For the detection of somatic structural variation (SV) in cancer genomes, long-read sequencing is advantageous over short-read sequencing with respect to mappability and variant phasing. However, most current long-read SV detection methods are not developed for the analysis of tumor genomes characterized by complex rearrangements and heterogeneity. Here, we present Severus, a breakpoint graph-based algorithm for somatic SV calling from long-read cancer sequencing. Severus works with matching normal samples, supports unbalanced cancer karyotypes, can characterize complex multibreak SV patterns and produces haplotype-specific calls. On a comprehensive multitechnology cell line panel, Severus consistently outperforms other long-read and short-read methods in terms of SV detection F1 score (harmonic mean of the precision and recall). We also illustrate that compared to long-read methods, short-read sequencing systematically misses certain classes of somatic SVs, such as insertions or clustered rearrangements. We apply Severus to several clinical cases of pediatric leukemia/lymphoma, revealing clinically relevant cryptic rearrangements missed by standard genomic panels. Complex structural variations and rearrangements in cancer are identified using long-read sequencing.

Spatial transcriptomics has revolutionized our understanding of tissue organization by simultaneously capturing gene expression and spatial localization within intact tissues. However, analyzing these increasingly complex datasets requires specialized expertise across computational biology, statistics, and biological context. To address this challenge, we introduce the Spatial Transcriptomics AI Agent (STAgent), an autonomous multimodal agentic AI that integrates multimodal large language models (LLMs) with specialized computational tools to transform weeks-long analysis tasks into minutes of automated processing. Unlike conventional machine learning approaches that are limited to narrow, predefined tasks, STAgent leverages the emergent capabilities of multimodal LLMs – such as flexible reasoning, contextual understanding, and cross-modal integration – which allow it to adapt to novel data, execute multi-step analyses, and generate biologically meaningful insights with minimal human input. STAgent enables autonomous deep research through integrated capabilities, including dynamic code generation for complex analytical workflows, visual reasoning for interpreting spatial patterns, real-time retrieval of relevant peer-reviewd scientific literature, and synthesis of comprehensive, actionable reports. We applied STAgent to investigate the in vivo maturation of human stem cell-derived pancreatic cells (SC-pancreas) transplanted into immunodeficient mice. We generated single-cell spatial transcriptomics data spanning multiple developmental timepoints. STAgent autonomously (1) identified the maturation of initially scattered endocrine cells into well-defined islet-like structures, with predominantly peripheral α-cells surrounding β-cell cores supported by an expanding mesenchymal network; (2) revealed strengthening endocrine-endocrine cell interactions over time and, through context-aware gene set analysis, uncovered spatially resolved biological processes driving maturation; (3) unlike traditional analytical approaches, STAgent offers mechanistic explanations of spatial patterns, contextualizing findings with relevant literatures and developing cohesive insights into human pancreatic development. This agentic approach establishes a new paradigm in spatial transcriptomics analysis by substantially lowering the expertise barrier and reducing analysis time, accelerating biological and biomedical discovery.

The accumulation of electrochemically produced bubbles is inevitable in gas-evolving reactions and can induce potential losses by theoretically increasing activation, concentration, and ohmic overpotentials. These effects are often either overstated or completely neglected in the literature, which complicates the accurate analysis of experimental results for gas evolution reactions. This study systematically identifies and quantifies the overpotential losses induced by bubbles by combining experimental results for hydrogen (HER) and oxygen evolution reactions (OER), obtained using the rotating disk electrode (RDE) technique, with simulations based on a two-dimensional transmission line model. Our results show that ohmic overpotential is the primary cause of apparent activity loss due to bubbles in RDE. This effect leads to catalyst activity misestimates exceeding 2 orders of magnitude, and Tafel slope errors of 100% at higher currents if left uncorrected. By identifying these effects, this work provides a robust framework for mitigating inaccuracies and improving the characterization of electrocatalysts for gas evolution reactions.

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024 (Second Terrestrial Very-Long-Baseline Atom Interferometry Workshop, Imperial College, April 2024), building on the initial discussions during the inaugural workshop held at CERN in March 2023 (First Terrestrial Very-Long-Baseline Atom Interferometry Workshop, CERN, March 2023). Like the summary of the first workshop (Abend et al. in AVS Quantum Sci. 6:024701, 2024), this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions (Memorandum of Understanding for the Terrestrial Very Long Baseline Atom Interferometer Study).

This article is concerned with qualitative and quantitative refinements of the concepts of the log-convexity and log-concavity of positive sequences. A new class of tempered sequences is introduced, its basic properties are established and several interesting examples are provided. The new class extends the class of log-balanced sequences by including the sequences of similar growth rates, but of the opposite log-behavior. Special attention is paid to the sequences defined by two- and three-term linear recurrences with constant coefficients. For the special cases of generalized Fibonacci and Lucas sequences, we graphically illustrate the domains of their log-convexity and log-concavity. For an application, we establish the concyclicity of the points a2na2n+1,1a2n+1 for some classes of Horadam sequences (an) with positive terms.

Parameterized Quantum Circuits (PQCs) with fixed structures severely degrade the performance of Quantum Machine Learning (QML). To address this, a Hybrid Quantum-Classical Classifier (HQCC) is proposed. It adaptively optimizes the PQC through a dynamic circuit generator driven by Long Short Term Memory (LSTM) and exploits architectural plasticity to balance the entanglement performance and expressiveness, opening up a practical pathway for efficiently deploying QML in the Noisy Intermediate-Scale Quantum (NISQ) era. Extensive experiments on MNIST and Fashion-MNIST demonstrate that HQCC achieves up to 99.86% accuracy in binary classification and 97.12% in multi-class tasks, surpassing state-of-the-art quantum and classical baselines.

