bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hierarchical Interpretation of Out-of-Distribution Cells Using Bottlenecked Transformer
Authors: Wang Qifei , Zhu He , Hu Yiwen , Chen Yanjie , Wang Yuwei , Zhang Xuegong , Zou James , Kellis Manolis , Li Yue , Liu Dianbo , Jiang Lan 
Published: December 2024

AbstractIdentifying the genetic and molecular drivers of phenotypic heterogeneity among individuals is vital for understanding human health and for diagnosing, monitoring, and treating diseases. To this end, international consortia such as the Human Cell Atlas and the Tabula Sapiens are creating comprehensive cellular references. Due to the massive volume of data generated, machine learning methods, especially transformer architectures, have been widely employed in related studies. However, applying machine learning to cellular data presents several challenges. One such challenge is making the methods interpretable with respect to both the input cellular information and its context. Another less explored challenge is the accurate representation of cells outside existing references, referred to as out-of-distribution (OOD) cells. The out-of-distribution could be attributed to various physiological conditions, such as comparing diseased cells, particularly tumor cells, with healthy reference data, or significant technical variations, such as using transfer learning from single-cell reference to spatial query data. Inspired by the global workspace theory in cognitive neuroscience, we introduce CellMemory, a bottlenecked Transformer with improved generalization capabilities designed for the hierarchical interpretation of OOD cells unseen during reference building. Even without pre-training, it exceeds the performance of large language models pre-trained with tens of millions of cells. In particular, when deciphering spatially resolved single-cell transcriptomics data, CellMemory demonstrates the ability to interpret data at the granule level accurately. Finally, we harness CellMemory’s robust representational capabilities to elucidate malignant cells and their founder cells in different patients, providing reliable characterizations of the cellular changes caused by the disease.
Mapping of suitable release areas for the parasitoidDolichogenidea gelechiidivoris(Marsh) for the classical biocontrol ofTuta absoluta(Meyrick) using temperature-dependent phenology models
Authors: Mujica Norma , Carhuapoma Pablo , Kroschel Jürgen , Kreuze Jan 
Published: December 2024

AbstractThe South American tomato leafminer,Tuta absoluta(Meyrick) is a major invasive pest of tomato (Lycopersicum esculentumMill.) worldwide. Among the different integrated pest management strategies, biological control is most promising.Dolichogenidea gelechiidivoris(Marsh) is a larval endoparasitoid native to the Neotropics, where it is the dominant biological agent ofT. absolutaalong the Peruvian coast. The determination of the parasitoid’s temperature-dependent development is crucial for better predicting the potential of the parasitoid to establish in new regions and to control the target pest. Therefore, the effect of temperature on the development and reproduction ofD. gelechiidivoriswas studied at five constant temperatures ranging from 10 to 30°C in its main hostT. absoluta. The Insect Life Cycle Modeling (ILCYM) software was used to fit nonlinear equations to collected life table data and to establish an overall phenology model to simulate life table parameters based on temperature. The parasitoid completed its life cycle at constant temperatures from 15 to 30°C; the temperature of 10°C was lethal to pupae. The theoretical lower threshold temperatures for the development of egg-larvae and pupae were 7.6°C and 10.9°C respectively. The egg-larval and pupae stages had the lowest mortality between the temperature range of 20-30°C. The lowest senescence rates for females and males were observed within the temperature range of 10–20°C. Oviposition time decreased significantly with increasing temperature from 16.7 days (10°C) to 1.6 days (35°C). Mean fecundity was highest at 20°C (74.4 eggs/female). Maximum population growth is expected around 24.3°C with a finite rate of increase, λ of 1.1088, which corresponds to a population doubling time of 6.7 days. The highest values for gross reproduction rate (GRR) and net reproduction rate (R0) were found between 20 and 21°C, and the shortest mean generation time (T) was observed at 30°C (19.9 d). Suitable release areas with a very high probability of establishment and potentially good control efficacy of the parasitoid are tropical and subtropical regions (e.g., countries in Southern Europe; Spain, Portugal). The potential use of the parasitoid in the context of classical biological control ofT. absolutais discussed.
Personalizing chemotherapy drug selection using a novel transcriptomic chemogram
Authors: Lin-Rahardja Kristi , Scarborough Jessica , Scott Jacob G 
Published: December 2024

