bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel glaucomatous representation method based on Radon and wavelet transform
Authors: Zou Beiji , Chen Changlong , Zhao Rongchang , Ouyang Pingbo , Zhu Chengzhang , Chen Qilin , Duan Xuanchu 
Published: December 2019

AbstractBackgroundGlaucoma is an irreversible eye disease caused by the optic nerve injury. Therefore, it usually changes the structure of the optic nerve head (ONH). Clinically, ONH assessment based on fundus image is one of the most useful way for glaucoma detection. However, the effective representation for ONH assessment is a challenging task because its structural changes result in the complex and mixed visual patterns.MethodWe proposed a novel feature representation based on Radon and Wavelet transform to capture these visual patterns. Firstly, Radon transform (RT) is used to map the fundus image into Radon domain, in which the spatial radial variations of ONH are converted to a discrete signal for the description of image structural features. Secondly, the discrete wavelet transform (DWT) is utilized to capture differences and get quantitative representation. Finally, principal component analysis (PCA) and support vector machine (SVM) are used for dimensionality reduction and glaucoma detection.ResultsThe proposed method achieves the state-of-the-art detection performance on RIMONE-r2 dataset with the accuracy and area under the curve (AUC) at 0.861 and 0.906, respectively.ConclusionIn conclusion, we showed that the proposed method has the capacity as an effective tool for large-scale glaucoma screening, and it can provide a reference for the clinical diagnosis on glaucoma.
DTA-SiST: de novo transcriptome assembly by using simplified suffix trees
Authors: Zhao Jin , Feng Haodi , Zhu Daming , Zhang Chi , Xu Ying 
Published: December 2019

Abstract
Background
Alternative splicing allows the pre-mRNAs of a gene to be spliced into various mRNAs, which greatly increases the diversity of proteins. High-throughput sequencing of mRNAs has revolutionized our ability for transcripts reconstruction. However, the massive size of short reads makes de novo transcripts assembly an algorithmic challenge.

Results
We develop a novel radical framework, called DTA-SiST, for de novo transcriptome assembly based on suffix trees. DTA-SiST first extends contigs by reads that have the longest overlaps with the contigs’ terminuses. These reads can be found in linear time of the lengths of the reads through a well-designed suffix tree structure. Then, DTA-SiST constructs splicing graphs based on contigs for each gene locus. Finally, DTA-SiST proposes two strategies to extract transcript-representing paths: a depth-first enumeration strategy and a hybrid strategy based on length and coverage. We implemented the above two strategies and compared them with the state-of-the-art de novo assemblers on both simulated and real datasets. Experimental results showed that the depth-first enumeration strategy performs always better with recall and also better with precision for smaller datasets while the hybrid strategy leads with precision for big datasets.

Conclusions
DTA-SiST performs more competitive than the other compared de novo assemblers especially with precision measure, due to the read-based contig extension strategy and the elegant transcripts extraction rules.
GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings
Authors: Zhong Xiaoshi , Kaalia Rama , Rajapakse Jagath C. 
Published: December 2019

Abstract
Background
Semantic similarity between Gene Ontology (GO) terms is a fundamental measure for many bioinformatics applications, such as determining functional similarity between genes or proteins. Most previous research exploited information content to estimate the semantic similarity between GO terms; recently some research exploited word embeddings to learn vector representations for GO terms from a large-scale corpus. In this paper, we proposed a novel method, named GO2Vec, that exploits graph embeddings to learn vector representations for GO terms from GO graph. GO2Vec combines the information from both GO graph and GO annotations, and its learned vectors can be applied to a variety of bioinformatics applications, such as calculating functional similarity between proteins and predicting protein-protein interactions.

Results
We conducted two kinds of experiments to evaluate the quality of GO2Vec: (1) functional similarity between proteins on the Collaborative Evaluation of GO-based Semantic Similarity Measures (CESSM) dataset and (2) prediction of protein-protein interactions on the Yeast and Human datasets from the STRING database. Experimental results demonstrate the effectiveness of GO2Vec over the information content-based measures and the word embedding-based measures.

