bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative transcriptome analysis to investigate the potential role of miRNAs in milk protein/fat quality
Authors: Wang Xuehui , Zhang Li , Jin Jing , Xia Anting , Wang Chunmei , Cui Yingjun , Qu Bo , Li Qingzhang , Sheng Chunyan 
Published: April 2018

AbstractmiRNAs play an important role in the processes of cell differentiation, biological development, and physiology. Here we investigated the molecular mechanisms regulating milk secretion and quality in dairy cows via transcriptome analyses of mammary gland tissues from dairy cows during the high-protein/high-fat, low-protein/low-fat or dry periods. To characterize the important roles of miRNAs and mRNAs in milk quality and to elucidate their regulatory networks in relation to milk secretion and quality, an integrated analysis was performed. A total of 25 core miRNAs were found to be differentially expressed (DE) during lactation compared to non-lactation, and these miRNAs were involved in epithelial cell terminal differentiation and mammary gland development. In addition, comprehensive analysis of mRNA and miRNA expression between high-protein/high-fat group and low-protein/low-fat groups indicated that, 38 miRNAs and 944 mRNAs were differentially expressed between them. Furthermore, 38 DE miRNAs putatively negatively regulated 253 DE mRNAs. The putative genes (253 DE mRNAs) were enriched in lipid biosynthetic process and amino acid transmembrane transporter activity. Moreover, putative DE genes were significantly enriched in fatty acid (FA) metabolism, biosynthesis of amino acids, synthesis and degradation of ketone bodies and biosynthesis of unsaturated FAs. Our results suggest that DE miRNAs might play roles as regulators of milk quality and milk secretion during mammary gland differentiation.
Enhancing performance of a InGaP/GaAs VCO by means of a switching architecture
Authors: Pantoli L. , Arena S. , Cavanna T. 
Published: April 2018

The introduction of a compact switching architecture in the design of a monolithic microwave integrated voltage‐controlled oscillator (VCO) is described, that is able to provide enhanced characteristics in term of both linearity and tunability. The proposed solution is applied to the design of a C‐band GaAs MMIC (monolithic microwave integrated circuit) VCO. The chip has been realised with the heterojunction bipolar transistor (HBT) HB20M technology process provided by UMS Foundry. Measurements show a relative tuning range of about 30%, an average sensitivity of about 83 MHz/V, a minimum output power of 11.4 dBm and a phase noise at 100 kHz of frequency offset from the carrier lower of −107 dBc/Hz.
Impact analysis of vibration on nearby municipal tunnel induced by metro running
Authors: Tang Liyun , Wang Bochao , Wu Yingming , Xi Jiami , Wu Di 
Published: April 2018

For researching the effect of vibration induced by metro running to normal use of municipal tunnel, the Kunming Road Municipal Tunnel (construction) at Xi'an is taken as a background. Based on the distribution of soil layer in actually engineering and the designing parameter of metro and municipal tunnel, the finite element model of ‘metro‐soil–municipal tunnel’ is established under three different positions, and the wheel–rail force at three different speeds (36, 72, and 108 km/h) is simplified by exciting force faction. Then the numerical model is analysed by transient dynamic analysis. The results show that the vibration of municipal tunnel wall is mainly characterised by vertical vibration, which increases sharply with the metro speed improved. The vibration magnitude is greatly affected by relative position between metro and municipal tunnel: the horizontal and vertical vibration magnitude above the position between two metro tunnels is 14.6–20.8% and 55.5–74.6%, respectively, of those at the top of the metro tunnel. Some suggestions are proposed that the vibration effect induced by metro running on normal use of nearby municipal tunnel could be reduced by choosing the reasonable relative position with some corresponding measures according to the metro speed.
Identification of Fungal Pathogens in Otomycosis and Their Drug Sensitivity: Our Experience
Authors: Ali Khaled , Hamed Mahmood , Hassan Hameda , Esmail Amira , Sheneef Abeer 
Published: April 2018

