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Using the linear references from the pangenome to discover missing autism variants
Abstract To better understand large-effect pathogenic variation associated with autism, we generated long-read sequencing (LRS) data to construct phased and near-complete genome assemblies (average contig N50 = 43 Mbp, QV = 56) for 189 individuals from 51 families with unsolved cases. We applied read- and assembly-based strategies to facilitate comprehensive characterization of de novo mutations, structural variants (SVs), and DNA methylation. Using LRS pangenome controls, we efficiently filtered >97% of common SVs exclusive to 87 offspring. We find no evidence of increased autosomal SV burden for probands when compared to unaffected siblings yet observe a suggestive trend toward an increased SV burden on the X chromosome among affected females. We establish a workflow to prioritize potential pathogenic variants by integrating autism risk genes and putative noncoding regulatory elements defined from ATAC-seq and CUT&Tag data from the developing cortex. In total, we identified three pathogenic variants in TBL1XR1 , MECP2 , and SYNGAP1 , as well as nine candidate de novo and biallelic inherited homozygous SVs, most of which were missed by short-read sequencing. Our work highlights the potential of phased genomes to discover complex more pathogenic mutations and the power of the pangenome to restrict the focus on an increasingly smaller number of SVs for clinical evaluation.
Case study report on design, manufacturing and digital representation of a DED-Arc steel node for construction
Abstract This report presents a comprehensive investigation into the design, manufacturing, and evaluation of a DED-Arc (also known as Wire Arc Additive Manufacturing, WAAM) Y-Node for the construction industry. While conventional steel nodes for such applications are typically fabricated by welding multiple segments from cut plates or by complex castings, DED-Arc enables individual near-net-shape production of geometrically complex, force-flow optimized components while reducing the need for manual welding and machining. Focusing on the challenges of slicing and manufacturing strategy, such as collision avoidance between the torch and the built component, and guaranteeing torch accessibility in regions with pronounced overhangs, the study highlights the relationship between geometric freedom, path planning complexity, and manufacturing optimization. It emphasizes the importance of early consideration of manufacturing process constraints to enhance design efficiency. The integration of design, manufacturing process, and geometry data within a framework aiming towards a Digital Twin (DT) structure is thoroughly explored with the goal to support a first-time-right fabrication without the need for prototyping, thus reducing material waste. Moreover, the paper demonstrates the role of DT data in predicting component behavior, offering insights into stress distribution predictions influenced by manufacturing strategy. This research contributes to advancing methods for component behaviour analysis and optimization, with significant implications for the construction industry.
Influence of follow-up, screening age, interval, and compliance on overdiagnosis of ductal carcinoma in situ (DCIS): A modelling study
Overdiagnosis estimates of ductal carcinoma in situ (DCIS) vary from 20–91%, which complicates screening communication and optimization. The aim was to quantify the influence of follow-up time and screening setting on overdiagnosis of DCIS. The fully validated micro-simulation Markov model for DCIS (SimDCIS) was used to estimate DCIS overdiagnosis in different screening settings with varying follow-up. DCIS overdiagnosis was defined as the number of diagnosed DCIS (screen-, clinically detected, or progressed to invasive breast cancer) in screening that would not have been diagnosed without screening. Outcomes were presented as overdiagnosed proportion and rate. The base cohort was screened biennially from age 50–74 with 76% compliance and 25 years (y) follow-up and compared to the non-screened cohort. Follow-up was varied from 2-25y, screening start 40-74y, screening interval 1-5y and compliance 50–100%. DCIS overdiagnosis was estimated at 20% of all diagnosed DCIS and 38.1 overdiagnosed DCIS per 100,000 women screened biennially from age 50–74 at 76% compliance and 25y follow-up. The proportion of overdiagnosed DCIS increased with shorter follow-up (27% at 2y to 20% at 25y), older screening start age (1% at 40y to 15% at 74y), decreased screening interval (23% at 1y to 12% at 5y), and increased compliance (16% at half to 20% at full participation). In conclusion, reliable DCIS overdiagnosis estimates require attention to screening setting and ≥20 years follow-up. Older women (74y) showed up to seven times more overdiagnosis at initial screening than younger women (50y). Improved estimates can provide guidance in screening communication and optimization.
Two types of axonal muscarinic acetylcholine receptors mediate formation of saliva cocktail in the tick Ixodes ricinus
Abstract Hard ticks depend upon an ability to precisely and dynamically regulate their saliva to successfully evade host haemostatic and immune defences during extended blood feeding. Although pilocarpine, an exogenous muscarinic acetylcholine receptor (mAChR) agonist, can stimulate salivation experimentally, the endogenous control of saliva secretion by acetylcholine remains poorly understood. Here, we identify and characterise two pharmacologically distinct mAChRs (type A and B) in the genome of the medically important tick Ixodes ricinus . Molecular dynamics simulations and targeted mutagenesis reveal that type B mAChRs exhibit an atypical muscarinic profile, suggesting unconventional receptor signalling. Combining immunolabelling, in vivo pharmacology, and proteomics, we show that specific central neurons interact with distinct salivary gland regions via mAChR type-specific axons, coordinating fluid and protein secretion through separate acini and likely acting upstream of a neuropeptide-dependent cascade. This previously unrecognised mechanism of neural control offers new insights into how ticks modulate their saliva advancing our understanding of vector-host interactions, with potential implications for disrupting pathogen transmission.
