bluuebunny/crossref_metadata_2025_split huggingface dataset:
Generalizable Model Design for Clinical Event Prediction using Graph Neural Networks
Authors: Tariq Amara , Kaur Gurkiran , Su Leon , Gichoya Judy , Patel Bhavik , Banerjee Imon 
Published: March 2023

AbstractWhile many machine learning and deep learning-based models for clinical event prediction leverage various data elements from electronic healthcare records such as patient demographics and billing codes, such models face severe challenges when tested outside of their institution of training. These challenges are rooted in differences in patient population characteristics and medical practice patterns of different institutions. We propose a solution to this problem through systematically adaptable design of graph-based convolutional neural networks (GCNN) for clinical event prediction. Our solution relies on unique property of GCNN where data encoded as graph edges is only implicitly used during prediction process and can be adapted after model training without requiring model re-training. Our adaptable GCNN-based prediction models outperformed all comparative models during external validation for two different clinical problems, while supporting multimodal data integration. These results support our hypothesis that carefully designed GCNN-based models can overcome generalization challenges faced by prediction models.
Homozygous ALS-linked mutations in TARDBP/TDP-43 lead to hypoactivity and synaptic abnormalities in human iPSC-derived motor neurons
Authors: Lépine Sarah , Nauleau-Javaudin Angela , Deneault Eric , Chen Carol X.-Q. , Abdian Narges , Franco-Flores Anna Krystina , Haghi Ghazal , Castellanos-Montiel María José , Maussion Gilles , Chaineau Mathilde , Durcan Thomas M. 
Published: March 2023

SummaryCytoplasmic mislocalization and aggregation of the RNA-binding protein TDP-43 is a pathological hallmark of the motor neuron (MN) disease amyotrophic lateral sclerosis (ALS). Furthermore, while mutations in theTARDBPgene (encoding TDP-43) have been associated with ALS, the pathogenic consequences of these mutations remain poorly understood. Using CRISPR/Cas9, we engineered two homozygous knock-in iPSC lines carrying mutations inTARDBPencoding TDP-43A382Tand TDP-43G348C, two common yet understudied ALS TDP-43 variants. MNs differentiated from knock-in iPSCs had normal viability and displayed no significant changes in TDP-43 subcellular localization, phosphorylation, solubility, or aggregation compared with isogenic control MNs. However, our results highlight synaptic impairments in both TDP-43A382Tand TDP-43G348CMN cultures, as reflected in synapse abnormalities and alterations in spontaneous neuronal activity. Collectively, our findings suggest that MN dysfunction may precede the occurrence of TDP-43 pathology and neurodegeneration in ALS and further implicate synaptic and excitability defects in the pathobiology of this disease.
BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans
Authors: Hendrickson Timothy J. , Reiners Paul , Moore Lucille A. , Lundquist Jacob T. , Fayzullobekova Begim , Perrone Anders J. , Lee Erik G. , Moser Julia , Day Trevor K.M , Alexopoulos Dimitrios , Styner Martin , Kardan Omid , Chamberlain Taylor A. , Mummaneni Anurima , Caldas Henrique A. , Bower Brad , Stoyell Sally , Martin Tabitha , Sung Sooyeon , Fair Ermias A. , Carter Kenevan , Uriarte-Lopez Jonathan , Rueter Amanda R. , Yacoub Essa , Rosenberg Monica D. , Smyser Christopher D. , Elison Jed T. , Graham Alice , Fair Damien A. , Feczko Eric 
Published: March 2023

AbstractObjectivesBrain segmentation of infant magnetic resonance (MR) images is vitally important for studying typical and atypical brain development. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here we introduce a deep neural network BIBSNet (Baby andInfantBrainSegmentation NeuralNetwork), an open-source, community-driven model for robust and generalizable brain segmentation leveraging data augmentation and a large sample size of manually annotated images.Experimental DesignIncluded in model training and testing were MR brain images from 90 participants with an age range of 0-8 months (median age 4.6 months). Using the BOBs repository of manually annotated real images along with synthetic segmentation images produced using SynthSeg, the model was trained using a 10-fold procedure. Model performance of segmentations was assessed by comparing BIBSNet, joint label fusion (JLF) inferred segmentation to ground truth segmentations using Dice Similarity Coefficient (DSC). Additionally, MR data along with the FreeSurfer compatible segmentations were processed with the DCAN labs infant-ABCD-BIDS processing pipeline from ground truth, JLF, and BIBSNet to further assess model performance on derivative data, including cortical thickness, resting state connectivity and brain region volumes.Principal ObservationsBIBSNet segmentations outperforms JLF across all regions based on DSC comparisons. Additionally, with processed derived metrics, BIBSNet segmentations outperforms JLF segmentations across nearly all metrics.ConclusionsBIBSNet segmentation shows marked improvement over JLF across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF, produces FreeSurfer-compatible segmentation labels, and can be easily included in other processing pipelines. BIBSNet provides a viable alternative for segmenting the brain in the earliest stages of development.
LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies
Authors: Ehsani Rezvan , Jonassen Inge , Akslen Lars A. , Kleftogiannis Dimitrios 
Published: March 2023

