bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of mutational signatures active in individual tumors
Authors: Krüger Sandra , Piro Rosario M 
Published: January 2018

The mutational processes responsible for the somatic mutations observed in tumor samples can significantly vary not only between tumor types but also among the individual cancers within a tumor class. Mutational processes can be represented by so called “mutational signatures” which reflect the occurrences of base changes within their sequence contexts (i.e., in dependence on their flanking bases).
We present a user-friendly R package, called decompTumor2Sig, that can be used to evaluate the contribution of Shiraishi signatures to the somatic mutations found in an individual tumor.
A Dynamic Bayesian Network model for simulation of disease progression in Amyotrophic Lateral Sclerosis patients
Authors: Zandonà Alessandro , Francescon Matilde , Bronfeld Maya , Calvo Andrea , Chiò Adriano , Di Camillo Barbara 
Published: January 2018

Background. Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting upper and lower motor neurons in the brain and spinal cord. The heterogeneity in the course of ALS clinical progression and ultimately survival, coupled with the rarity of this disease, make predicting disease outcome at the level of the individual patient very challenging. Besides, stratification of ALS patients has been known for years as a question of great importance to clinical practice, research and drug development.
Methods. In this work, we present a Dynamic Bayesian Network (DBN) model of ALS progression to detect probabilistic relationships among variables included in the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT), which provides records of over 10,700 patients from different clinical trials, and with over 2,869,973 longitudinally collected data measurements.
Results. Our model unravels new dependencies among clinical variables in relation to ALS progression, such as the influence of basophil count and creatine kinase on patients’ clinical status and the respiratory functional state, respectively. Furthermore, it provided an indication of ALS temporal evolution, in terms of the most probable disease trajectories across time at the level of both patient population and individual patient.
Conclusions. The risk factors identified by out DBN model could allow patients' stratification based on velocity of disease progression and a sensitivity analysis on this latter in response to changes in input variables, i.e. variables measured at diagnosis.
Personalised models for human – gut microbiota interaction
Authors: Gilbert David , Heiner Monika , Ghanbar Leila 
Published: January 2018

It is now becoming feasible to determine the composition of an individual gut microbiota (gut microflora), as well as the individual genome. In addition, whole genome scale metabolic models (GEMs) exist for a range of bacteria, and also for human. In principle this enables us to build models for gut microbiota by aggregating strain-specific models and also place this within the human context, and to make predictions on a personalised basis of the influence of gut microbiota on human metabolism, and how the interactions between these microbiota and also the human may evolve. Such aggregation, however, raises several challenges, which we discuss in this paper.
Furthermore, we present techniques and supporting tools which permit the development of personlised models for human – gut microbiota interaction. The construction of such models is supported by a suite of modelling and analysis tools which permit the exploration of the dynamic behaviour of the very large metabolic models, comprising Snoopy, Charlie, Prolog, MC2, and Marcie.
Our tools could be applied to populations of models in the context of human - gut microbiota in- teractions. Our approach that we have developed permits the description of the dynamic behavioural interaction between different bacterial strains and their human host on a personalised level within one aggregated model represented as a coloured Petri net. We use simulative model checking techniques over coloured traces to analyse the huge amounts of data generated by the dynamic simulation of these very large and hierarchically structured models.
Poster abstracts of the 25th German Conference on Bioinformatics
Authors: Nieselt Kay , Pfeifer Nico , Lupas Andrei , Kohlbacher Oliver 
Published: January 2018

The annual German Conference on Bioinformatics (GCB) has brought together renowned international scientists in bioinformatics, computational biology, biostatistics, biology and medicine since 1993. The 25th GCB was held in Tübingen from 18th to 21st of September 2017, making its second appearance at the vivid university city with its internationally renowned medical faculty as well as the faculty of science, and several top research institutes like the Max Planck Institute for Developmental Biology. This environment has created and attracted several internationally recognized research groups in bioinformatics, computational biology, and biomedical data science out of which many have contributed in organizing the GCB 2017.
Following are the abstracts of posters accepted for presentation at the conference, showing the wide range of different areas from bioinformatics infrastructure to theoretical approaches in single cell analyses.
The most complete ankylosaur skull ever found in the Wessex Sub-basin (Lower Cretaceous) of the Isle of Wight.
Authors: Pond Stuart 
Published: January 2018

