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Factors associated with the duration of telephone observation and consultation sessions provided by the Hiroshima Prefecture Follow-up Center in the later stages of the COVID-19 pandemic in Japan

Background Identifying the factors that affected the workload of Public Health Centers (PHCs) during the recent COVID-19 pandemic may help such institutions improve their preparedness and response to future pandemics and public health crises. Therefore, this study aimed to examine factors related to the duration of telephone observation and consultation sessions administered by nurses at the Hiroshima Prefecture Follow-Up Center to patients with COVID-19 recuperating at home during the pandemic. Methods This cross-sectional study used data from 22,490 telephone observation and consultation sessions collected using the COVID-19 Japan-Surveillance in Post-Extreme Emergencies and Disasters form by nurses at the Hiroshima Prefecture Follow-Up Center (Japan) from 1 November 2022 to7 May 2023. Long call duration was defined as ≥ 15 minutes. Study days were classified into low-, middle-, and high-incidence groups using the 25th and 75th percentiles of the 7-day moving average of newly diagnosed COVID-19 cases. Multivariable logistic regression models were fitted separately for each incidence group. Results Across the low-, middle-, and high-incidence groups, age ≥ 80 years, two or more symptoms, medical consultation on physical symptoms, and consultation about family members or close contacts were consistently associated with long telephone observation and consultation sessions. In the middle- and high-incidence groups, long telephone sessions were significantly more common at nighttime and among patients who requested physician involvement, and less common among pregnant women, whereas longer evening sessions were observed only in the high-incidence group. Conclusions In the later stages of the COVID-19 pandemic, several factors found to be associated with long telephone observation and consultation sessions depended on the number of newly diagnosed cases. These results may help PHCs and Follow-Up Centers plan staffing, triage, and workflows when personnel and time are limited.

The primate hippocampus constructs a dynamic internal model of rhythmic events

Comparison of two algorithms of APTT-based lupus anticoagulant assay, two Dilute Russell viper venom time reagents, and silica clotting time

Lupus anticoagulants (LA) are heterogeneous antiphospholipid antibodies that interfere with phospholipid-dependent coagulation assays, resulting in considerable variability among detection methods. Although international guidelines recommend a stepwise approach incorporating screening, mixing, and confirmatory testing, integrated strategies omitting routine mixing studies are widely used. Different percentile-based cutoffs have been proposed for defining LA positivity.We retrospectively analyzed 135 citrated plasma samples requested for LA testing. Five LA detection approaches were evaluated: 4 integrated assays and 1 activated partial thromboplastin time (APTT)–based approach following the ISTH-recommended stepwise algorithm with a mixing study. The integrated assays comprised silica clotting time, dilute Russell viper venom time (dRVVT) using 2 different reagent systems, and an APTT-based assay. Precision studies and reference intervals were established, and LA positivity rates were compared using 97.5th and 99th percentile cutoffs. Inter-assay agreement and associations with anticardiolipin (aCL) and anti–β2-glycoprotein I (aβ2GPI) antiphospholipid antibodies were assessed. LA positivity rates varied across procedures (19.3%–35.6%) at the 97.5th percentile. Application of the 99th percentile decreased positivity for dRVVT-HemosIL and APTT-based assays. Positivity increased at the 97.5th percentile in patients tested according to ISTH indications, with minimal impact in noncompliant cases. Inter-assay agreement ranged from fair to substantial and was influenced by assay type and cutoff definition. APTT-based assays showed the strongest associations with aCL and aβ2GPI antibodies. LA detection is strongly influenced by assay selection and cutoff strategy. Positivity rate–based evaluation provides a practical framework for comparing LA assays in laboratory practice, particularly in the absence of a reference standard.

