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Discovery of clocinizine as a potential oral drug against Schistosoma mansoni infection

Fine-scale associational effects: Single plant neighbours can alter susceptibility of focal plants to herbivores

The neighbourhood of plants in a patch can shape vulnerability of focal plants to herbivores, known as an associational effect. Associational effects of plant neighbourhoods are widely recognised. But whether a single neighbouring plant can exert an associational effect is unknown. Here, we tested if single neighbours indeed do influence the likelihood that a focal plant is visited and eaten by a mammalian herbivore. We then tested whether any refuge effect is strengthened by having more neighbours in direct proximity to a focal plant. We used native plant species and a browser/mixed feeder mammalian herbivore (swamp wallabies (Wallabia bicolor)) free-ranging in natural vegetation. We found that a single neighbouring plant did elicit associational effects. Specifically, plant pairs consisting of one high-quality seedling next to a single low-quality plant were visited and browsed by wallabies later and less than pairs of two high-quality seedlings. Having more neighbours did not strengthen these associational effects. Compared with no neighbours, one or five low-quality neighbours had the same effect in delaying time taken for wallabies to first visit a plot and browse on a high-quality focal seedling. While traditionally a ‘patch’ refers to a broad sphere-of-influence neighbouring plants have on a focal plant, our findings suggest the influence of plant neighbours can range from the nearest individual neighbour to the entire plant neighbourhood. Such fine-scale associational effects are fundamentally important for understanding intricate plant-herbivore interactions, and ecologically important by potentially having knock-on effects on plant survival, in turn influencing plant community structure.

Optimization of dynamic incentive strategies for public transportation based on reinforcement learning and network synergy effect

Abstract Aiming at the challenges of peak passenger congestion, user behavior heterogeneity and insufficient network synergy faced by public transportation systems in urbanization, this study proposed the Dynamic Incentive Strategy-Heterogeneous Response Synergy Model (DIS-HARM). The model integrated reinforcement learning, user heterogeneity modeling and small-world network synergy mechanism, adjusted the carbon credit intensity in real time by dynamic incentive generator, quantified the diminishing marginal utility effect of incentives for high-income groups by combining elastic user identifiers, and designed weather attenuation coefficients to optimize the spread of social influence. Simulation results showed that DIS-HARM significantly improves system efficiency and fairness: the peak hour passenger flow reduction rate reaches 72.2% (2.5% higher than the static strategy), the average peak hourly cost is reduced by 3.125%, and 36.5% of the incentive resources are tilted to the low-income group (83.1% coverage rate) at the same time. The model provided a theoretical tool for dynamic pricing and differentiated incentive strategies for urban transportation management, helping to achieve the dual goals of green travel and social equity.

Integrative molecular network analysis of genetic risk factors to infer biomarkers and therapeutic targets for rheumatoid arthritis

Background Understanding the interplay between genetic risk factors and molecular pathways in rheumatoid arthritis (RA) is essential for developing effective treatments. This study aims to utilize genetic risk factors of RA and identify their key pathways and potential therapeutic targets through an integrated multi-omics approach. Methods We developed a computational pipeline to construct a knowledge graph that combines genetic risk factor molecular networks with multi-omics enrichment analysis to estimate potential therapeutic target for RA. Genetic risk factors were extracted from the literature, curated, and annotated. Molecular interaction networks were constructed based on these genetic risk factors and their neighboring proteins. Enrichment analysis was performed to identify significantly impacted biological processes and pathways. Multi-omics knowledge graph was used to prioritize candidates potential therapeutic target for RA. Results Our analysis identified 35 significant genes associated with RA as potential therapeutic targets and biomarkers, categorized into three pathways: Cytokine Regulation and Production, Hematopoietic or Lymphoid Organ Development, and Myeloid Cell Differentiation. Among these, 25 genes were classified as risk genes, while 10 were neighboring genes. We identified nine novel risk proteins (RELA, ETS1, NFATC1, BATF, LCK, PIK3R1, PRKCB, RASGRP1,and FYN) as potential therapeutic targets for RA and they significantly contribute in the disease pathogenesis. Conclusion This study provides a comprehensive integrative molecular network and knowledge graph analysis of genetic risk factors in RA, offering a solid framework for integrating multi-omics data in RA research. These findings may contribute to more accurate clinical decision-making and the development of targeted treatment regimens. Additionally, this study highlights the importance of inferring hidden relationships across networks based on disease associations and functional similarities, further enhancing our understanding of RA pathogenesis.

