bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of affecting factors on patient safety culture in public and private hospitals in Iran
Authors: Khoshakhlagh Amir Hossein , Khatooni Elham , Akbarzadeh Isa , Yazdanirad Saeid , Sheidaei Ali 
Published: December 2019

Abstract
Background
Patient safety culture is one of the main components of the quality of health services and is one of the main priorities of health studies. Accordingly, this study aimed to determine and compare the views of healthcare staff on the patient safety culture and the impact of effective factors on patient safety culture in public and private hospitals in Tehran, Iran.

Methods
This cross-sectional study was carried out on a sample of 1203 health care workers employed in three public and three private hospitals in Tehran, Iran. Stratified random sampling was used in this study. Data were collected using the Maslach burnout inventory and patient safety culture questionnaire (HSOPSC). IBM SPSS v22 and Amos v23 were used to perform path analysis.

Results
Eight hundred sixty-seven (72.57%) females and 747 (27.43%) males with a mean age of 33.88 ± 7.66 were included. The average percentage of positive responses to the safety culture questionnaire in public and private hospitals was 65.5 and 58.3%, respectively. The strengths of patient safety culture in public hospitals were in three dimensions including non-punitive response to errors (80%), organizational learning—continuous improvement (79.77%), and overall perceptions of patient safety (75.16%), and in private hospitals, were three dimensions including non-punitive responses to errors (71.41%), organizational learning & continuous improvement (69.24%), and teamwork within units (62.35%). The type of hospital and work-shift hours influenced the burnout and patient safety questionnaire scores (P-value < 0.05). The path analysis results indicate the fitness of the proposed model (RMSEA = 0.024). The results showed a negative impact of a work shift (β = − 0.791), occupational burnout (β = − 0.554) and hospital type (β = − 0.147) on the observance of patient safety culture.

Conclusion
Providing feedback on errors and requirements for the frequent incident reporting, and patient information exchange seem necessary to promote the patient safety culture. Also, considering the negative impact of the shift work and burnout on patient safety culture, by planning and managing these factors appropriately, correct actions could be designed to improve the safety culture.
Multi-species annotation of transcriptome and chromatin structure in domesticated animals
Authors: Foissac Sylvain , Djebali Sarah , Munyard Kylie , Vialaneix Nathalie , Rau Andrea , Muret Kevin , Esquerré Diane , Zytnicki Matthias , Derrien Thomas , Bardou Philippe , Blanc Fany , Cabau Cédric , Crisci Elisa , Dhorne-Pollet Sophie , Drouet Françoise , Faraut Thomas , Gonzalez Ignacio , Goubil Adeline , Lacroix-Lamandé Sonia , Laurent Fabrice , Marthey Sylvain , Marti-Marimon Maria , Momal-Leisenring Raphaelle , Mompart Florence , Quéré Pascale , Robelin David , Cristobal Magali San , Tosser-Klopp Gwenola , Vincent-Naulleau Silvia , Fabre Stéphane , Pinard-Van der Laan Marie-Hélène , Klopp Christophe , Tixier-Boichard Michèle , Acloque Hervé , Lagarrigue Sandrine , Giuffra Elisabetta 
Published: December 2019

