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Geospatial and econometric approaches or older driver safety: Analysis of crash injury severity of regional highways

This study tried to focus on the older drivers’ group and explore the impact factors of injury severity involving older drivers from geo-spatial analysis. To reach the goal, a spatial analysis was proposed employing geographic information systems (GIS) with a case study application to two counties in Nevada. First, crash clusters were explored using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach to investigate the spatial crash pattern for older drivers, and determine high risk locations of injury severity. Next, Bayesian spatial binary probit model was presented in order to determine the significant impact factors of injury severity involving older drivers. It was found that at-fault driver condition and vehicle condition, not-at-fault vehicle action and road factors were significant factors for injury severity of older drivers. Results revealed that DBSCAN provides a solid option for hotspot identification of injury severity and Bayesian spatial binary probit model addresses the factor determinants spatially. The GIS-based spatial analysis can benefit more reliable older driver-concentrated evaluation and injury severity analysis.

“I just felt that everything came tumbling down around me”—Barriers in cancer care for patients with severe mental illness: A qualitative study

Background Patients with severe mental illness experience serious inequity when facing cancer treatment. They are less likely to be referred for cancer treatment following recommended guidelines and have poorer cancer survival than patients without mental illness. Relevant specialties such as psychiatry and general practice are rarely involved, and the patient perspective is rarely represented in research in the field. The present study investigated how patients with severe mental illness experience barriers to and facilitators of patient-centred cancer treatment and care. Methods In this qualitative case study, field observations, semi-structured interviews, and patient file analysis were performed with five patients with cancer in an adult psychiatric setting, included through purposeful sampling. Results Our analysis showed one major theme, “Complexity on many levels”, and four subthemes: “How the mental illness is affected by the cancer trajectory”, “The complexity of patient vulnerability”, “Fragmented healthcare system and lack of structure”, and “The role of the relationship between patient and health professional.” Barriers included the cancer trajectory leading to severe worsening of the mental illness, as well as fragmentation of the healthcare system and a lack of a systematic approach to the patient group. Facilitators included the health professionals acknowledging the patient’s own resources and approaching the patient as a person rather than a disease. Conclusion This study highlights critical focal points to improve care for patients with cancer who also struggle with severe mental illness. By addressing these target areas, healthcare providers can better tailor their approach to meet the unique needs of this population.

Effect of intra- and inter-specific plant interactions on the rhizosphere microbiome of a single target plant at different densities

Root and rhizosphere studies often focus on analyzing single-plant microbiomes, with the literature containing minimum empirical information about the shared rhizosphere microbiome of multiple plants. Here, the rhizosphere of individual plants was analyzed in a microcosm study containing different combinations and densities (1–3 plants, 24 plants, and 48 plants) of cover crops: Medicago sativa, Brassica sp., and Fescue sp. Rhizobacterial beta diversity was reduced by increasing plant density for all plant mixtures. Interestingly, plant density had a significant influence over beta diversity while plant diversity was found to be a less important factor since it did not have a significant change. Regardless of plant neighbor identity or density, a low number of rhizobacteria were strongly associated with each target species. Nonetheless, a few bacterial taxa were shown to have conditional associations such as being enriched within only high plant densities, which may alleviate plant competition between these species. Also, we found evidence of bacterial sharing of nitrogen fixers from alfalfa to fescue. Although rhizosphere bacterial networks had overlapping bacterial modules, the modules showing the largest percentage of the network changed depending on plant neighbor. In summary, this study found that for the most part plants maintained their rhizosphere microbiome despite escalating plant-plant competition.

Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions

As people’s material living standards continue to improve, the types and quantities of household garbage they generate rapidly increase. Therefore, it is urgent to develop a reasonable and effective method for garbage classification. This is important for resource recycling and environmental improvement and contributes to the sustainable development of production and the economy. However, existing deep learning-based garbage image classification models generally suffer from low classification accuracy, insufficient robustness, and slow detection speed due to the large number of model parameters. To this end, a new garbage image classification model is proposed, with the ResNet-50 network as the core architecture. Specifically, first, a redundancy-weighted feature fusion module is proposed, enabling the model to fully leverage valuable feature information, thereby improving its performance. At the same time, the module filters out redundant information from multi-scale features, reducing the number of model parameters. Second, the standard 3×3 convolutions in ResNet-50 are replaced with depth-separable convolutions, significantly improving the model’s computational efficiency while preserving the feature extraction capability of the original convolutional structure. Finally, to address the issue of class imbalance, a weighting factor is added to the Focal Loss, aiming to mitigate the negative impact of class imbalance on model performance and enhance the model’s robustness. Experimental results on the TrashNet dataset show that the proposed model effectively reduces the number of parameters, improves detection speed, and achieves an accuracy of 94.13%, surpassing the vast majority of existing deep learning-based waste image classification models, demonstrating its solid practical value.