Integrated effects of light intensity, spectral composition and photoperiod fragmentation on growth and nutrient removal of three duckweed species

Health outcomes and healthcare delivery systems for pregnant and postpartum individuals in immigration detention settings: A scoping review protocol

Pregnant and postpartum individuals in immigration detention may experience barriers to accessing timely and appropriate perinatal care. The existing literature remains fragmented across global settings. This scoping review protocol aims to map the extent, range, and nature of evidence on health outcomes and healthcare delivery systems for pregnant and postpartum individuals in immigration detention. Following the Arksey and O’Malley framework and PRISMA-ScR guidelines, peer-reviewed studies and relevant grey literature will be included without language restrictions, after January 2000. Two reviewers will independently screen sources, and a third will perform full text review and extract data. Findings will be synthesized descriptively and thematically.

Challenges and strategic assessment of biohydrogen development via a fuzzy Delphi-SWOT-QSPM framework

Editorial Note: Design and implementation of a graphene–polyimide-based H-slot terahertz antenna for wireless and biomedical applications

A quantum resistant chaos driven image encryption framework for secure visual data transmission in intelligent transportation systems

Cannabis use, sexual behaviors, and HIV prevention behaviors among young adults attending key-population-led sexual health clinics in Bangkok, Thailand: A mixed-method study

Background The intersection of cannabis and sexual behaviors poses important public health concerns, particularly following Thailand’s cannabis decriminalization. However, evidence on how cannabis use relates to sexual and HIV prevention behaviors among young adults in this policy context remains limited. This mixed-methods study aimed to assess cannabis use patterns and their associations with sexual behaviors and HIV prevention behaviors among young adults attending key population-led sexual health clinics in Bangkok, Thailand. Methods A self-report survey of 200 participants aged 18–24 assessed cannabis use, sexual behaviors, and HIV prevention behaviors using study-specific questionnaires, and the Cannabis Use Disorder Identification Test-Revised. Laboratory data, including HIV and Sexually Transmitted Infections (STIs) test results were extracted from medical records. Chi-square tests and Poisson regression examined associations between cannabis use, sexual behaviors, and HIV prevention behaviors. In-depth interviews with 15 cannabis users and 15 non-users explored perceptions of cannabis use in relation to sexual behaviors and HIV prevention behaviors to complement quantitative findings. Thematic analysis was conducted. Results Among the 200 participants (mean age = 21.45, SD = 1.89; 35% gay men, 32% transgender women), 22% were past-month cannabis users. Cannabis use was significantly associated with sex under the influence of any substances (Prevalence Ratio (PR)=1.24, 95%CI: 1.09,1.41), and alcohol (PR = 1.12, 95%CI: 1.02,1.24). No associations were found with other sexual behaviors, PrEP adherence, HIV or any STIs test results. Many participants cited disinhibitions as a pathway to risk, whilst some cannabis users emphasized individual responsibility as a more important determinant of behavior. Conclusions Cannabis use is linked to alcohol- or substance-influenced sex. Diverging views on cannabis and sexual risk suggest a need for tailored youth-centered harm reduction strategies within sexual health clinics that address risks and empower personal responsibility, particularly in the context of Thailand’s evolving cannabis policy.

A comparative study of machine and deep learning models for time-series-based bearing fault diagnosis of induction motors

Microscopic simulation of free riding speed dynamics in bicycle traffic: Modeling heterogeneous context-dependent effects

Simulation is a valuable tool for traffic planning that requires reliable modeling of traffic dynamics. Free flow speeds in bicycle traffic depend on the characteristics and preferences of the bicyclists, infrastructure design, and environmental conditions. However, existing models are limited in capturing changes in the speed during free riding, thereby reducing their applicability in bicycle traffic analysis. This study advances microscopic bicycle traffic simulation by developing and evaluating simulation models for free riding dynamics, aiming to capture the heterogeneous and context-dependent effects of infrastructure design (slopes, curves, and presence of intersections) and wind on bicyclist behavior. We implement three models within SUMO —(1) context-based speed distributions, (2) a speed regression model, and (3) a physics-based speed model derived from power output— and benchmark them against built-in models. All models are evaluated using empirical trajectory data from 57 bicycle commuters in semi-controlled experiments in Sweden and Germany. Results demonstrate that the proposed models outperform the existing baselines in replicating speed patterns. In this regard, the physics-based model provides the closest alignment to observed speeds. Omitting context dependency in free riding can result in substantially larger errors in speeds on uphills and downhills, and in deceleration on curves or when crossing intersections. Context-sensitive models enhance the accuracy of bicycle traffic simulation, thereby increasing their usefulness in planning and evaluating bicycling facilities that accommodate the diverse preferences of bicyclists.

