bluuebunny/crossref_metadata_2025_split huggingface dataset:
Polyamine Depletion by D, L-alpha-difluoromethylornithine Inhibits Ewing Sarcoma Metastasis by Inducing Ferroptosis
Authors: Offenbacher Rachel , Jackson Kyle W. , Hayashi Masanori , Zhang Jinghang , Peng Da , Tan Yuqi , Stewart Tracy Murray , Ciero Paul , Foley Jackson , Casero Robert A. , Cahan Patrick , Loeb David M. 
Published: June 2024

AbstractPolyamine metabolism and signaling play important roles in multiple cancers but have not previously been studied in Ewing sarcoma. Here, we show that blocking polyamine synthesis with D, L-alpha-difluoromethylornithine (DFMO) causes a G1 cell cycle arrest, dose-dependent decreases in sarcosphere formation from Ewing sarcoma cell lines growing in non-adherent conditions and a decrease in clonogenic growth in soft agar. Further, we utilized our orthotopic implantation/amputation model of Ewing sarcoma metastasis to demonstrate that DFMO slowed primary tumor growth in addition to limiting metastasis. RNA sequencing demonstrated gene expression patterns consistent with induction of ferroptosis caused by polyamine depletion. Induction of ferroptosis was validated in vitro by demonstrating that ferrostatin-1, an inhibitor of ferroptosis, allows sphere formation even in the presence of DFMO. Collectively, these results reveal a novel mechanism by which DFMO prevents metastasis – induction of ferroptosis due to polyamine depletion. Our results provide preclinical justification to test the ability of DFMO to prevent metastatic recurrence in Ewing sarcoma patients at high risk for relapse.
A unifying model of species abundance distribution
Authors: Gao Yingnan , Abdullah Ahmed , Wu Martin 
Published: June 2024

AbstractThe species abundance distribution (SAD) is one of the most fundamental and best-studied macroecological patterns at the core of any biodiversity theory. Remarkably, almost every community investigated to date shows a hollow curve, indicative of the presence of many rare species and a few abundant species. While the precise nature of SAD is believed to reflect fundamental ecological processes underlying community assembly, ecologists have yet to identify a single model that comprehensively explains all SADs. Recent studies using large datasets suggested that logseries best describes animal and plant communities1,2while lognormal is the best model for microbes3, thereby challenging the notion of a unifying SAD model across the tree of life. Using a large dataset of ∼30,000 globally distributed communities spanning animals, plants and microbes from diverse environments, here we show that powerbend distribution, predicted by a maximum information entropy-based theory of ecology, emerges as a unifying model that accurately captures SADs of all life forms, habitats and abundance scales, supporting the existence of universal ecological principles. Our findings reject the notion of pure neutrality and support the idea that community assembly is driven by both random fluctuations and deterministic mechanisms, such as interspecific trait variation and resource competition. We also show that the previously estimated one trillion microbial species existing on Earth might be orders of magnitude off.
MPAC: a computational framework for inferring cancer pathway activities from multi-omic data
Authors: Liu Peng , Page David , Ahlquist Paul , Ong Irene M. , Gitter Anthony 
Published: June 2024

AbstractFully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cancer (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC uses network relationships encoded in pathways using a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to prioritize proteins with potential clinical relevance. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available athttps://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.
Strategies for addressing pseudoreplication in multi-patient scRNA-seq data
Authors: Malfait Milan , Gilis Jeroen , Van den Berge Koen , Assefa Alemu Takele , Verbist Bie , Clement Lieven 
Published: June 2024

AbstractThe rapidly evolving field of single-cell transcriptomics has provided a powerful means for understanding cellular heterogeneity. Large-scale studies with multiple biological samples hold promise for discovering differentially expressed biomarkers with a higher level of confidence through a better characterization of the target population. However, the hierarchical nature of these experiments introduces a significant challenge for downstream statistical analysis. Indeed, despite the availability of numerous differential expression methods, only a select few can accurately address the within-patient correlation of single-cell expression profiles. Furthermore, due to the high computational costs associated with some of these methods, their practical use is limited.In this manuscript, we undertake a comprehensive assessment of different strategies to address the hierarchical correlation structure in multi-sample scRNA-seq data. We employ synthetic data generated from a simulator that retains the original correlation structure of multi-patient data while making minimal assumptions, providing a robust platform for benchmarking method performance. Our analyses indicate that neglecting within-patient correlation jeopardizes type I error control. We show that, in line with some previous reports and in contrast with others, Poisson Generalized Estimation Equations provide a useful and flexible framework for addressing these issues. We also show that pseudobulk approaches outperform single-cell level methods across the board. In this work, we resolve the conflicting results regarding the utility of GEEs and their performance relative to pseudobulk approaches. As such, we provide valuable guidelines for researchers navigating the complex landscape of gene expression modeling, and offer insights on choosing the most appropriate methods based on the specific structure and design of their datasets.
Towards Maps of Disease Progression: Biomedical Large Language Model Latent Spaces For Representing Disease Phenotypes And Pseudotime
Authors: Zamora-Resendiz Rafael , Khurram Ifrah , Crivelli Silvia 
Published: June 2024