ABSTRACTRecent advances in highly multiplexed imaging have provided unprecedented insights into the complex cellular organization of tissues, with many applications in translational medicine. However, downstream analyses of multiplexed imaging data face several technical limitations, and although some computational methods and bioinformatics tools are available, deciphering the complex spatial organisation of cellular ecosystems remains a challenging problem. To mitigate this problem, we develop a novel computational tool, LOCATOR (anaLysisOfCAncerTissue micrOenviRonment), for spatial analysis of cancer tissue microenvironments using data acquired from mass cytometry imaging (MCI) technologies. LOCATOR introduces a graph-based representation of tissue images to describe features of the cellular organisation and deploys downstream analysis and visualisation utilities that can be used for data-driven patient risk stratification. Our case studies using MCI data from two well-annotated breast cancer cohorts re-confirmed that the spatial organisation of the tumour-immune microenvironment is strongly associated with the clinical outcome in breast cancer. In addition, we report interesting potential associations between the spatial organization of macrophages and patients’ survival. Our work introduces an automated and versatile analysis tool for MCI data with many applications in future cancer research projects.Datasets and codes of LOCATOR are publicly available athttps://github.com/RezvanEhsani/LOCATOR.
Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification
Authors: Pfeifer Bastian , Chereda Hryhorii , Martin Roman , Saranti Anna , Clemens Sandra , Hauschild Anne-Christin , Beißbarth Tim , Holzinger Andreas , Heider Dominik 
Published: March 2023

AbstractFederated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Here, we present our Ensemble-GNN software package, which can be used to deploy federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplary show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA).
Neuro-GSTH: A Geometric Scattering and Persistent Homology Framework for Uncovering Spatiotemporal Signatures in Neural Activity
Authors: Bhaskar Dhananjay , Moore Jessica , Zhang Yanlei , Gao Feng , Rieck Bastian , Wolf Guy , Pushkarskaya Helen , Khasawneh Firas , Munch Elizabeth , Greco Valentina , Pittenger Christopher , Krishnaswamy Smita 
Published: March 2023

AbstractUnderstanding how neurons communicate and coordinate their activity is essential for unraveling the brain’s complex functionality. To analyze the intricate spatiotemporal dynamics of neural signaling, we developedGeometric Scattering Trajectory Homology (neuro-GSTH), a novel framework that captures time-evolving neural signals and encodes them into low-dimensional representations. GSTH integrates geometric scattering transforms, which extract multiscale features from brain signals modeled on anatomical graphs, with t-PHATE, a manifold learning method that maps the temporal evolution of neural activity. Topological descriptors from computational homology are then applied to characterize the global structure of these neural trajectories, enabling the quantification and differentiation of spatiotemporal brain dynamics.We demonstrate the power of neuro-GSTH in neuroscience by applying it to both simulated and biological neural datasets. First, we used neuro-GSTH to analyze neural oscillatory behavior in the Kuramoto model, revealing its capacity to track the synchronization of neural circuits as coupling strength increases. Next, we applied neuro-GSTH to neural recordings from the visual cortex of mice, where it accurately reconstructed visual stimulus patterns such as sinusoidal gratings. Neuro-GSTH-derived neural trajectories enabled precise classification of stimulus properties like spatial frequency and orientation, significantly outperforming traditional methods in capturing the underlying neural dynamics. These findings demonstrate that neuro-GSTH effectively identifies neural motifs—distinct patterns of spatiotemporal activity—providing a powerful tool for decoding brain activity across diverse tasks, sensory inputs, and neurological disorders. Neuro-GSTH thus offers new insights into neural communication and dynamics, advancing our ability to map and understand complex brain functions.
Genetic and dietary modulators of the inflammatory response in the gastro-intestinal tract of the BXD mouse genetic reference population
Authors: Li Xiaoxu , Morel Jean-David , Benegiamo Giorgia , Poisson Johanne , Bachmann Alexis , Rapin Alexis , Sulc Jonathan , Williams Evan , Perino Alessia , Schoonjans Kristina , Sleiman Maroun Bou , Auwerx Johan 
Published: March 2023

