bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plants and water: the search for a comprehensive understanding
Authors: Rockwell Fulton , Sage Rowan F 
Published: August 2022

Abstract
We learn early in life sciences classes that water is the solution of life, working in tandem with carbon to make life as we know it possible. Globally, the abundance of water can be misleading, as most of this water is unavailable, being overly salinized in the oceans or locked in deep underground reserves. On land, the critical supply is of freshwater, which is unevenly distributed in space and time. Even the wettest environments can experience episodic water deficit, and flash flooding periodically occurs in arid landscapes. While humanity can capture, store and transport freshwater over large distances to ensure sustained supply, such options are not apparent for plants except in an immediate local context. Plants must make do with the water in their immediate surroundings, whether it be abundant or scarce. How they do this has led to a myriad of adaptive solutions, involving capturing, storing and transporting water. The traits that enable them to optimize water use in a range of hydraulic environments, subject to multivariate selective constraints, are the essence of the discipline of plant–water relations.
MMGraph: a multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data
Authors: Zhang Shuangquan , Yang Lili , Wu Xiaotian , Sheng Nan , Fu Yuan , Ma Anjun , Wang Yan 
Published: August 2022

AbstractMotivationTranscription factor binding sites (TFBSs) prediction is a crucial step in revealing functions of transcription factors from high-throughput sequencing data. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) provides insight on TFBSs and nucleosome positioning by probing open chromatic, which can simultaneously reveal multiple TFBSs compare to traditional technologies. The existing tools based on convolutional neural network (CNN) only find the fixed length of TFBSs from ATAC-seq data. Graph neural network (GNN) can be considered as the extension of CNN, which has great potential in finding multiple TFBSs with different lengths from ATAC-seq data.ResultsWe develop a motif predictor called MMGraph based on three-layer GNN and coexisting probability of k-mers for finding multiple motifs from ATAC-seq data. The results of the experiment which has been conducted on 88 ATAC-seq datasets indicate that MMGraph has achieved the best performance on area of eight metrics radar score of 2.31 and could find 207 higher-quality multiple motifs than other existing tools.Availability and implementationMMGraph is wrapped in Python package, which is available at https://github.com/zhangsq06/MMGraph.gitSupplementary informationSupplementary data are available at Bioinformatics online.
Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions
Authors: Peng Wei , Liu Hancheng , Dai Wei , Yu Ning , Wang Jianxin 
Published: August 2022

AbstractMotivationDue to cancer heterogeneity, the therapeutic effect may not be the same when a cohort of patients of the same cancer type receive the same treatment. The anticancer drug response prediction may help develop personalized therapy regimens to increase survival and reduce patients’ expenses. Recently, graph neural network-based methods have aroused widespread interest and achieved impressive results on the drug response prediction task. However, most of them apply graph convolution to process cell line-drug bipartite graphs while ignoring the intrinsic differences between cell lines and drug nodes. Moreover, most of these methods aggregate node-wise neighbor features but fail to consider the element-wise interaction between cell lines and drugs.ResultsThis work proposes a neighborhood interaction (NI)-based heterogeneous graph convolution network method, namely NIHGCN, for anticancer drug response prediction in an end-to-end way. Firstly, it constructs a heterogeneous network consisting of drugs, cell lines and the known drug response information. Cell line gene expression and drug molecular fingerprints are linearly transformed and input as node attributes into an interaction model. The interaction module consists of a parallel graph convolution network layer and a NI layer, which aggregates node-level features from their neighbors through graph convolution operation and considers the element-level of interactions with their neighbors in the NI layer. Finally, the drug response predictions are made by calculating the linear correlation coefficients of feature representations of cell lines and drugs. We have conducted extensive experiments to assess the effectiveness of our model on Cancer Drug Sensitivity Data (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. It has achieved the best performance compared with the state-of-the-art algorithms, especially in predicting drug responses for new cell lines, new drugs and targeted drugs. Furthermore, our model that was well trained on the GDSC dataset can be successfully applied to predict samples of PDX and TCGA, which verified the transferability of our model from cell line in vitro to the datasets in vivo.Availability and implementationThe source code can be obtained from https://github.com/weiba/NIHGCN.Supplementary informationSupplementary data are available at Bioinformatics online.
Isoform function prediction by Gene Ontology embedding
Authors: Qiu Sichao , Yu Guoxian , Lu Xudong , Domeniconi Carlotta , Guo Maozu 
Published: August 2022