Vanillin as eco-friendly corrosion inhibitor for aluminum immersed in 0.5 M H2SO4

Blue and fin whales in the Northern Mariana Islands: Their call characteristics and occurrence

Many baleen whale vocalizations are species-, or even population-specific and can be used to monitor their occurrence. Although baleen whale occurrence has been well studied in parts of the Pacific Ocean, little is known about the seasonal distribution of blue and fin whales in the Western Pacific. Since 2010, a more concerted visual and acoustic survey effort has occurred around a very remote region of the western Pacific Ocean: the Northern Mariana Islands. Passive acoustic data were collected at two locations in the Northern Mariana Islands, Saipan and Tinian, from 2015–2017 and they were analyzed for call characteristics and occurrence of blue whale and fin whale calls. Low levels of Central North Pacific blue whale tonal calls were detected year-round with peaks in the winter (December) and summer (June). There was a clear seasonal pattern in fin whale calls (20 and 40 Hz calls), with the majority of detections occurring during winter and spring. Moreover, two unknown low-frequency sounds were detected, one tonal and one pulsed. The former was more common at Tinian and the latter at Saipan. By providing their acoustic features and occurrence patterns, we aim to facilitate future identification of their source. Additionally, the observed seasonal patterns in blue and fin whale call occurrences may offer insights into their movement patterns in this remote region.

Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis

Do regional trade agreements increase technology complexity of digital service export trade? Evidence from digital intellectual property rules

Based on the technology complexity data of bilateral digital service exports of 64 representative economies in the world from 2010 to 2021, this paper uses the gravity model to explore the heterogeneous trade effects of Regional Trade Agreements (RTAs) from the perspective of digital intellectual property rules. The results show that the higher the commitment level of digital intellectual property rules in RTAs signed between the contracting parties, the more conducive to the enhancement of the technology complexity of bilateral digital service trade exports, and the trade effect of RTAs is significant. The influence of the depth of digital intellectual property rules on the export technology complexity of digital service trade has heterogeneity in rule categories, economic development level of exporting countries, imitation ability of importing countries, and law enforcement gap between importing and exporting countries. The results of the mechanism test show that the digital intellectual property rules in RTAs can increase the technology complexity of digital service enterprises through the scale expansion effect of two-way FDI and the technological innovation effect of exporting countries. This paper provides policy implications for global digital intellectual property governance and the quality development of digital service trade.

Electrochemical loading enhances deuterium fusion rates in a metal target

Structural relaxation of ferroelectric phase in hard sodium lithium niobate solid solutions studied by solid-state NMR

Abstract Hard sodium lithium niobate (LNN) solid solutions offer a promising environmentally benign alternative to lead-based ferroelectric materials for electronic devices. A major challenge in their practical application is that their ferroelectric phase relaxes over a timescale ranging from weeks to years to an orthorhombic phase, limiting their long-term performance. In order to understand the structural changes in the relaxation process, high mechanical quality factor Li x Na 1− x NbO 3 solid-solutions were deliberately stored under ambient conditions for 24 months, without any specialized hermetic protection, to assess their stability over time. We utilized 1D and 2D  23 Na solid-state Nuclear Magnetic Resonance (ssNMR) to investigate short-range structural changes in the 24-months-old aged and unaged Li x Na 1− x NbO 3 solid-solutions. NMR results reveal a system with phase-changes as a function of aging time and temperature. The samples exhibit a multiphase structure compromised of crystalline R and Q orthorhombic domains, along with two types of amorphous regions. A significant amount of ferroelectric phase persists in the ceramics after 24 months of exposure to ambient conditions. A structural model based on short-range order of sodium was suggested and agrees well with the lattice parameter of the freshly prepared samples.

