bluuebunny/crossref_metadata_2025 huggingface dataset:
Toward Earth system modeling with resolved clouds and ocean submesoscales on heterogeneous many-core HPCs
Authors: Zhang Shaoqing , Xu Shiming , Fu Haohuan , Wu Lixin , Liu Zhao , Gao Yang , Zhao Chun , Wan Wubing , Wan Lingfeng , Lu Haitian , Li Chenling , Liu Yanfei , Lv Xiaojing , Xie Jiayu , Yu Yangyang , Gu Jun , Wang Xuantong , Zhang Yan , Ning Chenhui , Fei Yunlong , Guo Xiuwen , Wang Zhaoying , Wang Xue , Wang Zhenming , Qu Binglin , Li Mingkui , Zhao Haoran , Jiang Yingjing , Yang Guang , Lu Lv , Wang Hong , An Hong , Zhang Xin , Zhang Yu , Ma Wentao , Yu Fujiang , Xu Jing , Lin Xiaopei , Shen Xueshun
Published: March 2023

With the aid of the newly developed ‘Sunway’ heterogeneous-architecture supercomputer, which has world-leading HPC (high-performance computer) capability, a series of high-resolution coupled Earth system models (SW-HRESMs) with up to 5 km of atmosphere and 3 km of ocean have been developed. These models can meet the needs of multiscale interaction studies with different computational costs. Here we describe the progress of SW-HRESMs development, with an overview of the major advancements made by the international Earth science community in HR-ESMs. We also show the preliminary results of SW-HRESMs with regard to capturing major weather-climate extremes in the atmosphere and ocean, stressing the importance of permitted clouds and ocean submesoscale eddies in modeling tropical cyclones and eddy-mean flow interactions, and paving the way for further model development to resolve finer scales with even higher resolution and more realistic physics. Finally, in addition to increasing model resolution, the development procedure for a non-hydrostatic cloud and ocean submesoscale resolved ESM is discussed, laying out the major scientific directions of such a huge modeling advancement.
CMW-Net: an adaptive robust algorithm for sample selection and label correction
Authors: Shu Jun , Yuan Xiang , Meng Deyu
Published: March 2023

A class-aware sample weighting algorithm is developed for general label noise problems. The algorithm can effectively tackle complicated and diverse noisy label tasks, winning the Championship of the ‘Arena Contest’ Track 1 of 2022 Greater BayArea (Huangpu) International Algorithm Case Competition.
Patient and Epidemiological Factors Associated With Influenza Testing in Hospitalized Adults With Acute Respiratory Illnesses, 2016–2017 to 2019–2020
Authors: Dalton Alexandra F , Couture Alexia , DeSilva Malini B , Irving Stephanie A , Gohil Shruti , Rao Suchitra , Fink Rebecca V , Naleway Allison L , Guo Zijing , Sundaresan Devi , Birch Rebecca J , Ball Sarah , Zheng Kai , Ong Toan C , Reed Carrie , Bozio Catherine H
Published: March 2023

Background

Data are limited on influenza testing among adults with acute respiratory illness (ARI)–associated hospitalizations. We identified factors associated with influenza testing in adult ARI-associated hospitalizations across the 2016–2017 through 2019–2020 influenza seasons.

Methods

Using data from 4 health systems in the United States, we identified hospitalizations that had an ARI discharge diagnosis or respiratory virus test. A hospitalization with influenza testing was based on testing performed within 14 days before through 72 hours after admission. We used random forest analysis to identify patient characteristics and influenza activity indicators that were most important in terms of their relationship to influenza testing.

Results

Across 4 seasons, testing rates ranged from 14.8%–19.4% at 3 pooled sites and 60.1%–78.5% at a fourth site with different testing practices. Discharge diagnoses of pneumonia or infectious disease of noninfluenza etiology, presence of ARI signs/symptoms, hospital admission month, and influenza-like illness activity level were consistently among the variables with the greatest relative importance.

