bluuebunny/crossref_metadata_2025_split huggingface dataset:
Whole-genome sequences reveal zygotic composition in chimeric twins
Authors: Yoon Christopher J. , Nam Chang Hyun , Kim Taewoo , Lee Jeong Seok , Kim Ryul , Yi Kijong , Koh June-Young , Kim Jiye , Won Hyein , Oh Ji Won , Griffith Obi L. , Griffith Malachi , Sung Joohon , Kim Tae Yeul , Cho Duck , Choi Ji Seon , Ju Young Seok 
Published: December 2023

SummaryThe monochorionic placenta in dizygotic twins allowsin uteroexchange of embryonic cells, resulting in chimerism in the twins. In practice, this chimerism is incidentally identified on mixed ABO blood types or in the presence of cells with a discordant sex chromosome. Here, we applied whole-genome sequencing to one triplet and one twin families to precisely understand their zygotic compositions, using millions of genomic variants as barcodes of zygotic origins. Peripheral blood showed asymmetrical contributions from two sister zygotes, where one of the zygotes was the major clone in both twins. Single-cell RNA sequencing of peripheral blood tissues further showed differential contributions from the two sister zygotes across blood cell types. In contrast, buccal tissues were pure in genetic composition, suggesting thatin uterocellular exchanges were confined to the blood tissues. Our study illustrates the cellular history of twinning during human development, which is critical for managing the health of chimeric individuals in the era of genomic medicine.
Integration of multi-source gene interaction networks and omics data with graph attention networks to identify novel disease genes
Authors: Yang Kaiyuan , Cheng Jiabei , Cao Shenghao , Pan Xiaoyong , Shen Hong-Bin , Jin Cheng , Yuan Ye 
Published: December 2023

AbstractThe pathogenesis of diseases is closely associated with genes, and the discovery of disease genes holds significant importance for understanding disease mechanisms and designing targeted therapeutics. However, biological validation of all genes for diseases is expensive and challenging. In this study, we propose DGP-AMIO, a computational method based on graph attention networks, to rank all unknown genes and identify potential novel disease genes by integrating multi-omics and gene interaction networks from multiple data sources. DGP-AMIO outperforms other methods significantly on 20 disease datasets, with an average AUROC and AUPR exceeding 0.9. The superior performance of DGP-AMIO is attributed to the integration of multiomics and gene interaction networks from multiple databases, as well as triGAT, a proposed GAT-based method that enables precise identification of disease genes in directed gene networks. Enrichment analysis conducted on the top 100 genes predicted by DGP-AMIO and literature research revealed that a majority of enriched GO terms, KEGG pathways and top genes were associated with diseases supported by relevant studies. We believe that our method can serve as an effective tool for identifying disease genes and guiding subsequent experimental validation efforts.
Controllable Protein Design by Prefix-Tuning Protein Language Models
Authors: Luo Jiawei , Liu Xianliang , Li Jiahao , Chen Qingcai , Chen Junjie 
Published: December 2023

AbstractDesigning novel proteins tailored for specific purposes (e.g. drug discovery, vaccine design) presents a promising approach to address various biomedical challenges. Due to the similarity between protein sequences and natural languages, motivated by the remarkable success in NLP tasks that pre-trained language models have enabled text generation with human-like capabilities, protein language models (ProtLMs) are constructed to generate protein sequences with a predictable function across large protein families. The text generation can be controllable by constructing prefix-phase as control tags to prompt NLP language models. However, the vocabulary of protein sequences only contains 20 amino acid residues, which is not like natural language vocabulary to make up flexible control tags. In this study, we propose a controllable protein design method, named PrefixProt, which utilizes prefix tuning to learn virtual tokens as control tags, enabling to efficiently prompt the pre-trained ProtLM for protein generation tailored for specific purposes. The virtual tokens can be learned on any protein properties by data-driven and are flexible to be combined for fine-grained control. To demonstrate the effectiveness of PrefixProt, we train three virtual tokens on alpha-helix structure dataset, antimicrobial peptide (AMP) dataset and anticancer peptide (ACP) dataset, respectively. Our results show that prefix virtual tokens are efficient to prompt the pretrained ProtLM by optimizing fewer trainable parameters compared with fine-tuning, especially under low-data settings. When combining the virtual tokens, the proportion of generated proteins with multiple properties are significantly improved. Therefore, PrefixProt offers a flexible and controllable protein design solution. We anticipate that PrefixProt will contribute to drug discovery and biomedical advancement.Availability and implementationThe models and associated code are available at:https://github.com/chen-bioinfo/PrefixProt
DNA origami assembled spheroid for evaluating cytotoxicity and infiltration of chimeric antigen receptor macrophage (CAR-M)
Authors: Liu Junhua , He Xiaofang , Zhu Qingyao , Wang Heming , Shan Xiaojiao , Zhao Yicheng , Zhang Luo , Song Guangqi , Yin Xiushan 
Published: December 2023