Ankylosaur remains are frequently recovered from the Lower Cretaceous Wealden deposits of the Isle of Wight, although the vast majority of these fossils represent postcranial elements and osteoderms. The rarity of ankylosaur cranial material means any new specimens are important for understanding the morphology, palaeoecology and evolution of these taxa. Here we describe a well-preserved partial ankylosaur cranium recovered with associated ankylosaur remains from the Wessex Formation at Compton Bay. This is the most complete ankylosaur skull ever recovered from the Wessex Sub-basin and is now held at Dinosaur Isle Museum (DI), Sandown, IOW. A highly water worn specimen held at the Sedgwick Museum of Earth Sciences was found at Chilton Chine in the early 1990s and assigned to cf. Polacanthus, and the two crania are compared here.
The specimen consists of the posterior part of the basicranium and skull roof including the proximal paraoccipital processes, occipital condyle and basal tuberosity. Both skulls share characters including the position of some of the foramina exiting the endocranium and lateral curvature of the skull roof. The DI skull differs from the Sedgwick specimen by the presence of a well-defined, notched border of the supraoccipital, flat rostal-caudal dorsal cranial surface, the occipital condyle being more bulbous and angled more ventrally, a well-defined nuchal shelf and being smaller and less robust. Differences between the specimens may be due to sexual dimorphism, ontogeny or they may represent different taxa.
Implementing a multilayer framework for pathway data integration, analysis and visualization
Authors: Hammoud Zaynab , Kramer Frank 
Published: January 2018

Personalized medicine, i.e. a medicine focused on the individual and proactive in nature, promises an improved health care by customizing the treatment according to patient needs. The methods to analyze data, model knowledge and store interpretable results vary widely. A common approach is to use networks for modelling and organizing this information.
Network theory has been used for many years in the modelling and analysis of complex systems, as epidemiology, biology and biomedicine. As the data evolves and becomes more heterogeneous and complex, monoplex networks become an oversimplification of the corresponding systems. This imposes a need to go beyond traditional networks into a richer framework capable of hosting objects and relations of different scales, called Multilayered Network.
These complex networks have contributed in many contexts and fields, and they are very applicable in the investigation of biological networks.
In order to enrich this investigation, we aim to implement a multilayer framework that can be applicable in various domains, especially in the field of pathway modelling.
Our idea is to integrate pathways and their related knowledge into a multilayer model, where each layer represents one of their elements. The model offers a feature we call “Selective Inclusion of Knowledge”, as well as a collection of related knowledge into a single graph, like diseases and drugs. The main layers are mapped to the entities of the pathways and the additional knowledge, for instance, a convenient model would have 3 layers respectively representing proteins, drugs and diseases. The model imports knowledge from multiple sources like the Reactome database, PharmGKB, DrugBank, OMIM and other public sources.
The submitted poster will give an overview of the various models of multilayered networks, then it will describe the model we are building, and the workflow of implementing it into R as well as the future plan. The workflow consists of multiple R packages, of which we present the first implemented package, mully, that provides the multilayer layout. The data import and the integration will be done by another package to be implemented, Multipath.
A Gene Set Enrichment Analysis of multiomic celiac disease data
Authors: Del Prete Eugenio , Facchiano Angelo , Liò Pietro 
Published: January 2018