AbstractBackgroundComparative genomics studies are central in identifying the coding and non-coding elements associated with complex traits, and the functional annotation of genomes is a critical step to decipher the genotype-to-phenotype relationships in livestock animals. As part of the Functional Annotation of Animal Genomes (FAANG) action, the FR-AgENCODE project aimed to create reference functional maps of domesticated animals by profiling the landscape of transcription (RNA-seq), chromatin accessibility (ATAC-seq) and conformation (Hi-C) in species representing ruminants (cattle, goat), monogastrics (pig) and birds (chicken), using three target samples related to metabolism (liver) and immunity (CD4+ and CD8+ T cells).ResultsRNA-seq assays considerably extended the available catalog of annotated transcripts and identified differentially expressed genes with unknown function, including new syntenic lncRNAs. ATAC-seq highlighted an enrichment for transcription factor binding sites in differentially accessible regions of the chromatin. Comparative analyses revealed a core set of conserved regulatory regions across species. Topologically associating domains (TADs) and epigenetic A/B compartments annotated from Hi-C data were consistent with RNA-seq and ATAC-seq data. Multi-species comparisons showed that conserved TAD boundaries had stronger insulation properties than species-specific ones and that the genomic distribution of orthologous genes in A/B compartments was significantly conserved across species.ConclusionsWe report the first multi-species and multi-assay genome annotation results obtained by a FAANG project. Beyond the generation of reference annotations and the confirmation of previous findings on model animals, the integrative analysis of data from multiple assays and species sheds a new light on the multi-scale selective pressure shaping genome organization from birds to mammals. Overall, these results emphasize the value of FAANG for research on domesticated animals and reinforces the importance of future meta-analyses of the reference datasets being generated by this community on different species.
The NASSS framework for ex post theorisation of technology-supported change in healthcare: worked example of the TORPEDO programme
Authors: Abimbola Seye , Patel Bindu , Peiris David , Patel Anushka , Harris Mark , Usherwood Tim , Greenhalgh Trisha 
Published: December 2019

Abstract
Background
Evaluation of health technology programmes should be theoretically informed, interdisciplinary, and generate in-depth explanations. The NASSS (non-adoption, abandonment, scale-up, spread, sustainability) framework was developed to study unfolding technology programmes in real time—and in particular to identify and manage their emergent uncertainties and interdependencies. In this paper, we offer a worked example of how NASSS can also inform ex post (i.e. retrospective) evaluation.

Methods
We studied the TORPEDO (Treatment of Cardiovascular Risk in Primary Care using Electronic Decision Support) research programme, a multi-faceted computerised quality improvement intervention for cardiovascular disease prevention in Australian general practice. The technology (HealthTracker) had shown promise in a cluster randomised controlled trial (RCT), but its uptake and sustainability in a real-world implementation phase was patchy. To explain this variation, we used NASSS to undertake secondary analysis of the multi-modal TORPEDO dataset (results and process evaluation of the RCT, survey responses, in-depth professional interviews, videotaped consultations) as well as a sample of new, in-depth narrative interviews with TORPEDO researchers.

Results
Ex post analysis revealed multiple areas of complexity whose influence and interdependencies helped explain the wide variation in uptake and sustained use of the HealthTracker technology: the nature of cardiovascular risk in different populations, the material properties and functionality of the technology, how value (financial and non-financial) was distributed across stakeholders in the system, clinicians’ experiences and concerns, organisational preconditions and challenges, extra-organisational influences (e.g. policy incentives), and how interactions between all these influences unfolded over time.

Conclusion
The NASSS framework can be applied retrospectively to generate a rich, contextualised narrative of technology-supported change efforts and the numerous interacting influences that help explain its successes, failures, and unexpected events. A NASSS-informed ex post analysis can supplement earlier, contemporaneous evaluations to uncover factors that were not apparent or predictable at the time but dynamic and emergent.
Molecular evidence of Babesia in pet cats in mainland China
Authors: Zhang Xue-Lian , Li Xiao-Wen , Li Wen-Jun , Huang Hui-Lan , Huang Shu-Jian , Shao Jian-Wei 
Published: December 2019

AbstractBackgroundBabesiaspp. are important emerging tick-borne protozoan hemoparasites, and pose a great impact on companion animals. Canine babesiosis has been well described worldwide, while felis babesiosis has primarily been reported from South Africa. To the best of our knowledge,Babesiaspp. infections in dogs have been well elucidated in pet dog population in China, no report aboutBabesiaspp. infection in cat population in mainland China.ResultsIn this study, a total of 203 blood samples were collected from pet cats in Shenzhen city, and detected the presence ofBabesiaspp. with nested-PCR. Sequence comparison based on the 18S rRNA gene and ITS region revealed that three cats (1.48%) were infected withBabesia.vogeli. Notably, the sequences of ITS region obtained in this study shared the highest nucleotide identity with the sequence ofB.vogelistrain isolated in cat from Taiwan.ConclusionsThis study is the first report about babesiosis in domestic cats, and also provides molecular evidence ofBabesiaspp. infection in cat in mainland China. The data present in this study suggestB.vogelimay be circulating in cat population in mainland China. Further study to investigate the epidemiology ofBabesiainfection in cat nationwide is warranted.
Methicillin-resistant Staphylococcus aureus and glycopeptide-resistant enterococci in fecal samples of birds from South-Eastern Poland
Authors: Kutkowska Jolanta , Turska-Szewczuk Anna , Kucharczyk Marek , Kucharczyk Halina , Zalewska Joanna , Urbanik-Sypniewska Teresa 
Published: December 2019