Conclusions

Select ARI diagnoses and indicators of influenza activity were the most important factors associated with influenza testing among ARI-associated hospitalizations. Improved understanding of which patients are tested may enhance influenza burden estimates and allow for more timely clinical management of influenza-associated hospitalizations.
‘Such Cold, Dispassionate Terms Fail Us’: Reading Hansard as an ‘Archive of Feelings”
Authors: Leibel Miranda S
Published: March 2023

This article proposes a distinctly affective methodological approach to Hansard transcripts using feminist affect theory and Pierre Bourdieu’s reflexive methodologies. Expanding the ‘toolkit’ of analysis available to parliamentary and legislative researchers to understand the political worlds we inhabit, I contend scholarship engaging with Hansard is shaped—but not limited—by the format and editorial decisions that inform its publication. Feminist scholarship on affect enables both (i) generative methodological approaches to so-called limitations of Hansard, and (ii) empowers a critical advancement of how researchers can engage Hansard as data. Considering the Hansard corpus as a process (rather than a thing) enables analyses to begin not with the question of can affect and emotion be present in transcripts, but rather how can emotion and affect proliferate throughout a genre designed and mediated in such a way as to occlude these emotional and affective speech patterns. I conclude by offering methodological strategies through an exploration of research vignettes drawn from my work with Canadian provincial Hansards.
Polydopamine nanoparticle-mediated mild photothermal therapy for inhibiting atherosclerotic plaque progression by regulating lipid metabolism of foam cells
Authors: Tu Shuangshuang , Ren Wenzhi , Han Jinru , Cui Haijing , Dai Ting , Lu Haoxuan , Xie Yanqing , He Wenming , Wu Aiguo
Published: March 2023

Since apoptosis of foam, cells can induce plaque instability, reducing intracellular lipid content while protecting foam cells from apoptosis is beneficial for the safe and efficient therapy of atherosclerosis. In this study, osteopontin-coupled polydopamine (PDA-OPN) nanoparticles were synthesized and applied to target mild photothermal therapy (PTT) of atherosclerosis. The results from laser confocal microscopy indicate that PDA-OPN nanoparticles can be specially recognized and absorbed by foam cells. Under near-infrared laser irradiation, the mild photothermal generated by PDA-OPN decreases intracellular lipid accumulation but does not induce cell apoptosis. In vivo treatments demonstrate that mild PTT can substantially reduce plaque area and improve plaque stability by upregulating the expression of plaque fibrosis in ApoE−/− mice. Our findings reinforce that the PDA-OPN nanoparticle-mediated mild PTT can inhibit atherosclerotic progression, which provides new insights for developing safe and effective treatment methods for atherosclerosis.
Public Religion and Gendered Attitudes
Authors: Stewart Evan , Edgell Penny , Delehanty Jack
Published: March 2023

Do religious commitments hinder support for gender equality and contribute to the stalled gender revolution as a social problem? Answering this question requires specifying what kinds of religious commitments affect what specific gendered attitudes. Using a cultural approach to the study of religion, we distinguish personal religious commitments (piety and practice) from public religious commitments (preferences for religious order in social life). Using a large national survey, we demonstrate (1) that support for public religious authority has a stronger positive relationship with support for separate gender roles and ambivalent sexism than does personal piety; (2) that these relationships do not hold for gender identity salience; and (3) that support for separate gender roles mediates the relationship between support for public religious order and support for a gender-equitable policy: paid family leave. We argue that public religious commitments in the United States are semi-autonomous from personal religiosity, and we identify one specific public religious repertoire that provides support for a public order based on a binary and complementary understanding of gender.
The Gender Gap in the Returns From College Education in Japan: The Impact of Attending a High-Ranking College
Authors: LI Wen , URAKAWA Kunio , SUGA Fumihiko
Published: March 2023

This study investigates the gender gap present in the relationship between obtaining a college degree and labour earnings in Japan, using a dataset that contains detailed information about the colleges from which the respondents graduated. In particular, we focus on the ranking of colleges as a factor. We find that obtaining a college degree, regardless of the college ranking, is positively correlated with higher earnings even after controlling educational and cultural experiences in childhood, for both men and women, but that this relationship is stronger for men. Moreover, graduating from a high-ranking college is positively and significantly associated with higher earnings for male workers only. Further, the relationship between a high-ranking college degree and women’s spousal labour earnings is positive and significant. Thus, opportunities to obtain higher education benefits are most likely connected to the different paths that men and women take to attain economic success.
The Complete Genome Sequence Resource of Rhizospheric Soil-Derived Bacillus velezensis Yao, with Biocontrol Potential Against Fusarium solani-Induced Pepper Root Rot
Authors: Pei Dongli , Zhang Qingchen , Zhu Xiaoqin , Yao Xiaolin , Zhang Lei
Published: March 2023