Conclusion
Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GO and GOA graphs. Our results also demonstrate that GO annotations provide useful information for computing the similarity between GO terms and between proteins.
Refining modules to determine functionally significant clusters in molecular networks
Authors: Kaalia Rama , Rajapakse Jagath C. 
Published: December 2019

Abstract
Background
Module detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a novel modular refinement approach that helps finding functionally significant modules of molecular networks.

Results
The module refinement algorithm improves the quality of topological modules in protein-protein interaction networks by finding biologically functionally significant modules. The algorithm is based on the fact that functional modules in biology do not necessarily represent those corresponding to maximum modularity. Larger modules corresponding to maximal modularity are incrementally re-modularized again under specific constraints so that smaller yet topologically and biologically valid modules are recovered. We show improvement in quality and functional coverage of modules using experiments on synthetic and real protein-protein interaction networks. We also compare our results with six existing methods available for clustering biological networks.

Conclusion
The proposed algorithm finds smaller but functionally relevant modules that are undetected by classical quality maximization approaches for modular detection. The refinement procedure helps to detect more functionally enriched modules in protein-protein interaction networks, which are also more coherent with functionally characterised gene sets.
Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice
Authors: Ivan Fransiskus Xaverius , Kwoh Chee Keong 
Published: December 2019

Abstract
Background
Influenza A virus (IAV) poses threats to human health and life. Many individual studies have been carried out in mice to uncover the viral factors responsible for the virulence of IAV infections. Nonetheless, a single study may not provide enough confident about virulence factors, hence combining several studies for a meta-analysis is desired to provide better views. For this, we documented more than 500 records of IAV infections in mice, whose viral proteins could be retrieved and the mouse lethal dose 50 or alternatively, weight loss and/or survival data, was/were available for virulence classification.

Results
IAV virulence models were learned from various datasets containing aligned IAV proteins and the corresponding two virulence classes (avirulent and virulent) or three virulence classes (low, intermediate and high virulence). Three proven rule-based learning approaches, i.e., OneR, JRip and PART, and additionally random forest were used for modelling. PART models achieved the best performance, with moderate average model accuracies ranged from 65.0 to 84.4% and from 54.0 to 66.6% for the two-class and three-class problems, respectively. PART models were comparable to or even better than random forest models and should be preferred based on the Occam’s razor principle. Interestingly, the average accuracy of the models was improved when host information was taken into account. For model interpretation, we observed that although many sites in HA were highly correlated with virulence, PART models based on sites in PB2 could compete against and were often better than PART models based on sites in HA. Moreover, PART had a high preference to include sites in PB2 when models were learned from datasets containing the concatenated alignments of all IAV proteins. Several sites with a known contribution to virulence were found as the top protein sites, and site pairs that may synergistically influence virulence were also uncovered.

Conclusion
Modelling IAV virulence is a challenging problem. Rule-based models generated using viral proteins are useful for its advantage in interpretation, but only achieve moderate performance. Development of more advanced approaches that learn models from features extracted from both viral and host proteins shall be considered for future works.
Performance of rotation forest ensemble classifier and feature extractor in predicting protein interactions using amino acid sequences
Authors: Bustamam Alhadi , Musti Mohamad I. S. , Hartomo Susilo , Aprilia Shirley , Tampubolon Patuan P. , Lestari Dian 
Published: December 2019

Abstract
Background
There are two significant problems associated with predicting protein-protein interactions using the sequences of amino acids. The first problem is representing each sequence as a feature vector, and the second is designing a model that can identify the protein interactions. Thus, effective feature extraction methods can lead to improved model performance. In this study, we used two types of feature extraction methods—global encoding and pseudo-substitution matrix representation (PseudoSMR)—to represent the sequences of amino acids in human proteins and Human Immunodeficiency Virus type 1 (HIV-1) to address the classification problem of predicting protein-protein interactions. We also compared principal component analysis (PCA) with independent principal component analysis (IPCA) as methods for transforming Rotation Forest.