AbstractMotivationHigh-resolution annotation of gene functions is a central task in functional genomics. Multiple proteoforms translated from alternatively spliced isoforms from a single gene are actual function performers and greatly increase the functional diversity. The specific functions of different isoforms can decipher the molecular basis of various complex diseases at a finer granularity. Multi-instance learning (MIL)-based solutions have been developed to distribute gene(bag)-level Gene Ontology (GO) annotations to isoforms(instances), but they simply presume that a particular annotation of the gene is responsible by only one isoform, neglect the hierarchical structures and semantics of massive GO terms (labels), or can only handle dozens of terms.ResultsWe propose an efficacy approach IsofunGO to differentiate massive functions of isoforms by GO embedding. Particularly, IsofunGO first introduces an attributed hierarchical network to model massive GO terms, and a GO network embedding strategy to learn compact representations of GO terms and project GO annotations of genes into compressed ones, this strategy not only explores and preserves hierarchy between GO terms but also greatly reduces the prediction load. Next, it develops an attention-based MIL network to fuse genomics and transcriptomics data of isoforms and predict isoform functions by referring to compressed annotations. Extensive experiments on benchmark datasets demonstrate the efficacy of IsofunGO. Both the GO embedding and attention mechanism can boost the performance and interpretability.Availabilityand implementationThe code of IsofunGO is available at http://www.sdu-idea.cn/codes.php?name=IsofunGO.Supplementary informationSupplementary data are available at Bioinformatics online.
Streaming books: confluencers, Kindle Unlimited and the platform imaginary
Authors: Larson Christine 
Published: August 2022

Abstract
The Kindle’s 2007 launch transformed one of the oldest breeds of creative workers—authors—into one of the newest: platformized cultural producers. Although most research on such workers focuses on careers birthed by social media (i.e., influencers, YouTube creators), many cultural producers work in legacy industries where traditional and platformized practices coexist, come together and influence each other (i.e., publishing, music, film). Both groups—born-digital and “confluencers”—must negotiate precarious labor conditions, but confluencers can draw on both digital and pre-digital networks and traditions to do so. This project shows how one such group—indie romance authors—draws on its history to create collective imaginaries regarding Kindle Unlimited (KU). These imaginaries offer an emergent, if weak, base of resistance to Amazon’s platform power. I contribute to literature on platformized cultural production by: (a) distinguishing among groups of platformized producers; and (b) showing how historic context informs strategies for negotiating platform power.
Comparison of safety and outcomes related to remdesivir treatment among dialysis patients hospitalized with COVID-19
Authors: Zaki Kirollos E , Huang Cheng-Wei , Zhou Hui , Chung Joanie , Selevan David C , Rutkowski Mark P , Sim John J 
Published: August 2022

ABSTRACT

Background
Patients with end-stage kidney disease (ESKD) are highly susceptible to coronavirus disease 2019 (COVID-19) infection and its complications. Remdesivir has improved outcomes in COVID-19 patients but its use has been limited among ESKD patients due to insufficient data regarding safety outcomes. We sought to evaluate the safety of remdesivir among dialysis patients hospitalized with COVID-19.

Methods
This retrospective cohort study was conducted among patients age ≥18 years on maintenance dialysis and hospitalized with COVID-19 between 1 May 2020 and 31 January 2021 within an integrated health system who were treated or not treated with remdesivir. The primary outcome was 30-day all-cause mortality. Secondary outcomes were intensive care unit (ICU) stay, and transaminitis (AST/ALT &gt;5× normal). Pseudo-populations were created using inverse probability of treatment weights with propensity scoring to balance patient characteristics among the two groups. Multivariable Poisson regression with robust error was performed to estimate 30-day mortality risk ratio.