Migrant-friendly maternity care in Montreal, Canada: A cross-sectional study on migrant women’s care perspectives

Objective We assessed the extent to which recommended migrant-friendly maternity care (MFMC) components were provided to recently-arrived international migrants giving birth in Montreal, Canada, and the extent to which the provision of MFMC components was related to socioeconomic and migratory characteristics. Methods We conducted a cross-sectional study of migrant women giving birth in four hospitals in 2014–2015. Data were collected using the Migrant-Friendly Maternity Care Questionnaire (MFMCQ), focusing on access to prenatal care, communication facilitation, healthcare provider (HCP) support, and responsiveness to preferences for care. Data were analyzed descriptively and through logistic regression. Results Of 2636 participants, most reported always being kept informed (86.1%) and finding HCPs helpful (90.3%), although 22.9% reported barriers to accessing services during pregnancy, and only 11% or less were asked about care preferences. Of 847 needing interpreters, 84.7% reported not being offered any. Worse access to prenatal care was reported among women who had arrived more recently [OR 0.55, 95% CI 0.36, 0.85], had lower income [0.69 (0.52, 0.90)], or had less education [0.66 (0.47, 0.94)]. Low language ability was most often associated with inadequate MFMC [e.g., worse HCP support during pregnancy [0.56 (0.36, 0.87)] and worse responsiveness to preferences for care during labour [0.55 (0.31, 0.98)]]. Maternal region of birth was associated both positively and negatively with all MFMC components. Conclusion Although some MFMC has been implemented, gaps remain. Addressing language barriers remains a top priority. To deliver optimal MFMC, HCPs and policymakers should provide care that is responsive to women’s socioeconomic and migratory backgrounds.

Low-energy nuclear fusion boosted by electrochemistry

A call to action to address critical flaws and bias in laboratory animal experiments and preclinical research

Assessment of the psychometric properties of self-management measurement instruments for individuals with type 2 diabetes: A systematic review protocol

Context and objectives The type 2 diabetes mellitus (T2DM) negatively impacts patients’ quality of life, affecting their physical and mental functioning as well as social relationships. Self-management is essential for T2DM control, as it involves self-care behaviors and self-efficacy, leading to better health outcomes such as better glycemic control. There are a variety of instruments in the literature capable of measuring self-management in T2DM population. Therefore, the aim of this review is to identify the available T2DM self-management instruments and evaluate their measurement properties, as well as to analyze their contents based on the international classification of functioning, disability and health. Methods The systematic review will follow the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines, and its protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) (registration CRD42024605840). Searches will be conducted in MEDLINE, Web of Science, Scopus, PsycINFO, Embase, and CINAHL. Additionally, a manual search will be conducted in the databases: PROQOLID, PROMIS, and Medical Outcome Trust. Studies on the development and validation of patient-reported outcome measures assessing self-management in individuals with T2DM will be included, with no restrictions on language or publication date. Data extraction will use tools recommended by COSMIN. The modified Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach will determine the quality of the evidence. Instruments will be categorized according to COSMIN recommendations. All steps will be conducted by two independent reviewers, with a third reviewer consulted in case of discrepancies. Additionally, the content of the instruments will be analyzed and linked to the ICF. Discussion This systematic review may guide researchers and healthcare professionals to choose the most suitable instrument for their target population. Ethics and dissemination Ethical approval is not required, as this study is a review of published data. The results will be disseminated through publication in peer-reviewed journals.

Dying star reveals its inner structure

Integrating non-linear radon transformation for diabetic retinopathy grading

Syndemic interactions between HIV/AIDS, mental health conditions, and non-communicable diseases in sub-Saharan Africa: A scoping review of contributing factors