ABSTRACTGene expression signatures predictive of chemotherapeutic response have the potential to greatly extend the reach of precision medicine by allowing medical providers to plan treatment regimens on an individual basis for patients with and without actionable mutations. Most published gene signatures are only capable of predicting response for individual drugs, but currently, a majority of chemotherapy regimens utilize combinations of different agents. We propose a unified framework, called the chemogram, that uses predictive gene signatures to rank the relative predicted sensitivity of different drugs for individual tumor samples. Using this approach, providers could efficiently screen against many therapeutics to identify the drugs that would fit best into a patient’s treatment plan at any given time. This can be easily reassessed at any point in time if treatment efficacy begins to decline due to therapeutic resistance.To demonstrate the utility of the chemogram, we first extract predictive gene signatures using a previously established method for extracting pan-cancer signatures inspired by convergent evolution. We derived 3 signatures for 3 commonly used cytotoxic drugs (cisplatin, gemcitabine, and 5-fluorouracil). We then used these signatures in our framework to predict and rank sensitivity among the drugs within individual cell lines. To assess the accuracy of our method, we compared the rank order of predicted response to the rank order of observed response (fraction of surviving cells at a standardized dose) against each of the 3 chemotherapies. Across a majority of cancer types, chemogram-generated predictions were consistently more accurate than randomized prediction rankings, as well as prediction rankings made by randomly generated gene signatures. In addition to the chemogram’s ability to rank relative sensitivity for any given tumor, this framework is easily scalable for any number of drugs for which a predictive signature exists. We repeated the process described above for 10 drugs and found that the accuracy of the predicted sensitivity rankings was maintained as the number of drugs in the chemogram’s screen increased.Our proposed framework demonstrates the ability of transcriptomic signatures to not only predict chemotherapeutic response but correctly assign rankings of drug sensitivity on an individual basis. With further validation, the chemogram could be easily integrated in a clinical setting, as it only requires gene expression data, which is less expensive than an extensive drug screen and can be performed at scale.
HAIRpred: Prediction of human antibody interacting residues in an antigen from its primary structure
Authors: Sahni Ruchir , Kumar Nishant , Raghava Gajendra P. S. 
Published: December 2024

1.AbstractIn the past, several methods have been developed for predicting conformational B-cell epitopes in antigens that are not specific to any host. Our primary analysis of antibody-antigen complexes indicated a need to develop host-specific B-cell epitopes. In this study, we present a novel approach to predict conformational B-cell epitopes specific to human hosts by focusing on human antibody-interacting residues in antigens. We trained, test and evaluate our models on 277 complexes of human antibody-antigen complexes. Initially, we employed machine learning models based on the binary sequence profile of antigens, achieving a maximum area under the receiver operating characteristic curve (AUC) of 0.61. Performance of model improved significantly AUC from 0.61 to 0.67, when evolutionary profiles are used instead of binary profiles. Models developed using embeddings from fine-tuned large language models reached an AUC of 0.61. Additionally, models utilizing predicted surface relative solvent accessibility achieved an AUC of 0.67. Our ensemble model, which combined relative surface accessibility with evolutionary profiles, achieved highest precision with an AUCROC of 0.72. All models in this study were trained using five-fold cross-validation on a training dataset and evaluated on an independent dataset not used for training or testing. Our method outperforms existing approaches on the independent dataset. Furthermore, we used SHAP eXplainable AI (XAI) method to interpret the importance of individual features contributing to the predictions made by our models. To support the scientific community, we have developed a standalone software, and web server, HAIRpred, for predicting human antibody-interacting residues in proteins.https://webs.iiitd.edu.in/raghava/hairpred/.Author’s BiographyRuchir Sahni is currently studying as an integrated BS-MS student at Indian Institute of Science Education and Research (IISER) Pune, India. He is currently working as an Intern on Project position at Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT), New Delhi, India.Nishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT), New Delhi, India.Gajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT), New Delhi, India.
An in-depth dataset of northwestern European arthropod life histories and ecological traits
Authors: Logghe Garben , Batsleer Femke , Maes Dirk , Permentier Tristan , Berg Matty P. , Brosens Dimitri , Cooleman Stijn , De Smedt Pallieter , Hagge Jonas , Lambrechts Jorg , Pollet Marc , Verheyde Fons , Bonte Dries 
Published: December 2024

