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 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 ( https://github.com/jotech/gapseq ), 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. <<<

Le Shi, Zehui Sun, Yingxue Cao, Yuying Li, Wenjie Qing, Lingli Zhou, Mingming Xu, Xiaomei Mai, Lechun Ou, Xiaoping Yang, Delaram Shakiba, Kaoru Ri, Wangsheng Ou, Jun Chen, Avi Rosenberg, Jean-François Trempe, Edward A. Fon, Xialin Liu, Yihai Cao, Elia J. Duh, Wei Yi <<<

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 Mtfr1l 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. <<<

Enock Niyonkuru, J Harry Caufield, Leigh C Carmody, Michael A Gargano, Sabrina Toro, Patricia L Whetzel, Hannah Blau, Mauricio Soto Gomez, Elena Casiraghi, Leonardo Chimirri, Justin T Reese, Giorgio Valentini, Melissa A Haendel, Christopher J Mungall, Peter N Robinson <<<

Abstract Motivation 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. Results 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. Availability and implementation AutoMAxO is a Python package freely available at https://github.com/monarch-initiative/automaxo. <<<

Bishnu Joshi, Jacob J. Zulk, Camille Serchejian, Zainab A. Hameed, Addison B. Larson, Austen L. Terwilliger, Deepak Kumar, Indira U. Mysorekar, Robert A. Britton, Anthony W. Maresso, Kathryn A. Patras <<<

ABSTRACT Urinary tract infections (UTIs), primarily caused by uropathogenic Escherichia coli (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 in vitro and in vivo 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. In vivo , 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. <<<

Yan Hu, Max A. Horlbeck, Ruochi Zhang, Sai Ma, Rojesh Shrestha, Vinay K. Kartha, Fabiana M. Duarte, Conrad Hock, Rachel E. Savage, Ajay Labade, Heidi Kletzien, Alia Meliki, Andrew Castillo, Neva C. Durand, Eugenio Mattei, Lauren J. Anderson, Tristan Tay, Andrew S. Earl, Noam Shoresh, Charles B. Epstein, Amy J. Wagers, Jason D. Buenrostro <<<

Abstract 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 time1. 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. 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. 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.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.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. 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. <<<

Kellen K. Petersen, Marta Milà-Alomà, Yan Li, Lianlian Du, Chengjie Xiong, Duygu Tosun, Benjamin Saef, Ziad S. Saad, Lei Du-Cuny, Janaky Coomaraswamy, Yulia Mordashova, Carrie E. Rubel, Emily A. Meyers, Leslie M. Shaw, Jeffrey L. Dage, Nicholas J. Ashton, Henrik Zetterberg, Kyle Ferber, Gallen Triana-Baltzer, Michael Baratta, Erin G. Rosenbaugh, Carlos Cruchaga, Eric McDade, David M. Holtzman, John C. Morris, J. Martin Sabandal, Randall J. Bateman, Anthony W. Bannon, William Z. Potter, Suzanne E. Schindler, , <<<

Abstract Predicting not just if, but also when, cognitively unimpaired individuals are likely to develop onset of Alzheimerʼs disease (AD) symptoms would be useful to clinical trials and, eventually, clinical practice. Although clock models based on amyloid and tau positron emission tomography have shown promise in predicting the onset of AD symptoms, a model based on plasma biomarkers would be more accessible. Using longitudinal plasma %p-tau217 (the ratio of phosphorylated to non-phosphorylated tau at position 217) from two independent cohorts ( n = 258 and n = 345), clock models were used to estimate the age at plasma %p-tau217 positivity. The estimated age at plasma %p-tau217 positivity was associated with the age at onset of AD symptoms (adjusted R 2 of 0.337−0.612) with a median absolute error of 3.0−3.7 years. Notably, the time from %p-tau217 positivity to onset of AD symptoms was markedly shorter in older individuals. Similar models were constructed with data from one p-tau217/Aβ42 immunoassay and four plasma p-tau217 immunoassays. These findings suggest that the time until onset of AD symptoms can be estimated using a single blood test within a margin of error that is acceptable for use in clinical trials. <<<