Introduction Otomycosis is a common problem in otolaryngology practice. However, we usually encounter some difficulties in its treatment because many patients show resistance to antifungal agents, and present high recurrence rate.
Objectives To determine the fungal pathogens that cause otomycosis as well as their susceptibility to the commonly used antifungal agents. Additionally, to discover the main reasons for antifungal resistance.
Methods We conducted an experimental descriptive study on 122 patients clinically diagnosed with otomycosis from April 2016 to April 2017. Aural discharge specimens were collected for direct microscopic examination and fungal culture. In vitro antifungal susceptibility testing was performed against the commonly used antifungal drugs. We tested the isolated fungi for their enzymatic activity.
Results Positive fungal infection was found in 102 samples. The most common fungal pathogens were Aspergillus and Candida species, with Aspergillus niger being the predominant isolate (51%). The antifungal susceptibility testing showed that mold isolates had the highest sensitivity to voriconazole (93.48%), while the highest resistance was to fluconazole (100%). For yeast, the highest sensitivity was to nystatin (88.24%), followed by amphotericin B (82.35%), and the highest resistance was to terbinafine (100%), followed by Itraconazole (94.12%). Filamentous fungi expressed a high enzymatic ability, making them more virulent.
Conclusion The Aspergillus and Candida species are the most common fungal isolates in otomycosis. Voriconazole and Nystatin are the medications of choice for the treatment of otomycosis in our community. The high virulence of fungal pathogens is owed to their high enzymatic activity. Empirical use of antifungals should be discouraged.
Do Otolaryngology Patients Show Gender Preference When Choosing a Surgeon? — A Quantitative and Qualitative Analysis
Authors: Chitguppi Chandala , Brar Tripti 
Published: April 2018

Introduction Otorhinolaryngology has always been considered a gender-neutral specialty, whereas in several other specialties, such as obstetrics, gynecology and urology, gender preference has been consistently shown by patients when choosing their treating surgeon. To date, no study has been performed to analyze whether this practice of gender preference is prevalent in otorhinolaryngology patients too.
Objectives To identify if gender preference exists in the field of otorhinolaryngology, specifically in its four subspecialties, namely otology, pediatric otolaryngology, laryngology and head and neck surgery.
Methods Patients attending our outpatient department were asked to complete a preformed proforma. The pro forma consisted of two parts, demographic details of the subjects and gender preference in the following subspecialties: otology, pediatric otolaryngology, laryngology and head and neck oncosurgery.
Results A total of 1,112 subjects took part in the study, out of which 1,089 subjects were included in the final analysis. Female gender preference was highest in the field of pediatric otolaryngology, while male preference was highest for head and neck oncosurgery.
Conclusion Though otorhinolaryngology and head and neck surgery has been considered a gender-neutral field, subspecialties of this field show considerable gender preference.
Formation of Fe-rich intermetallics in Al–5.0Cu–0.5 Fe alloys with different Mn additions
Authors: Lin Bo , Xu Rui , Li Haoyu , Zhang Weiwen 
Published: April 2018

ABSTRACT The formation of Fe-rich intermetallics in gravity die cast and squeeze cast Al–5.0 wt-% Cu–0.5 wt-% Fe alloys with different Mn contents was investigated. In gravity die cast alloys, the predominant Fe-rich intermetallics are AlmFe and β-Fe with lower Mn contents, but α-Fe and Al6(FeMn) are predominant when the amount of Mn increases. A lower amount of added Mn is required in the squeeze cast alloys because the applied pressure is favourable for the formation of Chinese script Fe-rich intermetallics and for preventing precipitation of the needle-like intermetallics. The formation temperature increases for α-Fe and AlmFe but decreases for β-Fe with an increase in the applied pressure.
Improving radiative recombination efficiency of green light-emitting diodes
Authors: Ding Boning 
Published: April 2018