Results
A total of 486 (407 hemodialysis and 79 peritoneal dialysis) patients were hospitalized with COVID-19, among which 112 patients (23%) were treated with remdesivir [median treatment four days (interquartile range 2–5)]. The 30-day mortality rate was 24.1% among remdesivir-treated and 27.8% among non-treated patients. The estimated 30-day mortality rate was 0.74 (95% confidence interval 0.52–1.05) among remdesivir treated compared with non-treated patients. Liver injury and ICU admission rates were 1.8% and 14.3% among remdesivir-treated patients compared with 2.4% and 16% among non-treated patients.

Conclusion
Among dialysis patients hospitalized with COVID-19, remdesivir was not associated with higher rates of liver injury or ICU admissions, and demonstrated a trend toward lower 30-day mortality.
Chronic kidney disease as cardiovascular risk factor in routine clinical practice: a position statement by the Council of the European Renal Association
Authors: Ortiz Alberto , Wanner Christoph , Gansevoort Ron ,   ERA Council, Ortiz Alberto , Wanner Christoph , Gansevoort Ron T , Cozzolino Mario , Fliser Danilo , Gambaro Giovanni , Ong Albert , Rosenkranz Alexander R , Rychlık Ivan , Sarafidis Pantelis , Torra Roser , Tuglular Serhan 
Published: August 2022

Abstract

Abstract
The European Society of Cardiology 2021 guideline on cardiovascular (CV) disease (CVD) prevention in clinical practice has major implications for both CV risk screening and kidney health of interest to primary care physicians, cardiologists, nephrologists, and other professionals involved in CVD prevention. The proposed CVD prevention strategies require as first step the categorization of individuals into those with established atherosclerotic CVD, diabetes, familiar hypercholesterolaemia, or chronic kidney disease (CKD), i.e. conditions that are already associated with a moderate to very-high CVD risk. This places CKD, defined as decreased kidney function or increased albuminuria as a starting step for CVD risk assessment. Thus, for adequate CVD risk assessment, patients with diabetes, familiar hypercholesterolaemia, or CKD should be identified by an initial laboratory assessment that requires not only serum to assess glucose, cholesterol, and creatinine to estimate the glomerular filtration rate, but also urine to assess albuminuria. The addition of albuminuria as an entry-level step in CVD risk assessment should change clinical practice as it differs from the current healthcare situation in which albuminuria is only assessed in persons already considered to be at high risk of CVD. A diagnosis of moderate of severe CKD requires a specific set of interventions to prevent CVD. Further research should address the optimal method for CV risk assessment that includes CKD assessment in the general population, i.e. whether this should remain opportunistic screening or whether systematic screening.
Genome assembly of the chemosynthetic endosymbiont of the hydrothermal vent snailAlviniconcha adamantisfrom the Mariana Arc
Authors: Breusing Corinna , Klobusnik Nathan Hagen , Hauer Michelle A , Beinart Roxanne A 
Published: August 2022

AbstractChemosynthetic animal-microbe symbioses sustain hydrothermal vent communities in the global deep sea. In the Indo-Pacific Ocean, hydrothermal ecosystems are often dominated by gastropod species of the genus Alviniconcha, which live in association with chemosynthetic Gammaproteobacteria or Campylobacteria. While the symbiont genomes of most extant Alviniconcha species have been sequenced, no genome information is currently available for the gammaproteobacterial endosymbiont of Alviniconcha adamantis—a comparatively shallow living species that is thought to be the ancestor to all other present Alviniconcha lineages. Here, we report the first genome sequence for the symbiont of A. adamantis from the Chamorro Seamount at the Mariana Arc. Our phylogenomic analyses show that the A. adamantis symbiont is most closely related to Chromatiaceae endosymbionts of the hydrothermal vent snails Alviniconcha strummeri and Chrysomallon squamiferum, but represents a distinct bacterial species or possibly genus. Overall, the functional capacity of the A. adamantis symbiont appeared to be similar to other chemosynthetic Gammaproteobacteria, though several flagella and chemotaxis genes were detected, which are absent in other gammaproteobacterial Alviniconcha symbionts. These differences might suggest potential contrasts in symbiont transmission dynamics, host recognition, or nutrient transfer. Furthermore, an abundance of genes for ammonia transport and urea usage could indicate adaptations to the oligotrophic waters of the Mariana region, possibly via recycling of host- and environment-derived nitrogenous waste products. This genome assembly adds to the growing genomic resources for chemosynthetic bacteria from hydrothermal vents and will be valuable for future comparative genomic analyses assessing gene content evolution in relation to environment and symbiotic lifestyles.
Estimated Pulse Wave Velocity and All-Cause Mortality: Findings From the Health and Retirement Study
Authors: Heffernan Kevin S , Wilmoth Janet M , London Andrew S 
Published: August 2022