ABSTRACTIn this study, we propose a scientific framework to detect capability among biomedical large language models (LLMs) for organizing expressions of comorbid disease and temporal progression. We hypothesize that biomedical LLMs pretrained on next-token prediction produce latent spaces that implicitly capture "disease states" and disease progression, i.e., the transitions over disease states over time. We describe how foundation models may capture and transfer knowledge from explicit pretraining tasks to specific clinical applications. A scoring function based on Kullback-Leibler divergence was developed to measure "surprise" in seeing specialization when subsetting admissions along 13 biomedical LLM latent spaces. By detecting implicit ordering of longitudinal data, we aim to understand how these models self-organize clinical information and support tasks such as phenotypic classification and mortality prediction. We test our hypothesis along a case study for obstructive sleep apnea (OSA) in the publicly available MIMIC-IV dataset, finding ordering of phenotypic clusters and temporality within latent spaces. Our quantitative findings suggest that increased compute, conformance with compute-optimal training, and widening contexts promote better implicit ordering of clinical admissions by disease states, explaining 60.3% of the variance in our proposed implicit task. Preliminary qualitative findings suggest LLMs’ latent spaces trace patient trajectories through different phenotypic clusters, terminating at end-of-life phenotypes. This approach highlights the potential of biomedical LLMs in modeling disease progression, identifying new patterns in disease pathways and interventions, and evaluating clinical hypotheses related to drivers of severe illness. We underscore the need for larger, high-resolution longitudinal datasets to further validate and enhance understanding of the utility of LLMs in modeling patient trajectories along clinical text and advancing precision medicine.Key PointsQuestionDo LLMs sensibly organize cilnical data with respect to applications in precision medicine?FindingsBiomedically-trained LLMs show increasing potential in promoting the organization of patient data to reflect disease progression. In a subcohort of OSA patients, maps derived from LLMs’ latent representations reveal traceable disease trajectories.MeaningMaps of disease progression offer an explanation to the utility of LLMs in precision medicine. Following current pretraining conventions in foundation modeling, scientific inquiry into these maps may help anticipate progress in applications of LLMs for healthcare.
Inhibitor of cardiolipin biosynthesis-related enzyme MoGep4 confers broad-spectrum anti-fungal activity
Authors: Sun Peng , Zhao Juan , Sha Gan , Zhou Yaru , Zhao Mengfei , Li Renjian , Kong Xiaojing , Sun Qiping , Li Yun , Li Ke , Bi Ruiqing , Yang Lei , Qin Ziting , Huang Wenzheng , Wang Yin , Gao Jie , Chen Guang , Zhang Haifeng , Adnan Muhammad , Yang Long , Zheng Lu , Chen Xiao-Lin , Wang Guanghui , Ishikawa Toshiki , Li Qiang , Xu Jin-Rong , Li Guotian 
Published: June 2024

AbstractPlant pathogens cause devastating diseases, leading to serious losses to agriculture. Mechanistic understanding of pathogenesis of plant pathogens lays the foundation for the development of fungicides for disease control. Mitophagy, a specific form of autophagy, is important for fungal virulence. The role of cardiolipin, mitochondrial signature phospholipid, in mitophagy and pathogenesis is largely unknown in plant pathogenic fungi. The functions of enzymes involved in cardiolipin biosynthesis and relevant inhibitors were assessed using a set of assays, including genetic deletion, plant infection, lipidomics, chemical-protein interaction, chemical inhibition, and field trials. Our results showed that the cardiolipin biosynthesis-related geneMoGEP4of the rice blast fungusMagnaporthe oryzaeregulates growth, conidiation, cardiolipin biosynthesis, and virulence. Mechanistically, MoGep4 regulated mitophagy and Mps1-MAPK phosphorylation, which are required for virulence. Chemical alexidine dihydrochloride (AXD) inhibited the enzyme activity of MoGep4, cardiolipin biosynthesis and mitophagy. Importantly, AXD efficiently inhibited the growth of 10 plant pathogens and controlled rice blast and Fusarium head blight in the field. Our study demonstrated that MoGep4 regulates mitophagy, Mps1 phosphorylation and pathogenesis inM. oryzae. In addition, we found that the MoGep4 inhibitor, AXD, displays broad-spectrum antifungal activity and is a promising candidate for fungicide development.
CHD8 adulthood microglial knockout induces behavioral, morphological, and transcriptional changes in a sex-dependent manner
Authors: Weissberg Orly , Getselter Dmitrity , Elliott Evan 
Published: June 2024