Although the solid-state lighting industry has achieved huge successes in both red and blue part of the visible spectrum during the last 40 years, light-emitting diodes (LEDs) that emit green light consistently exhibit inferior efficiencies. Thanks to the use of down-conversion phosphors, white LEDs have been commercialised without using green LEDs. However, the efficiency problem of green LEDs still hinders many potential applications of solid-state lighting and limits the overall system efficiency. This review first attempts to conclude and comment on the complex factors that limit the performance of green LEDs with recent research progresses. Then the article focuses on reviewing various strategies to improve green light LED radiative recombination efficiencies. This review was chosen as a runner up of the 2018 Materials Literature Review Prize of the Institute of Materials, Minerals and Mining, run by the Editorial Board of MST. Sponsorship of the prize by TWI Ltd is gratefully acknowledged.
Household economic modelsof gill net fishermen at Madura strait
Authors: Primyastanto M 
Published: April 2018

Abstract
The purposes of this research was to analyze household economic models of gill net fishermen at Madura strait. 30 families of gillnet fishermenwere used for purposive sampling. Data analysis used descriptive qualitative and quantitative (regression analysis). Quantitative descriptive analysis was used to analyze research and compare to factors that affecting household economic models of gill net fishermen family. Research results showed tha thousehold economic models of gill net fishermen at Madura strait was production value level or fishermen revenue at sea was strongly influenced byp roduction asset production, education level, fuel, and work flow. Work flow rate of fishermen families affected by asset production, non fisheries workflow and number of male workforce. Non fishing income level was strongly influenced by non-fishery business assets, number of family members owned andnon-fishing work flow. Spending levels of gill net fishermen at Madura strait was affected by fishing income, non-fishing income, fishermen wife education and fishermen family members.
Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering
Authors: Ballnus Benjamin , Schaper Steffen , Theis Fabian J , Hasenauer Jan 
Published: April 2018

Abstract

Motivation
Mathematical models have become standard tools for the investigation of cellular processes and the unraveling of signal processing mechanisms. The parameters of these models are usually derived from the available data using optimization and sampling methods. However, the efficiency of these methods is limited by the properties of the mathematical model, e.g. non-identifiabilities, and the resulting posterior distribution. In particular, multi-modal distributions with long valleys or pronounced tails are difficult to optimize and sample. Thus, the developement or improvement of optimization and sampling methods is subject to ongoing research.

Results
We suggest a region-based adaptive parallel tempering algorithm which adapts to the problem-specific posterior distributions, i.e. modes and valleys. The algorithm combines several established algorithms to overcome their individual shortcomings and to improve sampling efficiency. We assessed its properties for established benchmark problems and two ordinary differential equation models of biochemical reaction networks. The proposed algorithm outperformed state-of-the-art methods in terms of calculation efficiency and mixing. Since the algorithm does not rely on a specific problem structure, but adapts to the posterior distribution, it is suitable for a variety of model classes.

Availability and implementation
The code is available both as Supplementary Material and in a Git repository written in MATLAB.

Supplementary information
Supplementary data are available at Bioinformatics online.
Training for translation between disciplines: a philosophy for life and data sciences curricula
Authors: Anton Feenstra K , Abeln Sanne , Westerhuis Johan A , Brancos dos Santos Filipe , Molenaar Douwe , Teusink Bas , Hoefsloot Huub C J , Heringa Jaap 
Published: April 2018

Abstract

Motivation
Our society has become data-rich to the extent that research in many areas has become impossible without computational approaches. Educational programmes seem to be lagging behind this development. At the same time, there is a growing need not only for strong data science skills, but foremost for the ability to both translate between tools and methods on the one hand, and application and problems on the other.

Results
Here we present our experiences with shaping and running a masters’ programme in bioinformatics and systems biology in Amsterdam. From this, we have developed a comprehensive philosophy on how translation in training may be achieved in a dynamic and multidisciplinary research area, which is described here. We furthermore describe two requirements that enable translation, which we have found to be crucial: sufficient depth and focus on multidisciplinary topic areas, coupled with a balanced breadth from adjacent disciplines. Finally, we present concrete suggestions on how this may be implemented in practice, which may be relevant for the effectiveness of life science and data science curricula in general, and of particular interest to those who are in the process of setting up such curricula.

