bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implication From Mineralogical and Geochemical Characteristics of Authigenic Micronodules in the Haima Cold Seeps for Understanding the Manganese Geochemistry in Active Seepage
Authors: Zhang Huan , Zhou Junming , Yuan Peng , Dong Yanhui , Fan Wenxiao , Yu Xing , Lu Jianggu 
Published: February 2023

AbstractThe multi‐valent manganese is ubiquitous in various marine environments and is involved in many biogeochemical processes with electron transfers between different oxidation states. The involvement of Mn (IV) in the anaerobic oxidation of methane (AOM) in cold seeps has long been proposed by experimental simulation and was suggested to serve as an important sink of methane in the paleo‐oceanic environment. Nonetheless, metal‐dependent AOM processes still lack explicit geological evidence. Meanwhile, research on micronodules gained momentum in recent years, but the authigenic manganese micronodules in active cold seeps were yet not well studied. In this study, Mn‐rich sediments were discovered at the active Haima cold seeps area in the South China Sea, from which Mn‐micronodules were then extracted. These micronodules are irregular or subspheroidal with rough and porous surfaces and are mainly composed of 7 Å and 10 Å phyllomanganates. They are enriched in Mn, and deficient in Fe, Co, Ni, Cu, and REYs. Characteristic microbial‐like mineralization structures are also observed. Based on these mineralogical/geochemical features and the abnormally high growth rate (calculated by the cobalt chronometer algorithm), these authigenic manganese micronodules were probably formed rapidly via biomineralization and chemical precipitation and were supplied with sufficient Mn by the metal‐rich seepage fluids. The occurrence of these micronodules demonstrates manganese bacterial activities, and indirectly reflects metal‐dependent AOM.
Disentangling In‐Stream Nitrate Uptake Pathways Based on Two‐Station High‐Frequency Monitoring in High‐Order Streams
Authors: Zhang Xiaolin , Yang Xiaoqiang , Hensley Robert , Lorke Andreas , Rode Michael 
Published: February 2023

AbstractIn‐stream nitrate (NO3−) uptake in rivers involves complex autotrophic and heterotrophic pathways, which often vary spatiotemporally due to biotic and abiotic drivers. High‐frequency monitoring of NO3− mass balance between upstream and downstream measurement sites can quantitatively disentangle multi‐path NO3− uptake dynamics at the reach scale. However, this approach remains limited to a few river types and has not been fully explored for higher‐order streams with varying hydro‐morphological and biogeochemical conditions. We conducted two‐station 15‐min monitoring in five high‐order stream reaches in central Germany, calculating the NO3−‐N mass balance and whole‐stream metabolism based on time series of NO3−‐N and dissolved oxygen, respectively. With thorough considerations of lateral inputs, the calculated net NO3−‐N uptake rates () differed substantially among campaigns (ranging from −151.1 to 357.6 mg N m2 d−1, with cases of negative values representing net NO3−‐N release), and exhibited higher during the post‐wet season than during the dry season. Subtracting autotrophic assimilation (, stoichiometrically coupled to stream metabolism) from , represented the net balance of heterotrophic NO3−‐N uptake ( > 0, the dominance of denitrification and heterotrophic assimilation) and NO3−‐N release ( < 0, the dominance of nitrification/mineralization). This rarely reported uptake pathway contributed substantially to patterns, especially during post‐wet seasons; moreover, it appeared to exhibit various diel patterns, and for > 0, diel minima occurred during the daytime. These findings advance our understanding of complex reach‐scale N‐retention processes and can help develop future modeling concepts at the river‐network scale.
Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework
Authors: Botterill Tom E. , McMillan Hilary K. 
Published: February 2023

