bluuebunny/crossref_metadata_2025_split huggingface dataset:
The solution of large-scale Minimum Cost SAT Problem as a tool for data analysis in bioinformatics
Authors: Felici Giovanni , Ferone Daniele , Festa Paola , Napoletano Antonio , Pastore Tommaso 
Published: January 2018

Data mining is one of the main activities in bioinformatics, specifically to extract knowledge from massive data sets related with gene expression measurement, CNV, DNA strings, and others. A long array of methods are used to perform such task, ranging from the more established parametric statistical analysis to non parametric techniques, to classification methods that have been developed in knowledge engineering and artificial intelligence. In this paper, we consider a method for extracting logic formulas from data that relies on a large body of literature in integer and logic optimization, originally presented in [1], that has been largely and successfully applied to different problems in bioinformatics ([2], [3], [4], [5], [6]). Such method is based on the iterative solution of Minimum Cost SAT Problems and is able to extract logic formulas in DNF form that possess interesting features for their interpretation. While leaving the discussion of the main features and motivations of this approach to the related literature, in this talk we focus on the problem of solving efficiently very large scale instances of this well known logic programming problem and propose a new GRASP approach that, being able to exploit the specific structure of the problem, largely outperforms other established solvers for the same problem.

References

[1] G. Felici, K. Truemper. A Minsat Approach for Learning in Logic Domains, INFORMS Journal on Computing 14(1): 20-36 (2002).
[2] P. Bertolazzi, G. Felici, E. Weitschek. Learning to classify species with barcodes, BMC Bioinformatics, 10:1-12 (2009).
[3] M. Arisi, R. D’Onofrio, A. Brandi, S. Felsani, G. Capsoni, G. Drovandi, G. Felici, E. Weitschek, P. Bertolazzi, A. Cattaneo. Gene Expression Biomarkers in the Brain of a Mouse Model for Alzheimer’s Disease: Mining of Microarray Data by Logic Classification and Feature Selection. Journal of Alzheimer's Disease, 24(4) 721-738 (2011).
[4] E. Weitschek, A. Lo Presti, G. Drovandi, G. Felici, M. Ciccozzi, M. Ciotti, P. Bertolazzi. Human polyomaviruses identification by logic mining techniques. BMC Virology Journal, 9:58 (2012).
[5] E. Weitschek, G. Fiscon, G. Felici. Supervised DNA Barcodes species classification: analysis, comparisons and results, BMC BioData Mining, 7:4 (2014).
[6] P. Bertolazzi, G. Felici, P. Festa, G. Fiscon, E. Weitschek. Integer Programming models for Feature Selection: new extensions and a randomized solution algorithm, European Journal of Operational Research, 250-389–399, 250 (2016).
Impact of birth weight and postnatal diet on the gut microbiota of young adult guinea pigs
Authors: Al Kait , Sarr Ousseynou , Dunlop Kristyn , Gloor Gregory B , Reid Gregor , Burton Jeremy , Regnault Timothy RH 
Published: January 2018

Background: The gastrointestinal tract (GIT) microbiota is essential to metabolic health, and the prevalence of the Western diet (WD) high in fat and sugar is increasing, with evidence highlighting a negative interaction between the GIT and WD resulting in liver dysfunction. Additionally, an adverse in utero environment such as placental insufficiency resulting in low birth weight (LBW) offspring, contributes to an increased risk of metabolic diseases such as fatty liver infiltration and liver dysfunction in later life. We sought to understand the potential interactive effects of exposure to a WD upon growing LBW offspring. We postulated that LBW offspring when challenged with a poor postnatal diet, would display an altered microbiota and more severe liver metabolic dysfunction.
Methods: The fecal microbiota of normal birth weight (NBW) and LBW young guinea pig offspring, weaned onto either a control diet (CD) or WD was determined with 16S rRNA gene next generation sequencing and liver blood chemistry at young adulthood following the early rapid growth phase after weaning. A liver blood chemistry profile was also performed.
Results: The life-long consumption of WD following weaning into young adulthood resulted in increased total cholesterol, triglycerides and alanine aminotransferase levels in association with an altered GIT microbiota when compared to offspring consuming CD. Neither birth weight nor sex were associated with any significant changes in microbiota alpha diversity, by measuring the Shannon’s diversity index. One hundred forty-eight operational taxonomic units were statistically distinct between the diet groups, independent of birth weight. In the WD group, significant decreases were detected in Barnesiella, Methanobrevibacter smithii and relatives of Oscillospira guillermondii, while Butyricimonas and Bacteroides spp. were increased.
Discussion: These results describe the GIT microbiota in a guinea pig model of LBW and WD associated metabolic syndrome and highlight several WD specific GIT alterations associated with human metabolic disease.
­Glial and stem cell expression of Fibroblast Growth Factor Receptor 1 in the embryonic and perinatal nervous system
Authors: Collette Jantzen C , Choubey Lisha , Smith Karen Muller 
Published: January 2018