Ventral tegmental area glutamatergic neurons suppress hippocampal ripples and induce head movements in mice

Geometric phase sensing using seismic waves for comprehensive volcano monitoring at Kı̄lauea Hawaii

Abstract Monitoring volcanic activity requires sensitive tools capable of detecting subtle subsurface changes across eruptive cycles. While seismic methods are widely used to study volcanic unrest, few provide continuous tracking throughout eruptive stages with clearly interpretable signals. We present a geometric phase sensing approach, rooted in topological acoustics, that encodes the intrinsic geometry of the seismic wavefield. By integrating signals across a station array, the geometric phase change (Δ η ) captures wavepath-integrated medium perturbations, providing a stable measure of evolving subsurface conditions distinct from conventional waveform coherence metrics. Applied to Kı̄lauea volcano, Δ η closely tracks precursory magmatic pressurization and co-eruptive caldera collapse during the 2018 eruption, identifies two major intrusion events and post-eruptive recovery spanning five eruptions from 2020 to 2024. Δ η exhibits systematic and interpretable signatures of eruptive transitions, even under strongly perturbed ambient noise conditions. Numerical simulations corroborate these observations, indicating a sensitivity of  ~15.0% per MPa to subsurface pressure changes. By leveraging geometric phase approach, we establish a monitoring framework applicable to volcanic and other dynamic Earth systems.

An approach of dual attention-driven multi-levelled graph neural architecture for rich-structural social graph analysis and representation learning

In recent years, graph neural networks (GNNs) have emerged as a dominant paradigm for learning from graph-structured data, achieving strong performance across a wide range of graph mining tasks, including node classification, clustering, and relational inference. Despite these advances, most existing GNN architectures primarily rely on message-passing mechanisms that aggregate smoothed high-order local neighborhood information. While effective for capturing local proximity patterns, such designs often struggle to model richer multi-level graph structures, particularly in scenarios where both fine-grained local interactions and holistic global organization are essential. From a structural perspective, graph representation learning inherently involves information distributed across multiple levels. Local proximity cues, such as node degrees and common neighbors, capture immediate relational patterns, whereas global structural characteristics, including long-range dependencies and overall graph topology, provide complementary high-order information. However, conventional GNN models typically entangle these heterogeneous signals within a unified aggregation process, leading to suboptimal representations. To address this limitation, we propose DAGRL, a dual attention-based graph representation learning framework that explicitly disentangles and integrates multi-level structural information. The proposed model employs dedicated graph neural components to learn structure-specific representations and utilizes a dual attention mechanism to adaptively fuse them into a unified embedding space. Extensive experiments demonstrate that DAGRL consistently outperforms existing methods across multiple benchmark datasets.

Functional and structural olfactory changes in post-COVID-19 patients detected by 7 Tesla MRI

Topological suppression of quantum tunnelling in a lanthanide single-ion molecular magnet

Abstract Quantum coherence can be preserved by exploiting topology, encoding information in global geometric properties that resist local perturbations. These properties depend on the trajectory of quantum operations and curvature in parameter space, offering a topology-based route to fault-tolerant quantum computation. While geometric phase interference (Berry phase) is widely studied to probe a system’s topology, its direct detection in 4f-based molecular magnets—promising qudit platforms—has remained elusive. We present a magneto-spectroscopic μSQUID-EPR approach to resolve tunnel splittings in the Gd-based molecular magnet [ 160 GdPc₂]⁻ (Pc = phthalocyanine). By irradiating single crystals with microwaves under transverse magnetic fields, we map the spin ( S  = 7/2) manifold and observe pronounced oscillations in tunnel splitting—a hallmark of quantum phase interference. These oscillations reveal topological quenching and higher-order anisotropy, underscoring the role of topology in 4f systems and opening pathways toward holonomic quantum computation.