AbstractHydrologic signatures are quantitative metrics that describe a streamflow time series. Examples include annual maximum flow, baseflow index and recession shape descriptors. In this paper, we use machine learning (ML) to learn encodings that are optimal ML equivalents of hydrologic signatures, and that are derived directly from the data. We compare the learned signatures to classical signatures, interpret their meaning, and use them to build rainfall‐runoff models in otherwise ungauged watersheds. Our model has an encoder–decoder structure. The encoder is a convolutional neural net mapping historical flow and climate data to a low‐dimensional vector encoding, analogous to hydrological signatures. The decoder structure includes stores and fluxes similar to a classical hydrologic model. For each timestep, the decoder uses current climate data, watershed attributes and the encoding to predict coefficients that distribute precipitation between stores and store outflow coefficients. The model is trained end‐to‐end on the U.S. CAMELS watershed data set to minimize streamflow error. We show that learned signatures can extract new information from streamflow series, because using learned signatures as input to the process‐informed model improves prediction accuracy over benchmark configurations that use classical signatures or no signatures. We interpret learned signatures by correlation with classical signatures, and by using sensitivity analysis to assess their impact on modeled store dynamics. Learned signatures are spatially correlated and relate to streamflow dynamics including seasonality, high and low extremes, baseflow and recessions. We conclude that process‐informed ML models and other applications using hydrologic signatures may benefit from replacing expert‐selected signatures with learned signatures.
A shape-shifting nuclease unravels structured RNA
Authors: Meze Katarina , Axhemi Armend , Thomas Dennis R. , Doymaz Ahmet , Joshua-Tor Leemor 
Published: February 2023

AbstractRNA turnover pathways ensure appropriate gene expression levels by eliminating unwanted transcripts. Dis3-like 2 (Dis3L2) is a 3′–5′ exoribonuclease that plays a critical role in human development. Dis3L2 independently degrades structured substrates, including coding and noncoding 3′ uridylated RNAs. While the basis for Dis3L2’s substrate recognition has been well characterized, the mechanism of structured RNA degradation by this family of enzymes is unknown. We characterized the discrete steps of the degradation cycle by determining cryogenic electron microscopy structures representing snapshots along the RNA turnover pathway and measuring kinetic parameters for RNA processing. We discovered a dramatic conformational change that is triggered by double-stranded RNA (dsRNA), repositioning two cold shock domains by 70 Å. This movement exposes a trihelix linker region, which acts as a wedge to separate the two RNA strands. Furthermore, we show that the trihelix linker is critical for dsRNA, but not single-stranded RNA, degradation. These findings reveal the conformational plasticity of Dis3L2 and detail a mechanism of structured RNA degradation.
Developing a standardized but extendable framework to increase the findability of infectious disease datasets
Authors: Tsueng Ginger , Cano Marco A. Alvarado , Bento José , Czech Candice , Kang Mengjia , Pache Lars , Rasmussen Luke V. , Savidge Tor C. , Starren Justin , Wu Qinglong , Xin Jiwen , Yeaman Michael R. , Zhou Xinghua , Su Andrew I. , Wu Chunlei , Brown Liliana , Shabman Reed S. , Hughes Laura D. ,   the NIAID Systems Biology Data Dissemination Working Group, Turkarslan Serdar 
Published: February 2023

AbstractBiomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.
The temporal behavior of the murine T cell receptor repertoire following Immunotherapy
Authors: Snir Tom , Philip Hagit , Gordin Miri , Zilberberg Alona , Efroni Sol 
Published: February 2023

AbstractImmunotherapy is now an essential tool for cancer treatment, and the unique features of an individual’s T cell receptor repertoire are known to play a key role in its effectiveness. The repertoire, famously vast due to a cascade of cellular mechanisms, can be quantified using repertoire sequencing. In this study, we sampled the repertoire over several time points following treatment with anti-CTLA-4, in a syngeniec mouse model for colorectal cancer, generating a longitudinal dataset of T cell clones and their abundance. The dynamics of the repertoire can be observed in response to treatment over a period of four weeks, as clonal expansion of specific clones ascends and descends. The data made available here can be used to determine treatment and predict its effect, while also providing a unique look at the behavior of the immune system over time.
A large dataset of semantic ratings and its computational extension
Authors: Wang Shaonan , Zhang Yunhao , Shi Weiting , Zhang Guangyao , Zhang Jiajun , Lin Nan , Zong Chengqing 
Published: February 2023