Celiac disease is a chronic condition, which can be described as inflammatory and autoimmune. The well-known treatment is a lifelong gluten-free diet, but it can be not totally effective for a high percentage of the patients. The aim of this work is to approach the celiac disease complexity from a bioinformatics point of view. The idea is to analyse the state of the art from GEO online repository and revisit the works, by integrating gene expression data and Gene Ontology (GO) terms.
Gene Set Enrichment Analysis (GSEA) is a set of statistical methods to classify genes in groups, which are related to common biological function, chromosomal location or regulation. The work is developed in R environment. The packages are downloaded by the online repository Bioconductor. The studies are not standardized. In these circumstances, the candidate genes subset is chosen with a trade-off among all the scores, thus the creation of a GO graph eludes the Fishers exact test, keeping its biological importance to define process clusters.
A little framework on the biological processes involved in each study on celiac disease is suggested: GSE11501, peptidyl-tyrosine phosphorylation, phosphatidylinositol 3-kinase signaling, and response to endoplasmic reticulum stress; GSE87629, mitosis regulation, microtubule cytoskeleton organisation, and protein destabilization; GSE72625, signaling pathway and cellular response about interferon-gamma; GSE61849a, immune response and immune system development; GSE61849b, protein phosphorylation, apoptotic process, and regulation of cell adhesion; GSE76168, cytokine mediate signaling pathways.
Visualizing mutation occurrence using Big Data
Authors: Albert Silvana Iuliana 
Published: January 2018

The volume of collected genetic data has been growing exponentially in the past few years and we need to improve the way we store, analyze and visualize it in order to be able to draw relevant conclusions that could improve the life quality of people. Extracting patterns and predicting future mutations and their impact will rely heavily on the efficient use of Big Data. Often a mutation on its own cannot provide enough information about a disorder or disease. Only if we combine the genetic information with the organism’s environment we can draw some conclusions about penetrance and expressively of the mutation. Because many genes can cause a single disease and at the same time a single gene can cause multiple diseases, we need to analyze the whole context of a person.
In this work, a distributed solution that provides demographics and metrics about diagnostics and mutations is pro posed. Seeing the occurrence of a mutation in a particular geographic region can help medical special ists narrow down the search for a patient’s mutations without sequencing the whole genome.
Quantifying geographic range measures and their utility as extinction risk proxies
Authors: Boyle James 
Published: January 2018

Geographic range is used as a correlate of extinction risk for extant and extinct organisms across the fields of conservation and paleobiology. However, the exact method used to measure geographic range, the biases, and the limitations of each are rarely discussed explicitly despite their potential to impact conclusions. Here I examine and quantify properties of five commonly used measures of geographic range (convex hull area, maximum pairwise great circle distance, latitudinal range, longitudinal range, and cell count) along with a rarely used measure (minimum spanning tree distance) in the context of three datasets. A simulated dataset of two shapes with known areal limits, a paleontological occurrence dataset of pre-Cenozoic brachiopod genera from the Paleobiology Database (PBDB), and 50000 occurrence records of birds species in the western hemisphere from the eBird database.
Simulated distributions indicate that when a distribution is simple (i.e. a rectangle) all measures are similarly accurate and precise at varying sample sizes and all measures converge toward the true value with increasing sample size. However, given a more complex distribution (i.e. horseshoe shape) the convex hull area becomes increasingly inaccurate as sample size increases even as it becomes more precise. Within the PBDB dataset the minimum spanning tree was heavily favored by Akaike Information Criterion as the most effective predictor of extinction risk. Analysis of the eBird data set indicated differences between IUCN Red List Categories were significant for all geographic range measures. Overall, these results suggest that for the purpose of categorical comparisons, such as those between victims and survivors of an extinction event, all six geographic range measures are interchangeable. However, the magnitude of the relationship between geographic range and extinction risk is strongly dependent on the choice of measure. Simple linear measures, such as latitudinal range, were relatively poor predictors while minimum spanning tree and cell count measures were strong predictors, especially after sampling was accounted for. The minimum spanning tree measure was found to perform at the same level or better than most other measures with the main drawback being that it is computationally expensive.
Characterization of antimicrobial resistance genes in Haemophilus parasuis isolated from pigs in China
Authors: Zhao Yongda , Guo Lili , Li Jie , Huang Xianhui , Fang Binghu 
Published: January 2018