Background. Fibroblast growth factors (FGFs) and their receptors (FGFRs) are involved in the development and function of multiple organs and organ systems, including the central nervous system (CNS). FGF signaling via FGFR1, one of the three FGFRs expressed in the CNS, stimulates proliferation of stem cells during prenatal and postnatal neurogenesis and participates in regulating cell-type ratios in many developing regions of the brain. Anomalies in FGFR1 signaling have been implicated in certain neuropsychiatric disorders. Fgfr1 expression has been shown, via in situ hybridization, to vary spatially and temporally throughout embryonic and postnatal development of the brain. However, in situ hybridization lacks sufficient resolution to identify which cell-types directly participate in FGF signaling. Furthermore, because antibodies raised against FGFR1 commonly cross-react with other members of the FGFR family, immunocytochemistry is not alone sufficient to accurately document Fgfr1 expression. Here, we elucidate the identity of Fgfr1 expressing cells in both the embryonic and perinatal mouse brain.
Methods. To do this, we utilized a tgFGFR1-EGFPGP338Gsat BAC line (tgFgfr1-EGFP+) obtained from the GENSAT project. The tgFgfr1-EGFP+ line expresses EGFP under the control of a Fgfr1 promoter, thereby causing cells endogenously expressing Fgfr1 to also present a positive GFP signal. Through simple immunostaining using GFP antibodies and cell-type specific antibodies, we were able to accurately determine the cell-type of Fgfr1 expressing cells.
Results. This technique revealed Fgfr1 expression in proliferative zones containing BLBP+ radial glial stem cells, such as the cortical and hippocampal ventricular zones, and cerebellar anlage of E14.5 mice, in addition to DCX+ neuroblasts. Furthermore, our data reveal Fgfr1 expression in proliferative zones containing BLBP+ cells of the anterior midline, hippocampus, cortex, hypothalamus, and cerebellum of P0.5 mice, in addition to the early-formed GFAP+ astrocytes of the anterior midline.
Discussion. Understanding when during development and where Fgfr1 is expressed is critical to improving our understanding of its function during neurodevelopment as well as in the mature CNS. This information may one day provide an avenue of discovery towards understanding the involvement of aberrant FGF signaling in neuropsychiatric disorders.
Freeze-thaw decellularization of the trabecular meshwork in an ex vivo eye perfusion model
Authors: Dang Yalong , Waxman Susannah , Wang Chao , Jensen Adrianna , Loewen Ralitsa T , Bilonick Richard A , Loewen Nils A 
Published: January 2018