AbstractEvidence from psychology and cognitive neuroscience indicates that the human brain’s semantic system contains several specific subsystems, each representing a particular dimension of semantic information. Word ratings on these different semantic dimensions can help investigate the behavioral and neural impacts of semantic dimensions on language processes and build computational representations of language meaning according to the semantic space of the human cognitive system. Existing semantic rating databases provide ratings for hundreds to thousands of words, which can hardly support a comprehensive semantic analysis of natural texts or speech. This article reports a large database, the Six Semantic Dimension Database (SSDD), which contains subjective ratings for 17,940 commonly used Chinese words on six major semantic dimensions: vision, motor, socialness, emotion, time, and space. Furthermore, using computational models to learn the mapping relations between subjective ratings and word embeddings, we include the estimated semantic ratings for 1,427,992 Chinese and 1,515,633 English words in the SSDD. The SSDD will aid studies on natural language processing, text analysis, and semantic representation in the brain.
An Open Dataset of Annotated Metaphase Cell Images for Chromosome Identification
Authors: Tseng Jenn-Jhy , Lu Chien-Hsing , Li Jun-Zhou , Lai Hui-Yu , Chen Min-Hu , Cheng Fu-Yuan , Kuo Chih-En 
Published: February 2023

AbstractChromosomes are a principal target of clinical cytogenetic studies. While chromosomal analysis is an integral part of prenatal care, the conventional manual identification of chromosomes in images is time-consuming and costly. This study developed a chromosome detector that uses deep learning and that achieved an accuracy of 98.88% in chromosomal identification. Specifically, we compiled and made available a large and publicly accessible database containing chromosome images and annotations for training chromosome detectors. The database contains five thousand 24 chromosome class annotations and 2,000 single chromosome annotations. This database also contains examples of chromosome variations. Our database provides a reference for researchers in this field and may help expedite the development of clinical applications.
Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information
Authors: Zhang Alice Tianbo , Gu Vincent Xinyi 
Published: February 2023

AbstractWe present one of the most comprehensive geo-referenced global dam databases to date. The Global Dam Tracker (GDAT) contains 35,000 dams with cross-validated geo-coordinates, satellite-derived catchment areas, and detailed attribute information. Combining GDAT with fine-scaled satellite data spanning three decades, we demonstrate how GDAT improves upon existing databases to enable the inter-temporal analysis of the costs and benefits of dam construction on a global scale. Our findings show that over the past three decades, dams have contributed to a dramatic increase in global surface water coverage, especially in developing countries in Asia and South America. This is an important step toward a more systematic understanding of the worldwide impact of dams on local communities. By filling in the data gap, GDAT would help inform a more sustainable and equitable approach to energy access and economic development.
Transcript annotation of Chinese sturgeon (Acipenser sinensis) using Iso-seq and RNA-seq data
Authors: Liao Xiaolin , Zhang Libin , Tian Hua , Yang Bo , Wang Ezhou , Zhu Bin 
Published: February 2023

