bluuebunny/crossref_metadata_2025_split huggingface dataset:
Postcopulatory sexual selection is associated with sperm aggregate quality in Peromyscus mice
Authors: Hook Kristin A , Weber W David , Fisher Heidi S 
Published: September 2021

Abstract
In some species, sperm form coordinated groups that are hypothesized to improve their swimming performance in competitive contexts or to navigate through the viscous fluids of the female reproductive tract. Here we investigate sperm aggregation across closely related species of Peromyscus mice that naturally vary by mating system to test the predictions that sperm aggregates 1) are faster than solitary sperm in species that females mate multiply to aid cells in sperm competition, and 2) outperform solitary sperm cells in viscous environments. We find significant variation in the size of sperm aggregates, which negatively associates with relative testis mass, a proxy for sperm competition risk, suggesting that postcopulatory sexual selection has a stabilizing effect on sperm group size. Moreover, our results show that sperm aggregates are faster than solitary sperm in some, but not all, species, and this can vary by fluid viscosity. Of the two species that produce the largest and most frequent groups, we find that sperm aggregates from the promiscuous P. maniculatus are faster than solitary sperm in every experimentally viscous environment but aggregation provides no such kinematic advantage under these same conditions for the monogamous P. polionotus. The reduced performance of P. polionotus aggregates is associated with less efficient aggregate geometry and the inclusion of immotile or morphological abnormal sperm. Our cross-species comparison yields insight into the evolution of sperm social behaviors, provides evidence of extensive variation in the Peromyscus lineage, and reveals that differences in sperm aggregate quality associate with postcopulatory sexual selection.
A cross-study analysis of drug response prediction in cancer cell lines
Authors: Xia Fangfang , Allen Jonathan , Balaprakash Prasanna , Brettin Thomas , Garcia-Cardona Cristina , Clyde Austin , Cohn Judith , Doroshow James , Duan Xiaotian , Dubinkina Veronika , Evrard Yvonne , Fan Ya Ju , Gans Jason , He Stewart , Lu Pinyi , Maslov Sergei , Partin Alexander , Shukla Maulik , Stahlberg Eric , Wozniak Justin M , Yoo Hyunseung , Zaki George , Zhu Yitan , Stevens Rick 
Published: September 2021

AbstractTo enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.
Network-based analysis revealed significant interactions between risk genes of severe COVID-19 and host genes interacted with SARS-CoV-2 proteins
Authors: Qi Hao-xiang , Shen Qi-dong , Zhao Hong-yi , Qi Guo-zhen , Gao Lei 
Published: September 2021

Abstract
Whether risk genes of severe coronavirus disease 2019 (COVID-19) from genome-wide association study could play their regulatory roles by interacting with host genes that were interacted with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins was worthy of exploration. In this study, we implemented a network-based approach by developing a user-friendly software Network Calculator (https://github.com/Haoxiang-Qi/Network-Calculator.git). By using Network Calculator, we identified a network composed of 13 risk genes and 28 SARS-CoV-2 interacted host genes that had the highest network proximity with each other, with a hub gene HNRNPK identified. Among these genes, 14 of them were identified to be differentially expressed in RNA-seq data from severe COVID-19 cases. Besides, by expression enrichment analysis in single-cell RNA-seq data, compared with mild COVID-19, these genes were significantly enriched in macrophage, T cell and epithelial cell for severe COVID-19. Meanwhile, 74 pathways were significantly enriched. Our analysis provided insights for the underlying genetic etiology of severe COVID-19 from the perspective of network biology.
Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients
Authors: Wang Hong , Zhang Jingqing , Lu Zhigang , Dai Weina , Ma Chuanjiang , Xiang Yun , Zhang Yonghong 
Published: September 2021

Abstract
After experiencing the COVID-19 pandemic, it is widely acknowledged that a rapid drug repurposing method is highly needed. A series of useful drug repurposing tools have been developed based on data-driven modeling and network pharmacology. Based on the disease module, we identified several hub proteins that play important roles in the onset and development of the COVID-19, which are potential targets for repositioning approved drugs. Moreover, different network distance metrics were applied to quantify the relationship between drug targets and COVID-19 disease targets in the protein–protein-interaction (PPI) network and predict COVID-19 therapeutic effects of bioactive herbal ingredients and chemicals. Furthermore, the tentative mechanisms of candidates were illustrated through molecular docking and gene enrichment analysis. We obtained 15 chemical and 15 herbal ingredient candidates and found that different drugs may play different roles in the process of virus invasion and the onset and development of the COVID-19 disease. Given pandemic outbreaks, our method has an undeniable immense advantage in the feasibility analysis of drug repurposing or drug screening, especially in the analysis of herbal ingredients.
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction
Authors: Basith Shaherin , Lee Gwang , Manavalan Balachandran 
Published: September 2021