AbstractIn response to the ongoing biodiversity crisis among arthropods, it is essential to implement efficient conservation strategies to safeguard both species diversity and the vital ecosystem services they provide. Developing such strategies requires reliable predictive models that can identify the species that are the most vulnerable to current and future threats, including those posed by climate and land-use change. Species life histories are central to these models, as they influence both population dynamics and spread rates. To support this effort, we compiled a dataset with key traits for arthropods based on several literature sources and expert knowledge. The dataset contains data on body size, life history, thermal niche and ecology for 4874 northwestern European species across 10 different orders. By gathering these essential trait data, we aim to create a robust foundation for predicting species vulnerability and anticipating shifts in arthropod communities in response to global change.
A generalized and versatile framework to train and evaluate autoencoders for biological representation learning and beyond: AUTOENCODIX
Authors: Joas Maximilian , Jurenaite Neringa , Prascevic Dusan , Scherf Nico , Ewald Jan 
Published: December 2024

ABSTRACTInsights and discoveries in complex biological systems, e.g. for personalized medicine, are gained by the combination of large, feature-rich and high-dimensional data with powerful computational methods uncovering patterns and relationships. In recent years, autoencoders, a family of deep learning-based methods for representation learning, are advancing data-driven research due to their variability and non-linear power of multi-modal data integration. Despite their success, current implementations lack standardization, versatility, comparability, and generalizability preventing a broad application. To fill the gap, we present AUTOENCODIX (https://github.com/jan-forest/autoencodix), an open-source framework, designed as a standardized and flexible pipeline for preprocessing, training, and evaluation of autoencoder architectures. These architectures, like ontology-based and cross-modal autoencoders, provide key advantages over traditional methods via explainability of embeddings or the ability to translate across data modalities. We show the value of our framework by its application to data sets from pan-cancer studies (TCGA), single-cell sequencing as well as in combination with imaging. Our studies provide important user-centric insights and recommendations to navigate through architectures, hyperparameters, and important trade-offs in representation learning. Those include reconstruction capability of input data, the quality of embedding for downstream machine learning models, or the reliability of ontology-based embeddings for explainability. In summary, our versatile and generalizable framework allows multi-modal data integration in biomedical research and any other data-driven fields of research. Hence, it can serve as a open-source platform for several major trends and research using autoencoders including architectural improvements, explainability, or training of large-scale pre-trained models.
WITHDRAWN: Structural Enzymology, Phylogenetics, Differentiation, and Symbolic Reflexivity at the Dawn of Biology
Authors: Carter Charles W. , Tang Guo Qing , Patra Sourav Kumar , Betts Laurie , Dieckhaus Henry , Kuhlman Brian , Douglas Jordan , Wills Peter R. , Bouckaert Remco , Popovic Milena , Ditzler Mark A. 
Published: December 2024

Withdrawal StatementThis manuscript was posted without the final consent of all authors. The authors have therefore withdrawn it. The authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author,carter@med.unc.edu.
Forbs in Viking lands: The effect of disturbing dominant graminoids on forb recruitment in tundra grasslands
Authors: Celis Gerardo , Ehrich Dorothee , Heskel Mary , Soininen Eeva , Ungar Peter , Bråthen Kari Anne 
Published: December 2024

