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Ancient DNA reveals pervasive directional selection across West Eurasia

Characterization of complex New Kingdom funerary resinous mixtures uncovers liquorice-derived substances and pine nut oil

Abstract Ancient Egyptian embalming substances and organic coatings applied to coffins and burial furnishings served both sacred and functional roles. However, their molecular characterization remains incomplete despite advances in analytical chemistry, highlighting the need for deeper approaches to refine compositional frameworks. This study investigates organic residues from the New Kingdom (2nd millennium BCE) funerary assemblage of Kha (outer bandages and middle and inner coffins), Merit (inner coffin), and a coeval little jar, all housed at the Museo Egizio in Turin, Italy. Gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-high resolution mass spectrometry (LC-HRMS) were combined to determine whether the substances were bituminous or resinous and to detail their composition. The analyses revealed resinous compounds, with no detectable evidence of bitumen, and a diverse organic matrix dominated by plant-derived biomarkers. Distinct molecular profiles emerged between body-related materials and coffin coatings, supporting function-specific formulations. Notably, 18-β-glycyrrhetinic acid (liquorice-derived compound) and pinolenic acid (pine nut oil marker) were assigned, providing the earliest molecular evidence to date for the use of liquorice in a New Kingdom funerary context. Overall, these findings reveal function-related funerary preparations and selective ingredient use, underscoring the potential of integrated HRMS workflows to detect unconventional markers and advance the study on ritual practices.

Cluster-randomized trial of Homework, Organization, and Planning Skills program compared to treatment as usual/waitlist for youth ages 11–14: Study protocol for conceptual replication

Background Organization, time management, and planning (OTMP) difficulties are associated with academic underachievement. OTMP skills training programs are effective in reducing OTMP deficits and improving academic performance. A randomized controlled trial of Homework, Organization, and Planning Skills (HOPS) for students ages 11–14 found it to be effective with medium to large effects. In that study, HOPS was provided by counselors employed by the research team. This study is a replication examining HOPS under more authentic conditions when providers are employed by schools serving enrolled students. The primary aim is to evaluate HOPS offered by school providers in relation to treatment-as-usual/waitlist (TAU/WL). To respond to limited school resources post-COVID-19, HOPS is also provided by research team members, creating the opportunity to replicate the findings from the prior trial and explore differential effectiveness when HOPS is implemented by school vs. research providers. Methods Students in about 30 schools serving students ages 11–14 will be enrolled. Schools are randomly assigned to HOPS vs. TAU/WL on a 2:1 ratio. Students assigned to HOPS schools are randomly assigned to a school vs. research provider on a 1:1 basis. Providers receive two hours of training and additional assistance on request. Child outcomes related to OTMP skills, homework, and academic performance are assessed at post-treatment, 6-month (from baseline) follow-up, and 12-month follow-up. HOPS sessions are video recorded for fidelity coding. Potential effect modifiers include student ADHD, oppositional defiant, and internalizing symptoms, and family socioeconomic level. Analyses will use mixed effects modeling. The goal of the study is to enroll 135 participants, yielding a minimal detectable effect size of 0.50, within the expected range based on prior research. Discussion The study is unique in examining intervention implementation and effectiveness when intervention is provided under authentic practice conditions. Trial registration This study was registered with clinicaltrials.gov (NCT04465708).

GARUDA: the first complete mitochondrial genome of the endangered Javan hawk-eagle (Nisaetus bartelsi) and its phylogenetic relationships within Accipitridae

Fog-Adaptive-YOLO: A lightweight model for insulator defect detection

Insulator defect detection under foggy conditions suffers from complex backgrounds, small targets, weak features, and severe weather interference, remaining a challenging task for UAV-based inspection. To address these issues, this paper proposes Fog-Adaptive-YOLO, a lightweight fog-adaptive detection network. The FogEnhance module suppresses fog noise and enhances weak defect features; the C3MSGR and C2fMSGR modules optimize lightweight multi-scale feature extraction and aggregation. Experimental results show that on the self-constructed InsDef-Fog dataset, the proposed model achieves 65.4% mAP50 with only 2.74M parameters. It obtains 60.3% mAP50 on the public IDID_FOG dataset and 80.2% mAP50 on the real-world WM-FOG dataset. The model also maintains stable precision on the cross-scene RTTS foggy dataset. These results demonstrate that Fog-Adaptive-YOLO achieves a favorable balance between detection accuracy and lightweight efficiency, well-suited for practical foggy insulator defect detection tasks.

Temporal and spatial changes of ecosystem services and their influencing factors in Shule River Basin

Structured knowledge representation of the South China Sea: An LLM-based knowledge graph approach

The South China Sea (SCS) presents a significant research challenge due to severe data fragmentation and semantic heterogeneity across disparate historical, legal, and geopolitical sources. Conventional linear research approaches often fail to systematically integrate these disparate records. This study develops and demonstrates an automated LLM-driven framework for constructing the South China Sea Knowledge Graph (SCS-KG). The three-stage process (extraction, normalization, and verification) converts heterogeneous textual sources into a coherent, machine-readable structure that integrates the region’s historical, cultural, and geopolitical dimensions. After verification, the SCS-KG comprises 59,836 entities and 652,018 triples. The resulting knowledge graph supports three analytical applications: (1) evidence-based query answering that synthesizes facts across centuries (e.g., from ancient textual records to modern legal declarations); (2) discovery of implicit, multi-hop relationships linking state-level governance and micro-level social practices; and (3) rapid, entity-centric profiling of complex geopolitical instruments. This LLM-based approach provides a replicable model for structured knowledge representation and enables integrated, evidence-based analysis in South China Sea studies.