Abstract
Background
The incidence of human infection and colonization with methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE) has increased in the recent years. Environmental sources, including bird droppings, might play an important role as resistance reservoirs.

Results
Fresh fecal samples were collected from rooks and wild-living birds during the autumn-winter period of 2016/2017, and tested for the presence of bacteria associated with human diseases. Besides bacteria representing the genera Enterococcus, Campylobacter, Escherichia, and Staphylococcus, Enterobacter, Citrobacter, Proteus, Hafnia, and Pseudomonas were also identified. The susceptibility of S. aureus and Enterococcus spp. isolates to methicillin, and vancomycin and teicoplanin, respectively, was analyzed to assess the avian wildlife as a reservoir of MRSA and VRE strains. Twenty-two percent of all S. aureus isolates were methicillin-resistant. These strains were screened by polymerase chain reaction (PCR), using the most widely used primer sets specific for the mecA gene. Twenty percent of all Enterococcus strains were phenotypically vancomycin-resistant. The presence of van resistance genes in these strains was investigated by PCR using vanA and vanB gene-specific primers. A good correlation between mecA gene detection and disc diffusion data was observed, while some discrepancy was noted between the PCR data and the vancomycin/teicoplanin phenotypic resistance pattern. The incidence of strains resistant to methicillin and glycopeptide antibiotics in wild-living birds was twice that in rooks.

Conclusions
The study suggests that rooks from urban areas and passerine birds from the natural habitat carry antibiotic-resistant Enterococcus spp. and S. aureus strains, probably reflecting the presence of such isolates in the environmental food sources.
Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs
Authors: Zhong Hua , Song Mingzhou 
Published: December 2019

Abstract
Background
Most statistical methods used to identify cancer driver genes are either biased due to choice of assumed parametric models or insensitive to directional relationships important for causal inference. To overcome modeling biases and directional insensitivity, a recent statistical functional chi-squared test (FunChisq) detects directional association via model-free functional dependency. FunChisq examines patterns pointing from independent to dependent variables arising from linear, non-linear, or many-to-one functional relationships. Meanwhile, the Functional Annotation of Mammalian Genome 5 (FANTOM5) project surveyed gene expression at over 200,000 transcription start sites (TSSs) in nearly all human tissue types, primary cell types, and cancer cell lines. The data cover TSSs originated from both coding and noncoding genes. For the vast uncharacterized human TSSs that may exhibit complex patterns in cancer versus normal tissues, the model-free property of FunChisq provides us an unprecedented opportunity to assess the evidence for a gene’s directional effect on human cancer.

Results
We first evaluated FunChisq and six other methods using 719 curated cancer genes on the FANTOM5 data. FunChisq performed best in detecting known cancer driver genes from non-cancer genes. We also show the capacity of FunChisq to reveal non-monotonic patterns of functional association, to which typical differential analysis methods such as t-test are insensitive. Further applying FunChisq to screen unannotated TSSs in FANTOM5, we predicted 1108 putative cancer driver noncoding RNAs, stronger than 90% of curated cancer driver genes. Next, we compared leukemia samples against other samples in FANTOM5 and FunChisq predicted 332/79 potential biomarkers for lymphoid/myeloid leukemia, stronger than the TSSs of all 87/100 known driver genes in lymphoid/myeloid leukemia.