Abstract

Background and Objectives
The gold standard method for the assessment of vascular aging is carotid–femoral pulse wave velocity (cfPWV). cfPWV can be estimated from 2 commonly assessed clinical variables—age and blood pressure. This analysis uses data from the Health and Retirement Study to examine the relationship between estimated pulse wave velocity (ePWV) and mortality among 9,293 middle age and older adults.

Research Design and Methods
Cox proportional hazard models were used to predict mortality occurring over a 10- to 12-year period. Controls were included for sociodemographic characteristics (age, gender, race, ethnicity, wealth, income, and education), health status (history of cardiovascular disease [CVD], diabetes, and stroke and related medication use), health behaviors (smoking, physical activity, and body mass index), and CVD-related biomarkers (systolic and diastolic blood pressure, C-reactive protein, cystatin c, hemoglobin A1c, total cholesterol, and high-density lipoprotein cholesterol).

Results
By 2018, 26.19% of the weighted analytic sample were reported as deceased. In the fully specified models that control for age, age-squared, systolic and diastolic blood pressure, sociodemographic variables, health status and behaviors, and biomarkers, ePWV was associated with a greater likelihood of mortality.

Discussion and Implications
An estimate of PWV derived from age and blood pressure is independently associated with an increased likelihood of death in a representative sample of middle age and older adults in the United States.
Association between SLICC/ACR damage index and outcomes for lupus patients after cardiac valve surgery
Authors: Hu Szu-Yen , Cheng Chiao-Feng , Yang Kelvin Jeason , Wang Chih-Hsien , Chi Nai-Hsin , Hsu Ron-Bin , Chen Yih-Sharng , Yu Hsi-Yu 
Published: August 2022

Abstract

Systemic lupus erythematosus (SLE) is associated with multi-organ damage including cardiac valve, which may need valvular operation. However, methods for outcome prediction and prosthetic valve selection are unclear in SLE patients undergoing cardiac valve surgery. Twenty-five SLE patients receiving valvular operation in a single institute between 2002 and 2020 were enrolled. Systemic Lupus International Collaborative Clinics/American College of Rheumatology Damage Index (SLICC/ACR damage index, SDI) was applied to evaluate the damage severity. Clinical outcomes were compared between patients with different SDI. The hospital survival rate was 88%, and long-term survival rate was 59.5% and 40.2% at 5 and 10 years. The median SDI was 4 (interquartile range 3–6) in our study, patients were then grouped into higher SDI (defined as SDI ≥ 5, n = 11) and lower SDI group (defined as SDI &lt; 5, n = 14). The in-hospital survival rate (72.2% vs 100%, P = 0.074) and 5-year survival rate (18.2% vs 92.9%, P &lt; 0.001) were lower in higher SDI group, compared to lower SDI group. SDI score was associated with long-term outcome for SLE patients receiving cardiac valve surgery. SDI ≥ 5 was associated with very poor long-term outcomes. This finding implicates that xenograft might be a reasonable choice for SLE patients with SDI ≥ 5.
Carbon sink price prediction based on radial basis kernel function support vector machine regression model
Authors: Yang Xing , Mi Jun-long , Jiang Jin , Li Jia-wen , Zhang Quan-shen , Geng Meng-meng 
Published: August 2022