AbstractCertain graminoids can be successful in grasslands to the extent it is a phenomenon called “the Viking Syndrome”. Nevertheless, forbs also make up a substantial part of vascular plant diversity in grasslands and are important resources of mammalian herbivores. Here we assess the hypothesis that forb recruitment is constrained by dominant graminoids, limiting access to safe sites for germination. We report on a disturbance experiment of plots with four different graminoid species in tundra grasslands of the Varanger Peninsula, Norway. Plots were selected to sample both rodent-disturbed and undisturbed areas. The dominant graminoids in each plot were removed, reducing their shading capabilities and belowground rhizome and root systems. Results show that forb recruitment one year following disturbance was significantly enhanced by manual graminoid removal. Dominant graminoid type, small rodent disturbance, initial forb abundance, and abiotic conditions had no effect on forb recruitment, whereas initial species richness had a positive relationship. Furthermore we found that manual disturbance had low impact on the species exchange ratio based on richness (SERr), suggesting that disturbance did not reduce the capacity of species to reside and move within the grassland. Our findings support the hypothesis that forb recruitment is limited by dominant graminoids.
A Low-Cost Markerless motion capture system to automate Functional Gait Assessment: Feasibility Study
Authors: Darici Osman , Cabak Chanel , Wong Jeremy D 
Published: December 2024

AbstractFunctional gait assessments in older adults have traditionally required manual in-person quantification of clinically-relevant metrics such as walking speed and step placement. This reliance on skilled labour hinders the frequency with which they are performed, and reduces their generalizability and replicability. To standardize and simplify gait assessments, we here leverage recently-developed open-source tools to produce a low-cost, AI driven markerless motion capture system. Validation of data collected with a traditional marker-based system on subjects (N=3) showed strong correlation to laboratory-standard measures of step length (R2=0.989), step width (R2=0.986), and body speed (R2=0.964), suggesting that open-source markerless systems are currently sufficient to be used in gait assessments. Older adult subjects (N=5) performed the complete functional gaita assessment (FGA), and the system demonstrated data consistent with previous findings. Additional standard measurements were proposed to supplement and systematize existing FGA workflows. This markerless motion capture system demonstrates a proof-of-principle system for performing analyses of functional gait.
ldentify potential feature genes and immune cell infiltration of HIRI based on branched-chain amino acid-related genes by machine learning and experimental validation
Authors: Zhao Jiahui , Wu Min , Li Shuangjiang , Li Shuang , Li Mingjuan , Li Qiuyun , Pan Guangdong , Ouyang Guoqing , Xu Honglai 
Published: December 2024

AbstractBackgroundBranched-chain amino acid metabolism is involved in the pathogenesis of various liver diseases. In this study, we investigate the potential role of branched-chain amino acid metabolism-related genes in the pathogenesis of hepatic ischemia reperfusion (HIRI).MethodsThe gene Expression profiles of HIRI were obtained from the Gene Expression Omnibu database. To determine the differential expression of branched-chain amino acid metabolism-related genes between HIRI and normal tissues. Then, the GO and KEGG analyses were performed, and the protein-protein interaction network was constructed. Next, the random forest and LASSO algorithms were used to screen hub genes, and machine learning techniques were used to build diagnostic models. immunoinfiltration was analyzed in both HIRI patients and controls and the ceRNA network was established. Finally, quantitative real-time PCR was used to verify the expression of hub gene.ResultsBased on data set GSE23649, three central DEGs (SLC7A5, SLC1A5, SLC43A2) were determined by the intersection of three machine learning algorithms and used to establish a nomogram that yielded a high predictive performance (area under the curve 0.733−0.922). In the external GSE15480 dataset, AUC value for three key genes is as high as 1.000. Further analysis of nomogram, decision curve and calibration curve also confirme the predictive efficacy of diagnosis. GSEA and GSVA suggest that these three marker genes were involved in multiple pathways associated with HIRI progression. Immunoinfiltration analysis suggest that the proportion of macrophages, neutrophils, aDCs, Treg, and Th1 cells in HIRI group is higher than that in control group, with statistical significance(P<0.05). The ceRNA network demonstrates the complex regulatory relationships among the three hub genes and these mRNA levels were further confirmed in mouse HIRI liver samples.ConclusionsOur study have provided a comprehensive understanding of the association between branched-chain amino acid and HIRI, may provide potential target for HIRI treatment and diagnosis. And provide new insights into the mechanisms of HIRI.Graphical Abstract
Intraguild predation among snails drives human schistosome amplification despite susceptible host regulation
Authors: Shaw Kelsey E , Syed Raeyan , Hartman Rachel B , Mangabat Danielle , Civitello David J 
Published: December 2024

