Abstract<br> <br> Objectives<br> To train and validate a machine learning model to distinguish paper mill publications from genuine cancer research articles, and to screen the cancer research literature to assess the prevalence of papers that have textual similarities to paper mill papers.<br> <br> <br> Design<br> Methodological and cross sectional study applying a BERT (bidirectional encoder representations from transformers) based, text classification model to article titles and abstracts.<br> <br> <br> Setting<br> Retracted paper mill publications listed in the Retraction Watch database were used for model training. The cancer research corpus was screened by the model using the PubMed database restricted to original cancer research articles published between 1999 and 2024.<br> <br> <br> Population<br> The model was trained on 2202 retracted paper mill papers and validated on independent data collected by image integrity experts. 2.6 million cancer research papers were screened.<br> <br> <br> Main outcome measures<br> Classification performance of the model. Prevalence of papers flagged as similar to retracted paper mill publications with 95% confidence intervals and their distribution over time, by country, publisher, cancer type, research area, and within high impact journals (top 10%).<br> <br> <br> Results<br> The model achieved an accuracy of 0.91. When applied to the cancer research literature, it flagged 261 245 of 2 647 471 papers (9.87%, 95% confidence interval 9.83 to 9.90) and revealed a large increase in flagged papers from 1999 to 2024, both across the entire corpus and in the top 10% of journals by impact factor. More than 170 000 papers affiliated with Chinese institutions were flagged, accounting for 36% of Chinese cancer research articles. Most publishers had published substantial numbers of flagged papers. Flagged papers were overrepresented in fundamental research and in gastric, bone, and liver cancer.<br> <br> <br> Conclusions<br> Paper mills are a large and growing problem in the cancer literature and are not restricted to low impact journals. Collective awareness and action will be crucial to address the problem of paper mill publications.<br> <<<

Board games have long served as complex decision-making benchmarks in artificial intelligence. In this field, search-based reinforcement learning methods such as AlphaZero have achieved remarkable success. However, their significant computational demands have been pointed out as barriers to their reproducibility. In this study, we propose a model-free reinforcement learning algorithm designed for board games to achieve more efficient learning. To validate the efficiency of the proposed method, we conducted comprehensive experiments on five board games: Animal Shogi, Gardner Chess, Go, Hex, and Othello. The results demonstrate that the proposed method achieves more efficient learning than existing methods across these environments. In addition, our extensive ablation study shows the importance of core techniques used in the proposed method. We believe that our efficient algorithm shows the potential of model-free reinforcement learning in domains traditionally dominated by search-based methods. <<<

The transition to the fault-tolerant era exposes the limitations of homogeneous quantum systems, where no single qubit modality simultaneously offers optimal operation speed, connectivity, and scalability. In this work, we propose a strategic approach to Heterogeneous Quantum Architectures (HQA) that synthesizes the distinct advantages of the superconducting (SC) and neutral atom (NA) platforms. We explore two architectural role assignment strategies based on hardware characteristics: (1) We offload the latency-critical Magic State Factory (MSF) to fast SC devices while performing computation on scalable NA arrays, a design we term MagicAcc, which effectively mitigates the resource-preparation bottleneck. (2) We explore a Memory-Compute Separation (MCSep) paradigm that utilizes NA arrays for high-density qLDPC memory storage and SC devices for fast surface-code processing. Our evaluation, based on a comprehensive end-to-end cost model, demonstrates that principled heterogeneity yields significant performance gains. Specifically, our designs achieve $752\times$ speedup over NA-only baselines on average and reduce the physical qubit footprint by over $10\times$ compared to SC-only systems. These results chart a clear pathway for leveraging cross-modality interconnects to optimize the space-time efficiency of future fault-tolerant quantum computers. <<<

AbstractSingle-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis. <<<

Abstract<br> <br> Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism’s genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present (<br> https://github.com/jotech/gapseq<br> ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.<br> <<<

<br> Mitochondrial dysfunction is a hallmark of aging and a key contributor to age-related diseases including cardiovascular disease. However, molecular pathways that safeguard mitochondrial homeostasis in the aging heart remain poorly understood. Here, we identify MTFR1L as a regulator of mitophagy that binds p-S65-Ub, a key signal amplifying the PINK1/Parkin axis. We find that MTFR1L is enriched in metabolically active tissues, particularly in the heart, where it regulates Parkin signaling. Genetic deletion of<br> Mtfr1l</i><br> in mice impairs stress-induced mitophagy and Parkin activation, leading to accumulation of damaged mitochondria, increased inflammation and senescence, and accelerated age-related cardiac dysfunction. Strikingly, cardiac expression of MTFR1L progressively decreases along with aging in mice, primates, and humans, coinciding with cardiomyocyte senescence and lipofuscin accumulation. Together, these findings uncover a role for MTFR1L in regulation of the p-S65-Ub/Parkin mitophagy axis and maintenance of mitochondrial homeostasis during cardiac aging and suggest that age-associated loss of MTFR1L may contribute to age-related cardiac dysfunction. Based on these findings, we propose a therapeutic paradigm for the prevention of heart aging by restoring MTFR1L function.<br> <<<