Abstract Time-multiplexed networks of degenerate optical parametric oscillators have demonstrated remarkable success in simulating coupled Ising spins, thus providing a promising route to solving complex combinatorial optimization problems. In these systems, referred to as coherent Ising machines, spins are encoded in the oscillator phases, and measured at the system output using phase-sensitive techniques, making intricate phase stabilization necessary. Here, we introduce an optical Ising machine based on spontaneous polarization symmetry breaking in a coherently driven fiber Kerr nonlinear resonator. In our architecture, the spins are encoded in the polarization state, allowing robust, all-intensity readout with off-the-shelf telecom components. By operating in a newly-discovered regime where nonlinearity and topology lock the system’s symmetry, we eliminate drift and bias, enabling uninterrupted Ising trials at optical speeds for over an hour, without manual intervention. This all-fiber platform not only simplifies the hardware but also opens a path to more stable, high-throughput coherent optical optimization devices for applications from finance to drug design and beyond. <<<

AbstractBreast cancer is a highly heterogeneous disease, and there are many forms of categorization for breast cancer based on gene expression profiles. Gene expression profiles are variables and may show differences if measured at different time points or under different conditions. In contrast, biological networks are relatively stable over time and under different conditions. In this study, we used a gene interaction network from a new point of view to explore the subtypes of breast cancer based on individual-specific edge perturbations measured by relative gene expression value. Our study reveals that there are four breast cancer subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of breast cancer show strong heterogeneity in prognosis, somatic mutations, phenotypic changes and enriched pathways. The network-based subtypes are closely related to the PAM50 subtypes and immunohistochemistry index. This work helps us to better understand the heterogeneity and mechanisms of breast cancer from a network perspective. <<<

When receiving a reward after a sequence of multiple events, how do we determine which event caused the reward? This problem, known as temporal credit assignment, can be difficult for humans to solve given the temporal uncertainty in the environment. Research to date has attempted to isolate dimensions of delay and reward during decision-making, but algorithmic solutions to temporal learning problems and the effect of uncertainty on learning remain underexplored. To further our understanding, we adapted a reward learning task that creates a temporal credit assignment problem by combining sequentially delayed rewards, intervening events, and varying uncertainty via the amount of information presented during feedback. Using computational modeling, two learning strategies were developed: an eligibility trace, whereby previously selected actions are updated as a function of the temporal sequence, and a tabular update, whereby only systematically related past actions (rather than unrelated intervening events) are updated. We hypothesized that reduced information uncertainty would correlate with increased use of the tabular strategy, given the model’s capacity to incorporate additional feedback information. Both models effectively learned the task, and predicted choices made by participants (N = 142) as well as specific behavioral signatures of credit assignment. Consistent with our hypothesis, the tabular model outperformed the eligibility model under low information uncertainty, as evidenced by more accurate predictions of participants’ behavior and an increase in tabular weight. These findings provide new insights into the mechanisms implemented by humans to solve temporal credit assignment and adapt their strategy in varying environments. <<<

Lachlan McGinness, Yutong Ma, Mohammad Attar, Andrew Carse, Yeming Chen, Thomas Green, Jeong-Yeon Ha, Yanbai Jin, Amy McWilliams, Theirry Panggabean, Zhengyu Peng, Jing Ru, Jiacheng She, Lujin Sun, Jialin Wang, Zilun Wei, Jiayuan Zhu <<<

We present Particle Builder, an online board game which teaches students about concepts from the Standard Model of Particle Physics at a high school level. This short activity resulted in a gain of 0.16, indicating that students learned a significant amount of particle physics knowledge. Students found the activity was more engaging and less difficult than a normal classroom lesson. <<<