AbstractChimeric antigen receptor (CAR) T-cell therapies have shown remarkable results in patients with hematological malignancies. However, their success in treating solid tumors has been limited. As an alternative candidate for the CAR approach, CAR-macrophages (CAR-M) have demonstrated activation and phagocytosis directed by tumor antigens, showing promise in the treatment of solid tumors. Nevertheless, the mechanisms by which CARs direct tumor chemotaxis and invasion of CAR-M remain poorly understood. In this study, we aim to investigate the role of CARs in CAR-M attachment and infiltration using 3D tumor micro-spheroids, which were created by utilizing a novel nucleic acid nanostructures decorated living cells (NACs) based origami assembly technique. First, the effectiveness of phagocytosis and killing conducted by CAR-M was validated in the conventional 2D well/plate-surface culture models. Then,peripheral blood mononuclear cell (PBMC) invasion assay confirmed that the 3D tumor micro-spheroids were feasible for cell invasion. Finally, our results demonstrated that CAR-M exhibited higher invasion and killing capacity in 3D tumor micro-spheroids. In summary, the 3D NACs-origami assembled tumor spheroid model provides a suitable platform for target screening and pharmacodynamic evaluation of CAR-M.
gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes
Authors: Arıkan Muzaffer , Muth Thilo 
Published: December 2023

AbstractBackgroundOver the past few years, the rise of omics technologies has offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to offer a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline specifically tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline.ResultsHere, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from two to four distinct omics data types including 16S rRNA gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration and visualization approaches, all aimed at providing a more comprehensive toolkit and insightful analysis of microbiomes. The functionality of these new features is showcased through the use of four microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives.ConclusionsgNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, opening up avenues for novel insights in the field of both host associated and free-living microbiome research. gNOMO2 is available freely athttps://github.com/muzafferarikan/gNOMO2.
The effects of alternative rabbit control methods on feral cat activity in an open, semi-arid landscape
Authors: Jansen Jeroen , Comte Sebastien , Dean Abbey T , Axford Geoff , Moseby Katherine E , Peacock David E , Brandle Robert , Jones Menna E 
Published: December 2023

AbstractThe availability of invasive prey often plays an important role in regulating cointroduced invasive predator populations. As predators have been shown to respond rapidly to declines in prey populations, our objective was to experimentally test how local population reduction of an invasive prey species, the European rabbit (Oryctolagus cuniculus), affects the activity of an introduced predator, the feral cat (Felis catus). To test the effectiveness of three different rabbit control methods, activity levels of cats were surveyed with remote infrared wildlife cameras in three treatment and four control sites. The rabbit control treatments were implemented in extensive open landscapes in the semi-arid zone of South Australia, and consisted of shooting of rabbits, destruction of rabbit warrens, and the targeted delivery of baits treated with RHDV. The results indicate that only the destruction of rabbit warrens has observable effects on the number of cat detections on cameras. Cat detections decreased in the areas where rabbit warrens were destroyed and increased in adjacent areas where rabbits were still abundant. This suggests that cats vacated the treated area and moved into surrounding areas of abundant introduced prey.
Landscape and functional repertoires of long noncoding RNAs in the pan-cancer tumor microenvironment using single-nucleus total RNA sequencing
Authors: Fan Tongqiang , Ni Shengyu , Chen Haide , Xu Ziye , Fan Longjiang , Wang Yongcheng 
Published: December 2023

AbstractIntratumor heterogeneity (ITH) plays crucial roles in tumor progression. However, the atlas of long noncoding RNAs (lncRNAs) in the context of ITH across multiple cancer types remains largely unexplored. Here, we analyze over 800,000 cells from ten different cancer types generated from the random-primed single-nucleus total RNA sequencing and provide a systematic landscape of lncRNAs in tumor microenvironment (TME) and malignant programs. Our study employe a robust cell annotation pipeline called scAnnotation, which allows us to identify 39 distinct cell types within the pan-cancer TME. By applying stringent criteria, we identify thousands of reliable marker genes, including both mRNAs and lncRNAs. Next, we identify sets of cell type-specific lncRNA-mRNA pairs by our LncPairs algorithm. Moreover, we identify nine expression meta-programs (MPs) associated with diverse biological processes in malignant cells across multiple cancer types. MP-specific lncRNA-transcription factor (TF) regulatory networks are further constructed and key lncRNAs and regulons that exert control over MP-specific gene expression are identified. The comprehensive atlas of lncRNAs in the pan-cancer context, coupled with the bioinformatics tools tailored for the random-primed datasets, is expected to accelerate advancements in the field of lncRNA research at the single-cell resolution.
Effects of greening-induced warming and cooling on tree phenology in temperate and boreal forests
Authors: Guo Jing , Wang Jinmei , Qiao Yuxin , Smith Nicholas G. , Liu Zhiyong , Zhang Rui , Chen Xiuzhi , Wu Chaoyang , Chen Lei 
Published: December 2023