AbstractMutations in CHD8 (chromodomain-helicase-DNA binding protein 8) are highly associated with autism spectrum disorders. It has been well established that CHD8 has a prominent role in the development of neurons. However, there is little knowledge of its specific roles in microglia, and its possible roles in cellular functions after development, i.e. adulthood. In addition, while microglial dysfunction has been characterized in autism, the roles of autism-associated genes in microglial function have not been well characterized. Using conditional transgenic mouse models, we determined that adulthood deletion of CHD8 in microglia induces robust changes in behavior, including anxiety, social deficits, and depression-like behavior, in association with changes in microglial activation and robust microglial gene expression changes, including expression of cytokines. Of great interest, many of these changes were seen specifically in male deletion mice, and not female deletion mice. In contrast, adulthood neuron knockout had more subtle effects on behavior, mainly on depression-like behavior, and induced subtle changes in gene transcription related to the Wnt/Beta-Catenin pathway. These changes were also only present in male neuron knockout mice. In summary, CHD8 is particularly important for microglial function in adulthood and has cellular effects that are specific to males.
Ccdc66 regulates primary cilium stability, disassembly and signaling important for epithelial organization
Authors: Deretic Jovana , Cengiz-Emek Seyma , Seyrek Ece , Firat-Karalar Elif Nur 
Published: June 2024

AbstractThe primary cilium is a conserved, microtubule-based organelle that transduces signaling pathways essential for development and homeostasis. It is a dynamic structure that assembles and disassembles in response to intrinsic and extrinsic stimuli while maintaining remarkable stability and tightly controlled length. Although cilium assembly is well-understood, less is known about the molecular players and pathways governing their stability, length and disassembly. Here, we elucidated the function of Ccdc66, a microtubule-associated protein linked to ciliopathies, in cilium maintenance and disassembly in mouse epithelial cells. We found that Ccdc66 depletion disrupts cilium disassembly, length and stability, but does not affect assembly in these cells. Live imaging of these processes revealed that cilia in Ccdc66-depleted cells frequently fluctuate in length and exhibit increased ectocytosis from the cilium tip. Phenotypic rescue experiments andin vitromicrotubule stabilization assays showed that Ccdc66 mediates these functions via regulating the stability of microtubules. Temporal proximity mapping of CCDC66 identified potential new regulators and molecular pathways involved in cilium disassembly. Additionally, depletion of CCDC66 compromised Hedgehog and Wnt pathway activation and disrupted epithelial cell organization and polarity in two-dimensional and three-dimensional cultures. Collectively, our results define Ccdc66 as a new microtubule-stabilizing factor that regulates cilium stability and disassembly, providing insights into the mechanisms of cilium homeostasis and the pathologies associated with Ccdc66.
Mechanical Drivers of Glycosaminoglycan Content Changes in Intact and Damaged Human Cartilage
Authors: Elahi Seyed Ali , Castro-Viñuelas Rocio , Tanska Petri , Maes Lauranne , Famaey Nele , Korhonen Rami K. , Jonkers Ilse 
Published: June 2024

AbstractArticular cartilage undergoes significant degeneration during osteoarthritis, currently lacking effective treatments. This study explores mechanical influences on cartilage health using a novel finite element-based mechanoregulatory model, predicting combined degenerative and regenerative responses to mechanical loading. Calibrated and validated through one-week longitudinal ex vivo experiments on intact and damaged cartilage samples, the model underscores the roles of maximum shear strain, fluid velocity, and dissipated energy in driving changes in cartilage glycosaminoglycan (GAG) content. It delineates the distinct regenerative contributions of fluid velocity and dissipated energy, alongside the degenerative contribution of maximum shear strain, to GAG adaptation in both intact and damaged cartilage under physiological mechanical loading. Remarkably, the model predicts increased GAG production even in damaged cartilage, consistent with our in vitro experimental findings. Beyond advancing our understanding of mechanical loading’s role in cartilage homeostasis, our model aligns with contemporary ambitions by exploring the potential of in silico trials to optimize mechanical loading in degenerative joint disease, fostering personalized rehabilitation.
Functional Control of Network Dynamical Systems: An Information Theoretic Approach
Authors: Singh Moirangthem Sailash , Pasumarthy Ramkrishna , Vaidya Umesh , Leonhardt Steffen 
Published: June 2024