AbstractInflammatory gut disorders, including inflammatory bowel disease (IBD), can be impacted by dietary, environmental and genetic factors. While the incidence of IBD is increasing worldwide, we still lack a complete understanding of the gene-by-environment interactions underlying inflammation and IBD. Here, we profiled the colon transcriptome of 52 BXD mouse strains fed with a chow or high-fat diet (HFD) and identified a subset of BXD strains that exhibit an IBD-like transcriptome signature on HFD, indicating that an interplay of genetics and diet can significantly affect intestinal inflammation. Using gene co-expression analyses, we identified modules that are enriched for IBD-dysregulated genes and found that these IBD-related modules sharecis-regulatory elements that are responsive to the STAT2, SMAD3, and REL transcription factors. We used module quantitative trait locus (ModQTL) analyses to identify genetic loci associated with the expression of these modules. Through a prioritization scheme involving systems genetics in the mouse and integration with external human datasets, we identifiedMuc4andEpha6as the top candidates mediating differences in HFD-driven intestinal inflammation. This work provides insights into the contribution of genetics and diet to IBD risk and identifies two candidate genes,MUC4andEPHA6, that may mediate IBD susceptibility in humans.
DIP-MS: A novel ultra-deep interaction proteomics for the deconvolution of protein complexes
Authors: Frommelt Fabian , Fossati Andrea , Uliana Federico , Wendt Fabian , Peng Xue , Heusel Moritz , Wollscheid Bernd , Aebersold Ruedi , Ciuffa Rodolfo , Gstaiger Matthias 
Published: March 2023

AbstractMost, if not all, proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing the majority of cellular processes in health and disease. Ever-advancing analytical capabilities promise to pinpoint lesions in proteome modularity driving disease phenotypes. Affinity purification of the protein of interest combined with LC-MS/MS (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can however not be resolved from a single AP-MS experiment but requires computational inference from multiple time-and resource-intensive reciprocal AP-MS experiments.In this study we introduce Deep Interactome Profiling by Mass Spectrometry (DIP-MS) which combines affinity enrichment with BN-PAGE separation, DIA mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment.We applied DIP-MS to probe the organisation of the human prefoldin (PFD) family of complexes, resolving distinct PFD holo- and sub-complex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution and thus represents a critical steppingstone to relate a proteome state to phenotype in both healthy and diseased conditions.
Molecular compartmentalization in a syncytium: restricted mobility of proteins within the sea urchin skeletogenic mesenchyme
Authors: Khor Jian Ming , Guerrero-Santoro Jennifer , Ettensohn Charles A. 
Published: March 2023

AbstractMultinucleated cells, or syncytia, are found in diverse taxa. Their biological function is often associated with the compartmentalization of biochemical or cellular activities within the syncytium. How such compartments are generated and maintained is poorly understood. The sea urchin embryonic skeleton is secreted by a syncytium, and local patterns of skeletal growth are associated with distinct sub-domains of gene expression within the syncytium. For such molecular compartments to be maintained and to control local patterns of skeletal growth: 1) the mobility of TFs must be restricted to produce stable differences in the transcriptional states of nuclei within the syncytium, and 2) the mobility of biomineralization proteins must also be restricted to produce regional differences in skeletal growth patterns. To test these predictions, we expressed fluorescently-tagged forms of transcription factors and biomineralization proteins in sub-domains of the skeletogenic syncytium. We found that both classes of proteins have restricted mobility within the syncytium and identified motifs that limit their mobility. Our findings have general implications for understanding the functional and molecular compartmentalization of syncytia.Summary statementTranscription factors and effector proteins have limited mobility within the skeletogenic syncytium of the sea urchin embryo.
Understanding the phytochemical diversity of plants: Quantification, variation and ecological function
Authors: Petrén Hampus , Anaia Redouan Adam , Aragam Kruthika Sen , Bräutigam Andrea , Eckert Silvia , Heinen Robin , Jakobs Ruth , Ojeda-Prieto Lina , Popp Moritz , Sasidharan Rohit , Schnitzler Jörg-Peter , Steppuhn Anke , Thon Frans , Tschikin Sebastian , Unsicker Sybille B. , van Dam Nicole M. , Weisser Wolfgang W. , Wittmann Meike J. , Yepes Sol , Ziaja Dominik , Müller Caroline , Junker Robert R. 
Published: March 2023