Dynamic driver drowsiness detection with attention enhanced convolutional neural networks for real time monitoring and road safety applications

Wave propagation in dual-porosity media under the combined effects of light, heat, elasticity, and gravity

Cytoplasmic lattices are megadalton storage complexes in mammalian oocytes

A method for constructing an installation accuracy control system for ultra-high-speed maglev supports considering known-point precision

mRNA vaccines engage unconventional pathways in CD8+ T cell priming

Seasonal monitoring of light intensity reveals design-driven variability in naturally lit broiler houses

Efficacy of sutureless and glue-free conjunctival autograft compared with sutured conjunctival autograft in pterygium surgery: a meta-analysis

A new Li/Mg paleothermometer from pteropod shells

Abstract Pteropods are promising proxy archives for paleoceanographic reconstructions but remain underexplored. Previous oxygen isotope analyses indicated that Heliconoides inflatus calcifies at shallow depths (50–75 m), suggesting its potential to record surface-ocean conditions. Here, we evaluate for the first time the applicability of Li/Mg thermometry in pteropod shells, a temperature proxy widely used in coral studies. We show that, in addition to recording environmental variability through shell carbon and oxygen isotopic composition, H. inflatus shells record upper-water temperature conditions, with Li/Mg ratios showing a robust relationship with ambient temperatures at sampling locations. Li/Mg ratios decrease exponentially with increasing temperature, enabling temperature reconstructions with an average precision of ± 1–2 °C. Pteropod shells (species H. inflatus , formerly known as Limacina inflata ) were collected along a latitudinal transect in the Atlantic Ocean (31° N to 38° S), spanning a surface-water temperature gradient of approximately 15 °C. The global distribution and abundance of this annual species in sediments makes it a suitable alternative for paleo-surface temperature reconstruction. The utility of pteropod shells extends further to seasonal paleotemperature reconstruction, as seasonal temperature variability is captured in the elemental ratios from the embryonic to later (adult) sections of the shell. These results, in combination with a basin-scale distribution, make the Li/Mg thermometer in pteropods an exciting new tool in paleoceanography and further cement this group as new proxy archives.

Aversive learning hijacks a brain sugar sensor to consolidate memory

Understanding tile drain discharge dynamics through saturated area observation

Abstract This study explored the potential of using saturated area patterns in understanding tile drain response from an agricultural hillslope. Using spatial analysis of time-lapse imagery gathered from the Hydrological Open-Air Laboratory (HOAL) in Petzenkirchen, Lower Austria, twelve events between 2015 and 2017 have been analyzed. Visual inspection of the analyzed events reveals connected or disconnected saturation patterns in the downslope direction of the hillslope. The connected events show consistently larger average maximum connected distance (> 32 m) and average saturated area (> 50 m 2 ) during the events. The results show that for the connected events, tile drain response is always low, while for the events that become connected, the tile drain response increases rapidly with connectivity distance. Overall, the observed saturated areas are shown to outperform traditional explanatory variables of tile drain response, with the exception of event groundwater levels, with a 59% and 62% of behaviour explained for peak tile drain discharge and event volume, respectively. Camera observations are sensitive to snow cover and surface vegetation, which necessitates a manual pre-screening of the captured events for possible use. Overall, the findings of this study suggest that observations of saturated areas are highly informative for predicting hillslope scale runoff and related hydrological processes because they provide spatially distributed rather than point scale information, which presents a cost-effective method with potential of much wider use in hydrology.

Author Correction: In vitro characterization of the human segmentation clock

Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing

Abstract Identifying patient diagnoses from hospital discharge letters is essential for large-scale cohort selection and epidemiological research, but traditional supervised approaches require extensive manual annotation, which is often impractical for large textual datasets. We present a weakly supervised Natural Language Processing (NLP) pipeline for classifying Italian discharge letters without document-level manual annotation. The method extracts diagnosis-related sentences, generates semantic embeddings using a transformer model further pre-trained on Italian medical documents, and applies a two-level clustering procedure to derive weak labels that are then used to train a document-level classifier. The approach was evaluated in a case study on bronchiolitis using 33,176 discharge letters of children admitted to 44 emergency rooms or hospitals in the Veneto Region, Italy, between 2017 and 2020. The best weakly supervised model achieved an AUROC of 77.68% ( $$\pm 4.30\%$$ ), an AUPRC of 73.13% ( $$\pm 4.93\%$$ ), and an F1-score of 78.14% ( $$\pm 4.89\%$$ ) against manually annotated data. Performance surpassed unsupervised baselines and approached fully supervised models, while reducing the need for manual annotation by more than 1,500 hours for a dataset of this size. Similar model rankings were observed in a secondary validation on a smaller bronchitis dataset (3,188 discharge letters, 2020-2025), where the best weakly supervised model achieved an AUPRC of 76.72% ( $$\pm 5.02\%$$ ). These results suggest the potential of weakly supervised NLP methods for scalable disease identification from clinical discharge letters.

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