AbstractChinese sturgeon (Acipenser sinensis) is a critically endangered fish inhabiting the Yangtze River and Chinese coastal waters. Numerous research projects and conservation efforts have focused on artificial propagation and release to restore this endangered species. However, genomic and full-length transcriptomic sequencing of Chinese sturgeon has rarely been reported. In this study, a total of 10 Chinese sturgeon tissues were used for PacBio Iso-seq and RNA-seq analyses. A total of 19,538 full-length transcripts were obtained with sizes from 51 bp to 7,033 bp. Moreover, cluster analysis of gene families and phylogenetic analysis of 14 species were performed. Furthermore, lncRNAs and coding sequence (CDS) were identified in all Chinese sturgeon tissues. Finally, gene expression profiles and differentially expressed genes (DEGs) were analyzed among 10 tissues in Chinese sturgeon. Taken together, full-length transcripts and the gene expression profile from Chinese sturgeon tissues will provide gene sequences and expression information for future functional genomic study and be very helpful for comprehensive understanding of the genetic mechanism of endangerment in Chinese sturgeon.
The predictive model for risk of chemotherapy-induced thrombocytopenia based on antineoplastic drugs for solid tumors in eastern China
Authors: Zhou Shishi , Song Bingxin , Li Chenghui , Tang Wanfen , Zhang Xia , Jin Xiayun , Xu Xifeng , Wang Qinghua , Zheng Hongjuan , Fu Jianfei 
Published: February 2023

AbstractChemotherapy-related thrombocytopenia (CIT) is a significant adverse event during chemotherapy, which can lead to reduced relative dose intensity, increased risk of serious bleeding and additional medical expenditure. Herein, we aimed to develop and validate a predictive nomogram model for prediction of CIT in patients with solid tumor. From Jun 1, 2018 to Sep 9, 2021, a total of 1541 patients who received 5750 cycles of chemotherapy were retrospectively enrolled. Cox regression analysis was performed to identify predictive factors to establish the nomogram model for CIT. The incidence of chemotherapy-induced thrombocytopenia was 21.03% for patient-based and 10.26% for cycles of chemotherapy. The top five solid tumors with CIT are cervix, gastric, bladder, biliary systemic, and ovarian. The incidence of chemotherapy dose delays in any cycle because of CIT was 5.39%. Multivariate analysis showed that tumor site, treatment line, AST, oxaliplatin, and capecitabine were significantly associated with CIT. Moreover, we established a nomogram model for CIT probability prediction, and the model was well calibrated (Hosme-Lemeshow P = 0.230) and the area under the receiver operating characteristic curve was 0.844 (Sensitivity was 0.625, Specificity was 0.901). We developed a predictive model for chemotherapy-induced thrombocytopenia based on readily available and easily assessable clinical characteristics. The predictive model based on clinical and laboratory indices represents a promising tool in the prediction of CIT, which might complement the clinical management of thrombocytopenia.
Development and validation of a robot social presence measurement dimension scale
Authors: Chen Na , Liu Xiaoyu , Zhai Yanan , Hu Xueyan 
Published: February 2023

AbstractRobots that can exhibit human-like behaviour, build social relationships with humans, and carry out social interaction that can be considered to have a social presence. Measuring the social presence of robots can provide an important basis for optimizing the design of robots’ human-like behaviour, but until now, there has been no research and discussion on the presence of robots from the perspective of social interaction. Based on related studies, such as the theory of the presence of interpersonal interaction society, the mechanism of network social presence measurement and human–computer interaction, this study constructs a theoretical model of robot social presence, and develops corresponding measurement scales with five dimensions, namely, the presence, attention distribution, interactive expression and information understanding, perceived emotional interdependence, and interaction behaviour perception. The 5-dimensional robot social presence model was finalized, and a 17-question questionnaire scale was developed. The validation of the model and the development of the scale includes one expert assessment (involving three experts), one user interview (involving five interviewees), and two questionnaire surveys (involving 172 valid questionnaires and 494 valid questionnaires respectively). The final model shows good reliability of the measurement questionnaire, high inter-factor correlation, high model fit, high internal consistency of the dimensions, high reliability of the scale, and high convergent validity of all dimensions. This study provides a theoretical basis for the study of robot social presence and human–robot collaboration and provides a reference measurement tool for social robot-related development and research.
Subspecies-level genome comparison of Lactobacillus delbrueckii
Authors: Baek Min-gyung , Kim Kwan Woo , Yi Hana 
Published: February 2023

