bluuebunny/crossref_metadata_2025_split huggingface dataset:
Características do perfil de indivíduos amputados atendidos em um instituto de reabilitação
Authors: Utiyama Daniela Mitiyo Odagiri , Santos Heyriane Martins dos , Papa Leticia Gori Alves del , Silva Nadja Moreira da , Sales Viviane Caroline , Ayres Denise Vianna Machado , Alfieri Fabio Marcon , Battistella Linamara Rizzo 
Published: December 2019

Objetivo: Apresentar características do perfil de indivíduos amputados unilaterais na fase pré protética atendidos em um instituto de reabilitação hospitalar. Método: Estudo retrospectivo a partir dos registros dos prontuários dos pacientes amputados do Instituto de Medicina Física e Reabilitação da Universidade de São Paulo que realizaram reabilitação hospitalar na instituição. Foram analisados os prontuários de julho de 2015 até novembro de 2018. Nestes prontuários foram coletadas as informações sobre: idade, gênero, tipo de lesão, tempo da lesão, etiologia da lesão, mobilidade funcional por meio do teste Timed Up and Go (TUG), equilíbrio/funcionalidade por meio do teste Amputee Mobility Predictor (AMP), capacidade de caminhar por meio do teste de caminhada de 2 minutos (TC2Min). Análises descritivas foram feitas para avaliar as características demográficas e clínicas dos pacientes. Resultados: A média de idade foi de 43,59±16,52 anos, com tempo de amputação de 21,9±32,7 meses. Houve predominância do gênero masculino e a etiologia traumática e amputação transfemoral foram as mais prevalentes. Os dados clínicos encontrados foram: tempo de execução do teste TUG: 18,7 segundos, da AMP: 31,2 pontos, TC2Min: 111,2 metros. Conclusão: Na fase pré-protética, a origem traumática foi predominante, sendo a maioria do gênero masculino. A amputação transfemoral foi superior às demais. O tempo médio da amputação até a internação é quase de dois anos. Os testes clínicos embora apresentem bons resultados, mostram a necessidade de serem aprimoradas as questões como, por exemplo, a mobilidade funcional que é condizente com risco de quedas nestes indivíduos.
Association Between Physicians’ Appropriate Use of Echocardiography and Subsequent Healthcare Use and Outcomes in Patients With Heart Failure
Authors: Tharmaratnam Tharmegan , Bouck Zachary , Sivaswamy Atul , Wijeysundera Harindra C. , Chu Cherry , Yin Cindy X. , Nesbitt Gillian C. , Edwards Jeremy , Yared Kibar , Wong Brian , Weinerman Adina , Thavendiranathan Paaladinesh , Rakowski Harry , Dorian Paul , Anderson Geoff , Austin Peter C. , Dudzinski David M. , Ko Dennis T. , Weiner Rory B. , Bhatia R. Sacha 
Published: December 2019

Background
There is little understanding of whether a physician's tendency to order an inappropriate cardiac service is associated with the use of other cardiac services and clinical outcomes in their patients with heart failure (HF).

Methods and Results

We conducted a secondary analysis of 35 Ontario‐based cardiologists who participated in the control arm of the Echo
WISELY
(Will Inappropriate Scenarios for Echocardiography Lessen Significantly) trial. Transthoracic echocardiograms, ordered during the trial, were classified as rarely appropriate (
rA
), appropriate, or maybe appropriate on the basis of the 2011 appropriate use criteria. Cardiologists were grouped into tertiles of
rA
transthoracic echocardiogram ordering frequency: low ordering (bottom tertile), n=11; moderate ordering, n=12; or high ordering (top tertile), n=12. The main outcomes were measures of cardiac service use, including cardiology‐related physician visits, tests, and medications. Among 1677 patients with heart failure and an outpatient visit to 1 of 35 cardiologists, we found no significant association between
rA
transthoracic echocardiogram ordering frequency (by tertile) and cardiac testing use, although patients of cardiologists in the high ordering group had fewer physician visits, on average, than patients seen by low ordering cardiologists. In addition, patients of cardiologists in the highest
rA
ordering tertile had significantly lower odds of receiving potentially effective interventions, such as β blockers (odds ratio, 0.62; 95%
CI
, 0.43–0.89), than the low ordering group.