Conclusions
This study demonstrated the advantage of FunChisq in revealing directional association, especially in detecting non-monotonic patterns. Here, we also provide the most comprehensive catalog of high-quality biomarkers that may play a causative role in human cancers, including putative cancer driver noncoding RNAs and lymphoid/myeloid leukemia specific biomarkers.
MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks
Authors: Hui Ying , Wei Pi-Jing , Xia Junfeng , Wang Yu-Tian , Zheng Chun-Hou 
Published: December 2019

Abstract
Background
Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network.

Methods
We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population.

Results
We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature.

Conclusions
We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation’s effect on the transcriptional network, but also assesses the differential expression’s effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes.
A network clustering based feature selection strategy for classifying autism spectrum disorder
Authors: Tang Lingkai , Mostafa Sakib , Liao Bo , Wu Fang-Xiang 
Published: December 2019

Abstract
Background
Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance.

Methods
In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification.

Results
The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network.

Conclusion
It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.
A new method for mining information of co-expression network based on multi-cancers integrated data
Authors: Hou Mi-Xiao , Gao Ying-Lian , Liu Jin-Xing , Shang Junliang , Zhu Rong , Yuan Sha-Sha 
Published: December 2019

AbstractBackgroundGene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction.MethodsIn this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes.ResultsWe uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation.ConclusionsThe results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.
Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network
Authors: Song Junrong , Peng Wei , Wang Feng , Wang Jianxin 
Published: December 2019

Abstract
Background
Cancer as a kind of genomic alteration disease each year deprives many people’s life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance.

Methods
Considering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN).

Results
This method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes.

Conclusion
The final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.
Genome analysis and knowledge-driven variant interpretation with TGex
Authors: Dahary Dvir , Golan Yaron , Mazor Yaron , Zelig Ofer , Barshir Ruth , Twik Michal , Iny Stein Tsippi , Rosner Guy , Kariv Revital , Chen Fei , Zhang Qiang , Shen Yiping , Safran Marilyn , Lancet Doron , Fishilevich Simon 
Published: December 2019

AbstractBackgroundThe clinical genetics revolution ushers in great opportunities, accompanied by significant challenges. The fundamental mission in clinical genetics is to analyze genomes, and to identify the most relevant genetic variations underlying a patient’s phenotypes and symptoms. The adoption of Whole Genome Sequencing requires novel capacities for interpretation of non-coding variants.ResultsWe present TGex, the Translational Genomics expert, a novel genome variation analysis and interpretation platform, with remarkable exome analysis capacities and a pioneering approach of non-coding variants interpretation. TGex’s main strength is combining state-of-the-art variant filtering with knowledge-driven analysis made possible by VarElect, our highly effective gene-phenotype interpretation tool. VarElect leverages the widely used GeneCards knowledgebase, which integrates information from > 150 automatically-mined data sources. Access to such a comprehensive data compendium also facilitates TGex’s broad variant annotation, supporting evidence exploration, and decision making. TGex has an interactive, user-friendly, and easy adaptive interface, ACMG compliance, and an automated reporting system. Beyond comprehensive whole exome sequence capabilities, TGex encompasses innovative non-coding variants interpretation, towards the goal of maximal exploitation of whole genome sequence analyses in the clinical genetics practice. This is enabled by GeneCards’ recently developed GeneHancer, a novel integrative and fully annotated database of human enhancers and promoters. Examining use-cases from a variety of TGex users world-wide, we demonstrate its high diagnostic yields (42% for single exome and 50% for trios in 1500 rare genetic disease cases) and critical actionable genetic findings. The platform’s support for integration with EHR and LIMS through dedicated APIs facilitates automated retrieval of patient data for TGex’s customizable reporting engine, establishing a rapid and cost-effective workflow for an entire range of clinical genetic testing, including rare disorders, cancer predisposition, tumor biopsies and health screening.ConclusionsTGex is an innovative tool for the annotation, analysis and prioritization of coding and non-coding genomic variants. It provides access to an extensive knowledgebase of genomic annotations, with intuitive and flexible configuration options, allows quick adaptation, and addresses various workflow requirements. It thus simplifies and accelerates variant interpretation in clinical genetics workflows, with remarkable diagnostic yield, as exemplified in the described use cases.TGex is available athttp://tgex.genecards.org/