AbstractLactobacillus delbrueckii comprises six subspecies, L. delbrueckii subsp. bulgaricus, L. delbrueckii subsp. lactis, L. delbrueckii subsp. jakobsenii, L. delbrueckii subsp. delbrueckii, L. delbrueckii subsp. sunkii, and L. delbrueckii subsp. indicus. We investigated the evolution of the six subspecies of L. delbrueckii using comparative genomics. While the defining feature of the species was the gene number increment driven by mobile elements and gene fragmentation, the repertoire of subspecies-specific gene gains and losses differed among the six subspecies. The horizontal gene transfer analyses indicated that frequent gene transfers between different subspecies had occurred when the six subspecies first diverged from the common ancestor, but recent gene exchange was confined to a subspecies implying independent evolution of the six subspecies. The subspecies bulgaricus is a homogeneous group that diverged from the other subspecies a long time ago and underwent convergent evolution. The subspecies lactis, jakobsenii, delbrueckii, and sunkii were more closely related to each other than to other subspecies. The four subspecies commonly show increasing genetic variability with increasing genome size. However, the four subspecies were distinguished by specific gene contents. The subspecies indicus forms a branch distant from the other subspecies and shows an independent evolutionary trend. These results could explain the differences in the habitat and nutritional requirements of the subspecies of L. delbrueckii.
Distilling functional variations for human UGT2B4 upstream region based on selection signals and implications for phenotypes of Neanderthal and Denisovan
Authors: Wang Pin-Yi , Yang Yuan , Shi Xiao-Qian , Chen Ying , Liu Shao-Dong , Wang Hong-Yan , Peng Tao , Shi Qiang , Zhang Wei , Sun Chang 
Published: February 2023

AbstractOur previous work identified one region upstream human UGT2B4 (UDP glucuronosyltransferase family 2 member B4) which is associated with breast cancer and under balancing selection. However, the distribution, functional variation and molecular mechanism underlying breast cancer and balancing selection remain unclear. In current study, the two haplotypes with deep divergence are described by analyzing 1000 genomes project data and observed to be with high frequencies in all human populations. Through population genetics analysis and genome annotation, the potential functional region is identified and verified by reporter gene assay. Further mutagenesis indicates that the functional mutations are rs66862535 and rs68096061. Both SNPs can alter the interaction efficiency of transcription factor POU2F1 (POU class 2 homeobox 1). Through chromosome conformation capture, it is identified that the enhancer containing these two SNPs can interact with UGT2B4 promoter. Expression quantitative trait loci analysis indicates that UGT2B4 expression is dependent on the genotype of this locus. The common haplotype in human is lost in four genomes of archaic hominins, which suggests that Neanderthal and Denisovan should present relatively lower UGT2B4 expression and further higher steroid hormone level. This study provides new insight into the contribution of ancient population structure to human phenotypes.
Use of access port covers in transport incubators to improve thermoregulation during neonatal transport
Authors: Fukuyama Takahiro , Arimitsu Takeshi 
Published: February 2023

AbstractHypothermia in newborns increases the risk of health complications and mortality. This study aimed to evaluate the effectiveness of using covers over snap-open access ports of a transport incubator to maintain the temperature within. The change in temperature inside the transport incubator was evaluated over a 15-min period at three ambient room temperatures (20 °C, 24 °C, and 28 °C), as well as for three snap-open access port conditions: closed, where ports are closed; open, where the two ports on one side are open; and covered, where the two ports on one side are open but a cover is used. The automatic temperature control of the incubator was set to 37 °C for all conditions. We repeated the same experiments three times. The temperature decrease inside the incubator was greater for the open than for the closed or covered access port conditions at all three 4 °C-increasing room temperatures (p < 0.05). The incubator temperature decreased as a function of decreasing room temperature only for the open condition, with no significant difference between the closed and covered conditions. Therefore, snap-open access port covers provide an option to maintain a constant temperature within the transport incubator, which may lower the risk of neonatal hypothermia.