Conclusions

Although patients of cardiologists who frequently order
rA
transthoracic echocardiograms do not appear more (or less) likely to have subsequent cardiac tests, these patients have fewer follow‐up visits and lower odds of receiving evidence‐based medications.

Clinical Trial Registration

URL
:
http://www.clinicaltrials.gov
. Unique identifier:
NCT
02038101.
DNLC: differential network local consistency analysis
Authors: Lu Jianwei , Lu Yao , Ding Yusheng , Xiao Qingyang , Liu Linqing , Cai Qingpo , Kong Yunchuan , Bai Yun , Yu Tianwei 
Published: December 2019

AbstractBackgroundThe biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages.ResultsIn this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible.ConclusionsThe new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available athttps://cran.r-project.org/web/packages/DNLC.
Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection
Authors: Shi Jinhong , Yan Yan , Links Matthew G. , Li Longhai , Dillon Jo-Anne R. , Horsch Michael , Kusalik Anthony 
Published: December 2019

AbstractBackgroundAntimicrobial resistance (AMR) is a major threat to global public health because it makes standard treatments ineffective and contributes to the spread of infections. It is important to understand AMR’s biological mechanisms for the development of new drugs and more rapid and accurate clinical diagnostics. The increasing availability of whole-genome SNP (single nucleotide polymorphism) information, obtained from whole-genome sequence data, along with AMR profiles provides an opportunity to use feature selection in machine learning to find AMR-associated mutations. This work describes the use of a supervised feature selection approach using deep neural networks to detect AMR-associated genetic factors from whole-genome SNP data.ResultsThe proposed method, DNP-AAP (deep neural pursuit – average activation potential), was tested on aNeisseria gonorrhoeaedataset with paired whole-genome sequence data and resistance profiles to five commonly used antibiotics including penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime. The results show that DNP-AAP can effectively identify known AMR-associated genes inN. gonorrhoeae, and also provide a list of candidate genomic features (SNPs) that might lead to the discovery of novel AMR determinants. Logistic regression classifiers were built with the identified SNPs and the prediction AUCs (area under the curve) for penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime were 0.974, 0.969, 0.949, 0.994, and 0.976, respectively.ConclusionsDNP-AAP can effectively identify known AMR-associated genes inN. gonorrhoeae. It also provides a list of candidate genes and intergenic regions that might lead to novel AMR factor discovery. More generally, DNP-AAP can be applied to AMR analysis of any bacterial species with genomic variants and phenotype data. It can serve as a useful screening tool for microbiologists to generate genetic candidates for further lab experiments.
A robustness metric for biological data clustering algorithms
Authors: Lu Yuping , Phillips Charles A. , Langston Michael A. 
Published: December 2019

Abstract
Background
Cluster analysis is a core task in modern data-centric computation. Algorithmic choice is driven by factors such as data size and heterogeneity, the similarity measures employed, and the type of clusters sought. Familiarity and mere preference often play a significant role as well. Comparisons between clustering algorithms tend to focus on cluster quality. Such comparisons are complicated by the fact that algorithms often have multiple settings that can affect the clusters produced. Such a setting may represent, for example, a preset variable, a parameter of interest, or various sorts of initial assignments. A question of interest then is this: to what degree do the clusters produced vary as setting values change?

Results
This work introduces a new metric, termed simply “robustness”, designed to answer that question. Robustness is an easily-interpretable measure of the propensity of a clustering algorithm to maintain output coherence over a range of settings. The robustness of eleven popular clustering algorithms is evaluated over some two dozen publicly available mRNA expression microarray datasets. Given their straightforwardness and predictability, hierarchical methods generally exhibited the highest robustness on most datasets. Of the more complex strategies, the paraclique algorithm yielded consistently higher robustness than other algorithms tested, approaching and even surpassing hierarchical methods on several datasets. Other techniques exhibited mixed robustness, with no clear distinction between them.

Conclusions
Robustness provides a simple and intuitive measure of the stability and predictability of a clustering algorithm. It can be a useful tool to aid both in algorithm selection and in deciding how much effort to devote to parameter tuning.
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

Abstract
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

Abstract
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

Abstract
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

AbstractBackgroundProtein 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.ResultsHere, 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/ConclusionsMADOKA 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

Abstract
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

Abstract
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

Abstract
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.