Antiretroviral therapies and status of people living with HIV in Japan: An update from hospital survey and national database

No updated data on people living with HIV (PLHIV) in Japan have been available since 2015, leaving a critical gap in understanding the current status of care and treatment. Therefore, this study aimed to conduct a nationwide evaluation of the second and third goals of the “90-90-90 target” defined by UNAIDS between 2016 and 2020. The study utilized data from approximately 360 core hospitals through structured questionnaires and the National Database of Health Insurance Claims and Specific Health Checkups (NDB). Key findings revealed that over 95% of diagnosed outpatients were retained in care (second 90), and more than 99% achieved successful viral suppression (third 90). A significant transition to single-tablet regimens and newer, highly effective antiretroviral drugs was observed, optimizing treatment adherence and outcomes. These results underscore the efficacy of Japan’s universal health insurance system in ensuring consistent access to HIV care and treatment, supporting both individual patient outcomes and national surveillance efforts.

Factors associated with receiving a Functional Disorder diagnostic label: A systematic review

Objectives Functional Disorders (FD) are highly prevalent conditions that are diagnosed based on the presence of specific patterns of somatic symptoms. Examples of FDs include Fibromyalgia and Irritable Bowel Syndrome. Many patients who meet the criteria do not receive a formal diagnostic label. This systematic review aims to assess factors associated with receiving an FD diagnostic label. Methods A systematic search of PubMed, PsycINFO, and Embase was performed following the PRISMA guidelines. All research methodologies and languages were included with a focus on experiences and impacts of receiving/having an FD diagnostic label. Excluded studies were those not mentioning diagnostic labels, only involving single pain symptoms, and studies solely focusing on functional neurological symptoms. Screening, data extraction and quality ratings (using the QuADS instrument) were performed by two independent reviewers. Results 15 Studies were identified (10 quantitative and 5 qualitative). Our results show that female patients were more likely to receive an FD diagnostic label for their symptoms; other associations were less consistent and only found for specific labels or research designs. In general, quality of life and healthcare use did not seem to differ between patients with and without an FD diagnostic label. From the healthcare professional’s perspective there was doubt about giving an FD diagnostic label, mainly due to concerns of harm for patients. Quality of included studies was rated low to moderate. Conclusion Better understanding of factors associated with receiving or having an FD diagnostic label, independently from symptom development can help healthcare professionals make evidence-based decisions in labelling or not; however, high quality studies on this topic are urgently needed.

Age-related change in inhibitory processes when controlling working memory capacity and processing speed: A confirmatory factor analysis

The main purpose of this study was to examine the age-related changes in inhibitory control of 450 children at the ages of 7–8, 11–12, and 14–16 when controlling for working memory capacity (WMC) and processing speed to determine whether inhibition is an independent factor far beyond its possible reliance on the other two factors. This examination is important for several reasons. First, empirical evidence about age-related changes of inhibitory control is controversial. Second, there are no studies that explore the organization of inhibitory functions by controlling for the influence of processing speed and WMC in these age groups. Third, the construct of inhibition has been questioned in recent research. Multigroup confirmatory analyses suggested that inhibition can be organized as a one-dimension factor in which processing speed and WMC modulate the variability of some inhibition tasks. The partial reliance of inhibitory processes on processing speed and WMC demonstrates that the inhibition factor partially explains the variance of inhibitory tasks even when WMC and processing speed are controlled and some methodological concerns are addressed.

Correction: Severe, but not moderate asthmatics share blood transcriptomic changes with post-traumatic stress disorder and depression

Exploring the impact of urban spatial morphology on land surface temperature: A case study in Linyi City, China

The increasing population density and impervious surface area have exacerbated the urban heat island effect, posing significant challenges to urban environments and sustainable development. Urban spatial morphology is crucial in mitigating the urban heat island effect. This study investigated the impact of urban spatial morphology on land surface temperature (LST) at the township scale. We proposed a six-dimensional factor system to describe urban spatial morphology, comprising Atmospheric Quality, Remote Sensing Indicators, Terrain, Land Use/Land Cover, Building Scale, and Socioeconomic Factors. Spatial autocorrelation and spatial regression methods were used to analyze the impact. To this end, the township-scale data of Linyi City from 2013 to 2022 were collected. The results showed that LST are significantly influenced by urban spatial morphology, with the strongest correlations found in the factors of land use types, landscape metrics, and remote sensing indices. The global Moran’s I value of LST exceeds 0.7, indicating a strong positive spatial correlation. The High-High LISA values are distributed in the central and western areas, and the Low-Low LISA values are found in the northern regions and some scattered counties. The Geographically Weighted Regression (GWR) model outperforms the Spatial Error Model (SEM) and Ordinary Least Squares (OLS) model, making it more suitable for exploring these relationships. The findings aim to provide valuable references for town planning, resource allocation, and sustainable development.