AbstractTree phenology, periodic biological events in trees, is highly sensitive to climate change. It has been reported that forest greening can influence the local climate by altering the seasonal surface energy budget. However, tree phenological responses to forest greening remains poorly understood at large spatial scales. Combining remote-sensing derived phenological and leaf area indices since 2001, herein we show that forest greening led to earlier spring (−1.05 ± 0.17 d) and autumn phenology (−1.95 ± 0.14 d) in temperate and boreal forests. Our results show that forest greening in winter and spring decreased surface albedo and thus resulted in biophysical warming that caused earlier spring leaf phenology. In contrast, forest greening in summer and autumn triggered biophysical cooling by enhancing evapotranspiration, which led to earlier autumn leaf phenology. These findings suggest that forest greening could significantly alter tree phenology through seasonal biophysical impacts. Therefore, it is essential to incorporate these complicated biophysical impacts of greening into tree phenology models to accurately predict future shifts in tree phenology under future climate change.
Seabird traits and seasonality modulate nutrient dynamics of terrestrial and marine habitats on atolls
Authors: Appoo Jennifer , Bunbury Nancy , Letori Jake , Hector Aurelie , Gendron Annie , Graham Nicholas A.J. , Rocamora Gerard , Corre Matthieu Le , Jaquemet Sébastien 
Published: December 2023

AbstractMarine nutrients underpin productivity and functioning of oceanic island ecosystems. Seabirds represent a primary source of marine nutrients. In tropical regions, some of the largest seabird populations nest on atolls, yet there is limited information available on seabird contributions to atoll ecosystem nutrient dynamics. To investigate the spatial and seasonal dynamics of seabird contributions, we assessed seabird colonies of different taxa, including red-footed boobies and terns, nesting on separate islands of Farquhar Atoll, Seychelles. We assessed nutrient concentrations of guano, soil, coastal plants and nearby seagrass in seabird colonies and at a control island with no seabirds, during the wet and dry seasons. Sooty terns contributed the highest quantities of nutrients, estimated at 71.2 N tonne.yr-1and 52.2 P tonne.yr-1. Seabird-derived nutrient transfer occurred year-round from seabird colonies to soil, coastal plants and seagrass. Soil macro- and inorganic nutrients were higher in the high-density, ground-nesting tern colony and during the dry season, coinciding with the breeding period of sooty terns. Both red-footed booby and tern colonies maintained high nitrogen levels (%N) in coastal plants year-round, while phosphorus levels (%P) did not differ between islands or seasons. Seabird-derived nitrogen reversed nitrogen limitation (log C:N) of seagrass during the dry season. We provide the first insights into seabird nutrient contributions to atoll ecosystems in Seychelles, with recommendations for seabird conservation to boost and support atoll and island ecosystem resilience. Our results from a relatively undisturbed atoll serve as a baseline with which more impacted atolls and future changes can be assessed.
Overcoming Observation Bias for Cancer Progression Modeling
Authors: Schill Rudolf , Klever Maren , Lösch Andreas , Hu Y. Linda , Vocht Stefan , Rupp Kevin , Grasedyck Lars , Spang Rainer , Beerenwinkel Niko 
Published: December 2023

AbstractCancers evolve by accumulating genetic alterations, such as mutations and copy number changes. The chronological order of these events is important for understanding the disease, but not directly observable from cross-sectional genomic data. Cancer progression models (CPMs), such as Mutual Hazard Networks (MHNs), reconstruct the progression dynamics of tumors by learning a network of causal interactions between genetic events from their co-occurrence patterns. However, current CPMs fail to include effects of genetic events on the observation of the tumor itself and assume that observation occurs independently of all genetic events. Since a dataset contains by definition only tumors at their moment of observation, neglecting any causal effects on this event leads to the “conditioning on a collider” bias: Events that make the tumor more likely to be observed appear anti-correlated, which results in spurious suppressive effects or masks promoting effects among genetic events. Here, we extend MHNs by modeling effects from genetic progression events on the observation event, thereby correcting for the collider bias. We derive an efficient tensor formula for the likelihood function and learn two models on somatic mutation datasets from the MSK-IMPACT study. In colon adenocarcinoma, we find a strong effect on observation by mutations in TP53, and in lung adenocarcinoma by mutations in EGFR. Compared to classical MHNs, this explains away many spurious suppressive interactions and uncovers several promoting effects.The data, code, and results are available athttps://github.com/cbg-ethz/ObservationMHN.
Limitation of switching sensory information flow in flexible perceptual decision making
Authors: Luo Tianlin , Xu Mengya , Zheng Zhihao , Okazawa Gouki 
Published: December 2023

