bluuebunny/crossref_metadata_2025 huggingface dataset:
CSA: a web service for the complete process of ChIP-Seq analysis
Authors: Li Min , Tang Li , Wu Fang-Xiang , Pan Yi , Wang Jianxin
Published: December 2019

Background

Chromatin immunoprecipitation sequencing (ChIP-seq) is a technology that combines chromatin immunoprecipitation (ChIP) with next generation of sequencing technology (NGS) to analyze protein interactions with DNA. At present, most ChIP-seq analysis tools adopt the command line, which lacks user-friendly interfaces. Although some web services with graphical interfaces have been developed for ChIP-seq analysis, these sites cannot provide a comprehensive analysis of ChIP-seq from raw data to downstream analysis.

Results

In this study, we develop a web service for the whole process of ChIP-Seq Analysis (CSA), which covers mapping, quality control, peak calling, and downstream analysis. In addition, CSA provides a customization function for users to define their own workflows. And the visualization of mapping, peak calling, motif finding, and pathway analysis results are also provided in CSA. For the different types of ChIP-seq datasets, CSA can provide the corresponding tool to perform the analysis. Moreover, CSA can detect differences in ChIP signals between ChIP samples and controls to identify absolute binding sites.

Conclusions

The two case studies demonstrate the effectiveness of CSA, which can complete the whole procedure of ChIP-seq analysis. CSA provides a web interface for users, and implements the visualization of every analysis step. The website of CSA is available at http://CompuBio.csu.edu.cn
Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data
Authors: Chung Neo Christopher , Miasojedow BłaŻej , Startek Michał , Gambin Anna
Published: December 2019

Background

A survey of presences and absences of specific species across multiple biogeographic units (or bioregions) are used in a broad area of biological studies from ecology to microbiology. Using binary presence-absence data, we evaluate species co-occurrences that help elucidate relationships among organisms and environments. To summarize similarity between occurrences of species, we routinely use the Jaccard/Tanimoto coefficient, which is the ratio of their intersection to their union. It is natural, then, to identify statistically significant Jaccard/Tanimoto coefficients, which suggest non-random co-occurrences of species. However, statistical hypothesis testing using this similarity coefficient has been seldom used or studied.

Results

We introduce a hypothesis test for similarity for biological presence-absence data, using the Jaccard/Tanimoto coefficient. Several key improvements are presented including unbiased estimation of expectation and centered Jaccard/Tanimoto coefficients, that account for occurrence probabilities. The exact and asymptotic solutions are derived. To overcome a computational burden due to high-dimensionality, we propose the bootstrap and measurement concentration algorithms to efficiently estimate statistical significance of binary similarity. Comprehensive simulation studies demonstrate that our proposed methods produce accurate p-values and false discovery rates. The proposed estimation methods are orders of magnitude faster than the exact solution, particularly with an increasing dimensionality. We showcase their applications in evaluating co-occurrences of bird species in 28 islands of Vanuatu and fish species in 3347 freshwater habitats in France. The proposed methods are implemented in an open source R package called (https://cran.r-project.org/package=jaccard).

Conclusion

We introduce a suite of statistical methods for the Jaccard/Tanimoto similarity coefficient for binary data, that enable straightforward incorporation of probabilistic measures in analysis for species co-occurrences. Due to their generality, the proposed methods and implementations are applicable to a wide range of binary data arising from genomics, biochemistry, and other areas of science.
An automated 3D modeling pipeline for constructing 3D models of MONOGENEAN HARDPART using machine learning techniques
Authors: Teo Bee Guan , Dhillon Sarinder Kaur
Published: December 2019

Background

Studying structural and functional morphology of small organisms such as monogenean, is difficult due to the lack of visualization in three dimensions. One possible way to resolve this visualization issue is to create digital 3D models which may aid researchers in studying morphology and function of the monogenean. However, the development of 3D models is a tedious procedure as one will have to repeat an entire complicated modelling process for every new target 3D shape using a comprehensive 3D modelling software. This study was designed to develop an alternative 3D modelling approach to build 3D models of monogenean anchors, which can be used to understand these morphological structures in three dimensions. This alternative 3D modelling approach is aimed to avoid repeating the tedious modelling procedure for every single target 3D model from scratch.