Supplementary information
Supplementary data are available at Bioinformatics online.
Covariate-dependent negative binomial factor analysis of RNA sequencing data
Authors: Zamani Dadaneh Siamak , Zhou Mingyuan , Qian Xiaoning 
Published: April 2018

AbstractMotivationHigh-throughput sequencing technologies, in particular RNA sequencing (RNA-seq), have become the basic practice for genomic studies in biomedical research. In addition to studying genes individually, for example, through differential expression analysis, investigating co-ordinated expression variations of genes may help reveal the underlying cellular mechanisms to derive better understanding and more effective prognosis and intervention strategies. Although there exists a variety of co-expression network based methods to analyze microarray data for this purpose, instead of blindly extending these methods for microarray data that may introduce unnecessary bias, it is crucial to develop methods well adapted to RNA-seq data to identify the functional modules of genes with similar expression patterns.ResultsWe have developed a fully Bayesian covariate-dependent negative binomial factor analysis (dNBFA) method—dNBFA—for RNA-seq count data, to capture coordinated gene expression changes, while considering effects from covariates reflecting different influencing factors. Unlike existing co-expression network based methods, our proposed model does not require multiple ad-hoc choices on data processing, transformation, as well as co-expression measures and can be directly applied to RNA-seq data. Furthermore, being capable of incorporating covariate information, the proposed method can tackle setups with complex confounding factors in different experiment designs. Finally, the natural model parameterization removes the need for a normalization preprocessing step, as commonly adopted to compensate for the effect of sequencing-depth variations. Efficient Bayesian inference of model parameters is derived by exploiting conditional conjugacy via novel data augmentation techniques. Experimental results on several real-world RNA-seq datasets on complex diseases suggest dNBFA as a powerful tool for discovering the gene modules with significant differential expression and meaningful biological insight.Availability and implementationdNBFA is implemented in R language and is available at https://github.com/siamakz/dNBFA.
mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion
Authors: Jokinen Emmi , Heinonen Markus , Lähdesmäki Harri 
Published: April 2018

AbstractMotivationProteins are commonly used by biochemical industry for numerous processes. Refining these proteins’ properties via mutations causes stability effects as well. Accurate computational method to predict how mutations affect protein stability is necessary to facilitate efficient protein design. However, accuracy of predictive models is ultimately constrained by the limited availability of experimental data.ResultsWe have developed mGPfusion, a novel Gaussian process (GP) method for predicting protein’s stability changes upon single and multiple mutations. This method complements the limited experimental data with large amounts of molecular simulation data. We introduce a Bayesian data fusion model that re-calibrates the experimental and in silico data sources and then learns a predictive GP model from the combined data. Our protein-specific model requires experimental data only regarding the protein of interest and performs well even with few experimental measurements. The mGPfusion models proteins by contact maps and infers the stability effects caused by mutations with a mixture of graph kernels. Our results show that mGPfusion outperforms state-of-the-art methods in predicting protein stability on a dataset of 15 different proteins and that incorporating molecular simulation data improves the model learning and prediction accuracy.Availability and implementationSoftware implementation and datasets are available at github.com/emmijokinen/mgpfusion.Supplementary informationSupplementary data are available at Bioinformatics online.
LONGO: an R package for interactive gene length dependent analysis for neuronal identity
Authors: McCoy Matthew J , Paul Alexander J , Victor Matheus B , Richner Michelle , Gabel Harrison W , Gong Haijun , Yoo Andrew S , Ahn Tae-Hyuk 
Published: April 2018

Abstract

Motivation
Reprogramming somatic cells into neurons holds great promise to model neuronal development and disease. The efficiency and success rate of neuronal reprogramming, however, may vary between different conversion platforms and cell types, thereby necessitating an unbiased, systematic approach to estimate neuronal identity of converted cells. Recent studies have demonstrated that long genes (&gt;100 kb from transcription start to end) are highly enriched in neurons, which provides an opportunity to identify neurons based on the expression of these long genes.