Introduction The syndemic framework provides a critical lens for understanding the complex interplay between HIV/AIDS, mental health (MH) conditions, and non-communicable diseases (NCDs) in Africa. This scoping review explores how these conditions converge to form a syndemic that disproportionately affects vulnerable populations – particularly people living with HIV/AIDS (PLWH). Contextual factors such as stigma, lower socioeconomic resulting in poverty, gender, resource limitations, and fragmented healthcare systems exacerbate these interrelated conditions, posing significant challenges to individuals and their health. Methods A scoping review was conducted to examine the syndemic interactions between HIV/AIDS, MH, and NCDs across Africa. Utilizing the PRISMA-ScR framework and a predefined inclusion criterion, literature searches were conducted in the following databases: PubMed/Medline (OVID), Web of Science (all databases), Web of Science (core collection), Global Health, Cumulative Index of Allied Health Literature (CINAHL), MEDLINE OVID, Psychinfo (OVID), Psychinfo (proquest); and Psychinfo (psychnet) in March 2024. Articles were screened independently by two peer reviewers and conflicts were resolved by a third reviewer. Data were extracted to summarize study characteristics, prevalence rates, and the contextual factors that underpin syndemic interactions among HIV/AIDS, MH and NCDs. Results An initial search retrieved 5937 articles, with 2913 articles remaining after removal of duplicates. Title and abstract screening further excluded 2706 articles. In total, 207 full-text articles were assessed, of which 17 publications were extracted and included in the review. The scoping review identified a significant prevalence of multi-morbidities amongst PLWH, particularly within hypertension, diabetes, and depression. Women and older adults were disproportionately affected, with gender and age disparities shaping health outcomes. Contextual factors such as stigma, socioeconomic barriers, and fragmented healthcare systems were consistently reported as key contributors to worsening such multi-morbidities. In many publications, NCDs and MH conditions were undiagnosed or poorly managed, complicating HIV treatment and reducing the quality of life. Individual and structural resource limitations, along with poor healthcare integration, further hindered effective care. Conclusion This scoping review underscores the urgent need for integrated healthcare models to address the syndemic of HIV/AIDS, NCDs, and MH in Africa. Interventions should prioritize stigma reduction, capacity building, and comprehensive care to address the underlying socioeconomic determinants of health among PLWH. Strengthening healthcare systems and promoting holistic, patient-centered care is essential for reducing disparities, improving health outcomes, and achieving the Sustainable Development Goals. Future research should expand geographic and demographic coverage to capture the full scope of these syndemic relationships in diverse African contexts.

Dual-stream hybrid architecture with adaptive multi-scale boundary-aware mechanisms for robust urban change detection in smart cities

A lightweight cross-scale feature fusion model based on YOLOv8 for defect detection in sewer pipeline

Sewer pipeline defect detection is a critical task for ensuring the normal operation of urban infrastructure. However, the sewer environment often presents challenges such as multi-scale defects, complex backgrounds, lighting changes, and diverse defect morphologies. To address these issues, this paper proposes a lightweight cross-scale feature fusion model based on YOLOv8. First, the C2f module in the backbone network is replaced with the C2f-FAM module to enhance multi-scale feature extraction capabilities. Second, the HS-BiFPN module is adopted to replace the original structure, leveraging cross-layer semantic fusion and feature re-weighting mechanisms to improve the model’s ability to distinguish complex backgrounds and diverse defect morphologies. Finally, DySample is introduced to replace traditional sampling operations, enhancing the model’s ability to capture details in complex environments. This study uses the Sewer-ML dataset to train and evaluate the model, selecting 1,158 images containing six types of typical defects (CK, PL, SG, SL, TL, ZW), and expanding the dataset to 1,952 images through data augmentation. Experimental results show that compared to the YOLOv8n model, the improved model achieves a 3.8% increase in mAP, while reducing the number of parameters by 35%, floating-point operations by 21%, and model size by 33%. By improving detection accuracy while achieving model lightweighting, the model demonstrates potential for application in pipeline defect detection.

A lightweight and explainable CNN model for empowering plant disease diagnosis

Abstract Crop disease is a significant challenge in agriculture, requiring quick and precise detection to safeguard yields and reduce economic losses. Traditional diagnostic methods are slow, labor-intensive, and rely on expert knowledge, limiting scalability for large-scale operations. To overcome these challenges, a novel architecture called Mob-Res, combining residual learning with the MobileNetV2 feature extractor, is introduced in this work. Despite having only 3.51 million parameters, Mob-Res is lightweight and well-suited for mobile applications while delivering exceptional performance. The proposed model is assessed using two benchmark datasets: Plant Disease Expert, consisting of 199,644 images across 58 classes, and PlantVillage, with 54,305 images across 38 classes. Through a rigorous training strategy, Mob-Res demonstrates robust performance, achieving 97.73% average accuracy on the Plant Disease Expert dataset and 99.47% on the PlantVillage dataset. The cross-domain validation rate (CDVR) is computed to assess its cross-domain adaptability, with the model showing competitive results compared to other pre-trained models. Additionally, Mob-Res outperforms prominent pre-trained CNN architectures, surpassing ViT-L32 while maintaining a significantly lower parameter count and achieving faster inference times. The proposed model enhances interpretability by utilizing Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, and Local Interpretable Model-agnostic Explanations (LIME). These techniques provide visual insights into the neural regions influencing the predictions. The experimental results conducted in the current work highlight Mob-Res as a promising solution for automated plant disease detection, supporting large-scale agricultural operations and advancing global food security.