Results
The results show that using global encoding and PseudoSMR as a feature extraction method successfully represents the amino acid sequence for the Rotation Forest classifier with PCA or with IPCA. This can be seen from the comparison of the results of evaluation metrics, which were >73% across the six different parameters. The accuracy of both methods was >74%. The results for the other model performance criteria, such as sensitivity, specificity, precision, and F1-score, were all >73%. The data used in this study can be accessed using the following link: https://www.dsc.ui.ac.id/research/amino-acid-pred/.

Conclusions
Both global encoding and PseudoSMR can successfully represent the sequences of amino acids. Rotation Forest (PCA) performed better than Rotation Forest (IPCA) in terms of predicting protein-protein interactions between HIV-1 and human proteins. Both the Rotation Forest (PCA) classifier and the Rotation Forest IPCA classifier performed better than other classifiers, such as Gradient Boosting, K-Nearest Neighbor, Logistic Regression, Random Forest, and Support Vector Machine (SVM). Rotation Forest (PCA) and Rotation Forest (IPCA) have accuracy, sensitivity, specificity, precision, and F1-score values >70% while the other classifiers have values <70%.
Gut bacterial diversity and physiological traits of Anastrepha fraterculus Brazilian-1 morphotype males are affected by antibiotic treatment
Authors: Juárez María Laura , Pimper Lida Elena , Bachmann Guillermo Enrique , Conte Claudia Alejandra , Ruiz María Josefina , Goane Lucía , Medina Pereyra Pilar , Castro Felipe , Salgueiro Julieta , Cladera Jorge Luis , Fernández Patricia Carina , Bourtzis Kostas , Lanzavecchia Silvia Beatriz , Vera María Teresa , Segura Diego Fernando 
Published: December 2019

Abstract
Background
The interaction between gut bacterial symbionts and Tephritidae became the focus of several studies that showed that bacteria contributed to the nutritional status and the reproductive potential of its fruit fly hosts. Anastrepha fraterculus is an economically important fruit pest in South America. This pest is currently controlled by insecticides, which prompt the development of environmentally friendly methods such as the sterile insect technique (SIT). For SIT to be effective, a deep understanding of the biology and sexual behavior of the target species is needed. Although many studies have contributed in this direction, little is known about the composition and role of A. fraterculus symbiotic bacteria. In this study we tested the hypothesis that gut bacteria contribute to nutritional status and reproductive success of A. fraterculus males.

Results
AB affected the bacterial community of the digestive tract of A. fraterculus, in particular bacteria belonging to the Enterobacteriaceae family, which was the dominant bacterial group in the control flies (i.e., non-treated with AB). AB negatively affected parameters directly related to the mating success of laboratory males and their nutritional status. AB also affected males’ survival under starvation conditions. The effect of AB on the behaviour and nutritional status of the males depended on two additional factors: the origin of the males and the presence of a proteinaceous source in the diet.

Conclusions
Our results suggest that A. fraterculus males gut contain symbiotic organisms that are able to exert a positive contribution on A. fraterculus males’ fitness, although the physiological mechanisms still need further studies.
Assessment of the Bacteria community structure across life stages of the Chinese Citrus Fly, Bactrocera minax (Diptera: Tephritidae)
Authors: Andongma Awawing Anjwengwo , Wan Lun , Dong Yong-Cheng , Wang Yu-Lei , He Jin , Niu Chang-Ying 
Published: December 2019

