Browse Articles

Structure of the pre-initiation complex explains CMGE biogenesis

Abstract When cells enter S phase, bidirectional DNA replication is initiated through the kinase-regulated recruitment of three activators (Cdc45, GINS and Pol ε) to a duplex-DNA-loaded double hexamer of minichromosome maintenance (MCM) ATPases. Together, these proteins form two CMGE helicases that establish divergent replication forks as they become separated 1 . Here, to gain an understanding of CMGE biogenesis, we reconstituted the pre-initiation complex with purified yeast proteins. The cryo-electron-microscopy structure shows a set of firing factors caught in the act of assembling two symmetrical CMGEs. We show how stepwise complex formation reshapes MCM in preparation for DNA opening, and we explain how ATP promotes firing-factor ejection and CMGE maturation. We find that although Sld2 facilitates the recruitment of GINS to MCM, as expected, it also aids the efficient separation of the CMGE dimer, and is essential for the ejection of the lagging strand from MCM. These findings have direct implications for our understanding of the metazoan Sld2 orthologue, RECQL4, and point to a replication-fork establishment mechanism that is conserved across eukaryotes.

Scalable Topochemical Synthesis of Black Phosphorene Nanoribbons

Expression of concern: Engineering Pseudomonas protegens Pf-5 for nitrogen fixation and its application to improve plant growth under nitrogen-deficient conditions

Targeting the HMGB1–TLR4 Axis Alleviates Neuropathic Pain-Associated Cognitive Deficits

Cognitive deficits associated with chronic pain pose a significant burden on a patient's quality of life. Emerging evidence indicates that Toll-like receptor 4 (TLR4), a pattern recognition receptor implicated in neuroinflammatory signaling, can disrupt synaptic plasticity and memory processes. However, the specific involvement of TLR4 in the development of neuropathic pain-related cognitive deficits has not been fully elucidated. In this investigation, we observed an upregulation of TLR4 expression within hippocampal neurons in male mice subjected to chronic constriction injury (CCI) relative to the sham group. Notably, in separate experimental cohorts, TLR4 knock-out and neuron-specific TLR4 knockdown mice exhibited improved cognitive function compared with wild-type controls, alongside attenuated neuroinflammatory responses, reduced neuronal apoptosis, and enhanced preservation of hippocampal neuroplasticity. Concurrently, elevated concentrations of high-mobility group box 1 (HMGB1), a damage-associated molecular pattern molecule, were detected in the sciatic nerve, serum, and hippocampal tissues following CCI. Furthermore, increased colocalization of HMGB1 with TLR4 was evident in the hippocampus. Exogenous administration of HMGB1 augmented HMGB1 and TLR4 levels in the hippocampus and worsened memory functions that depend on hippocampal integrity. Conversely, inhibition of HMGB1 with glycyrrhizin, which subsequently attenuates TLR4 activation, ameliorated cognitive impairments induced by CCI. Collectively, these results support a model in which HMGB1, elevated during chronic neuropathic pain, contributes to cognitive deficits via a TLR4-dependent mechanism, triggering downstream inflammatory and apoptotic cascades and impairing synaptic plasticity.

Tailoring Local–Global Structures via Hot Deformation for High-Performance BiSbSe3 Thermoelectrics

Abstract Enhancing carrier concentration (n) is widely regarded as a core strategy for advancing high-performance thermoelectric (TE) materials. However, this approach is often limited by a concomitant decline in carrier mobility (μ). To surmount this trade-off, a synergistic integration combining composite engineering and hot deformation processing was employed to synergistically optimize both n and μ in BiSbSe3. Microstructural analysis reveals that this dual processing route drives a local–global structural evolution, involving texture formation, dynamic recrystallization, precipitation of Cu-rich secondary phases (CuSbSe2), incorporation of interstitial Cu atoms, and enhanced short-range ordering. As a result, the optimized n and tailored carrier transport pathways lead to reproducible and substantially enhanced electrical conductivity and power factor. Meanwhile, interstitial atoms, dislocations, subgrain boundaries, and heterogeneous interfaces collectively create a multiscale phonon scattering network, effectively reducing lattice thermal conductivity. Consequently, the peak ZT value along the out-of-plane direction is dramatically enhanced from ∼0.06 for pristine BiSbSe3 to ∼1.3 at 723 K for the BiSbSe3 + 2 mol % CuI + 1.8 mol % Cu sample subjected to single-pass hot deformation. This peak ZT value surpasses the highest reported value at the same temperature, with the Vickers hardness of the modified sample concurrently improved. This work elucidates the micromechanisms through which hot deformation synergistically regulates TE properties via tailoring of local–global structural modifications, laying a solid foundation for future commercialization.