Background: Haemophilus parasuis is a common porcine respiratory disease that causes high rates of morbidity and mortality in farmed swine. We performed a molecular characterization of antimicrobial resistance genes harbored by H. parasuis from pig farms in China.
Methods: We screened 143 H. parasuis isolates for the presence of 64 antimicrobial resistance genes by PCR amplification and DNA sequence analysis. We determined quinolone resistance determining region mutations of DNA gyrase (gyrA and gyrB) and topoisomerase IV (parC and parE). The genetic relatedness among the strains was analyzed by pulsed-field gel electrophoresis.
Results: We found 14 antimicrobial resistance genes were present in these isolates, including TEM-1, ROB-1.ermB,ermA ,flor, catl,tetB,tetC, rmtB, rmtD, aadA1, aac(3’)-ⅡC, sul1, and sul2 genes. Interestingly, one isolate carried 5 antibiotic resistance genes (tetB, tetC, flor, rmtB, sul1). The genes tetB, rmtB, and flor were the most prevalent resistance genes in H. parasuis in China. Alterations in the gyrA gene (S83F/Y, D87Y/N/H/G) were detected in 81% of the strains and parC mutations were often accompanied by a gyrA mutation. pulsed-field gel electrophoresis typing revealed 51 unique patterns in the isolates carrying antibiotic resistance genes indicating considerable genetic diversity and suggesting the genes were spread horizontally.
Discussion: The current study demonstrated that the high antibiotic resistance of H. parasuis in piglets is a combination of transferable antibiotic resistance genes and multiple target gene mutations. GyrA gene mutation also was the most important role in quinolone resistance. These data provide novel insights for the better understanding of the prevalence and epidemiology of antimicrobial resistance in H. parasuis.
Enhancing knowledge of head and neck anatomy in preclinical medical students using low fidelity simulation
Authors: Buckley Lisa , St. Hilaire Rose Clarie , Gilbert Gregory , Leighton Kim , Barremkala Mallikarjuna , Callender Diana , Pederson David 
Published: January 2018

Background: Simulation-based medical education is more prevalent in undergraduate preclinical medical education and acts as a foundation for clinical learning in years three and four. Currently, there is a call to teach clinical application of basic science material in preclinical years one and two.
Methods: Two groups of students participated in this investigation; a historical control of 270 students from the 2010 matriculating class and students receiving the intervention (anatomy lecture plus airway management simulation) from the 2012 matriculating class (n=337). Descriptive statistics were calculated for demographic and academic performance variables. Unadjusted and adjusted odds of passage of mid-term and final assessment were calculated. The final assessment was defined as one correct, two correct, and all three questions correct.
Results: Adjusted odds of passage of mid-term exam for the control group were 3.9 (95% CI: 2.7-5.9), virtually unchanged from the unadjusted odds of passage. Control group results for final exam passage as defined as one correct increased from .7 to .9 when adjusted for variables (95% CI:.3-2.5). Odds of passage of final assessment, for the control group, for adjusted models for two or greater correct increased from 4.1 to 5.6 (95% CI:2.6-13.7) and from 34.1 to 44.0 (95% CI: 21.7-102.5) when three answers (or 100%) are needed for passage.
Conclusions: When passage criteria for the final exam were defined as one correct, addition of a simulation exercise to the anatomy lecture increased the rate of passage by 11% after adjusting for covariates. However, when passage criteria for the final exam was defined as two or three correct, addition of a simulation exercise to the anatomy lecture decreased the rate of passage.
MCLEAN: Multilevel Clustering Exploration As Network
Authors: Alcaide Daniel , Aerts Jan 
Published: January 2018