AbstractProtein post-translational modification (PTM) is an important regulatory mechanism that plays a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs owing to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the in silico identification of Kace sites have been developed. Of those, a few are prokaryotic species-specific. Despite their attractive advantages and performances, these methods have certain limitations. Therefore, this study proposes a novel predictor STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION), containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed 11 different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the predicted values from baseline models were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictor on independent tests. To expedite direct accessibility to the STALLION models, a user-friendly online predictor was implemented, which is available at: http://thegleelab.org/STALLION.
DeepHost: phage host prediction with convolutional neural network
Authors: Ruohan Wang , Xianglilan Zhang , Jianping Wang , Shuai Cheng L I 
Published: September 2021

Abstract
Next-generation sequencing expands the known phage genomes rapidly. Unlike culture-based methods, the hosts of phages discovered from next-generation sequencing data remain uncharacterized. The high diversity of the phage genomes makes the host assignment task challenging. To solve the issue, we proposed a phage host prediction tool—DeepHost. To encode the phage genomes into matrices, we design a genome encoding method that applied various spaced $k$-mer pairs to tolerate sequence variations, including insertion, deletions, and mutations. DeepHost applies a convolutional neural network to predict host taxonomies. DeepHost achieves the prediction accuracy of 96.05% at the genus level (72 taxonomies) and 90.78% at the species level (118 taxonomies), which outperforms the existing phage host prediction tools by 10.16–30.48% and achieves comparable results to BLAST. For the genomes without hits in BLAST, DeepHost obtains the accuracy of 38.00% at the genus level and 26.47% at the species level, making it suitable for genomes of less homologous sequences with the existing datasets. DeepHost is alignment-free, and it is faster than BLAST, especially for large datasets. DeepHost is available at https://github.com/deepomicslab/DeepHost.
Characterization of gastric cancer stem-like molecular features, immune and pharmacogenomic landscapes
Authors: Wei Chen , Chen Mingkai , Deng Wenying , Bie Liangyu , Ma Yijie , Zhang Chi , Liu Kangdong , Shen Wei , Wang Shuyi , Yang Chaogang , Luo Suxia , Li Ning 
Published: September 2021

Abstract
Cancer stem cells (CSCs) actively reprogram their tumor microenvironment (TME) to sustain a supportive niche, which may have a dramatic impact on prognosis and immunotherapy. However, our knowledge of the landscape of the gastric cancer stem-like cell (GCSC) microenvironment needs to be further improved. A multi-step process of machine learning approaches was performed to develop and validate the prognostic and predictive potential of the GCSC-related score (GCScore). The high GCScore subgroup was not only associated with stem cell characteristics, but also with a potential immune escape mechanism. Furthermore, we experimentally demonstrated the upregulated infiltration of CD206+ tumor-associated macrophages (TAMs) in the invasive margin region, which in turn maintained the stem cell properties of tumor cells. Finally, we proposed that the GCScore showed a robust capacity for prediction for immunotherapy, and investigated potential therapeutic targets and compounds for patients with a high GCScore. The results indicate that the proposed GCScore can be a promising predictor of prognosis and responses to immunotherapy, which provides new strategies for the precision treatment of GCSCs.
Deep learning in retrosynthesis planning: datasets, models and tools
Authors: Dong Jingxin , Zhao Mingyi , Liu Yuansheng , Su Yansen , Zeng Xiangxiang 
Published: September 2021

Abstract
In recent years, synthesizing drugs powered by artificial intelligence has brought great convenience to society. Since retrosynthetic analysis occupies an essential position in synthetic chemistry, it has received broad attention from researchers. In this review, we comprehensively summarize the development process of retrosynthesis in the context of deep learning. This review covers all aspects of retrosynthesis, including datasets, models and tools. Specifically, we report representative models from academia, in addition to a detailed description of the available and stable platforms in the industry. We also discuss the disadvantages of the existing models and provide potential future trends, so that more abecedarians will quickly understand and participate in the family of retrosynthesis planning.
RNA–RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection
Authors: Zhang Song , Amahong Kuerbannisha , Zhang Chenyang , Li Fengcheng , Gao Jianqing , Qiu Yunqing , Zhu Feng 
Published: September 2021