Abstract
At present, the main pricing methods for carbon sink trading are marginal cost pricing, shadow price pricing and physical option pricing. The most serious defect of these three methods is that their theoretical basis is the extremely idealized effective market hypothesis, which obviously does not conform to the fractal and chaotic behavior characteristics of the actual carbon sink trading market. It is particularly important to study new pricing technologies and methods for a complex, nonlinear and dissipative system. Therefore, this paper constructs a carbon sink price prediction method based on the radial basis kernel function support vector machine (RBF-SVM) model. The main findings are the following:
• Compared with the build nonlinear support vector machine (SVM) models by the three kernel functions: linear kernel function (LF), radial basis function (RBF) and sigmoid kernel function, the RBF-SVM has the highest prediction accuracy.
• The adaptability of the RBF-SVM model is tested by using the price fluctuation data of EU allowance (EUA). It is proved that the adaptability of the RBF-SVM model for the price predictions of heterogeneous carbon products is also excellent.
• The forecast results of the RBF-SVM model for carbon sink price for the period of September 2021 to August 2022 show that the lowest carbon sink price of 20 euros is expected to occur around November 2021 and the highest price of 38.5 euros around August 2022.
Therefore, the established RBF-SVM model can be used for carbon sink trading market price prediction, which is more scientific and applicable than the previous three methods.
Genomic insights into the conservation and population genetics of two Chinese native goat breeds
Authors: Wang Xiaopeng , Li Guixin , Jiang Yongchuang , Tang Jianhong , Fan Yin , Ren Jun 
Published: August 2022

Abstract
Chinese goats are an important group of goats worldwide. However, there are few studies on the conservation priority, genetic relationship, and potential gene flow between Chinese and global goat breeds. Here, we genotyped 239 goats from conservation populations of the Chinese Guangfeng and Ganxi breeds using the GoatSNP50 BeadChip. The conservation priority, population structure, selection signatures and introgression of these goats were analyzed in the context of 36 global goat breeds. First, we showed that Guangfeng and Ganxi goats had the largest effective population sizes across the global breeds 13 generations ago. Nevertheless, Ganxi goats have recently experienced a high degree of inbreeding, resulting in their conservation priority based on total gene and allelic diversities being lower than that of most other Chinese breeds (including Guangfeng goats). Population structure and admixture analyses showed that an average of 18% of Guangfeng genomic components were introgressed from Boer goats approximately 18-yr ago. Next, we reconstructed the subfamily structure of the core populations of Guangfeng and Ganxi goats, and proposed reasonable conservation strategies for inbreeding management. Moreover, a list of candidate genes under selection for fertility, immunity, growth, and meat quality were detected in Guangfeng and Ganxi goats. Finally, we identified some genes related to body development and reproduction, which were introgressed from Boer goats and may be beneficial for improving performance and productivity of Guangfeng goats. In conclusion, this study not only provides new insights into the conservation and utilization of Guangfeng and Ganxi goats but also enriches our understanding of artificial introgression from exotic goats into Chinese local goats.
Purification and characterization of protease M, a yeast mitochondrial nucleotide-stimulated metal protease: its identification as CYM1 gene product, a mitochondrial presequence peptidase
Authors: Yasuhara Toshimasa , Nakai Toshiki , Fujiki Yukio 
Published: August 2022

Abstract
A chelator-sensitive protease in the mitochondrial matrix of the yeast, Saccharomyces cerevisiae (Biochem. Biophys. Res. Commun. 144, 277, 1987), was purified and characterized. The purified enzyme, termed protease M, specifically hydrolyzes peptide substrates on the N-side of the paired basic residues. When mastoparan was used as substrate, it cleaved Ala8-Leu9 and Lys11-Lys12 bonds as well as the N-side of Lys11-Lys12 residues. Nucleotide triphosphates stimulated the activity 3-fold at 2.5 mM. The genomic DNA sequence showed that protease M was a gene product of CYM1 known as mitochondrial presequence protease homologue in S. cerevisiae, encoding a 989-amino acid-long precursor protein. The N-terminal sequence of the purified enzyme indicated that protease M has 16-residue signal sequence and the ‘mature’ protein consists of 973 amino acids with a molecular mass of 110 kDa. Protease M contained consensus sequence motifs of ATP-binding site very near the carboxyl terminus. The alignment of the two ATP-binding motifs is an inverted version of the common alignment. Gene disruption of the enzyme generates mixed subunits in tetrameric MnSOD formed with 23-kDa mature and 24-kDa partial presequence-containing subunits. This report describes newly identified enzyme properties of the CYM1 gene product, protease M and abnormal MnSOD complex formation of the disruption mutant.