The pepper rhizospheric soil-derived Bacillus velezensis Yao from the Shangqiu region of the Henan province in China possesses antagonistic activity against Fusarium solani, which causes pepper root rot. In this report, we introduced the entire genomic sequence of B. velezensis Yao, which is 3,951,864 bp long, with 46.61% G+C content, and 4,097 genes. Using antiSMASH analysis, we predicted 12 gene clusters that encode for secondary antimicrobial metabolites and multiple genes that regulate plant bacterial interactions. The B. velezensis Yao genome data may be a valuable resource as this strain may serve as an effective biocontrol agent against pepper root rot.
Correlates of Risk for Disinhibited Behaviors in the Million Veteran Program Cohort
Authors: Barr Peter B. , Bigdeli Tim B. , Meyers Jacquelyn L. , Peterson Roseann E. , Sanchez-Roige Sandra , Mallard Travis T. , Dick Danielle M. , Harden K. Paige , Wilkinson Anna , Graham David P. , Nielsen David A. , Swann Alan , Lipsky Rachele K. , Kosten Thomas , Aslan Mihaela , Harvey Philip D. , Kimbrel Nathan A. , Beckham Jean C.
Published: March 2023

Background

Many psychiatric outcomes are thought to share a common etiological pathway reflecting behavioral disinhibition, generally referred to as externalizing disorders (EXT). Recent genome-wide association studies (GWAS) have demonstrated the overlap between EXT and important aspects of veterans’ health, such as suicide-related behaviors, substance use disorders, and other medical conditions.

Methods

We conducted a series of phenome-wide association studies (PheWAS) of polygenic scores (PGS) for EXT, and comorbid psychiatric problems (depression, schizophrenia, and suicide attempt) in an ancestrally diverse cohort of U.S. veterans (N= 560,824), using diagnostic codes from electronic health records. We conducted ancestry-specific PheWASs of EXT PGS in the European, African, and Hispanic/Latin American ancestries. To determine if associations were driven by risk for other comorbid problems, we performed a conditional PheWAS, covarying for comorbid psychiatric problems (European ancestries only). Lastly, to adjust for unmeasured confounders we performed a within-family analysis of significant associations from the main PheWAS in full-siblings (N = 12,127, European ancestries only).

Results

The EXT PGS was associated with 619 outcomes across all bodily systems, of which, 188 were independent of risk for comorbid problems of PGS. Effect sizes ranged from OR = 1.02 (95% CI = 1.01, 1.03) for overweight/obesity to OR = 1.44 (95% CI = 1.42, 1.47) for viral hepatitis C. Of the significant outcomes 73 (11.9%) and 26 (4.5%) were significant in the African and Hispanic/Latin American results, respectively. Within-family analyses uncovered robust associations between EXT and consequences of substance use disorders, including liver disease, chronic airway obstruction, and viral hepatitis C.

Conclusion

Our results demonstrate a shared polygenic basis of EXT across populations of diverse ancestries and independent of risk for other psychiatric problems. The strongest associations with EXT were for diagnoses related to substance use disorders and their sequelae. Overall, we highlight the potential negative consequences of EXT for health and functioning in the US veteran population.
Bias Amplification in Intersectional Subpopulations for Clinical Phenotyping by Large Language Models
Authors: Pal Ridam , Garg Hardik , Patel Shashwat , Sethi Tavpritesh
Published: March 2023

Large Language Models (LLMs) have demonstrated remarkable performance across diverse clinical tasks. However, there is growing concern that LLMs may amplify human bias and reduce performance quality for vulnerable subpopulations. Therefore, it is critical to investigate algorithmic underdiagnosis in clinical notes, which represent a key source of information for disease diagnosis and treatment. This study examines prevalence of bias in two datasets - smoking and obesity - for clinical phenotyping. Our results demonstrate that state-of-the-art language models selectively and consistently underdiagnosed vulnerable intersectional subpopulations such as young-aged-males for smoking and middle-aged-females for obesity. Deployment of LLMs with such biases risks skewing clinicians’ decision-making which may lead to inequitable access to healthcare. These findings emphasize the need for careful evaluation of LLMs in clinical practice and highlight the potential ethical implications of deploying such systems in disease diagnosis and prognosis.
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

While 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

Cytoplasmic 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

Objectives

Brain 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 Design

Included 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 Observations

BIBSNet segmentations outperforms JLF across all regions based on DSC comparisons. Additionally, with processed derived metrics, BIBSNet segmentations outperforms JLF segmentations across nearly all metrics.