Abstract
Some studies reported that genomic RNA of SARS-CoV-2 can absorb a few host miRNAs that regulate immune-related genes and then deprive their function. In this perspective, we conjecture that the absorption of the SARS-CoV-2 genome to host miRNAs is not a coincidence, which may be an indispensable approach leading to viral survival and development in host. In our study, we collected five datasets of miRNAs that were predicted to interact with the genome of SARS-CoV-2. The targets of these miRNAs in the five groups were consistently enriched immune-related pathways and virus-infectious diseases. Interestingly, the five datasets shared no one miRNA but their targets shared 168 genes. The signaling pathway enrichment of 168 shared targets implied an unbalanced immune response that the most of interleukin signaling pathways and none of the interferon signaling pathways were significantly different. Protein–protein interaction (PPI) network using the shared targets showed that PPI pairs, including IL6-IL6R, were related to the process of SARS-CoV-2 infection and pathogenesis. In addition, we found that SARS-CoV-2 absorption to host miRNA could benefit two popular mutant strains for more infectivity and pathogenicity. Conclusively, our results suggest that genomic RNA absorption to host miRNAs may be a vital approach by which SARS-CoV-2 disturbs the host immune system and infects host cells.
Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data
Authors: Wang Yue , Zhao Yunpeng , Pan Qing 
Published: September 2021

AbstractCoronavirus disease 2019 (COVID-19) has attracted research interests from all fields. Phylogenetic and social network analyses based on connectivity between either COVID-19 patients or geographic regions and similarity between syndrome coronavirus 2 (SARS-CoV-2) sequences provide unique angles to answer public health and pharmaco-biological questions such as relationships between various SARS-CoV-2 mutants, the transmission pathways in a community and the effectiveness of prevention policies. This paper serves as a systematic review of current phylogenetic and social network analyses with applications in COVID-19 research. Challenges in current phylogenetic network analysis on SARS-CoV-2 such as unreliable inferences, sampling bias and batch effects are discussed as well as potential solutions. Social network analysis combined with epidemiology models helps to identify key transmission characteristics and measure the effectiveness of prevention and control strategies. Finally, future new directions of network analysis motivated by COVID-19 data are summarized.
Dissecting and predicting different types of binding sites in nucleic acids based on structural information
Authors: Jiang Zheng , Xiao Si-Rui , Liu Rong 
Published: September 2021

Abstract
The biological functions of DNA and RNA generally depend on their interactions with other molecules, such as small ligands, proteins and nucleic acids. However, our knowledge of the nucleic acid binding sites for different interaction partners is very limited, and identification of these critical binding regions is not a trivial work. Herein, we performed a comprehensive comparison between binding and nonbinding sites and among different categories of binding sites in these two nucleic acid classes. From the structural perspective, RNA may interact with ligands through forming binding pockets and contact proteins and nucleic acids using protruding surfaces, while DNA may adopt regions closer to the middle of the chain to make contacts with other molecules. Based on structural information, we established a feature-based ensemble learning classifier to identify the binding sites by fully using the interplay among different machine learning algorithms, feature spaces and sample spaces. Meanwhile, we designed a template-based classifier by exploiting structural conservation. The complementarity between the two classifiers motivated us to build an integrative framework for improving prediction performance. Moreover, we utilized a post-processing procedure based on the random walk algorithm to further correct the integrative predictions. Our unified prediction framework yielded promising results for different binding sites and outperformed existing methods.
An evo-devo perspective of the female reproductive tract
Authors: Major Andrew T , Estermann Martin A , Roly Zahida Y , Smith Craig A 
Published: September 2021