4-Nonylphenol regulates cell proliferation and apoptosis in cervical carcinoma through the MEG3/PI3K/AKT signaling pathway
Correction: Low-frequency transmission and persistence of antimicrobial-resistant bacteria and genes from livestock to agricultural soil and crops through compost application
Homotypic membrane-powered electrochemical microfluidic analysis of extracellular vesicles for precise cancer diagnosis
Expression, transport, and storage of fetuin-B in human granulosa cells
Abstract Fetuin-B is crucial in fertilization through inhibiting ovastacin, an enzyme that hardens the zona pellucida. Primarily synthesized in the liver, fetuin-B is also found in the placenta, tongue, and follicular fluid produced by granulosa cells. This study investigated fetuin-B localization and production in granulosa cells. Samples from 45 women undergoing intracytoplasmic sperm injection therapy at the University Medical Center Mainz were analyzed. Serum and follicular fluid samples were retrieved during egg collection. Granulosa cells were cultured and sampled at different time points. FETUB gene expression was quantified by RT-qPCR, with HepG2 cells as a positive control. Fetuin-B levels in granulosa cells and supernatants were measured by ELISA. Fetuin-B was localized using immunocytochemical staining. HepG2 cells showed strong FETUB expression, while granulosa cells exhibited minimal transcription. Fetuin-B levels were highest in serum and significantly lower in follicular fluid ( p < 0.001). Supernatant fetuin-B peaked on day 1 ( p = 0.004), while intracellular fetuin-B exceeded extracellular ( p = 0.008). Immunocytochemistry revealed a largely uniform intracellular distribution, with notable accumulations in filopodia. These findings indicate that granulosa cells store, release, and re-internalize fetuin-B but do not synthesize it. The early release at culture onset suggests a regulatory role in supporting fertilization conditions.
Integrated assessment of brackish water induced deleterious impacts: Analysis of hemato-biochemistry, erythrocytic toxicity, histopathology and oxidative stress biomarkers of mrigal carp
Salinity is recognized as one of the most detrimental abiotic stressors, inducing a range of cellular and biochemical disruptions in both marine and freshwater organisms worldwide. In this study, 84 healthy specimens of mrigal carp ( Cirrhinus mrigala ) were randomly selected and assigned to four experimental groups (T0, T1, T2, and T3). Each group was maintained under standardized laboratory conditions and exposed to different salinity levels (0, 3, 5, and 7 ppt) in brackish water to evaluate the resulting toxico-physiological responses over 90 days interval. Hematological analysis revealed a significant reduction in erythrocyte count, hemoglobin concentration, hematocrit values, and lymphocyte percentage in salinity-exposed (5, and 7 ppt) fish. In contrast, a notable elevation in total white blood cell (WBC) count and neutrophil population was observed in fish exposed to salinity (5, and 7 ppt) at days 60 and 90 of study. Biochemical assessment of serum parameters demonstrated a marked decline in albumin triglycerides and glucose levels, whereas concentrations of lactate dehydrogenase (LDH), cholesterol, alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), creatinine, and urea increased significantly in fish at days 60 and 90 of trial subjected to salinity (5, and 7 ppt). A significant elevation in oxidative stress biomarkers was observed in fish exposed to brackish water (5, and 7 ppt), accompanied by a substantial decline in the activity of key antioxidant enzymes, including glutathione (GSH), catalase (CAT), and superoxide dismutase (SOD) in erythrocytes. Histopathological examination of visceral organs—such as the gills, kidneys, liver, brain, and heart—revealed pronounced tissue damage and pathological alterations, particularly in fish exposed to higher salinity levels (5and 70 ppt). Overall, the findings indicate that elevated salinity in brackish water can provoke oxidative stress, impair hematological and biochemical homeostasis, suppress antioxidant defense mechanisms in erythrocytes, and induce microscopic ailments in different visceral organs of fresh water fish in a time dependent response. In conclusion, hematological and biochemical biomarkers are useful tools and provide reliable information about immune potential, level of stress, intoxication and severity of histopathological lesions.