Automated classification of natural habitats using ground-level imagery

Accurate classification of terrestrial habitats is critical for biodiversity conservation, ecological monitoring, and land use planning. Several habitat classification schemes are in use, typically based on analysis of satellite imagery and validation by field ecologists. Here, a methodology is presented for classification of habitats based solely on ground-level imagery (photographs), offering improved validation and enhanced ability to classify habitats at scale (e.g., using imagery from citizen science). In collaboration with Natural England, a public sector organisation with responsibility for nature/biodiversity conservation in England, this study develops a classification system that applies deep learning to ground-level habitat photographs, categorising each image into one of 16 distinct classes following the established ‘Living England’ framework. Images were pre-processed using resizing, normalisation, and augmentation techniques, while resampling was used to balance classes in the training data and enhance model robustness. A custom deep learning classifier based on the DeepLabV3-ResNet101 architecture was developed and fine-tuned to assign a habitat class label to ground-level photographs. Using five-fold cross-validation, the model demonstrated strong overall performance across 16 habitat classes, with accuracy and F1-scores varying between classes. This approach supports robust, scalable habitat classification based on balanced and well-prepared training data. Across all folds, the model achieved a mean F1-score of 0.63, with some habitat classes such as Bare Sand (BS) and Coniferous Woodland (CW) reaching values above 0.87. High performance was achieved for visually distinct habitats and lower performance for visually mixed or ambiguous classes. These findings demonstrate the potential of this approach for ecological monitoring. Ground-level imagery is easily obtained and accurate computational methods for habitat classification based on such data have many potential applications. To support use by practitioners, a simple web application is also provided that allows classification of uploaded images using the trained model.

Computer-aided diagnosis system for thoracic computed tomography of rib fractures in older emergency patients: A preliminary study

The peculiarities of older individuals related to osteoporosis and hyperostosis may lead to a higher rate of misdiagnosis of rib fractures on computed tomography (CT) images in older than in middle-aged/young people when using radiologist-only reading. However, none of these studies on rib fracture computer-aided diagnostic (CAD) systems grouped patients by age or evaluated the value of using CAD in older patients. To address these gaps, we divided 1,012 blunt chest trauma emergency patients who underwent chest CT into middle-aged/young and older groups with a cutoff age of 60 years. CT images were read by six radiologists from three institutions (each with 7 years of experience in thoracic CT diagnosis) using two reading methods, radiologist-only and radiologist-CAD reading, to explore the value of a deep learning (DL)-based CAD system for detecting rib fractures in emergency older patients. The final findings of the independent panel consisting of two senior radiologists with or without a thoracic surgeon were set as the reference standard. The sensitivity was calculated by dividing the number of true positives by the overall number of fractures, as confirmed by an expert panel. The false positives per patient (FPPP) was calculated by dividing the number of false positives by the overall number of patients. Sensitivity and FPPP were used to evaluate the diagnostic efficiency. Sensitivity, FPPP, and reading time were compared between the two groups, as well as reading methods. The results showed the following: (1) Sensitivity for detecting fresh fractures using radiologist-only reading was lower in the older than in the middle-aged/young group (86.7% [95% confidence interval (CI): 86.3%, 91.7%] vs. 91.5% [95% CI: 89.9%, 92.9%], p  < 0.05). With the assistance of the CAD system, the sensitivity increased in the older group to the same level as that in the middle-aged/young group using radiologist-only reading (92.5% [95% CI: 90.4%, 94.2%] vs. 91.5% [95% CI: 89.9%, 92.9%], p  > 0.05). (2) The FPPP of fresh fractures with radiologist-only reading was higher in the older than in the middle-aged/young group (0.47 vs. 0.37, p  < 0.05). With the assistance of the CAD system, the FPPP in the older group decreased to the same level as that in the middle-aged/young group when using radiologist-only or radiologist-CAD reading (0.37 vs. 0.37/0.39, p  > 0.05). (3) The reading time of fresh fractures when using radiologist-only reading was longer in the older than in the middle-aged/young group (6.1 vs 5.4 min, p  < 0.05). With the assistance of the CAD system, the reading time in the older group was reduced by approximately 36% ( p  < 0.05). We conclude that the efficiency of intermediate-level radiologists in diagnosing fresh rib fractures by radiologist-only reading in older emergency patients was lower than that in middle-aged/young patients. When a DL-based CAD system assists radiologists, the diagnostic efficiency of identifying fresh fractures in older patients improves to the same level as independent radiologist-only reading in middle-aged/young patients while reducing the reading time.

