivity OSA burden for different levels of global warming were estimated. Globally, higher temperatures (99th vs. 25th; 27.3 vs. 6.4 °C) were associated with a 45% higher probability of having OSA on a given night (mean [95% confidence interval]; 1.45 [1.44, 1.47]). Warming-related increase in OSA prevalence in 2023 was estimated to be associated with a loss of 788,198 (489,226, 1,087,170) healthy life years (in 29 countries), and a workplace productivity loss of 30 (21 to 40) billion United States dollars. Scenarios with projected temperatures ≥1.8 °C above pre-industrial levels would incur a further 1.2 to 3-fold increase in OSA burden by 2100.

Coordinated action of multiple active histone modifications shapes the zygotic genome activation in teleost embryos

Daily briefing: ‘Glimmer of hope’ at UN Ocean Conference as 50 countries ratify the High Seas Treaty

People were wrecking the climate 140 years ago — we just lacked the tech to spot it

Mice with human cells developed using ‘game-changing’ technique

Forensic Footwear Comparison: Leveraging AI Tools for Enhanced Analysis

Footwear impressions serve as a crucial type of evidence in forensic investigations, effectively linking suspects to crime scenes with remarkable accuracy. This research explores the efficacy of three widely utilized casting materials - Plaster of Paris, Latex Rubber, and Dental Stone when applied to various soil types. Additionally, the study examines the impact of artificial intelligence (AI) in enhancing the analysis of these impressions. The main objectives were to investigate footwear impressions differ across diverse surfaces, identify which casting material captures the most precise details, and evaluate whether AI tools can provide more dependable comparisons than traditional visual inspection. Digital images of both the original and cast impressions were analyzed using an AI algorithm that included grayscale conversion and histogram analysis. Statistical technique chi-square tests, indicated significant differences based on the type of surface and material used. The findings revealed that original impressions exhibited superior clarity and detail compared to cast impressions. This study highlights the necessity of careful material selection and the incorporation of AI to enhance accuracy and reliability in the forensic examination of footwear impressions.

Modelling the Dynamics of Crime Against Women by Incorporating Role of Technology

that, despite attempts, remains a substantial challenge. It can have serious and long-term consequences for their physical, emotional, and psychological well being. Technology can help combat crime against women by providing tools and resources for preventing, reporting, and dealing with abuse and harassment. Aim: To combat crime against women in India and create a safe environment for them. Objectives: To investigate the effectiveness of technology in lowering crime against women. Material: A nonlinear mathematical model has been constructed taking into account five interacting variables namely, targeted, susceptible, criminals, victims, and technology. Result: Our model shows the presence of two equilibria: crime-free and crime persistent. The crime-free equilibria (CFE) always exist, while crime-persistent equilibria (CPE) exist when criminality reproduction number ( 0 c 0 c R ) is greater than 1. Further, stability analysis has been carried out. Analysis demonstrated that when R >1, crime-persistent equilibria is nonlinearly asymptotically stable under some restrictions on parameters. The impact of key parameters on the dynamics of crime against women is also investigated through numerical simulation. Conclusion: According to the model study, the use of technology like installing Safetipin or another emergency app, addressing areas with poor safety, introducing wearable tech etc can effectively minimize crime against women in society

An Investigation into Cybercrime Awareness and Security Measures

The widespread adaptation of the internet has integrated it into the daily routines of a vast majority of individuals for various transactions. Correspondingly, the user base of the internet has experienced significant growth, paralleled by a rise in cybercrimes. Cybercrime, executed through computers and networks, poses a persistent and escalating threat across professional and personal fields. The arising of the internet has transformed traditional crimes into new forms. This research aims to raise awareness about contemporary cybercrimes and promote heightened cybersecurity measures. Furthermore, it endeavours to examine the levels of computer crime alertness among inter web users of various age groups and educational backgrounds. Utilizing a Linear predictive model, this study analyses both objectives. The findings reveal a correlation between respondents’ age groups and educational qualifications. Therefore, it is incumbent upon all internet users to remain vigilant regarding cybercrime and cybersecurity, as well as to actively foster awareness among others.

Challenging Locard’s Exchange Principle: Knowing Former Postulates of Forensic Science to Overhaul the Fundamentals

Fundamental principles of forensic science have one of the most popular of these the exchange principle by Edmund Locard. Locard’s exchange principle is summed up in a statement as “Every contact leaves a trace”. In practical case scenarios it has been observed that such exchange of evidences might not occur at all in a perfectly committed crime. Since, the origins of Forensic Science were not supported by the larger scientific communities due to its indecisive nature. The fundamentals of any discipline of science have a lot of importance in its development in the long years. There has been no mathematical or statistical explanation for Locard’s exchange principle and thus, the reliability of the same has been always questioned. Some studies have suggested likelihood ratio or Bayes factor to support the exchange principle. While few suggest modification of the statement to bring clarity of this principle. It is necessary to match the criteria required for a statement to become a scientific principle with reference to Locard’s principle. The current article is an effort to measure the validity of Locard’s principle including its merit/demerits and encourage the scientific community for complying to standards and corrections required for wider application in field of criminal investigation

Social Media Under Scrutiny: A Data-Driven Study of Privacy and Security Challenges

Social media has completely changed how individuals engage with one another and has become a crucial component of modern communication. It has made it possible for people and organizations to communicate with one another, exchange ideas and information, and create communities on a global scale. Social media has also been linked to a number of detrimental effects, including addiction, cyber bullying, false information, and the dissemination of propaganda and hate speech. As a result, it is crucial that users of social media use them properly and critically assess the content they come across online. Users should be aware of social media privacy and security concerns in order to safeguard their personal information and stop illegal access to their accounts. In this paper researchers going to found the security and privacy concern of customer while using social media and its impact on their mental health. For analyzing the data JASP software has been used. It is found that a large number of paper have been concerned of privacy and security of social media. Social media has been effected the youngster more than others. The sample size has been 350. Researchers have been collect the sample though questionnaire. Social media is causes of many crimes and unlawful activity. For resolving such type of problems researchers conducted this research on social media and its privacy and concern.

The Potential of Machine Learning and Artificial Intelligence inForensicScience: An Overview of Key Applications, Challenges, andFutureDirections

Forensic science has evolved signi𿿿cantly in recent years with the integrationof machine learning (ML) and arti𿿿cial intelligence (AI). The use of these technologies has created new possibilities and the ability to completely transform the industry. The ability to quickly and precisely analyze large volumes of data has signi𿿿cantlyimproved the effectiveness and accuracy of forensic analysis. In the context of arti𿿿cial intelligence and machine learning, this paper explores the functions of image analysis, pattern recognition, bloodstain pattern analysis, anomaly detection, cybersecurity, intrusion detection, age, sex, ancestry estimation, facial reconstruction, skeletal trauma analysis, DNA analysis, genomics, 𿿿ngerprint identi𿿿cation, and oice recognition. The function of numerous technologies, including virtual reality, neural networks, support vector machines, computer vision, CBR, and NLP, is also investigated. Despite the advantages, there are several drawbacks to using these technologies, including the requirement for high-quality data and the possibility of algorithmic bias. Therefore, it is imperative to provide moral and practical criteria for the application of AI and machine learning in forensic science. The main applications, dif𿿿culties, and potential future directions of machine learning and AI in orensics are outlined in this review paper. We aim to shed light on the possible advantages and dif𿿿culties of these technologies and provide insights into the future of forensic science by analyzing the state of the𿿿eld today