Conclusions

BIBSNet 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

Recent 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

Federated 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

Understanding 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

Inflammatory 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

Most, 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

Multinucleated 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 statement

Transcription 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

Plants 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

ORFanage 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

Metabolic 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

Fibrosis, 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.
Large-scale epidemiological study on feline autosomal dominant polycystic kidney disease and identification of novelPKD1gene variants
Authors: Shitamori Fumitaka , Nonogaki Ayaka , Motegi Tomoki , Matsumoto Yuki , Sakamoto Mika , Tanizawa Yasuhiro , Nakamura Yasukazu , Yonezawa Tomohiro , Momoi Yasuyuki , Maeda Shingo
Published: March 2023

Autosomal dominant polycystic kidney disease (ADPKD) is a common inherited disease in cats. In most cases, the responsible abnormality is a nonsense single nucleotide polymorphism in exon 29 of thePKD1gene (chrE3:g.42858112C>A, the conventionalPKD1variant). Epidemiological studies on feline ADPKD caused by the conventionalPKD1variant have been conducted in several countries, including Japan. However, they were limited to Persian cats or cats already suspected of ADPKD. There are no data on the prevalence of the conventionalPKD1variant among a wider cat population in Japan that is not limited to certain breeds or clinical backgrounds. This large-scale epidemiological study involving 1,281 cats aimed to establish a new genotyping assay to detect the conventionalPKD1variant rapidly. Among the cats examined in this study, 1.8% (23/1,281) harbored the conventionalPKD1variant. The odds of having the conventionalPKD1variant were significantly higher in Persian cats, Scottish Folds, and Exotic Shorthairs than in the other breeds. Furthermore, targeted resequencing ofPKD1was performed in cats with ADPKD and healthy cats to search for new variants that may be involved in ADPKD. We identified four variants unique to ADPKD cats that were not found in healthy cats, all of which were in exon 15. This indicates that variants in exon 15 ofPKD1, in addition to the conventional variant in exon 29, are key factors in the pathogenesis of ADPKD in cats.
Rainbow trout in the inlet tributaries of Lake Chinishibetsu, Shiretoko Peninsula
Authors: Sahashi Genki , Kuroki Mari , Nobetsu Takahiro , Morita Kentaro
Published: March 2023

Rainbow trout, Oncorhynchusmykiss, is one of the most widely introduced fish species in the world, and its impacts on native fishes and ecosystems are of considerable concern. One of the rivers inhabited by rainbow trout in the Shiretoko Peninsula is the Chinishibetsu River, and the origin of rainbow trout in this river is thought to be Lake Chinishibetsu in the upper reaches of the system, where the private stocking of rainbow trout was conducted in the 1960s. However, the basic biology of rainbow trout in the Lake Chinishibetsu area is currently unknown. This study addresses this knowledge gap by examining the biology of rainbow trout in the inlet tributaries of Lake Chinishibetsu based on sampling conducted during the rainbow trout spawning season. A total of 104 rainbow trout, ranging in age from 1+ to 8+ years, were collected from the two inlet tributaries of Lake Chinishibetsu. White-spotted charrSalvelinus leucomaenisand Siberian stone loachBarbatula oreas, neither of which is native to the Shiretoko Peninsula, were also collected and had presumably invaded the area at the same time as the rainbow trout. The sampled rainbow trout included immature and mature males and females. The distribution of fork lengths of mature females was bimodal, and the sex ratio of mature rainbow trout was male-biased. Our results indicate that the rainbow trout population in the inlet tributaries of Lake Chinishibetsu is reproducing continuously and exhibits a dimorphic life history with river residents and lake migrants of both sexes. Additionally, rainbow trout continue to be collected downstream of the Chinishibetsu River, which is the primary habitat for this species in the Shiretoko Peninsula. Therefore, unless rainbow trout are eliminated from Lake Chinishibetsu, which serves as a source of non-native species upstream of the Chinishibetsu River, it will be difficult to control rainbow trout distributions and minimize population sizes on the Shiretoko Peninsula.
Uncovering Hidden Cancer Self-Dependencies through Analysis of shRNA-Level Dependency Scores
Authors: Toghrayee Zohreh , Montazeri Hesam
Published: March 2023