Abstract<br> <br> Motivation<br> Structured representations of clinical data can support computational analysis of individuals and cohorts, and ontologies representing disease entities and phenotypic abnormalities are now commonly used for translational research. The Medical Action Ontology (MAxO) provides a computational representation of treatments and other actions taken for clinical management. Currently, manual biocuration is used to annotate MAxO terms to rare diseases. However, it is challenging to scale manual curation to comprehensively capture information about medical actions for the more than 10 000 rare diseases.<br> <br> <br> Results<br> We present AutoMAxO, a semi-automated workflow that leverages Large Language Models (LLMs) to streamline MAxO biocuration. AutoMAxO first uses LLMs to retrieve candidate curations from abstracts of relevant publications. Next, the candidate curations are matched to ontology terms from MAxO, Human Phenotype Ontology (HPO), and MONDO disease ontology via a combination of LLMs and post-processing techniques. Finally, the matched terms are presented in a structured form to a human curator for approval. We used this approach to process abstracts related to 37 rare genetic diseases and identified 958 novel treatment annotations that were transferred to the MAxO annotation dataset.<br> <br> <br> Availability and implementation<br> AutoMAxO is a Python package freely available at https://github.com/monarch-initiative/automaxo.<br> <<<

ABSTRACT<br> <br> <br> <br> Urinary tract infections (UTIs), primarily caused by uropathogenic<br> Escherichia coli</i><br> (UPEC), affect millions annually. UPEC gains access to the urinary tract through mucosal reservoirs, including the vaginal tract. With rising antibiotic resistance and frequent recurrence, alternative non-antibiotic strategies like bacteriophage (phage) therapy are gaining attention. We explored the potential of a lytic phage, ΦHP3, as well as a phage cocktail to decolonize UPEC from the urogenital tract using<br> in vitro</i><br> and<br> in vivo</i><br> models. Phage demonstrated replication and lytic activity in both bacteriologic medium and simulated vaginal fluid. Pretreatment of human vaginal epithelial cells (VK2/E6E7) and bladder carcinoma cells (HTB-9) with phage reduced adhesion and invasion of UPEC compared with controls. Phage treatment was further able to reduce intracellular UPEC in VK2 cells. Notably, phage pretreatment did not impact phage-resistant UPEC strains, indicating that phage lysis was the primary driver of phenotypes. Live confocal microscopy confirmed the interaction of phage particles with UPEC and with both epithelial cell lines.<br> In vivo</i><br> , daily intravaginal ΦHP3 administration in humanized microbiota mice significantly reduced vaginal UPEC burden after 4 days. Treatment with a phage cocktail also reduced vaginal and cervical tissue burdens by day 7 post-treatment. UPEC dissemination was observed in uterine and kidney tissues, but burdens were not different between phage and mock-treated groups. In conclusion, we demonstrate that phage and phage cocktails can modestly reduce UPEC urogenital colonization, highlighting the potential of phage therapy as a viable prevention strategy for UTI.<br> <br> <<<

Cis-regulatory elements (CREs) control gene expression and are dynamic in their structure and function, reflecting changes in the composition of diverse effector proteins over time. However, methods for measuring the organization of effector proteins at CREs across the genome are limited, hampering efforts to connect CRE structure to their function in cell fate and disease. Here we developed PRINT, a computational method that identifies footprints of DNA-protein interactions from bulk and single-cell chromatin accessibility data across multiple scales of protein size. Using these multiscale footprints, we created the seq2PRINT framework, which uses deep learning to allow precise inference of transcription factor and nucleosome binding and interprets regulatory logic at CREs. Applying seq2PRINT to single-cell chromatin accessibility data from human bone marrow, we observe sequential establishment and widening of CREs centred on pioneer factors across haematopoiesis. We further discover age-associated alterations in the structure of CREs in murine haematopoietic stem cells, including widespread reduction of nucleosome footprints and gain of de novo identified Ets composite motifs. Collectively, we establish a method for obtaining rich insights into DNA-binding protein dynamics from chromatin accessibility data, and reveal the architecture of regulatory elements across differentiation and ageing. <<<

Although ageing can be understood in terms of associated hallmarks and biomarkers, the processes which connect and cause these phenotypes are ill-defined. Here we suggest a unifying model of ageing as four distinct processes which connect the major observations and evidence into a single framework. It explains, from a single initial cause to the ultimate outcomes and diseases, why we age and die. <br>We suggest that although DNA damage is crucial to shift homeostasis, ageing itself is not caused by simple DNA damage accumulation. Instead, only specific sites of damage are relevant when they affect selection and the resulting ageing processes. For clarity, each process is given a name. <br>The first process, celerisis, results from the natural course of tissue-level selection for cells with elevated metabolic and proliferative rate. If the damaged DNA site gives the cell a selective advantage, it can spread within the tissue causing hyperfunctional diseases including cancer and fibrosis. However, many ageing phenotypes are more associated with hypofunction. Therefore, we suggest that our tissues have a mechanism to prevent the spread of hyperfunctional cells.<br>In proliferative tissues, a second process, intrinsic ageing, is the result of this defence mechanism induced through cell communication via Notch. Slower metabolising mutants induce epigenetic changes in faster cells, and then epigenetically slowed cells slow other cells, causing gradual metabolic slowdown across tissues.<br>The third process, extrinsic ageing, could then result directly from metabolic slowdown as body cells use less ATP. Mitochondria reduce catabolism, restoring ATP levels by burning less glucose and lipid. Build-up of these fuels in the cytoplasm reduces import, restoring equilibrium but inducing insulin resistance (IR), while the excess fuel is diverted to the adipose, causing weight gain, chronic inflammation, and metabolic syndrome. These outcomes could then combine with intrinsic ageing to induce age-related disease. <br>The final process of mitochondrial selection induces intrinsic ageing of single celled life as well as post-mitotic tissues and organisms. Together, the four processes produce a detailed mechanistic map that explains the evolutionary significance of ageing, removing old paradoxes, and connecting the hallmarks into a causal framework that furthers our understanding. <<<