AbstractIn neurological networks, the emergence of various causal interactions and information flows among nodes is governed by the structural connectivity in conjunction with the node dynamics. The information flow describes the direction and the magnitude of an excitatory neuron’s influence to the neighbouring neurons. However, the intricate relationship between network dynamics and information flows is not well understood. Here, we address this challenge by first identifying a generic mechanism that defines the evolution of various information routing patterns in response to modifications in the underlying network dynamics. Moreover, with emerging techniques in brain stimulation, designing optimal stimulation directed towards a target region with an acceptable magnitude remains an ongoing and significant challenge. In this work, we also introduce techniques for computing optimal inputs that follow a desired stimulation routing path towards the target brain region. This optimization problem can be efficiently resolved using non-linear programming tools and permits the simultaneous assignment of multiple desired patterns at different instances. We establish the algebraic and graph-theoretic conditions necessary to ensure the feasibility and stability of information routing patterns (IRPs). We illustrate the routing mechanisms and control methods for attaining desired patterns in biological oscillatory dynamics.Author SummaryA complex network is described by collection of subsystems or nodes, often exchanging information among themselves via fixed interconnection pattern or structure of the network. This combination of nodes, interconnection structure and the information exchange enables the overall network system to function. These information exchange patterns change over time and switch patterns whenever a node or set of nodes are subject to external perturbations or stimulations. In many cases one would want to drive the system to desired information patterns, resulting in desired network system behaviour, by appropriately designing the perturbating signals. We present mathematical framework to design perturbation signals that drive the system to the desired behaviour. We demonstrate the applicability of our framework in the context of brain stimulation and in modifying causal interactions in gene regulatory networks.
Approach and rotation of reconnecting topological defect lines in liquid crystal
Authors: Zushi Yohei , Schimming Cody D. , Takeuchi Kazumasa A. 
Published: June 2024

Topological defects are a universal concept across many disciplines, such as crystallography, liquid-crystalline physics, low-temperature physics, cosmology, and even biology. In nematic liquid crystals, topological defects called disclinations have been widely studied. For their three-dimensional (3D) dynamics, however, only recently have theoretical approaches dealing with fully 3D configurations been reported. Further, recent experiments have observed 3D disclination line reconnections, a phenomenon characteristic of defect line dynamics, but detailed discussions were limited to the case of approximately parallel defects. In this paper, we focus on the case of two disclination lines that approach at finite angles and lie in separate planes, a more fundamentally 3D reconnection configuration. Observing and analyzing such reconnection events, we find the square-root law of the distance between disclinations and the decrease of the interdisclination angle over time. We compare the experimental results with theory and find qualitative agreement on the scaling of distance and angle with time, but quantitative disagreement on distance and angle relative mobilities. To probe this disagreement, we derive the equations of motion for systems with reduced twist constant and also carry out simulations for this case. These, together with the experimental results, suggest that deformations of disclinations may be responsible for the disagreement.

Published by the American Physical Society
2024
ChatGPT: reflections from the UK higher education institutions, accountancy bodies and BIG4s
Authors: Sales de Aguiar Thereza Raquel 
Published: June 2024

Purpose
This study aims to explore ChatGPT in the context of the UK higher education (HE) and accountancy profession.

Design/methodology/approach
Narrative research that applies deductive thematic analysis to investigate secondary data produced by the UK HE governing bodies, the ancient UK universities, accountancy bodies and BIG4s.

Findings
Discussions held by the governing bodies of HE in the UK follow the advice found in the literature on how to acknowledge contributions from artificial intelligence. However, these discussions are informal. Universities provide guidance on the use of ChatGPT; however, these documents differ, compromising a consistent approach across institutions. Similarly, accountancy researchers lack uniform guidance. This is concerning because the data analysis indicates that ChatGPT’s limitations may have a detrimental impact on compliance with the UK Concordat to support research integrity. Moreover, accountancy bodies are predicting major changes in the accountancy profession due to automation, which will potentially change the job market and the content of qualification examinations. BIG4s have begun automating, with some negative impacts already evident. Thus, the field should be ready for future challenges.

Originality/value
This study explores ChatGPT in the context of the UK HE and accountancy profession. It provides a detailed analysis of the risks and opportunities associated with the use of ChatGPG, while also providing suggestions for risk mitigation to assist accountancy students, researchers and practitioners.