Navigating the shots: Parental willingness to immunize their children with COVID-19 vaccines in Saudi Arabia explored through a systematic review and meta-analysis

Introduction Although COVID-19 vaccines have been recommended for children and adolescents since 2021, suboptimal vaccination uptake has been documented. No previous systematic review/meta-analysis (SRMA) investigated parents’ willingness to administer COVID-19 vaccines for their children in Saudi Arabia. Accordingly, this SRMA aimed to estimate parents’ willingness to immunize their children with COVID-19 vaccines in Saudi Arabia and to identify reasons and determinants influencing parents’ decisions. Methods This SRMA adhered to the PRISMA guidelines and the protocol was registered on PROSPERO (ID: CRD42023492760). An extensive systematic search was performed across electronic databases including Pub Med, Pub Med Central, ISI Web of science, Web of Science Core Collection, Medline, KCI-Korean Journal Database, ProQuest, and SciELO, to identify relevant studies published from January 1, 2020 to October 30, 2023. A random-effects model was utilized to estimate the pooled effects considering the expected variability across studies. Heterogeneity, risk of bias, publication bias and quality of studies were considered and evaluated by relevant appropriate tests to ensure robust results. Results Twenty-five studies with 30,844 parents were included. The overall pooled rate of parents who intended to immunize their children with COVID-19 vaccines was 48.0% (95% CI: 41.0–54.0%) with high heterogeneity (I2 = 99.42%). The main reason for parents to vaccinate children was to protect child, family and community from COVID-19. Perceived efficacy/safety of vaccines were the most significant determinants associated with parents’ willingness to vaccinate children. Conclusion This was the first SRMA from Saudi Arabia which emphasized the priority to focus on vaccine-related factors as main/key strategy of COVID-19 vaccines’ drivers to convince parents in a logical way based on accurate cumulative and emerging scientific data about efficacy and safety of COVID-19 vaccines to optimize their uptake by children/adolescents. This SRMA can provide valuable insights for development of evidence-based policies to improve parental willingness to vaccinate children, which is crucial for controlling SARS-CoV-2 spread and promoting herd immunity in the community particularly if the virus continues to pose a major threat.

Enhancing MaaS user satisfaction through strategic marketing: The synergy of sustainability and service experience

As urbanization intensifies and the need for sustainable transportation grows, Mobility as a Service (MaaS) emerges as a promising solution to urban mobility challenges. This study seeks to explore the underlying mechanisms of MaaS from a sustainability perspective and to assess its impact on service experience and user satisfaction. Additionally, it examines how user satisfaction influences the broader adoption of MaaS. To address these objectives, relevant hypotheses were posited, and hypothetical models were constructed based on a comprehensive review of the literature. The interconnections among sustainability, service experience, and user satisfaction within MaaS were rigorously analyzed employing both a survey methodology and structural equation modeling for data analysis. The findings support five hypotheses, affirming that sustainability significantly influences the MaaS service experience, which in turn impacts user satisfaction. Furthermore, sustainability directly contributes to user satisfaction and is crucial for its enhancement. User satisfaction also positively affects the dissemination of MaaS services. Notably, the study identifies a critical mediating role of service experience in the utilization of MaaS, linking sustainability and user satisfaction. This research offers both theoretical insights and practical guidance for understanding the operational dynamics of MaaS and improving the user experience.

Development of a service blueprint for blockchain services

As blockchain has been actively applied in various services, a tool for visualizing the complex service processes reflecting the characteristics of blockchain has been required. A service blueprint is a tool to visualize all key systems and encounters in service delivery. Although several blueprints already exist, they have limitations to systematically visualize and analyze blockchain service processes. This study develops a Blockchain Service Blueprint (BSB) specialized in visualizing and analyzing blockchain service processes. A comprehensive literature review and an analysis of blockchain services were conducted to identify characteristics of blockchain services and limitations of existing blueprints. The BSB was developed based on the derived key components of blockchain service processes, so that it has the optimal structure with key elements to visualize complex processes of blockchain services. The usefulness of the BSB was verified by both comparisons with traditional blueprints and expert interviews. The proposed BSB can intuitively and clearly visualize a service process between customers and service providers in blockchain services. Using the Blockchain Service Blueprint (BSB), providers can identify and improve service processes to enhance sustainability, and this study offers researchers cases and a development process that demonstrate the BSB’s effectiveness across various blockchain services.