OBJECTIVE Vestibular impairment may be present in and contribute to imbalance in patients with hereditary neuropathies. We examined the vestibulo-ocular reflex (VOR) characteristics in peripheral myelin protein 22 neuropathies using the video head-impulse test (vHIT). METHODS 23 patients with Charcot-Marie-Tooth disease 1A (CMT1A) and 17 with hereditary neuropathy with liability to pressure palsies (HNPP) were recruited. Three-dimensional vHIT was performed. VOR-gain and latency, refixation-saccade prevalence and first-saccade amplitude, onset-latency, peak-velocity and duration were examined and compared against age-matched controls. RESULTS In CMT1A and HNPP gait imbalance was reported in 78.3 % and 58.8 % of patients, resulting in recurrent falls in 65.2 % and 23.5 %. Reduced VOR-gain affecting the posterior-canals (PCs) was found in 47.8 % of CMT1A and 11.7 % of HNPP patients. First saccade amplitude and peak-velocities higher in horizontal-canal (HC) and PC in the CMT1A group compared to controls (p < 0.05). In HNPP, first saccades were larger in HC and anterior-canal (AC) planes; saccade peak-velocity was higher in AC and PC planes compared to controls (p < 0.05). In CMT1A, VOR-gain impairment was associated with higher Charcot-Marie-Tooth Examination Score, longer disease duration, and higher total Overall Neuropathy Limitation Scale score (p < 0.05) and VOR-gain was lower for PC in patients with a history of recurrent falls (p < 0.05). VOR-latency was significantly longer in HC and PCs in CMT1A compared to controls (p < 0.05). CONCLUSIONS VOR impairment and slowing of the VOR-latency is found in CMT1A but not the HNPP cohort. These findings may relate to demyelinating processes affecting the vestibular nerves and thus the VOR pathways. SIGNIFICANCE VHIT allows detection of VOR impairment which could be an additional contributor to imbalance and falls in patients with CMT1A.

Endothelial progenitor cells (EPCs) play a crucial role in vascular repair, and their depletion has been involved in coronary artery disease (CAD) severity. This study examines the relationship between circulating EPC levels and CAD complexity, as quantified by the Syntax Score I. A total of 85 patients undergoing coronary angiography were enrolled, with EPCs quantified using flow cytometry. EPC proportion showed a significant inverse relationship with CAD severity, measured by Syntax Score I. Additionally, we investigated EPC levels in patients presenting with acute coronary syndrome (ACS) and found that EPC depletion was more pronounced in this group compared to non-ACS patients (median EPC count: 0.35 vs. 0.61, p = 0.027). These findings suggest that lower EPC levels are indicative of more severe CAD and ACS, reinforcing their potential as biomarkers for cardiovascular risk stratification, monitoring disease advancement, and identifying patients at risk of adverse events.

Aging residential buildings in urban areas require effective thermal insulation to enhance energy efficiency and indoor comfort. In Bosnia and Herzegovina (BiH), expanded polystyrene (EPS) is the most commonly used insulation material due to its affordability, despite concerns regarding its flammability and environmental impact. While regulatory changes since 2019 have recommended rock wool for high-rise buildings, the absence of binding fire safety regulations has allowed the continued use of EPS, often driven by financial constraints. This study examines energy efficiency refurbishments in Sarajevo’s high-rise residential buildings and analyze the implications of the partial implementation of recommended measures. Using case studies, surveys, and expert interviews, this research identifies key challenges, such as limited funding, fragmented renovations, and inconsistent coordination between stakeholders. The findings indicate that facade insulation is often prioritized over comprehensive upgrades, including window replacement and heating system improvements, leading to suboptimal energy savings and minimal cost reductions for residents. Additionally, the complexity of multi-apartment ownership structures hinders uniform improvements in energy efficiency. Despite these challenges, property values tend to increase after renovation, highlighting the long-term financial benefits. To maximize energy savings and ensure sustainable urban housing, stronger interdisciplinary collaboration, improved funding mechanisms, and adherence to fire-safety standards are necessary. These measures would enhance the effectiveness of renovations and support long-term energy efficiency strategies.

Climate change is predicted to drive geographical range shifts that will result in changes in species diversity and functional composition and have potential repercussions for ecosystem functioning. However, the effect of these changes on species composition and functional diversity (FD) remains unclear, especially for mammals, specifically bats. We used species distribution models and a comprehensive ecological and morphometrical trait database to estimate how projected future climate and land‐use changes could influence the distribution, composition, and FD of the European bat community. Future bat assemblages were predicted to undergo substantial shifts in geographic range and trait structure. Range suitability decreased substantially in southern Europe and increased in northern latitudes. Our findings highlight the potential for climate change to drive shifts in bat FD, which has implications for ecosystem function and resilience at a continental scale. It is important to incorporate FD in conservation strategies. These efforts should target species with key functional traits predicted to be lost and areas expected to experience losses in FD. Conservation strategies should include habitat and roost protection, enhancing landscape connectivity, and international monitoring to preserve bat populations and their ecosystem services.