Objective: Trabecular meshwork (TM) is the primary substrate of outflow resistance in glaucomatous eyes. Repopulating diseased TM with fresh, functional TM cells might represent a novel therapeutic breakthrough. Various decellularized TM scaffolds were developed by ablating existing cells with suicide gene therapy or saponin, but always with incomplete cell removal or dissolve the extracellular matrix. We hypothesized that a chemical-free, freeze-thaw method would be able to produce a fully decellularized TM scaffold for cell transplantation.
Materials and Methods: We obtained 24 porcine eyes from a local abattoir, dissected and mounted them in an anterior segment perfusion and pressure transduction system within two hours of sacrifice. After they stabilized for 72 hours, eight eyes each were assigned to freeze-thaw (F) ablation (-80°C×2), to 0.02% saponin (S) treatment, or the control group (C), respectively. The trabecular meshwork was transduced with an eGFP expressing feline immunodeficiency viral (FIV) vector and tracked via fluorescent microscopy to confirm ablation. Following treatment, the eyes were perfused with standard tissue culture medium for 180 hours. We assessed histological changes by hematoxylin and eosin staining. TM cell viability was evaluated with a calcein AM/propidium iodide (PI) assay. We measured IOP and modeled it with a linear mixed effects model using a B-spline function of time with 5 degrees of freedom.
Results: F and S experienced a similar IOP reduction by 30% from baseline (P=0.64). IOP reduction of about 30% occurred in F within 24 hours and in S within 48 hours. Live visualization of eGFP demonstrated that F conferred a complete ablation of all TM cells and only a partial ablation in S. Histological analysis confirmed that no TM cells survived in F while the extracellular matrix remained. The viability assay showed very low PI and no calcein staining in F in contrast to numerous PI-labeled dead TM cells and calcein-labeled viable TM cells in S.
Conclusion: We developed a rapid TM ablation method that uses cyclic freezing that is free of biological or chemical agents and able to produce a decellularized TM scaffold with preserved TM excellular matrix in an organotypic perfusion culture.
Rapid learning curve assessment in an ex vivo training system for microincisional glaucoma surgery
Authors: Dang Yalong , Waxman Susannah , Wang Chao , Parikh Hardik A , Bussel Igor I , Loewen Ralitsa T , Xia Xiaobo , Lathrop Kira L , Bilonick Rick , Loewen Nils A 
Published: January 2018

Purpose: Microincisional glaucoma surgeons operate in a highly confined space, making it difficult to learn by observation or assistance alone. We hypothesized that an ex vivo model would allow for better refinement of technique, quantification of progress, and computation of a learning curve.
Methods: Seven trainees without angle surgery experience performed nine ab interno trabeculectomies in pig eyes (n=63) after preparing with training slides and videos. Trainees placed the eyes on a tiltable mannequin head, visualized the trabecular meshwork gonioscopically through an ophthalmic microscope, and removed it by trabectome-mediated ablation. An expert surgeon observed, guided, and rated the procedure using an Operating Room Score (ORS). The extent of accessed outflow beds was estimated with canalograms using fluorescent microspheres. Data was fit using mixed effect models.
Results: ORS reached a half-maximum on an asymptote after only 2.5 eyes. Surgical time decreased by 0.9 minutes per eye in linear fashion. The ablation arc followed an asymptotic function with a half-maximum inflection point after 5.3 eyes. The mean ablation arc improved from 73 to 135 degrees. Canalograms revealed that this progress did not correlate well with improvement in outflow instead suggesting that about 30 eyes are needed for true mastery.
Conclusion: This inexpensive pig eye model provides a safe and effective training model for ab interno trabeculectomy and allows for quantification of outcomes. Trainees without prior angle surgery experience improved quickly. Actual outflow improvements progressed at a slower rate, which serves as a reminder to remain humbly committed to training.
A comprehensive simulation study on classification of RNA-Seq data
Authors: Zararsiz Gokmen , Göksülük Dinçer , Korkmaz Selçuk , Eldem Vahap , Ertürk Zararsız Gözde , Duru İzzet Parug , Unver Turgay , Öztürk Ahmet 
Published: January 2018