Result

An automated 3D modeling pipeline empowered by an Artificial Neural Network (ANN) was developed. This automated 3D modelling pipeline enables automated deformation of a generic 3D model of monogenean anchor into another target 3D anchor. The 3D modelling pipeline empowered by ANN has managed to automate the generation of the 8 target 3D models (representing 8 species: Dactylogyrus primaries, Pellucidhaptor merus, Dactylogyrus falcatus, Dactylogyrus vastator, Dactylogyrus pterocleidus, Dactylogyrus falciunguis, Chauhanellus auriculatum and Chauhanellus caelatus) of monogenean anchor from the respective 2D illustrations input without repeating the tedious modelling procedure.

Conclusions

Despite some constraints and limitation, the automated 3D modelling pipeline developed in this study has demonstrated a working idea of application of machine learning approach in a 3D modelling work. This study has not only developed an automated 3D modelling pipeline but also has demonstrated a cross-disciplinary research design that integrates machine learning into a specific domain of study such as 3D modelling of the biological structures.
MADOKA: an ultra-fast approach for large-scale protein structure similarity searching
Authors: Deng Lei , Zhong Guolun , Liu Chenzhe , Luo Judong , Liu Hui
Published: December 2019

Background

Protein comparative analysis and similarity searches play essential roles in structural bioinformatics. A couple of algorithms for protein structure alignments have been developed in recent years. However, facing the rapid growth of protein structure data, improving overall comparison performance and running efficiency with massive sequences is still challenging.

Results

Here, we propose MADOKA, an ultra-fast approach for massive structural neighbor searching using a novel two-phase algorithm. Initially, we apply a fast alignment between pairwise structures. Then, we employ a score to select pairs with more similarity to carry out a more accurate fragment-based residue-level alignment. MADOKA performs about 6–100 times faster than existing methods, including TM-align and SAL, in massive alignments. Moreover, the quality of structural alignment of MADOKA is better than the existing algorithms in terms of TM-score and number of aligned residues. We also develop a web server to search structural neighbors in PDB database (About 360,000 protein chains in total), as well as additional features such as 3D structure alignment visualization. The MADOKA web server is freely available at:http://madoka.denglab.org/

Conclusions

MADOKA is an efficient approach to search for protein structure similarity. In addition, we provide a parallel implementation of MADOKA which exploits massive power of multi-core CPUs.
RNA editing-based classification of diffuse gliomas: predicting isocitrate dehydrogenase mutation and chromosome 1p/19q codeletion
Authors: Chen Sean Chun-Chang , Lo Chung-Ming , Wang Shih-Hua , Su Emily Chia-Yu
Published: December 2019

Background

Accurate classification of diffuse gliomas, the most common tumors of the central nervous system in adults, is important for appropriate treatment. However, detection of isocitrate dehydrogenase (IDH) mutation and chromosome1p/19q codeletion, biomarkers to classify gliomas, is time- and cost-intensive and diagnostic discordance remains an issue. Adenosine to inosine (A-to-I) RNA editing has emerged as a novel cancer prognostic marker, but its value for glioma classification remains largely unexplored. We aim to (1) unravel the relationship between RNA editing and IDH mutation and 1p/19q codeletion and (2) predict IDH mutation and 1p/19q codeletion status using machine learning algorithms.

Results

By characterizing genome-wide A-to-I RNA editing signatures of 638 gliomas, we found that tumors without IDH mutation exhibited higher total editing level compared with those carrying it (Kolmogorov-Smirnov test, p < 0.0001). When tumor grade was considered, however, only grade IV tumors without IDH mutation exhibited higher total editing level. According to 10-fold cross-validation, support vector machines (SVM) outperformed random forest and AdaBoost (DeLong test, p < 0.05). The area under the receiver operating characteristic curve (AUC) of SVM in predicting IDH mutation and 1p/19q codeletion were 0.989 and 0.990, respectively. After performing feature selection, AUCs of SVM and AdaBoost in predicting IDH mutation were higher than that of random forest (0.985 and 0.983 vs. 0.977; DeLong test, p < 0.05), but AUCs of the three algorithms in predicting 1p/19q codeletion were similar (0.976–0.982). Furthermore, 67% of the six continuously misclassified samples by our 1p/19q codeletion prediction models were misclassifications in the original labelling after inspection of 1p/19q status and/or pathology report, highlighting the accuracy and clinical utility of our models.