All‑cause excess mortality in Germany peaked during the late‑2022 influenza period, exceeding peaks during SARS‑CoV‑2 waves (2020–2023)

Introduction Numerous studies have examined mortality during the SARS-CoV-2 pandemic in Germany and worldwide. In Germany, excess mortality was highest in 2022 compared to the other pandemic years. In a small-scale analysis in Frankfurt am Main, Germany, the excess mortality 2022 was associated with an influenza wave at the end of the year. The aim of this study was to investigate this for the whole of Germany. Methods We used publicly available data for the number of deaths, for the population data and for notifications of SARS-CoV-2 and influenza. Standardized mortality ratios (SMR) were estimated for Germany for the years 2020–2023, for seven SARS-CoV-2 waves and for the influenza wave at the end of 2022. Expected numbers of deaths were estimated by two methods: in the first, average mortality in the pre-pandemic years 2016–2019 was used; in the latter, an exponential extrapolation was used to consider the increase in life expectancy. Results Relative excess mortality was highest in 2022 (SMR = 1.069 (95% confidence interval: 1.066–1.071) by method 1, SMR = 1.094 (1.092–1.096) by method 2). During the influenza wave from calendar week 47 / 2022 to calendar week 1 / 2023, the SMR was higher than that of any SARS-CoV-2 wave: SMR = 1.252 (95% CI: 1.246–1.258) by method 1, and SMR = 1.374 (95% CI: 1.367–1.380) by method 2. Among all waves considered, the mean number of excess deaths per week was highest during the influenza wave by both methods (5,043, and 6,812, respectively). Age-stratified analyses showed that the excess mortality during the influenza wave at the end of 2022 was highest in individuals aged 70 years and older. Discussion During an influenza wave at the end of 2022, excess mortality was higher than in any SARS-CoV-2 wave in 2020–2022 in Germany. Because this study is based on all‑cause mortality and population‑level surveillance indicators, it cannot establish causation or quantify the proportion of excess deaths directly attributable to influenza infection.

Correction: Global learning opportunities within social innovation in health (GLOWS): A modified Delphi process to identify and pilot core competencies for learning

Modeling diffuse midline glioma through triple-electrode in utero electroporation of the developing mouse pons

The leading cause of brain cancer–related death in children is diffuse midline glioma (DMG). A particularly aggressive DMG subtype is pontine DMG (formerly diffuse intrinsic pontine glioma, DIPG), which is caused by the histone mutation H3.3K27M. Because of its diffuse growth and location in a critical brainstem structure, therapeutic options are limited, and pontine DMG is considered universally fatal. The lack of appropriate animal models has hindered our understanding of the developmental origins and progression of pontine DMG, which in turn has limited the development of effective therapeutics. To address this barrier, several labs have developed mouse in utero electroporation approaches to express canonical DMG mutant oncogenes in the developing pons. In this manuscript and accompanying protocol, we describe a modified in utero electroporation strategy to generate DMG tumors in the developing mouse pons. Our protocol incorporates a single plasmid construct that expresses canonical DMG oncogenes, eliminating the need to co-electroporate multiple plasmids. We also employ a triple-electrode configuration to precisely target neural progenitors lining the fourth ventricle, which give rise to cells in the pons. As the embryos continue to develop in utero and postnatally, they form large, diffuse brainstem tumors with molecular characteristics of pediatric pontine DMG, allowing us to model the formation and progression of this deadly pediatric brain cancer.

Cortical development dynamics across autism spectrum disorder mouse models

Abstract Despite the functional diversity of over 100 causal genes 1–3 , phenotypic convergence across models may reveal common neurobiological processes in autism spectrum disorder (ASD). Here we profiled 251 samples from 11 monogenic mouse models of ASD using single-nucleus multi-omic sequencing across three developmental stages, both sexes and two brain regions. Despite genetic heterogeneity, ASD-linked mutations converged on perturbations of the radial glial cell lineage. These alterations reflect a transient developmental delay rather than lasting lineage misspecification and resolve by postnatal stages. Molecularly, the largest transcriptional differences emerged in neurons at early postnatal stages. These changes included downregulation of synaptic and ion channel-related genes, consistent with homeostatic adaptation or delayed maturation. Network analysis showed molecular convergence across models within each developmental stage, suggesting that diverse mutations linked to ASD impinge on common, stage-specific processes. Convergence becomes less pronounced by postnatal day 14, highlighting the dynamic nature of ASD-associated changes. Cross-genotype heterogeneity is superimposed on stage-specific effects. Electrophysiology corroborated this pattern: mutants generally showed altered neuronal excitability and synaptic properties with model-specific nuances. Our study also highlighted sex-specific gene expression alterations, with female mice often displaying larger effect sizes than male mice. Together, our findings provide a comprehensive view of developmental cellular and molecular dynamics across models of ASD.

