Browse Articles

Quadruple pegRNA enables programmable and efficient large genomic insertion

A comparative analysis of preoperative single-dose antimicrobial therapy versus extended postoperative antimicrobial regimen in elective laparoscopic cholecystectomy

Structural insights into predicted protein–protein interactions and protein complexes in the human proteome

Academic early warning model based on machine learning and model application

Deciphering physiological mechanisms of biopolymeric seed coatings in oilseed crops

Predicting depth of anaesthesia from single-channel EEG using a deep TCN-BiLSTM-attention model with EWMA

Abstract Accurately determining the depth of anaesthesia (DoA) is crucial for ensuring patient safety and providing individualised anaesthetic management. Widely used monitors, such as the Bispectral Index (BIS), rely on proprietary multichannel EEG algorithms, which limit transparency and accessibility. This study presents a single-lead EEG framework for continuous BIS estimation that is window-level causal, end-to-end, and suitable for near–real-time deployment. The approach integrates a Temporal Convolutional Network (TCN) with power-of-two dilations, a compact bidirectional LSTM for temporal refinement, and additive attention pooling, followed by an exponentially weighted moving average (EWMA) to stabilize predictions over time. This design captures multi-scale temporal dependencies directly from raw EEG while preserving interpretability and low latency. Using a random segment-level split on a public perioperative EEG–BIS dataset, the proposed model achieved a mean absolute error (MAE) of 4.499, root mean square error (RMSE) of 7.499, Pearson’s correlation coefficient (r) of 0.903, and Lin’s concordance correlation coefficient (CCC) of 0.897 relative to reference BIS values. Under a stricter subject-independent 5-fold GroupKFold evaluation, performance decreased as expected. Still, it remained stable across folds, with the best configuration achieving an MAE of 6.03 ± 0.38 and a CCC of 0.819 ± 0.050 after EWMA smoothing. With approximately 1.07 million parameters and a throughput of about 430 segments per second, the proposed framework offers an efficient and transparent solution for single-sensor DoA monitoring. Overall, this work advances data-driven BIS estimation beyond feature-based methods while explicitly addressing both within-dataset performance and generalization to unseen subjects.

DC traction grid modernization strategy to support EV chargers integration, enhance grid performance and profitability

Abstract This paper proposes a multi-stage modernization strategy based on return on investment (ROI) analysis for integrating EV chargers, battery storage and renewable energy sources (RES) into DC traction grids. A power converter interface (PCI), which consists of multiple power converters and a Smart Grid–based integration concept is proposed for modifying the existing infrastructure. The approach combines technical and economic considerations to support decision-making under varying regulatory and operational conditions. The effectiveness of the approach is evaluated using representative cost models, showing that additional services enabled by the PCI, such as EV charging, power quality improvement and RES integration, can achieve ROI values in the range of 10–20%, depending on electricity tariffs, penalty coefficients and utilization levels. The results indicate that, under suitable conditions, staged modernization of traction substations can improve power quality, reduce overloads and increase infrastructure utilization.

Ebola can be stopped — but only if world leaders prioritize public health

Spatiotemporal dynamics and nonlinear drivers of ecosystem service synergies and trade-offs in Shaanxi, China

Blue Origin rocket explosion rattles NASA’s mission to put humans back on the Moon

A lightweight remote sensing small-object detection approach with scale-based dynamic loss and efficient multi-scale attention

Abstract Accurate small-object detection is crucial for automated processes in remote sensing imagery, such as environmental monitoring and aerial surveillance. Yet it remains challenging due to limited feature representation, complex backgrounds, and localization errors. To address these challenges, this work proposes a lightweight detection approach that integrates a scale-based dynamic loss and efficient multi-scale attention. A lightweight backbone, dubbed A2C2fLite, is designed based on the A2C2f module from the most recent YOLO variant to reduce redundant parameters and computational overhead. An EMA module is embedded before the final detection heads to enhance multi-scale feature representation and improve target-background discrimination. The SD loss is used to effectively address scale variation and occlusion, improving localization accuracy beyond existing Intersection-over-Union-based losses. Evaluated on a representative remote sensing dataset, the proposed approach achieves 93.6% precision, 86.6% recall, and 92.6% $$\hbox {mAP}_{50}$$ , with only 2.0M parameters and 5.7 GFLOPs. Compared to the baseline YOLOv13, it achieves a superior accuracy-efficiency trade-off by reducing parameter counts by 20% and computational overhead by 8.1%, while improving $$\hbox {mAP}_{50}$$ by 1.5%. Visualization results demonstrate that it suppresses missed alarms and refines bounding-box localization. By minimizing memory footprint and computational bottlenecks, this lightweight design provides clear practical advantages for real-time small-object detection on resource-constrained edge devices, such as onboard Unmanned Aerial Vehicle systems and embedded aerial sensors. These findings highlight that the proposed approach offers an efficient, robust, and highly deployable solution for complex remote sensing applications.