Finding useful patterns in datasets has attracted considerable interest in the field of visual analytics. One of the most common tasks is the identification and representation of clusters. However, this is non-trivial in heterogeneous datasets since the data needs to be analyzed from different perspectives. Indeed, highly variable patterns may mask underlying trends in the dataset. Dendrograms are graphical representations resulting from agglomerative hierarchical clustering and provide a framework for viewing the clustering at different levels of detail. However, dendrograms become cluttered when the dataset gets large, and the single cut of the dendrogram to demarcate different clusters can be insufficient in heterogeneous datasets.
In this work, we propose a visual analytics methodology called MCLEAN that offers a general approach for guiding the user through the exploration and detection of clusters. Powered by a graph-based transformation of the relational data, it supports a scalable environment for representation of heterogeneous datasets by changing the spatialization. We thereby combine multilevel representations of the clustered dataset with community finding algorithms. Our approach entails displaying the results of the heuristics to users, providing a setting from which to start the exploration and data analysis.
To evaluate our proposed approach, we conduct a qualitative user study, where participants are asked to explore a heterogeneous dataset, comparing the results obtained by MCLEAN with the dendrogram. These qualitative results reveal that MCLEAN is an effective way of aiding users in the detection of clusters in heterogeneous datasets. The proposed methodology is implemented in an R package available at https://bitbucket.org/vda-lab/mclean
Time Correlations of Lightning Flash Sequences in Thunderstorms Revealed by Fractal Analysis
Authors: Gou Xueqiang , Chen Mingli , Zhang Guangshu 
Published: January 2018

AbstractBy using the data of lightning detection and ranging system at the Kennedy Space Center, the temporal fractal and correlation of interevent time series of lightning flash sequences in thunderstorms have been investigated with Allan factor (AF), Fano factor (FF), and detrended fluctuation analysis (DFA) methods. AF, FF, and DFA methods are powerful tools to detect the time‐scaling structures and correlations in point processes. Totally 40 thunderstorms with distinguishing features of a single‐cell storm and apparent increase and decrease in the total flash rate were selected for the analysis. It is found that the time‐scaling exponents for AF (αAF) and FF (αFF) analyses are 1.62 and 0.95 in average, respectively, indicating a strong time correlation of the lightning flash sequences. DFA analysis shows that there is a crossover phenomenon—a crossover timescale (τc) ranging from 54 to 195 s with an average of 114 s. The occurrence of a lightning flash in a thunderstorm behaves randomly at timescales <τc but shows strong time correlation at scales >τc. Physically, these may imply that the establishment of an extensive strong electric field necessary for the occurrence of a lightning flash needs a timescale >τc, which behaves strongly time correlated. But the initiation of a lightning flash within a well‐established extensive strong electric field may involve the heterogeneities of the electric field at a timescale <τc, which behave randomly.
Investigating the Linear Dependence of Direct and Indirect Radiative Forcing on Emission of Carbonaceous Aerosols in a Global Climate Model
Authors: Chen Yanju , Wang Hailong , Singh Balwinder , Ma Po‐Lun , Rasch Philip J. , Bond Tami C. 
Published: January 2018

AbstractThe relationship between forcing and emission is investigated for black carbon (BC) and primary organic carbon (OC) emitted from North America and Asia. Direct and indirect radiative forcing (DRF and IRF) of BC and OC are simulated with CAM5.1. Two diagnostics are introduced to aid policy‐relevant discussions: linearity and emission‐normalized forcing. DRF is linearly related to emission for both BC and OC from two regions, and the linear relationship is similar, within 15%. IRF is linear in emissions when emissions are lower and regions are far from sources (North American BC and OC). Indirect radiative forcing is sublinear for strong sources and near‐source regions (Asian OC). Emission‐normalized IRF in North America is two to four times higher than that in Asia. The difference among regions and species is primarily caused by particle density as high density of BC results in fewer emitted particles and by the processes for accumulation mode particles to become cloud condensation nuclei and then to activate into cloud droplet. Lower emission‐normalized IRF in more polluted regions means that reductions of OC in these regions would be relatively climate‐neutral rather than causing significant warming via IRF reduction. An optimal aggregation area (30° × 30°) is identified for analysis of the forcing‐to‐emission relationship. For IRF, only 15–40% of the Earth's surface is significantly affected by an emission region, but forcing in these regions comprises most of the global impact. Emission‐normalized forcing can be used to estimate forcing changes due to emission reductions, as long as causes of nonlinearity are identified and considered.