Phage mediated growth inhibition and biofilm disruption of the endodontic pathogen Enterococcus faecalis

The failure of endodontic procedures such as root canal therapy is primarily indicated by persistent microbial infections. Root canal therapy involves the removal of decaying dental pulp (internal blood vessels and nerves of teeth), sanitizing the canal, and filling the space with biocompatible materials. Improper cleaning or the breakdown of these materials can lead to secondary infections. These infections, if left untreated, can lead to severe pain, bone and tooth loss, and potentially systemic infection. The bacterium Enterococcus faecalis is one of the most commonly associated organisms with failed root canal therapy, in addition to its prevalence in urinary tract infections, endocarditis, wounds, and sepsis. E. faecalis is known to survive in low nutrient environments and produce extensive biofilms, making it difficult to eradicate. In addition to antibiotic treatment, bacteriophages (phages), which are bacteria-specific viruses that kill their host are an interesting companion or alternative to antibiotics. In this study we isolated and characterized 14 E. faecalis phages from wastewater samples by testing their host range, growth inhibition, and biofilm eradication capabilities against several E. faecalis strains including two that were orally derived. Several phages showed broad host ranges (up to 16 strains), strong bacterial growth inhibition even when applied at low concentrations, and significant eradication of mature biofilms (97% reduction). The phages presented here represent a unique repertoire of antibacterial agents for use in treating endodontic infections and add to the growing library of E. faecalis phages to treat diverse infections.

Construction and validation of an instrument to assess the university dropout intention formation process UDIFP-29

A significant body of research supports the importance of addressing university students’ dropout intention, the process through which university students form the intention to dropout from their studies. However, available measurement tools failed to account for how dropout intention is formed over time. This research aimed to fill that gap by developing and validating a multidimensional self-report instrument to assess how the intention to dropout from the university is formed. Three studies were designed for this purpose. Study 1 used focus groups to explore how the intention to dropout emerges in freshman students enrolled at a university, with the purpose of developing a theoretical model to explain how the intention to dropout emerges. Study 2 focused on the content validation of the preliminary version of the instrument, which was carried out by expert judgment. Study 3 examined the construct validity of the instrument using exploratory and confirmatory factor analysis and analyzing its convergent and divergent validity. The results showed a model with good adjustment and adequate internal consistency indexes. In general, the instrument has adequate psychometric properties, proving to be reliable and valid for measuring the process of forming early university dropout intention. Use of this instrument will allow the design of timely strategies and interventions to reduce dissatisfaction, dropout ideation and dropout.

CHPO coordinates chilling recovery and nitrogen use in rice

A semantic segmentation model to predict subcellular glycogen localization using transmission electron microscopy images

Transmission electron microscopy (TEM) is the gold standard for assessing subcellular glycogen localization in skeletal muscle fibres, but conventional manual analysis is extremely time-consuming and limits large-scale studies. Here, we developed and validated a deep learning–based semantic segmentation approach to automate quantification of glycogen particles across defined subcellular compartments in human skeletal muscle. Skeletal muscle biopsies were obtained from seven healthy men under conditions of normal, depleted, and supercompensated glycogen content. TEM images were acquired from myofibrillar and subsarcolemmal regions and manually annotated to train two complementary attention U-Net models: a region model identifying subcellular structures (intermyofibrillar space, intramyofibrillar regions including A-band, I-band and Z-disc, and mitochondria) and a glycogen model detecting individual glycogen particles. Combining the two models enabled estimation of compartment-specific glycogen areal densities. The model’s outcome was evaluated against manual point-counting. At the fibre level, estimates based on 10–12 images per region achieved biases below 15% and coefficient of variation below 26% for all compartments. Importantly, model-derived total glycogen volume density showed strong concordance with biochemically determined muscle glycogen content across biopsies. In conclusion, this validated semantic segmentation workflow provides an objective and highly time-efficient tool for quantifying subcellular glycogen distribution in skeletal muscle. The model substantially reduces analysis time and enables high-throughput investigations of compartmentalized glycogen metabolism, with model weights and code made openly available.