Weakly-supervised deep learning on pathological whole-slide images for cutaneous vasculitis and its mimickers: a high-performance diagnostic support tool

War and Famine

Bespoke immune cells stave off ravages of cirrhosis

Mechanism of Artemisia annua active component quercetin in treating psoriasis by regulating ferroptosis

Kidney Transplantation after Clearing Anti-HLA Antibodies with CD19 CAR T Cells

Gold keeps glittering courtesy of surface chemistry

Association between behavioral addiction and psychological disorders among medical students in Egypt, Sudan, and Libya: a cross-sectional study

Abstract We aimed to assess the prevalence of problematic internet use, gaming disorder symptoms, or problematic pornography consumption among medical students in Egypt, Sudan, and Libya, and their associations with psychological disturbances. A cross-sectional study was conducted via an online questionnaire. We used the Problematic Internet Use Scale (IAT), the Internet Gaming Disorder Scale–Short-Form (IGDS9-SF), and the Problematic Pornography Consumption Scale (PPCS). Chi-square tests were used to assess the variation between participants on the basis of their nationality. Multivariable multiple linear regression was used to assess the associations between behavioral addiction and psychological disorders and other confounders. A total of 1284 patients completed the questionnaire. The prevalence rates were 28.82%, 6.78%, and 7.55% for problematic internet use, gaming disorder symptoms, or problematic pornography consumption, respectively. Compared with other students, Egyptian medical students had a significant increase in all behavioral addictions. A positive association between increased behavioral addictions and anxiety or depression was observed. Stress was positively associated with problematic internet use and negatively associated with gaming disorder and pornography addiction. Sleeping less than 6 h was negatively associated with gaming disorder symptoms or problematic pornography consumption, whereas sleeping more than 9 h was positively associated with gaming disorder symptoms or problematic pornography consumption. Our study revealed a moderate level of problematic internet use, with a low level of gaming disorders and pornography addiction, alongside significant regional variation in prevalence.

All-Oral Treatment of Newly Diagnosed Acute Myeloid Leukemia

Beyond composite scores in chronotype assessment: item-level predictive patterns in the Morningness–Eveningness Questionnaire

Abstract Chronotype represents individual differences in circadian preferences that influence sleep-wake patterns, cognitive performance, and clinical outcomes across the lifespan. Given its growing relevance for clinical research and application, efficient and reliable chronotype assessment is essential. However, widely used tools like the Morningness–Eveningness-Questionnaire (MEQ) rely on composite scores, despite methodological concerns about multidimensionality and unequal item contributions. Addressing this limitation, the current study applied machine learning techniques to enhance theoretical understanding and empirical precision in chronotype assessment. Using the German MEQ in the Dortmund Vital Study (ClinicalTrials.gov NCT05155397), a prospective cohort study on healthy cognitive aging, we identified item-level predictive hierarchies and response patterns distinguishing morning, neutral, and evening types. Item 19 (“Which chronotype do you think you are?“) demonstrated exceptional predictive utility, showing threefold greater importance than any other item. This metacognitive self-assessment appears to capture relatively accurate chronotype identification rooted in lived experience. Chronotype-specific analyses revealed distinct predictive patterns across types, each relying on different item combinations for optimal classification. Partial dependence analysis identified non-linear response patterns including sigmoid curves, threshold effects, and plateau regions. A six-item combination achieved robust overall classification, potentially reducing assessment burden by 70%. Overall, this research advances chronotype assessment by uncovering non-linear and heterogeneous classification mechanisms to circadian preference. The findings provide evidence-based foundations for developing abbreviated screening tools for primary care consultations, large-scale epidemiological studies, and clinical contexts. An open-access R tutorial ensures reproducibility and facilitates adaptation across diverse populations and assessment instruments, supporting widespread implementation in research and practice.