AbstractBackgroundSymbiotic bacteria play a critical role in insect’s biology. They also offer great opportunities to improve on current pest management techniques. In order to exploit and integrate the roles played by the gut microbiota on pest management programs, a better understanding of the structural organization of the microbial community in the Chinese citrus flyBactrocera minaxis essential.ResultsThe results revealed a total of 162 OTUs at 97% similarity interval. The dominant bacteria phyla were Proteobacteria, Bacteroidetes, Antinobacteria and Firmicutes, with the Proteobacteria having the highest relative abundance (more than 80% in all life stages). There was also a shift in the dominant OTUs from the early developmental stages to the late developmental stages and adult stages inB. minax. These OTUs related toKlebsiella pneumoniae,Providencia rettgeriandEnterobacter aerogenes, respectively. Six bacteria OTU were shared by all the life stages. These belonged to the Enterobacteriaceae and the Enterococcaceae families.ConclusionThe common bacteria groups shared by all the life stages and other fruit flies could be important targets for further research. This should aim towards realizing how these bacteria affect the biology of the fly and how their relationship could be exploited in the development of sustainable management strategies against fruit flies.
Another Whipple’s triad? Pericardial, myocardial and valvular disease in an unusual case presentation from a Canadian perspective
Authors: Thornton Christina S. , Wang Yinong , Köebel Martin , Bernard Kathryn , Burdz Tamara , Maitland Andrew , Ferraz Jose G. , Beck Paul L. , Ferland Andre 
Published: December 2019

Abstract
Background
Whipple’s disease is a clinically relevant multi-system disorder that is often undiagnosed given its elusive nature. We present an atypical case of Whipple’s disease involving pan-valvular endocarditis and constrictive pericarditis, requiring cardiac intervention. A literature review was also performed assessing the prevalence of atypical cases of Whipple’s disease.

Case presentation
A previously healthy 56-year-old male presented with a four-year history of congestive heart failure with weight loss and fatigue. Notably, he had absent gastrointestinal symptoms. He went on to develop pan-valvular endocarditis and constrictive pericarditis requiring urgent cardiac surgery. A clinical diagnosis of Whipple’s disease was suspected, prompting duodenal biopsy sampling which was unremarkable, Subsequently, Tropheryma whipplei was identified by 16S rDNA PCR on the cardiac valvular tissue. He underwent prolonged antibiotic therapy with recovery of symptoms.

Conclusions
Our study reports the first known case of Whipple’s disease involving pan-valvular endocarditis and constrictive pericarditis. A literature review also highlights this presentation of atypical Whipple’s with limited gastrointestinal manifestations. Duodenal involvement was limited and the gold standard of biopsy was not contributory. We also highlight the Canadian epidemiology of the disease from 2012 to 2016 with an approximate 4% prevalence rate amongst submitted samples. Routine investigations for Whipple’s disease, including duodenal biopsy, in this case may have missed the diagnosis. A high degree of suspicion was critical for diagnosis of unusual manifestations of Whipple’s disease.
Response to Correspondence from Kolstoe and colleagues concerning our paper entitled, Research approvals iceberg: How a ‘low-key’ study in England needed 89 professionals to approve it and how we can do better
Authors: Petrova Mila , Barclay Stephen 
Published: December 2019

AbstractIn their letter to the Editor in this issue, Kolstoe and Carpenter challenge a core aspect of our recently published case study of research approvals [BMC Medical Ethics 20:7] by arguing that we conflate research ethics with governance and funding processes. Amongst the key concerns of the authors are: 1) that our paper exemplifies a typical conflation of concepts such as governance, integrity and ethics, with significant consequences for claims around the responsibility and accountability of the organisations involved; 2) that, as a consequence of this conflation, we misrepresent the ethics review process, including in fundamental aspects such as the ethics approval-opinion distinction; 3) that it is difficult to see scope for greater integration of processes such as applying for funding, research approvals, Patient and Public Involvement, etc., as suggested by us. Here we present an alternative point of view towards the concerns raised.
Integrative subspace clustering by common and specific decomposition for applications on cancer subtype identification
Authors: Guo Yin , Li Huiran , Cai Menglan , Li Limin 
Published: December 2019