Results
We have developed a versatile R package, LONGO, to analyze gene expression based on gene length. We propose a systematic analysis of long gene expression (LGE) with a metric termed the long gene quotient (LQ) that quantifies LGE in RNA-seq or microarray data to validate neuronal identity at the single-cell and population levels. This unique feature of neurons provides an opportunity to utilize measurements of LGE in transcriptome data to quickly and easily distinguish neurons from non-neuronal cells. By combining this conceptual advancement and statistical tool in a user-friendly and interactive software package, we intend to encourage and simplify further investigation into LGE, particularly as it applies to validating and improving neuronal differentiation and reprogramming methodologies.

Availability and implementation
LONGO is freely available for download at https://github.com/biohpc/longo.

Supplementary information
Supplementary data are available at Bioinformatics online.
Bayesian networks for mass spectrometric metabolite identification via molecular fingerprints
Authors: Ludwig Marcus , Dührkop Kai , Böcker Sebastian 
Published: April 2018

Abstract

Motivation
Metabolites, small molecules that are involved in cellular reactions, provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem mass spectrometry to identify the thousands of compounds in a biological sample. Recently, we presented CSI:FingerID for searching in molecular structure databases using tandem mass spectrometry data. CSI:FingerID predicts a molecular fingerprint that encodes the structure of the query compound, then uses this to search a molecular structure database such as PubChem. Scoring of the predicted query fingerprint and deterministic target fingerprints is carried out assuming independence between the molecular properties constituting the fingerprint.

Results
We present a scoring that takes into account dependencies between molecular properties. As before, we predict posterior probabilities of molecular properties using machine learning. Dependencies between molecular properties are modeled as a Bayesian tree network; the tree structure is estimated on the fly from the instance data. For each edge, we also estimate the expected covariance between the two random variables. For fixed marginal probabilities, we then estimate conditional probabilities using the known covariance. Now, the corrected posterior probability of each candidate can be computed, and candidates are ranked by this score. Modeling dependencies improves identification rates of CSI:FingerID by 2.85 percentage points.

Availability and implementation
The new scoring Bayesian (fixed tree) is integrated into SIRIUS 4.0 (https://bio.informatik.uni-jena.de/software/sirius/).
Association mapping in biomedical time series via statistically significant shapelet mining
Authors: Bock Christian , Gumbsch Thomas , Moor Michael , Rieck Bastian , Roqueiro Damian , Borgwardt Karsten 
Published: April 2018

AbstractMotivationMost modern intensive care units record the physiological and vital signs of patients. These data can be used to extract signatures, commonly known as biomarkers, that help physicians understand the biological complexity of many syndromes. However, most biological biomarkers suffer from either poor predictive performance or weak explanatory power. Recent developments in time series classification focus on discovering shapelets, i.e. subsequences that are most predictive in terms of class membership. Shapelets have the advantage of combining a high predictive performance with an interpretable component—their shape. Currently, most shapelet discovery methods do not rely on statistical tests to verify the significance of individual shapelets. Therefore, identifying associations between the shapelets of physiological biomarkers and patients that exhibit certain phenotypes of interest enables the discovery and subsequent ranking of physiological signatures that are interpretable, statistically validated and accurate predictors of clinical endpoints.ResultsWe present a novel and scalable method for scanning time series and identifying discriminative patterns that are statistically significant. The significance of a shapelet is evaluated while considering the problem of multiple hypothesis testing and mitigating it by efficiently pruning untestable shapelet candidates with Tarone’s method. We demonstrate the utility of our method by discovering patterns in three of a patient’s vital signs: heart rate, respiratory rate and systolic blood pressure that are indicators of the severity of a future sepsis event, i.e. an inflammatory response to an infective agent that can lead to organ failure and death, if not treated in time.Availability and implementationWe make our method and the scripts that are required to reproduce the experiments publicly available at https://github.com/BorgwardtLab/S3M.Supplementary informationSupplementary data are available at Bioinformatics online.