AbstractHumans can flexibly change rules to categorize sensory stimuli, but their performance degrades immediately after a task switch. This switch cost is believed to reflect a limitation in cognitive control, although the bottlenecks responsible for this remain controversial. Here, we show that humans exhibit a brief reduction in the efficiency of converting sensory inputs into decision evidence immediately after changing rules in perceptual tasks. Participants performed a flexible face categorization task in which they classified parametrically generated face stimuli based on one of two rules, switching every few trials. Although participants were always informed of a rule switch by a context cue, they showed a specific pattern of increase in reaction times, regardless of the time they were given to prepare for the switch. Psychophysical reverse correlation and computational modeling revealed a reduction in sensory weighting immediately after a rule switch, which recovered within a few hundred milliseconds after stimulus presentation. Furthermore, this cost depends on the sensory features being switched, suggesting a major bottleneck at the stage of adjusting the sensory information flow. We propose that decision-making circuits cannot fully adjust their sensory readout based on an abstract context cue alone, but rather require the presence of an actual stimulus to tune it, leading to a limitation in flexible perceptual decision making.
A Data Science Pipeline Applied to Australia’s 2022 COVID-19 Omicron Waves
Authors: Trauer James M , Hughes Angus E , Shipman David S , Meehan Michael T , Henderson Alec S , McBryde Emma S , Ragonnet Romain 
Published: December 2023

AbstractThe field of software engineering is advancing at astonishing speed, with packages now available to support many stages of data science pipelines. These packages can support infectious disease modelling to be more robust, efficient and transparent, which has been particularly important during the COVID-19 pandemic. We developed a package for the construction of infectious disease models, integrated this with several open-source libraries and applied this pipeline to multiple data sources that provided insights into Australia’s 2022 COVID-19 epidemic. We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters. Extending the base model to include mobility effects slightly improved model fit to data, but including the effect of 2022 vaccination programs on transmission did not. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30 to 60% of recently acquired natural immunity and that natural immunity lasted only one to eight months. We documented our analyses algorithmically and present our methods in conjunction with interactive online notebooks.
Metabolic variation reflects dietary intake in a multi-ethnic Asian population
Authors: Low Dorrain Yanwen , Mina Theresia Handayani , Sadhu Nilanjana , Wong Kari E , Jain Pritesh Rajesh , Dalan Rinkoo , Ng Hong Kiat , Xie Wubin , Lam Benjamin , Tay Darwin , Wang Xiaoyan , Yew Yik Weng , Best James , Sarangarajan Rangaprasad , Elliott Paul , Riboli Elio , Lee Jimmy , Lee Eng Sing , Ngeow Joanne , Sheridan Patricia A , Michelotti Gregory A , Loh Marie , Chambers John 
Published: December 2023

AbstractDietary biomarkers reflecting habitual diet are explored largely in European and American populations. However, the “food metabolome” is highly complex, with its composition varying to region and culture. Here, by assessing 1,055 plasma metabolites and 169 foods/beverages in 8,391 comprehensively phenotyped individuals from the multi-ethnic Asian HELIOS cohort (69% Chinese, 12% Malay, 19% South Asian), we report novel observations for ethnic-relevant and common foods. Using machine-learning feature selection approach, we developed dietary multi-biomarker panels (3-39 metabolites each) for key foods and beverages in respective training sets. These panels comprised distinct and shared metabolite networks, and captured variances in intake prediction models in test sets better than single biomarkers. Composite metabolite scores, derived from the biomarker panels, associated significantly and more strongly with clinical phenotypes (HOMA-IR, type 2 diabetes, BMI, fat mass index, carotid intima-media thickness and hypertension), compared to self-reported intakes. Lastly, in 235 individuals that returned for a repeat visit (averaged 322 days apart), diet-metabolite relationships were robust over time, with predicted intakes, derived from biomarker panels and metabolite scores, showing better reproducibility than self-reported intakes. Altogether, our findings show new insights into multi-ethnic diet-related metabolic variations and new opportunity to link exposure to health outcomes in Asian populations.