AbstractPlants produce a great number of phytochemical compounds mediating a variety of different functions. Recently, phytochemical diversity (chemodiversity), a way which to quantify the complex phenotype formed by sets of phytochemicals, has been suggested to be important for function. However, no study has systematically examined the potential (in)direct functional importance of chemodiversity on a general level, partly due to a lack of an agreement on how to quantify this aspect of the plant phenotype. This paper has four aims: 1) We discuss how chemodiversity (deconstructed into components of richness, evenness and disparity) may quantify different aspects of the phenotype that are ecologically relevant. 2) We systematically review the literature on chemodiversity to examine methodological practices, explore ecological patterns of variability in diversity across different levels of biological organization, and investigate the functional role of this diversity in interactions between plants and other organisms. 3) We provide a framework facilitating decisions on which measure of chemodiversity is best used in different contexts. 4) We outline open questions and avenues for future research in this area. A more thorough understanding of phytochemical diversity will increase our knowledge on the functional role phytochemical compounds, and how they shape ecological interactions between plants and their environment.
Investigating Open Reading Frames in Known and Novel Transcripts using ORFanage
Authors: Varabyou Ales , Erdogdu Beril , Salzberg Steven L. , Pertea Mihaela 
Published: March 2023

AbstractORFanage is a system designed to assign open reading frames (ORFs) to both known and novel gene transcripts while maximizing similarity to annotated proteins. The primary intended use of ORFanage is the identification of ORFs in the assembled results of RNA sequencing (RNA-seq) experiments, a capability that most transcriptome assembly methods do not have. Our experiments demonstrate how ORFanage can be used to find novel protein variants in RNA-seq datasets, and to improve the annotations of ORFs in tens of thousands of transcript models in the RefSeq and GENCODE human annotation databases. Through its implementation of a highly accurate and efficient pseudo-alignment algorithm, ORFanage is substantially faster than other ORF annotation methods, enabling its application to very large datasets. When used to analyze transcriptome assemblies, ORFanage can aid in the separation of signal from transcriptional noise and the identification of likely functional transcript variants, ultimately advancing our understanding of biology and medicine.
Flux exponent control predicts metabolic dynamics from network structure
Authors: Xiao Fangzhou , Li Jing Shuang , Doyle John C. 
Published: March 2023

AbstractMetabolic dynamics such as stability of steady states, oscillations, lags and growth arrests in stress responses are important for microbial communities in human health, ecology, and metabolic engineering. Yet it is hard to model due to sparse data available on trajectories of metabolic fluxes. For this reason, a constraint-based approach called flux control (e.g., flux balance analysis) was invented to split metabolic systems into known stoichiometry (plant) and unknown fluxes (controller), so that data can be incorporated as refined constraints, and optimization can be used to find behaviors in scenarios of interest. However, flux control can only capture steady state fluxes well, limiting its application to scenarios with days or slower timescales. To overcome this limitation and capture dynamic fluxes, this work proposes a novel constraint-based approach, flux exponent control (FEC). FEC uses a different plant-controller split between the activities of catalytic enzymes and their regulation through binding reactions. Since binding reactions effectively regulate fluxes’ exponents (from previous works), this yields the rule of FEC, that cells regulate fluxes’ exponents, not the fluxes themselves as in flux control. In FEC, dynamic regulations of metabolic systems are solutions to optimal control problems that are computationally solvable via model predictive control. Glycolysis, which is known to have minute-timescale oscillations, is used as an example to demon-strate FEC can capture metabolism dynamics from network structure. More generally, FEC brings metabolic dynamics to the realm of control system analysis and design.
Liposomal aggregates sustain the release of rapamycin and protect cartilage from friction
Authors: Bordon Gregor , Ramakrishna Shivaprakash N. , Edalat Sam G. , Eugster Remo , Arcifa Andrea , Aleandri Simone , Bertoncelj Mojca Frank , Isa Lucio , Crockett Rowena , Distler Oliver , Luciani Paola 
Published: March 2023

AbstractFibrosis, low-grade inflammation, and increased friction are together with degradation of cartilage key culprits for debilitating pain in osteoarthritis (OA), which is one of the most common chronic diseases of today’s aging population. Intraarticular administration of bio-lubricants loaded with a pharmaceutically active component recently showed promise to improve therapy. Liposomes have emerged as exceptional lubricant biomaterial, but their small size leads to rapid clearance from the synovium, causing a need for more frequent administration. We recently developed a liposomal drug delivery system based on aggregation of negatively charged liposomes with physiologically present divalent cations. Here, we expanded our platform by replacing calcium with zinc, reported to exert anti-inflammatory action. The liposomal aggregates extend the release of rapamycin (RAPA) beyond the free liposomes and have a diameter of nearly 100 μm, which was previously established to improve retention in synovial joints. Electron microscopy showed that RAPA alters the irregular morphology of liposomal clusters, which are irreversible upon dilution. RAPA recently showed great promise bothin vitroandin vivoat protecting the joints from inflammation and cartilage from further degradation. Our study adds to this by showing that RAPA is also able to dampen the fibrotic response in human OA synovial fibroblasts. Finally, the tribological properties were assessed on nano- and macro-scales on silicon surface andex vivoporcine cartilage, which showed an excellent protective ability of the system against friction on both scales. Taken together, our study shows that liposomal aggregates have the potential of improving local OA therapy.