Detection of non-invasive sexing of early chick embryos in intact eggs using laser speckle contrast imaging and deep neural networks

The ability to image blood flow in early-stage avian embryos has significant applications in developmental biology, drug and vaccine testing, as well as determining sex differentiation. In this project, a recently developed laser speckle contrast imaging (LSCI) system was used to non-invasively image extraembryonic blood vessels and used these images to attempt early sex identification of chick embryos. Specifically, blood vessels images were captured from 1,251 living chicken embryos between day three and day four of incubation. Then, deep neural network (DNN) models were applied to evaluate whether it is possible to differentiate sex based on vascular patterns. Using ResNetBiT and YOLOv5s-cls models, our results indicate that sex differentiation from extraembryonic blood vessel images was not achievable with sufficiently high accuracy or statistical significance for practical use. Specifically, ResNetBiT had a five-fold cross-validated average accuracy of 56% ± 4% (p-value of 0.28 across cross-validation folds) at day 3 and 57% ± 3% (p-value of 0.07 across cross-validation folds) at day 4. YOLOv5s-cls had a five-fold cross-validated average accuracy of 55% ± 2% (p-value of 0.13 across folds) at day 3 and 57% ± 3% (p-value of 0.10 across folds) at day 4. Our findings suggest that under the current experimental conditions and modeling approaches, per-egg evaluation did not produce sufficiently accurate or statistically robust results for early sex classification.

Image segmentation of track bolts based on improved YOLO model

dITP-induced remodeling activates the filamentous effector complex in Kongming anti-phage defense

The individual and combined effects of air pollution mixtures on the risk of cardiovascular diseases in patients with Cardiovascular-Kidney-Metabolic syndrome at stages 0–3

Background This study aims to employ a prospective cohort design to quantitatively assess the association between exposure levels of common ambient air pollutants and the risk of cardiovascular disease (CVD) in patients across stages 0–3 of Cardiovascular-kidney-metabolic (CKM) syndrome. By doing so, it addresses a critical knowledge gap in environmental exposure research within this specific clinical context. Methods We analyzed baseline differences between CVD cases/controls using descriptive statistics, parametric/nonparametric tests, and Pearson correlations for air pollutants (PM₁, PM2.5, PM₁₀, NO₂, O₃). Cox regression (Models 1–3, adjusting for sociodemographic/behavioral factors) and RCS assessed pollutant-CVD associations. WQS/qgcomp models evaluated mixture effects via weighted indices and directional weighting. Sensitivity analyses used BKMR, WQS-CVD exposure-response curves, and 2-year lag to address reverse causality. Results In single-pollutant analyses, per-interquartile range (IQR) increases in PM₁, PM₂.₅, PM₁₀, and NO₂ were associated with 30% (HR = 1.30, 95% CI 1.17–1.45), 35% (HR = 1.35, 95% CI 1.21–1.51), 52% (HR = 1.52, 95% CI 1.35–1.70), and 30% (HR = 1.30, 95% CI 1.17–1.45) elevations in CVD risk, respectively. No significant association was found for O₃. In mixture analyses, all three quantile g-computation (qgcomp) models linked combined pollutant exposure to significantly higher CVD risk (Model 1: HR = 1.10, 95% CI 1.04–1.17; Model 2: HR = 1.11, 95% CI 1.04–1.18; Model 3: HR = 1.12, 95% CI 1.05–1.19). PM₁₀ emerged as the dominant driver of the mixture effect. Conclusion Higher exposure levels to ambient air pollutants are associated with an increased risk of cardiovascular disease in patients with Stage 0–3 CKM syndrome.