Large-scale short hairpin RNA (shRNA) screens on well-characterized human cancer cell lines have been widely used to identify novel cancer dependencies. However, the off-target effects of shRNA reagents pose a significant challenge in the analysis of these screens. To mitigate these off-target effects, various approaches have been proposed that aggregate different shRNA viability scores targeting a gene into a single gene-level viability score. Most computational methods for discovering cancer dependencies rely on these gene-level scores. In this paper, we propose a computational method, named NBDep, to find cancer self-dependencies by directly analyzing shRNA-level dependency scores instead of gene-level scores. The NBDep algorithm begins by removing known batch effects of the shRNAs and selecting a subset of concordant shRNAs for each gene. It then uses negative binomial random effects models to statistically assess the dependency between genetic alterations and the viabilities of cell lines by incorporating all shRNA dependency scores of each gene into the model. We applied NBDep to the shRNA dependency scores available at Project DRIVE, which covers 26 different types of cancer. The proposed method identified more well-known and putative cancer genes compared to alternative gene-level approaches in pan-cancer and cancer-specific analyses. Additionally, we demonstrated that NBDep controls type-I error and outperforms statistical tests based on gene-level scores in simulation studies.

Author Summary

Large-scale shRNA screening is increasingly being used in cancer genomics to discover genes involved in cancer by analyzing the viabilities of cell lines upon knocking down a gene using a pool of short hairpin RNAs (shRNA). However, off-target effects, which result from the knockdown of unintended genes, are a major issue in RNAi screening. To address this issue, various computational methods have been developed to aggregate shRNA viability scores into gene-level dependency scores. In this paper, we propose a method called NBDep to identify cancer gene drivers that directly addresses the challenge of off-target effects at the shRNA level. NBDep identifies cancer gene drivers in three classes: amplification, missense, and non-missense alterations. In this method, we first remove known batch effects, select a subset of the most consistent shRNAs of each gene, and then perform a negative binomial mixed-effect model. The NBDep method not only identifies well-recognized and novel cancer driver genes but also has more statistical power than gene-level-score methods while controlling type-error. In summary, NBDep presents a new technique for analyzing shRNA screens and has the potential to uncover previously unknown cancer dependencies.
Endogenous viral elements constitute a complementary source of antigens for personalized cancer vaccines
Authors: Garde Christian , Pavlidis Michail A. , Garces Pablo , Lange Emma J. , Ramarathinam Sri H. , Sokač Mateo , Pandey Kirti , Faridi Pouya , Ahrenfeldt Johanne , Chung Shanzou , Friis Stine , Kleine-Kohlbrecher Daniela , Birkbak Nicolai J. , Kringelum Jens V. , Rønø Birgitte , Purcell Anthony W. , Trolle Thomas
Published: March 2023

Personalized cancer vaccines (PCVs) largely leverage neoantigens arising from somatic mutations limiting their application to patients with relatively high tumor mutational burden (TMB). This underscores the need for alternative antigens to design PCVs for low TMB cancers. To this end, we substantiate endogenous retroviral elements (EVEs) as tumor antigens through large-scale genomic analyses of healthy tissues and solid cancers. These analyses revealed that the breadth of EVE expression in tumors stratify checkpoint inhibitor treated melanoma patients into groups with differential overall and progression-free survival. To enable the design of PCVs containing EVE-derived epitopes with therapeutic potential, we developed a computational pipeline, ObsERV. We show that EVE-derived peptides are presented as epitopes on tumors and can be predicted by ObsERV. Preclinical testing of ObsERV demonstrates induction of sustained poly-functional CD4+ and CD8+ T-cell responses as well as long-term tumor protection. As such, EVEs may facilitate and improve PCVs especially for low-TMB patients.
Cancer Immunotherapy through Tissue Adhering Polymers
Authors: Borthwick Neil J. , Maikawa Caitlin L. , Weller Sven , Andresen Thomas L. , Hansen Anders E. , Autzen Anton A.A.
Published: March 2023

TLR 7/8 agonists are highly potent immunostimulators, though their clinical translation has been met with mixed success, due to their high toxicity as a result of an unregulated systemic immune activation. There is enormous potential to augment cancer immunotherapies with synthetic TLR 7/8 agonists, though a thorough control of pharmacokinetics and localization is needed for the general use of TLR 7/8 agonists in cancer immunotherapy. Herein, we control localization of TLR 7/8 agonists, by exploiting the extensive tissue retention of poly(acrylic acid-co-styrene). In a murine CT26 model, we find that covalently attaching TLR 7/8 agonists to the copolymer allows for retaining the drug in the tumor microenvironment for at least 15 weeks, after intratumoral injection, and results in a curative monotherapy. The copolymer itself is a new avenue for attaining prolonged tissue rentention for covalently attached drugs.