Conclusions

The study represents the first genome-wide analysis of glioma editome and identifies RNA editing as a novel prognostic biomarker for glioma. Our prediction models provide standardized, accurate, reproducible and objective classification of gliomas. Our models are not only useful in clinical decision-making, but also able to identify editing events that have the potential to serve as biomarkers and therapeutic targets in glioma management and treatment.
Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter
Authors: Lu Weizhong , Tang Ye , Wu Hongjie , Huang Hongmei , Fu Qiming , Qiu Jing , Li Haiou
Published: December 2019

Background

RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for this problem, with encouraging results regarding their predictive accuracy; however, their performances are usually limited by the requirements of the learning model and over-fitting, which requires use of a fixed number of training features. Because most natural biological sequences have variable lengths, the sequences have to be truncated before the features are employed by the learning model, which not only leads to the loss of information but also destroys biological-sequence integrity.

Results

To address this problem, we propose an adaptive sequence length based on deep-learning model and integrate an energy-based filter to remove the over-fitting base pairs.

Conclusions

Comparative experiments conducted on an authoritative dataset RNA STRAND (RNA secondary STRucture and statistical Analysis Database) revealed a 12% higher accuracy relative to three currently used methods.
Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network
Authors: Xiao Qingxin , Li Weilu , Kai Yuanzhong , Chen Peng , Zhang Jun , Wang Bing
Published: December 2019

Background

The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, machine learning and especially deep learning methods have been widely used in many fields and have achieved good results.

Methods

First, this papaer used the common Aprioro algorithm to find the association rules between weather factors and the occurrence of cotton pests. Then, in this paper, the problem of predicting the occurrence of pests and diseases is formulated as time series prediction, and an LSTM-based method was developed to solve the problem.

Results

The association analysis reveals that moderate temperature, humid air, low wind spreed and rain fall in autumn and winter are more likely to occur cotton pests and diseases. The discovery was then used to predict the occurrence of pests and diseases. Experimental results showed that LSTM performs well on the prediction of occurrence of pests and diseases in cotton fields, and yields the Area Under the Curve (AUC) of 0.97.

Conclusion

Suitable temperature, humidity, low rainfall, low wind speed, suitable sunshine time and low evaporation are more likely to cause cotton pests and diseases. Based on these associations as well as historical weather and pest records, LSTM network is a good predictor for future pest and disease occurrences. Moreover, compared to the traditional machine learning models (i.e., SVM and Random Forest), the LSTM network performs the best.
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

Background

Glaucoma 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.

Method

We 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.

Results

The 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.

Conclusion

In 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

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

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

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

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

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

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

Background

Symbiotic 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.

Results

The 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.

Conclusion

The 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

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.
Correction To: Effects of a lifestyle intervention in routine care on prenatal physical activity – findings from the cluster-randomised GeliS trial
Authors: Hoffmann Julia , Günther Julia , Geyer Kristina , Stecher Lynne , Rauh Kathrin , Kunath Julia , Meyer Dorothy , Sitzberger Christina , Spies Monika , Rosenfeld Eva , Kick Luzia , Oberhoffer Renate , Hauner Hans
Published: December 2019

Following publication of the original article [1], the author notified us about incorrectly formatted of Table 2 and Table 3.
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

In 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

Background

Recent 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.

Methods

In 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.

Results

The 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.