DNA from hunter-gatherer teeth reveals secrets of ancient plague

REPROGRAM: REsilience PROmotion with GeRoprotectors: AssessMent of biological effect: Rationale and protocol for a trial of biological effect

Background Ageing is associated with reduced resilience to physiological stressors such as infection and surgery. This reduced resilience is believed to be underpinned by the hallmarks of ageing, the key biological mechanisms driving the aged phenotype. Geroprotectors are drugs that are proposed to slow down the ageing process and promote longevity and healthspan. Despite this, mechanistic studies in healthy older adults are lacking. Methods and analysis This trial will test the hypothesis that geroprotectors targeted towards biological mechanisms associated with poor resilience can reverse these pathways within a three-week period. Three geroprotectors with a good safety profile in older adults and evidence of effect on the hallmarks of ageing will be administered to 60 (30 female; 30 male) adults 70 + . Participants will be randomised to one of three arms (Metformin MR 1500 mg, Fisetin 100 mg or Spermidine 15 mg). Participants will be extensively clinically characterised at baseline. Blood, abdominal adipose tissue and stool samples will be taken at baseline and following the three-week intervention. The primary research question will answer whether a three-week course of Metformin, Spermidine, or Fisetin reduce the number of senescent cells as measured by SA-β-GAL in adipose biopsies in healthy older volunteers. Additionally, there will be assessment of the effect of the geroprotectors on other hallmarks of ageing, including autophagy, immunosenescence, chronic inflammation, dysregulated mTOR signalling, epigenetic age, DNA damage, dysregulated metabolism, stem cell exhaustion and microbial composition. Ethics and dissemination Ethical approval is in place (24/LO/0549). The main trial report and any sub-studies will be published in high impact peer-reviewed gerontology journals, presented at academic conferences and through a series of public engagement events. Participants enrolled in the study will be informed of the results by a written summary. Trial registration REPROGRAM was registered with ISRCTN on 10/09/24. ISRCTN47919839. Available at https://www.isrctn.com/search?q=47919839 .

Reimagining machine vision with optical computing

“You never know what’s in front of you”: A mixed methods study of barriers and facilitators to physical activity among blind and low-vision adults with type 2 diabetes

Individuals living with type 2 diabetes and vision loss experience unique, under-researched barriers to physical activity, despite the important role exercise plays in diabetes management. This study examined physical activity participation among blind and low-vision adults with type 2 diabetes ( n  = 30). Participants, ages 44–83, resided in Bexar County, Texas. Closed-ended surveys (conducted in English or Spanish) were administered to participants, followed by in-depth, semi-structured interviews. Interviews were audio-recorded, transcribed verbatim, and double-coded. Participants’ physical activity levels were determined using the Exercise Behaviors Scale. Data were analyzed using a convergent inductive-deductive design. Consistent with the Social-Ecological Model, five main themes and 11 subthemes emerged, depicting how individual/intrapersonal, interpersonal, institutional/organizational, community, and policy-level factors influenced participants’ physical activity participation. These factors can be specifically targeted both at local and broader levels to promote physical activity among individuals with concurrent type 2 diabetes and blindness/vision loss.

A hybrid transformer-BiLSTM model optimized with Firefly Algorithm for network traffic anomaly detection

Network Traffic Anomaly Detection (NTAD) is essential for proactive cyber defense against increasingly sophisticated threats. This paper presents a data-driven framework that integrates adaptive signal decomposition, a hybrid attention-recurrent architecture, and metaheuristic optimization for timely anomaly prediction. Raw traffic sequences are first preprocessed via Empirical Mode Decomposition (EMD) to mitigate non-stationarity and suppress noise, yielding denoised intrinsic mode functions. The refined signal is then modeled by a hybrid deep network that couples a multi-head self-attention mechanism—capturing global, long-range dependencies—with a Bidirectional Long Short-Term Memory (BiLSTM) network that encodes bidirectional temporal dynamics. To circumvent the sensitivity of deep models to hyperparameter selection, the Firefly Algorithm (FA) is employed for automated, population-based optimization. Extensive evaluations on benchmark datasets demonstrate that the proposed EMD-FA-Transformer-BiLSTM model attains state-of-the-art performance, outperforms baseline and state-of-the-art models across all evaluated metrics, with statistically significant improvements in both regression error and classification F 1 -score.