AbstractBackgroundRecent high throughput technologies have been applied for collecting heterogeneous biomedical omics datasets. Computational analysis of the multi-omics datasets could potentially reveal deep insights for a given disease. Most existing clustering methods by multi-omics data assume strong consistency among different sources of datasets, and thus may lose efficacy when the consistency is relatively weak. Furthermore, they could not identify the conflicting parts for each view, which might be important in applications such as cancer subtype identification.MethodsIn this work, we propose an integrative subspace clustering method (ISC) by common and specific decomposition to identify clustering structures with multi-omics datasets. The main idea of our ISC method is that the original representations for the samples in each view could be reconstructed by the concatenation of a common part and a view-specific part in orthogonal subspaces. The problem can be formulated as a matrix decomposition problem and solved efficiently by our proposed algorithm.ResultsThe experiments on simulation and text datasets show that our method outperforms other state-of-art methods. Our method is further evaluated by identifying cancer types using a colorectal dataset. We finally apply our method to cancer subtype identification for five cancers using TCGA datasets, and the survival analysis shows that the subtypes we found are significantly better than other compared methods.ConclusionWe conclude that our ISC model could not only discover the weak common information across views but also identify the view-specific information.
Functional Analysis of the SIM1 Variant p.G715V in 2 Patients With Obesity
Authors: Blackburn Patrick R , Sullivan Adrienne E , Gerassimou Alexis G , Kleinendorst Lotte , Bersten David C , Cooiman Mellody , Harris Kimberly G , Wierenga Klaas J , Klee Eric W , van Gerpen Jay A , Ross Owen A , van Haelst Mieke M , Whitelaw Murray L , Caulfield Thomas R , Atwal Paldeep S 
Published: December 2019

Abstract

Context
Single-minded homologue 1 (SIM1) is a transcription factor with several physiological and developmental functions. Haploinsufficiency of SIM1 is associated with early-onset obesity with or without Prader-Willi–like (PWL) features and may exhibit incomplete penetrance.

Case Description
Next-generation sequencing was performed for 2 male patients with obesity, including 1 man presenting with intellectual disability (ID), body mass index (BMI) of 47.4, and impulse-control disorder, and the other man with early obesity (BMI of 36); sequencing revealed a missense variant in SIM1 (c.2144G&gt;T; p.G715V) in both individuals. Previous studies have identified several disease-associated variants that fall near the p.G715V variant within the C-terminal domain of SIM1. We examined p.G715V variant stability and activity in a doxycycline-inducible stable cell line transfected with an artificial reporter construct and either ARNT or ARNT2 as a partner protein.

Conclusions
Functional testing of the p.G715V variant revealed a significant reduction in SIM1-mediated transcriptional activity. We also generated the first ab initio hybrid protein model for full-length SIM1 to show the predicted spatial relationship between p.G715V and other previously described variants in this region and identified a putative mutation hotspot within the C-terminus. Significant clinical heterogeneity has been observed in patients with SIM1 variants, particularly with regards to the PWL phenotype. In the patient with ID, a second variant of uncertain significance in CHD2 was identified that may contribute to his ID and behavioral disturbances, emphasizing the role of additional genetic modifiers.
Systematic and integrative reviews: synthesising evidence for community nursing practice
Authors: Kennedy Catriona , Ramukumba Mokholelana Margaret 
Published: December 2019

Ease of access to vast amounts of information presents significant opportunities and challenges for nurses in the community as they seek to base their practice on the best available evidence. Growing expectations around evidence-based practice have developed alongside developments in evidence synthesis, which adopts robust approaches to identifying, appraising and synthesising key evidence for clinical decision-making. The context in which evidence-based practice occurs is key, and this article discusses the skills and knowledge needed for community nurses to discern how evidence and information should influence their decisions to review and change approaches to clinical practice. Importantly, if nurses understand the status of evidence underpinning areas of practice, they can ensure that the preferences and needs of patients and families are met.