Conclusion

We 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

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>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.
Misinterpretation and Limitations of pEEG Monitoring During Multimodal General Anesthesia: A Case Report
Authors: Salgado-Seixas Francisco , Pereira Rui , Machado Humberto , Cavaleiro Carla
Published: December 2019

The adequate titration of hypnotic agents can be achieved using processed electroencephalogram (pEEG) monitors. pEEG monitors with a spectrogram, such as bilateral bispectral index (BIS), are useful because they can show different signatures that suggest the effect of a given drug on brain activity. Multimodal general anesthesia is based on a combination of hypnotic and antinociceptive agents. We report a case of awareness in a patient monitored with bilateral BIS during multimodal general anesthesia. This case illustrates the limitations of the index and the value of the spectrogram in the assessment of hypnosis.
Postoperative Dexmedetomidine-Induced Polyuria in a Patient With Schizophrenia: A Case Report
Authors: Takekawa Daiki , Kubota Mirei , Saito Junichi , Hirota Kazuyoshi
Published: December 2019

We present a patient with schizophrenia who developed dexmedetomidine-induced polyuria after superficial parotidectomy. Two hours after starting the dexmedetomidine infusion, urine output increased from a baseline rate of 80 mL/h to a 7-hour average rate of 400 mL/h (range, 280–560 mL/h), the serum sodium concentration increased from 132 to 139 mEq/L, and urine-specific gravity was 1.006. Following dexmedetomidine discontinuation, the urine output decreased to an average of 66 mL/h (range, 40–100 mL/h). Close monitoring of urine output and serum sodium concentration may be indicated during dexmedetomidine infusion.
Selective targeting of mRNA and following protein synthesis of CaMKIIα at the long-term potentiation-induced site
Authors: Nihonmatsu Itsuko , Ohkawa Noriaki , Saitoh Yoshito , Okubo-Suzuki Reiko , Inokuchi Kaoru
Published: December 2019

Late-phase long-term potentiation (L-LTP) in hippocampus, thought to be the cellular basis of long-term memory, requires new protein synthesis. Neural activity enhances local protein synthesis in dendrites, which in turn mediates long-lasting synaptic plasticity. Ca2+/calmodulin-dependent protein kinase IIα (CaMKIIα) is a locally synthesized protein crucial for this plasticity, as L-LTP is impaired when its local synthesis is eliminated. However, the distribution of Camk2a mRNA during L-LTP induction remains unclear. In this study, we investigated the dendritic targeting of Camk2a mRNA after high-frequency stimulation, which induces L-LTP in synapses of perforant path and granule cells in the dentate gyrus in vivo. In situ hybridization studies revealed that Camk2a mRNA was immediately but transiently targeted to the site receiving high-frequency stimulation. This was associated with an increase in de novo protein synthesis of CaMKIIα. These results suggest that dendritic translation of CaMKIIα is locally mediated where L-LTP is induced. This phenomenon may be one of the essential processes for memory establishment.
MAIN BUSINESS ENVIRONMENT DEVELOPMENT FACTORS OF THE BORDER REGIONS IN MODERN CONDITIONS
Authors: Kuzavko Anton Sergeevich , Kirillova Elena Aleksandrovna
Published: December 2019

Identification of the main factors, which are determining opportunities and major threats in the business environment in the border region.

Methodology the article used mainly general logical methods of analysis and synthesis, generalization and modeling of the subject area.

Results: There are many studies of both Russian and foreign scientists on the business environment study. At the same time, its analysis in the context of such an important factor as the border location of the territories was practically not considered, the methods and methods of its assessment are described in fragments. While this factor is essential and determines a number of key parameters for assessing the business environment of the region, some aspects of the analysis in which become insignificant, and others on the contrary – critical.

The article presents the analysis of business environment development factors of border areas, assessment of foreign and Russian experience in this area, on the basis of which identified the main factors that determine the opportunities and major threats in the formation of a business unit in the border region.

Practical implications the results of the study will be useful for researchers and specialists involved in the development of border regions, as well as innovation management, taking into account regional specifics.
BANKS OF THE CASPIAN REGION COUNTRIES IN THE BANKING SYSTEM OF RUSSIA: CURRENT TRENDS AND DEVELOPMENT PROBLEMS
Authors: Bogacheva Olga Viktorovna , Lepekhin Oleg Alekseevich
Published: December 2019

The mutual penetration of capital from one country into another has become a characteristic feature of the world financial system. This is largely due to the current globalization processes in the global economy, the continuous development of banking technologies, the need to maintain foreign economic activity of business entities and the rationale for expanding the scales of business activity as a tool to increase operating efficiency.