AbstractCompetitors and predators of hosts have the potential to alter transmission dynamics within host- parasite systems. Biocontrol aims to harness these effects to mitigate diseases of human or agricultural importance, but without a detailed understanding of the mechanisms of how hosts and non-hosts interact over time these attempts may backfire. We investigated the role that two resource competitors that have been employed in biocontrol,Melanoides tuberculataandPhysa acuta, play in theBiomphalaria glabrata-Schistosoma mansoni system. We created experimental communities with differing compositions in mesocosms and tracked resource availability, host snail abundance, size, reproduction, and parasite production over 16 weeks. We found thatMelanoidesacted as a typical, albeit weak, resource competitor ofBiomphalaria,reducing host body size and per capita cercarial production but with no appreciable influence on host abundance.Physa, however, reduced host abundance but increased resource availability and cercarial production, which represents biocontrol failure and is inconsistent with solely acting as a resource competitor. In follow-up experiments, we determined thatPhysais a voracious consumer of host eggs. We then built a model representing this intraguild predation effect, which was able to explain these initially counterintuitive results. These divergent results from two putative resource competitors of host snails underscore the importance of establishing the mechanisms through which hosts, non-hosts, and parasites interact, especially for anticipating the potential outcomes of biocontrol interventions.
Response of Serum-isolated Extracellular Vesicles to Focused Ultrasound Blood-Brain Barrier Opening
Authors: Kline-Schoder Alina R. , Tsitsos Fotios N. , Batts Alec J. , DiBenedetto Melody R. , Liu Keyu , Bae Sua , Konofagou Elisa E. 
Published: December 2024

AbstractThe blood-brain barrier (BBB) limits drug delivery to the brain and the movement of neurological biomarkers between the brain and blood. Focused ultrasound-mediated blood-brain barrier opening (FUS-BBBO) noninvasively opens the BBB, allowing increased molecular transport to and from the brain parenchyma. Despite being initially developed as a drug delivery method, FUS-BBBO has shown promise both as a neuroimmunotherapeutic modality, and as a way of improving neurological disease diagnosis via amplification of disease biomarker circulation.Recently, the role of extracellular vesicles (EVs) in modulating the neuroimmune system and in improving biomarker detection has sparked research interest. However, despite their potential role in modulating FUS-BBBO-induced neuroimmunotherapy and their ability to improve biomarker specificity after treatment, the EV response to FUS-BBBO had not been extensively characterized prior to this study.In this study, we investigated the effect of FUS-BBBO on EV concentration and content in the serum of mice and Alzheimer’s Disease (AD) patients. We observed a 164% increase in murine EV concentration one hour after treatment, as well as an increase in EV RNA associated with FUS-BBBO neuroimmunotherapy. Patient EV concentration also increased one hour after treatment and was dependent on the volume of BBB opening three days post-treatment. Furthermore, EV isolation was found to significantly enhance the amplification of AD biomarker detection by FUS-BBBO.Overall, we present the first evidence of altered murine and AD patient EV concentration and content in response to FUS-BBBO, providing evidence of EVs’ role within FUS-BBBO neuroimmunotherapy as well as their utility in improving FUS-BBBO biomarker amplification.