Background RNA sequencing (RNA-Seq) is a powerful technique for transcriptome profiling of the organisms that uses the capabilities of next-generation sequencing (NGS) technologies. Recent advances in NGS let to measure the expression levels of tens to thousands of transcripts simultaneously. Using such information, developing expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of disease. Microarray based classifiers cannot be directly applied due to the discrete nature of RNA-Seq data. One way is to develop count-based classifiers, such as poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA). Other way is to transform the data hierarchically closer to microarrays and apply microarray-based classifiers. In most of the studies, the data overdispersion seems to be an another challenge in modeling RNA-Seq data. In this study, we aimed to examine the effect of dispersion parameter and classification algorithms on RNA-Seq classification. We also considered the effect of other parameters (i) sample size, (ii) number of genes, (iii) number of class, (iv) DE (differential expression) rate, (v) transformation method on classification performance.
Methods We designed a comprehensive simulation study, also used two miRNA and two mRNA experimental datasets. Simulated datasets are generated from negative binomial distribution under different scenarios and real datasets are obtained from publicly available resources. We compared the results of several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF).
Results Results from the simulated and real datasets revealed that increasing the sample size, differential expression rate, number of genes and decreasing the dispersion parameter and number of groups lead to an increase in the classification accuracy. To make an overall assessment, power transformed PLDA, RF and SVM classifiers performed the highest classification accuracies.
Discussion Overdispersion seems to be an important challenge in RNA-Seq classification studies. Similar with differential expression studies, classification of RNA-Seq data requires careful attention on handling data overdispersion. We conclude that, as a count-based classifier, power transformed PLDA; as a microarray based classifier vst or rlog transformed RF and SVM (bagSVM for high sample sized data) classifiers may be a good choice for classification. However, there is still a need to develop novel classifiers or transformation approaches for classification of RNA-Seq data. An R/BIOCONDUCTOR package MLSeq with a vignette is freely available at http://www.bioconductor.org/packages/2.14/bioc/html/MLSeq.html .
Angle stability and outflow in excisional ab interno trabeculectomy with active versus passive chamber management
Authors: Wang Chao , Dang Yalong , Waxman Susannah , Xia Xiaobo , Weinreb Robert N , Loewen Nils A 
Published: January 2018

Purpose: To compare the intraoperative angle stability and the postoperative outflow of two ab interno trabeculectomy devices that excise the trabecular meshwork. The newer device has an active aspiration and irrigation port while the older requires viscoelastic to maintain the anterior chamber. We hypothesized that anterior segment optical coherence tomography (ASOCT) allows quantifying the intraoperative behavior in a microincisional glaucoma surgery (MIGS) pig eye training model.
Methods: Twelve freshly enucleated porcine eyes were measured with ASOCT at baseline, at the beginning of the procedure and at its conclusion to determine the anterior chamber depth (ACD) and the nasal angle in degrees. The right and left eye of pairs were randomly assigned 24 to an active dual blade goniectome (aDBG) and a passive dual blade goniectome (pDBG) group, 25 respectively. The aDBG had an irrigation and aspiration port while the pDBG required surgery 26 under viscoelastic. We performed the procedures using our MIGS training system with a 27 standard, motorized ophthalmic operating microscope. We estimated outflow by obtaining 28 canalograms with fluorescent spheres.
Results: In aDBG, the nasal angle remained wide open during the procedure at above 90° and 30 did not change towards the end (100±10%, p=0.9). In contrast, in pDBG, ACD decreased by 31 51±19% to 21% below baseline (p<0.01) while the angle progressively narrowed by 40±12% 32 (p<0.001). Canalograms showed a similar extent of access to the outflow tract with the aDBG 33 and the pDBG (p=0.513). The average increase for the aDBG in the superonasal and inferonasal 34 quadrants was between 27 to 31% and for the pDBG between 15 to 18%.
Conclusion: ASOCT demonstrated that active irrigation and aspiration improved anterior 36 chamber maintenance and ease of handling with the aDBG in this MIGS training model. The 37 immediate postoperative outflow was equally good with both devices.
Prediction of protein function using a deep convolutional neural network ensemble
Authors: Zacharaki Evangelia I 
Published: January 2018

Background. The availability of large databases containing high resolution three-dimensional (3D) models of proteins in conjunction with functional annotation allows the exploitation of advanced supervised machine learning techniques for automatic protein function prediction.
Methods. In this work, novel shape features are extracted representing protein structure in the form of local (per amino acid) distribution of angles and amino acid distances, respectively. Each of the multi-channel feature maps is introduced into a deep convolutional neural network (CNN) for function prediction and the outputs are fused through Support Vector Machines (SVM) or a correlation-based k-nearest neighbor classifier. Two different architectures are investigated employing either one CNN per multi-channel feature set, or one CNN per image channel.
Results. Cross validation experiments on enzymes (n = 44,661) from the PDB database achieved 90.1% correct classification demonstrating the effectiveness of the proposed method for automatic function annotation of protein structures.
Discussion. The automatic prediction of protein function can provide quick annotations on extensive datasets opening the path for relevant applications, such as pharmacological target identification.
pISA-tree: standard directory tree as a support for reproducible research
Authors: Blejec Andrej , Gruden Kristina 
Published: January 2018