Improving polygenic score prediction for underrepresented groups through transfer learning
Correction: Growth of single crystalline films on lattice-mismatched substrates through 3D to 2D mode transition
Bio-waste derived fluorescent carbon dots based biodegradable mechanically strong hydrogel for stimuli-responsive nonsteroidal anti-inflammatory drug (NSAID) delivery
In this study, biowaste derived carbon dots (BCDs) were synthesized from sugarcane bagasse via a hydrothermal route and incorporated into biodegradable, mechanically robust hydrogels for stimuli-responsive nonsteroidal anti-inflammatory drug (NSAID) delivery. BCDs, characterized by FTIR, Raman spectroscopy, and photoluminescence analyses, exhibited abundant surface functional groups and excellent fluorescence properties. The integration of BCDs into the hydrogel matrix enhanced the mechanical strength, imparted pH-responsive drug release behavior, and maintained the structural integrity under physiological conditions. In vitro release studies using ibuprofen as a model drug demonstrated faster release under acidic conditions (pH 3.8) than under neutral and alkaline pH, aligning with pathological microenvironments. The drug release kinetics were best described by the Higuchi model, indicating a diffusion-controlled mechanism. Cytotoxicity assays confirmed the high biocompatibility of both free BCDs and BCD-loaded hydrogels, with sustained cell viability over five days. The combination of bio-derived nanomaterials, tunable release properties, and excellent cytocompatibility highlight the potential of this system for targeted and environmentally sustainable drug delivery applications. This approach provides a scalable and green route for developing advanced biomedical platforms from agricultural waste with significant implications for controlled drug release and tissue-compatible therapeutic interventions.
An H5N1 clade 2.3.4.4b virus vaccine that elicits cross-protective antibodies against conserved domains of H5 and N1 glycoproteins
Enhancement of electroless copper coatings by triazole dithiocarbamate and green additives
AI-enhanced multi-timescale optimization strategy for virtual power plants: Advancing losad forecasting and dynamic demand response integration
The integration of renewable energy sources (RESs) introduces significant challenges related to uncertainty and intermittency in power grids. While Artificial Intelligence (AI) offers promising solutions for Virtual Power Plants (VPP) optimization, existing approaches often treat load forecasting, system dispatch, and demand response as loosely coupled components, limiting their ability to holistically manage these deep uncertainties. To address this, we propose a novel AI-enhanced multi-timescale optimization strategy that creates a synergistic, integrated framework. Methodologically, the approach begins with an attention-augmented Bidirectional Long Short-Term Memory (BiLSTM) model that generates high-fidelity spatiotemporal load forecasts, providing crucial spatial-aware inputs often overlooked by traditional models. These enhanced forecasts are then leveraged by a Model Predictive Control (MPC) strategy for more robust and proactive day-ahead and intraday dispatch. Crucially, the framework integrates a dynamic demand response (DDR) mechanism that is directly coupled with real-time MPC outputs, ensuring that load flexibility is mobilized based on immediate system needs rather than static signals alone. Simulations, driven by real-world operational data, confirm that this integrated strategy not only reduces operational costs and improves forecasting accuracy but also establishes a more resilient and adaptive VPP operational paradigm compared to prior AI-based methods.
Co-translational determination of quaternary structures in chaperone factories
Prediction of the displacements of the pile tops and ground surface around piles based on machine learning algorithms
Rethinking emergency risk assessment: A single-center look at shock index and its variants in COVID-19
Background The Shock Index (SI) is a validated prognostic tool in conditions such as severe trauma and obstetric hemorrhage. During the COVID-19 pandemic, it was used to identify patients at higher risk of clinical deterioration, but results have been inconsistent. This study aimed to evaluate the prognostic value of the SI and its variants in predicting mortality, need for mechanical ventilation, and hospital length of stay in patients with moderate COVID-19. Methods and findings This longitudinal analytical observational study was conducted at a high-complexity hospital in southwestern Colombia and included adults over 18 years of age with moderate COVID-19 treated between 2020 and 2022, using data from the institutional RECOVID registry. A total of 283 patients were analyzed (median age: 61 years; 58.7% male), with cardiovascular and renal comorbidities being predominant. On admission, vital signs were stable (NEWS2: 3.0; shock index: 0.7). ICU admission was required in 29.3% of cases, and overall mortality was 12%. ROC curves and diagnostic accuracy parameters were used to assess the discriminatory ability of the SI and its variants. Most SI variants showed low discriminatory power (AUC < 0.58), except for the estimated shock index and the heart rate–adjusted shock index for mortality, with AUCs of 0.70 and 0.68, respectively, and positive predictive values exceeding 93%. Conclusions Early identification of patients at risk for complications in moderate COVID-19 is essential for optimizing hospital resources. The shock index and its variants showed limited utility as standalone predictors for mortality, ICU admission, and hospital length of stay. Combining SI with other clinical parameters may offer some benefit, but heterogeneity limits generalizability. Future studies should develop and prospectively validate multivariable models integrating clinical, laboratory, and imaging biomarkers.