Dual-hybrid intrusion detection system to detect False Data Injection in smart grids

Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures. This paper addresses this gap by proposing a novel IDS that utilizes hybrid feature selection and deep learning classifiers to detect FDIAs in smart grids. The main objective is to enhance the accuracy and robustness of IDS in smart grids. The proposed methodology combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for hybrid feature selection, ensuring the selection of the most relevant features for detecting FDIAs. Additionally, the IDS employs a hybrid deep learning classifier that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture the smart grid data’s spatial and temporal features. The dataset used for evaluation, the Industrial Control System (ICS) Cyber Attack Dataset (Power System Dataset) consists of various FDIA scenarios simulated in a smart grid environment. Experimental results demonstrate that the proposed IDS framework significantly outperforms traditional methods. The hybrid feature selection effectively reduces the dimensionality of the dataset, improving computational efficiency and detection performance. The hybrid deep learning classifier performs better in key metrics, including accuracy, recall, precision, and F-measure. Precisely, the proposed approach attains higher accuracy by accurately identifying true positives and minimizing false negatives, ensuring the reliable operation of smart grids. Recall is enhanced by capturing critical features relevant to all attack types, while precision is improved by reducing false positives, leading to fewer unnecessary interventions. The F-measure balances recall and precision, indicating a robust and reliable detection system. This study presents a practical dual-hybrid IDS framework for detecting FDIAs in smart grids, addressing the limitations of existing IDS techniques. Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.

Identification of key regulators in pancreatic ductal adenocarcinoma using network theoretical approach

Pancreatic Ductal Adenocarcinoma (PDAC) is a devastating disease with poor clinical outcomes, which is mainly because of delayed disease detection, resistance to chemotherapy, and lack of specific targeted therapies. The disease’s development involves complex interactions among immunological, genetic, and environmental factors, yet its molecular mechanism remains elusive. A major challenge in understanding PDAC etiology lies in unraveling the genetic profiling that governs the PDAC network. To address this, we examined the gene expression profile of PDAC and compared it with that of healthy controls, identifying differentially expressed genes (DEGs). These DEGs formed the basis for constructing the PDAC protein interaction network, and their network topological properties were calculated. It was found that the PDAC network self-organizes into a scale-free fractal state with weakly hierarchical organization. Newman and Girvan’s algorithm (leading eigenvector (LEV) method) of community detection enumerated four communities leading to at least one motif defined by G (3,3). Our analysis revealed 33 key regulators were predominantly enriched in neuroactive ligand-receptor interaction, Cell adhesion molecules, Leukocyte transendothelial migration pathways; positive regulation of cell proliferation, positive regulation of protein kinase B signaling biological functions; G-protein beta-subunit binding, receptor binding molecular functions etc. Transcription Factor and mi-RNA of the key regulators were obtained. Recognizing the therapeutic potential and biomarker significance of PDAC Key regulators, we also identified approved drugs for specific genes. However, it is imperative to subject Key regulators to experimental validation to establish their efficacy in the context of PDAC.

From impact metrics and open science to communicating research: Journalists’ awareness of academic controversies

This study sheds light on how journalists respond to evolving debates within academia around topics including research integrity, improper use of metrics to measure research quality and impact, and the risks and benefits of the open science movement. It does so through a codebook thematic analysis of semi-structured interviews with 19 health and science journalists from the Global North. We find that journalists’ perceptions of these academic controversies vary widely, with some displaying a highly critical and nuanced understanding and others presenting a more limited awareness. Those with a more in-depth understanding report closely scrutinizing the research they report, carefully vetting the study design, methodology, and analyses. Those with a more limited awareness are more trusting of the peer review system as a quality control system and more willing to rely on researchers when determining what research to report on and how to vet and frame it. While some of these perceptions and practices may support high-quality media coverage of science, others have the potential to compromise journalists’ ability to serve the public interest. Results provide some of the first insights into the nature and potential implications of journalists’ internalization of the logics of science.