Basic idea of science is reproducibility of phenomena and experiments. Reproducibility of data analyses and reports is becoming more and more important. It requires structured organization of data, augmented with enought metadata for future re-use of data. Our aim is to provide a system to store data in a way that can be used for small and moderate size projects and fulfill minimal requirements of ISA-tab and FAIR paradigm.
Standard directory trees are applicable to research data storage. The main condition is that information is organized in files and we are not interested in the system that enables access to individual line/record or column/variable in a tabular data structure in the file. The tree structure is generated on the fly by use of batch files (on Windows platform) that generate necessary folders and meta-data template files.
We implemented the system of standard directory trees for support of the research in our research unit. Most often, our research projects can be hierarchically structured into, what can be called, Investigation which is composed of several Studies. Each individual study can have one or more Assays. To reflect this hierarchy, we named such directory tree the pISA-tree. To make new levels we provide three batch files: makeInvestigation, makeStudy, and makeAssay. Special attention is given to Description files that contain meta-information about research, protocols, samples, features et cetera. They are in line with the standards accepted for particular assays (e.g. MIQE, MIAMI, MIRIAM,...) which allow exchange of data with other data management services. In particular, we have in mind FAIRdom platform and some digital notebooks (e.g. SciNote).
pISA-tree structure rely on directories that are readily available on any computer platform and familiar to use by the researchers. Since the translation of meta-data into ISA-tab standard format is not too complex, pISA-tree is a step towards the FAIR paradigm.
Effect of achievement motive on purpose in life, social participation, role expectation, self-efficacy, and physical function by a longitudinal research study in Japan: using multilevel structural equation modeling
Authors: Sano Nobuyuki , Kyougoku Makoto 
Published: January 2018

Objective: The purpose of this study was to demonstrate the effects of the achievement motive on important variables such as the purpose in life and social participation with objective physical functions in predicting within-person fluctuations and between-person differences using longitudinal research in community-dwelling Japanese elderly people.
Methods: The final dataset consisted of 227 persons (men: 109, women: 118) from day-service centers through testing at three time points. The tests comprised a questionnaire on the achievement motive, purpose in life and so on and the measurement of physical functions. We verified the following hypothesis model; 1) achievement motive works on improvement of the purpose in life, social participation, self-efficacy, and role expectation, 2) social participation and role expectation improve the purpose in life, 3) hobbies and going out affect the purpose in life, 4) social participation and physical function affect the increase in going out, 5) achievement motive and social participation improve physical function and increase role expectation and hobbies, and 6) self-efficacy improves social participation and the purpose in life. Our hypothesized model was based on previous research and was analyzed using a multilevel structural equation modeling approach.
Results: The modified hypothesis model without hobby, grip strength, and gait speed exhibited an adequate model fit: comparative fit index = 0.904, Tucker-Lewis index = 0.775, root mean square error of approximation = 0.064, Akaike’s information criterion = 25201.17, Bayesian information criterion = 25389.59, and adjusted BIC = 25256.24. Within level, the achievement motive had significant direct effects on the purpose in life, social participation, and self-efficacy. In addition, there were significant indirect effects of the achievement motive on the purpose in life through social participation and on social participation through self-efficacy. Between level, the achievement motive had significant direct effects on all variables and significant indirect effects on the purpose in life, social participation, and going out.
Conclusion: Our findings highlight the implications of achievement motive for the purpose in life, social participation and self-efficacy of the participation at the within person and between people levels. Consequently, we understood that the achievement motive could strongly affect between-person differences more than within-person fluctuations during a short period of six months.