AbstractThe vertebrate female reproductive tract has undergone considerable diversification over evolution, having become physiologically adapted to different reproductive strategies. This review considers the female reproductive tract from the perspective of evolutionary developmental biology (evo-devo). Very little is known about how the evolution of this organ system has been driven at the molecular level. In most vertebrates, the female reproductive tract develops from paired embryonic tubes, the Müllerian ducts. We propose that formation of the Müllerian duct is a conserved process that has involved co-option of genes and molecular pathways involved in tubulogenesis in the adjacent mesonephric kidney and Wolffian duct. Downstream of this conservation, genetic regulatory divergence has occurred, generating diversity in duct structure. Plasticity of the Hox gene code and wnt signaling, in particular, may underlie morphological variation of the uterus in mammals, and evolution of the vagina. This developmental plasticity in Hox and Wnt activity may also apply to other vertebrates, generating the morphological diversity of female reproductive tracts evident today.
Intervention versus surveillance in patients with common bile duct stones detected by intraoperative cholangiography: a population-based registry study
Authors: Johansson E , Österberg J , Sverdén E , Enochsson L , Sandblom G 
Published: September 2021

Abstract

Background
Each year 13 000 patients undergo cholecystectomy in Sweden, and routine intraoperative cholangiography (IOC) is recommended to minimize bile duct injuries. The risk of requiring endoscopic retrograde cholangiopancreatography (ERCP) following cholecystectomy for common bile duct (CBD) stones where IOC is omitted and in patients with CBD stones left in situ is not well known.

Methods
Data were retrieved from the population-based Swedish Registry of Gallstone Surgery and ERCP between 1 January 2009 and 10 December 2019. Primary outcome was risk for postoperative ERCP for retained CBD stones.

Results
A total of 134 419 patients that underwent cholecystectomy were included and 2691 (2.0 per cent) subsequently underwent ERCP for retained CBD stones. When adjusting for emergency or planned cholecystectomy, preoperative symptoms suggestive of CBD stones, sex and age, there was an increased risk for ERCP when IOC was not performed (hazard ratio (HR) 1.4, 95 per cent c.i. 1.3 to 1.6). The adjusted risk for ERCP was also increased if CBD stones identified by IOC were managed with surveillance (HR 5.5, 95 per cent c.i. 4.8 to 6.4). Even for asymptomatic small stones (less than 4 mm), the adjusted risk for ERCP was increased in the surveillance group compared with the intervention group (HR 3.5, 95 per cent c.i. 2.4 to 5.1).

Conclusion
IOC plus an intervention to remove CBD stones identified during cholecystectomy was associated with reduced risk for retained stones and unplanned ERCP, even for the smallest asymptomatic CBD stones.
Nationwide paediatric cohort study of a protective association between allergy and complicated appendicitis
Authors: Omling E , Salö M , Stenström P , Merlo J , Gudjonsdottir J , Rudolfson N , Hagander L 
Published: September 2021

Abstract

Background
In a nationwide cohort the potentially protective association between allergy and complicated appendicitis was analysed, and the influence of seasonal antigens, antihistamine treatment, and timing of allergy onset assessed.

Methods
Some 1 112 571 children born between 2000 and 2010 were followed from birth until the end of 2014. A cross-sectional analysis of appendicitis cases, with comparison of allergic versus non-allergic children for absolute risk and odds of complicated appendicitis was first undertaken. This was followed by a longitudinal analysis of children with allergy and matched controls who had never had an allergy, for incidence rate and hazard of subsequent complicated or simple appendicitis.

Results
Of all children, 20.4 per cent developed allergy and 0.6 per cent had appendicitis during follow-up. Among children with appendicitis, complicated appendicitis was more common among non-allergic children (18.9 per cent, 948 of 5016) than allergic children (12.8 per cent, 173 of 1351) (P &lt; 0.001), and allergic children had a lower adjusted odds of complicated appendicitis (adjusted odds ratio (OR) 0.80, 95 per cent c.i. 0.67 to 0.96; P = 0.021 ). The risk of complicated appendicitis among children with manifest allergy was reduced by one-third in the longitudinal analysis (incidence rate 0.13 versus 0.20 per 1000 person-years; hazard ratio (HR) 0.68, 95 per cent c.i. 0.58 to 0.81; P &lt; 0.001), whereas the risk of simple appendicitis remained unchanged (incidence rate 0.91 versus 0.91; HR 1.00, 0.94 to 1.07; P = 0.932 ). Seasonal antigen exposure was a protective factor (adjusted OR 0.82, 0.71 to 0.94; P = 0.004) and ongoing antihistamine medication a risk factor (adjusted OR 2.28, 1.21 to 4.28; P = 0.012).

Conclusion
Children with allergy have a lower risk of complicated appendicitis, but the same overall risk of simple appendicitis. Seasonal antigen exposure reduced, and antihistamine treatment increased, the risk of complicated disease.