Genomic characteristics and molecular epidemiology of MRSA from medical centers in Mexico: Results from the Invifar network

Introduction The methicillin-resistant Staphylococcus aureus (MRSA) genome varies by geographical location. This study aims to determine the genomic characteristics of MRSA using whole-genome sequencing (WGS) data from medical centers in Mexico and to explore the associations between antimicrobial resistance genes and virulence factors. Methods This study included 27 clinical isolates collected from sterile sites at eight centers in Mexico in 2022 and 2023. Antibiotic susceptibility testing was performed using VITEK 2. In addition, WGS was performed using a NovaSeq platform, and a bioinformatic analysis was conducted using several tools. Results In this study, 21 strains were CC5, five were CC8, and one was CC93. Moreover, six strains were identified as ST5(CC5)-MRSA-IIa- t895, four strains were found to be ST1011(CC5)-MRSA-IIa-t895, five strains were found to be ST1011(CC5)-MRSA-IIa-t9364, one strain was found to be ST1011(CC5)-MRSA-IIa-t8116, another was found to be ST1011(CC5)-MRSA-IIa-t62, three were found to be ST8(CC8)-MRSA-IVa-t8, one strain was ST5(CC5)-MRSA-IVa-t2, one strain was as ST93(CC93)-MRSA-IVa-t3949, two strains were ST9003(CC8)-MRSA-IVa-t18492, and three strains were ST9034(CC5)-MRSA-V-t2. All SCCmec IIa strains showed resistance to levofloxacin and ciprofloxacin, and all but two strains were resistant to clindamycin. Among the strains that harbored the type IIa cassette, most had the aadD, blaZ, and ermA_SDS genes and the erm A gene. Multiple genes for adhesion, enzymes, immune evasion, and secretion system were detected, regardless of SCCmec type. Of the SCCmec IVa strains, most harbored the Panton-Valentine leukocidin encoding genes. Conclusion In this study, the most frequently detected CC was CC5, followed by CC8, and CC93, and the most frequently detected MRSA ST was ST1011, followed by ST5. Most SCCmec elements were found to be type IIa, followed by type IVa. High MIC values were observed for ciprofloxacin, erythromycin, and clindamycin, particularly within SCCmec IIa. Of the SCCmec IVa strains, most harbored the lukS-PV and lukF-PV genes.

Using machine learning to forecast peak health care service demand in real-time during the 2022–23 winter season: A pilot in England, UK

During winter months, there is increased pressure on health care systems in temperature climates due to seasonal increases in respiratory illnesses. Providing real-time short-term forecasts of the demand for health care services helps managers plan their services. During the Winter of 2022–23 we piloted a new forecasting pipeline, using existing surveillance indicators which are sensitive to increases in respiratory syncytial virus (RSV). Indicators including telehealth cough calls and emergency department (ED) bronchiolitis attendances, both in children under 5 years. We utilised machine learning techniques to train and select models that would best forecast the timing and intensity of peaks up to 28 days ahead. Forecast uncertainty was modelled usings a novel generalised additive model for location, scale and shape (gamlss) approach which enabled prediction intervals to vary according to the level of the forecast activity. The winter of 2022–23 was atypical because the demand for healthcare services in children was exceptionally high, due to RSV circulating in the community and increased concerns around invasive group A streptococcal (iGAS) infections. However, our short-term forecasts proved to be adaptive forecasting a new higher peak once the increasing demand due to iGAS started. Thus, we have demonstrated the utility of our approach, adding forecasts to existing surveillance systems.

Water-regulated viscosity-plasticity phase transitions in a peptide self-assembled muscle-like hydrogel

Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics

Nonlinearity of the post-spinel transition and its expression in slabs and plumes worldwide

Abstract Phase transitions in the mantle control its internal dynamics and structure. The post-spinel transition marks the upper–lower mantle boundary, where ringwoodite dissociates into bridgmanite plus ferropericlase, and its Clapeyron slope regulates mantle flow across it. This interaction has previously been assumed to have no lateral spatial variations, based on the assumption of a linear post-spinel boundary in pressure and temperature. Here we present laser-heated diamond anvil cell experiments with synchrotron X-ray diffraction to better constrain this boundary, especially at higher temperatures. Combining our data with results from the literature, and using a global analysis based on machine learning, we find a pronounced nonlinearity in the post-spinel boundary, with its slope ranging from –4 MPa/K at 2100 K, to –2 MPa/K at 1950 K, and to 0 MPa/K at 1600 K. Changes in temperature over time and space can therefore cause the post-spinel transition to have variable effects on mantle convection and the movement of subducting slabs and upwelling plumes.