The purpose of the article is to study the current internalization process in the banking systems of the Caspian region countries in terms of the penetration of the banks from the Caspian region countries into the Russian banking sector.

Methodology. We have identified the sample of the Russian credit institutions fully controlled by the shareholders from the Caspian region states. Using the data, presented in turnover balance reports (Form 101), we have calculated key performance indicators in 2015–2018. We used the Bank of Russia’s practice to estimate the aggregate balance sheet of the 30 largest credit institutions as the basis for our algorithm. We have analyzed tendencies in funding base, structure of assets and operating efficiency.

Results. The analysis of the financial statements shows that within the chosen business model, the banks of the sample are more likely to focus on international financial settlements, servicing foreign trade transactions, than on traditional types of banking business such as lending to non-financial enterprises and individuals. Their performance is significantly lower than the industry benchmarks, which so far does not allow us to expect an increase in their scale of presence in the Russian market in the near future.

Practical implications. The analysis of current processes in the field of internationalization of banking systems can be used to assess the financial and economic integration of the countries of the Caspian countries and to develop corrective measures aimed at its further development and deepening.
Reminiscence intervention for community-dwelling older adults without dementia: a literature review
Authors: Shropshire Michele
Published: December 2019

The aim of the present integrative literature review is to summarise empirical evidence supporting the positive health benefits of reminiscence intervention for older adults without dementia who reside in community and long-term care settings. Reminiscence intervention may be used to improve cognitive ability in older adults by prompting them to share life stories and recall past events. Using Garrard's matrix method, 15 studies were identified and included in this review, with a total of 815 participants. The health outcomes of reminiscence intervention for older adults residing in the community and long-term care settings were improvements in depressive symptoms; greater wellbeing, peace and life satisfaction; and improvements in quality of life, social engagement, anxiety and cognitive skills/memory. Non-pharmacological approaches such as reminiscence intervention may contribute to a rich base for reformulating cognitive interpretations, increasing cognitive abilities, and improving social skills among older adults.
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.
Golden Rules of communication
Authors: Ellwood Fiona
Published: December 2019

Communication skills are quite often challenged and in more ways than one. Fiona Ellwood explains

Aim

Introduce the principles of effective communication and the potential barriers. It also provides a snapshot of the notion of Ego States, and a suggestion of further reading in order to consider the application to working in a dental setting.

Objectives

Recognise a number of communication forms and align them to the working day Analyse the notion of barriers to communication Appraise the concept of Ego States by Berne and the applicability to the smooth running of the dental settings Formulate a frank discussion based upon communication skills and the GDC principles. GDC development outcome A
Face facts: aesthetic treatments
Authors: Bissett Julie
Published: December 2019

What role, if any, does the dental nurse have in the increasingly popular market of facial cosmetic procedures? Julie Bissett investigates
Dementia Friends
Authors: Cheshire Angela
Published: December 2019

Dental nurse Angela Cheshire on what training adds to her care in practice
Life on the other side of the chair
Authors: Cairns Lilly-Rose
Published: December 2019

Fiona Ellwood investigates dental nursing career opportunities, with a look at how passion and hard work paid off for one of the students at Warwick University, orthodontic therapist Lilly-Rose Cairns
Meet the Gum Chums
Authors: Binns Keysha
Published: December 2019

Keysha Binns talks about the experiences that led her to create an innovative way to engage children in good toothbrushing habits
Back to the classroom?
Authors: Walsh Dawn
Published: December 2019

Dawn Walsh discusses why dental nurses should consider the ‘teaching’ role of oral health educator
Oral health champions
Authors: Gordon Ailsa , Moodie Miranda
Published: December 2019

A look at how Ailsa Gordon and Miranda Moodie are helping to change the oral health status of some of Scotland's young and old
Proactive, proud and passionate
Authors: Jackson Sophie
Published: December 2019

Dental nurse Sophie Jackson shares her career journey and her enthusiasm for all things dental
Acupuncture in dentistry
Authors: MacLeavy Christine
Published: December 2019

Dental therapist Christine MacLeavy considers this complementary therapy in a clinical dental setting
The essential skill of sedation
Authors: Verow Susie
Published: December 2019

Susie Verow on how she is supporting phobic patients