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Background: Single-molecule techniques have emerged as incisive approaches for addressing a wide range of questions arising in contemporary biological research [Trends Biochem Sci 38:30-37, 2013; Nat Rev Genet 14:9-22, 2013; Curr Opin Struct Biol 2014, 28C:112-121; Annu Rev Biophys 43:19-39, 2014]. The analysis and interpretation of raw single-molecule data benefits greatly from the ongoing development of sophisticated statistical analysis tools that enable accurate inference at the low signal-to-noise ratios frequently associated with these measurements. While a number of groups have released analysis toolkits as open source software [J Phys Chem B 114:5386-5403, 2010; Biophys J 79:1915-1927, 2000; Biophys J 91:1941-1951, 2006; Biophys J 79:1928-1944, 2000; Biophys J 86:4015-4029, 2004; Biophys J 97:3196-3205, 2009; PLoS One 7:e30024, 2012; BMC Bioinformatics 288 11(8):S2, 2010; Biophys J 106:1327-1337, 2014; Proc Int Conf Mach Learn 28:361-369, 2013], it remains difficult to compare analysis for experiments performed in different labs due to a lack of standardization. Results: Here we propose a standardized single-molecule dataset (SMD) file format. SMD is designed to accommodate a wide variety of computer programming languages, single-molecule techniques, and analysis strategies. To facilitate adoption of this format we have made two existing data analysis packages that are used for single-molecule analysis compatible with this format. Conclusion: Adoption of a common, standard data file format for sharing raw single-molecule data and analysis outcomes is a critical step for the emerging and powerful single-molecule field, which will benefit both sophisticated users and non-specialists by allowing standardized, transparent, and reproducible analysis practices. [ABSTRACT FROM AUTHOR]

Background: The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET) versus time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well. Results: The VBEM algorithm returns the model's evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model's parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem. Conclusions: The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics. [ABSTRACT FROM AUTHOR]

Background: There is great interest to study how gene pathways change their structure across different tissues. The assessment of inter-study reliability of pathway changes across tissues can inform on the fraction of tissues with specific functional changes in network structure. However, there is a lack of agreement measures among studies that independently observe how a group of observations change across conditions. We, therefore, propose λ, a new inter-study reliability measure that determines the consistency to distinguish observations by condition., Results: We derived λ's distributional characteristics, determine its reliability properties and compared it with Cohen's κ. We studied the co-expression structure of 287 gene pathways across four brain regions in two transcriptomic studies and applied λ to assess the inter-study reliability of the pathways' brain-regional changes. Brain-related pathways showed highest λ; the top value was for the nicotine addiction pathway whose structure was reliably distinguishable among regions with dopaminergic projections., Conclusion: Our results offer novel substantial evidence that changes in network structure across tissues can be inferred independently of samples, algorithms and experiments (RNA-sequencing or microarrays). Reliability measures, such as λ, can inform on the tissues where changes in a network's structure are likely functional. An R package is available at https://github.com/isglobal-brge/lambda .

Background: Several charters of rights have been issued in Europe to solemnly proclaim the rights of children during their hospital stay. However, notwithstanding such general declarations, the actual implementation of hospitalized children's rights is unclear. The purpose of this study was to understand to which extent such rights, as established by the two main existing charters of rights, are actually implemented and respected in Italian pediatric hospitals and the pediatric units of Italian general hospitals, as perceived by the nurses working in them., Methods: Cross-sectional study. A 12-item online questionnaire was set up and an invitation was sent by email to Italian pediatric nurses using professional mailing lists and social networks. Responders were asked to score to what extent each right is respected in their hospital using a numeric scale from 1 (never) to 5 (always)., Results: 536 questionnaires were returned. The best implemented right is the right of children to have their mothers with them (mean score 4.47). The least respected one is the right of children to express their opinion about care (mean 3.01). Other rights considered were the right to play (4.29), the right to be informed (3.95), the right to the respect of privacy (3.75), the right to be hospitalized with peers (3.39), the right not to experience pain ever (3.41), and the right to school (3.07). According to the majority of nurses, the most important is the right to pain relief. Significant differences in the implementation of rights were found between areas of Italy and between pediatric hospitals and pediatric units of general hospitals., Conclusion: According to the perception of pediatric nurses, the implementation of the rights of hospitalized children in Italian pediatrics units is still limited.

Background: Unintentional injury leads all other causes of death for those 1 to 45 years old. The expense of medical care for injured people is estimated to exceed $406 billion annually. Given this burden on the population, the Centers for Disease Control and Prevention consistently refers to injury prevention as a national priority. We postulated that exposure to crime and the density of alcohol outlets in one's neighborhood will be positively associated with the incidence of hospitalization for and mortality from traumatic injuries, independent of other neighborhood characteristics., Methods: We conducted a cross-sectional study with ecological and individual analyses. Patient-level data for traumatic injury, injury severity, and hospital mortality due to traumatic injury in 2010 were gathered from the Dallas-Fort Worth Hospital Council Foundation. Each case of traumatic injury or death was geospatially linked with neighborhood of origin information from the 2010 U.S. Census within Dallas County, Texas. This information was subsequently linked with crime data gathered from 20 local police departments and the Texas Alcoholic Beverage Commission alcohol outlet dataset. The crime data are the Part One crimes reported to the Federal Bureau of Investigation., Results: The proportion of persons 65 years old or older was the strongest predictor of the incidence of hospitalization for traumatic injury (b = 12.64, 95% confidence interval (CI) 8.73 to 16.55). In turn, the incidence of traumatic injury most strongly predicted the severity of traumatic injury (b = 0.008, 95% CI 0.0003 - 0.0012). The tract-level unemployment rate was associated with a 5% increase in the odds of hospital mortality among hospitalized trauma patients., Conclusions: Several neighborhood characteristics were associated with the incidence, severity, and hospital mortality from traumatic injury. However, crime rates and alcohol outlet density carried no such association. Prevention efforts should focus on neighborhood characteristics such as population density, mean age of the residents, and unemployment rate, regardless of crime rates and alcohol outlet density.

Background: High-throughput RNA sequencing (RNA-seq) offers unprecedented power to capture the real dynamics of gene expression. Experimental designs with extensive biological replication present a unique opportunity to exploit this feature and distinguish expression profiles with higher resolution. RNA-seq data analysis methods so far have been mostly applied to data sets with few replicates and their default settings try to provide the best performance under this constraint. These methods are based on two well-known count data distributions: the Poisson and the negative binomial. The way to properly calibrate them with large RNA-seq data sets is not trivial for the non-expert bioinformatics user., Results: Here we show that expression profiles produced by extensively-replicated RNA-seq experiments lead to a rich diversity of count data distributions beyond the Poisson and the negative binomial, such as Poisson-Inverse Gaussian or Pólya-Aeppli, which can be captured by a more general family of count data distributions called the Poisson-Tweedie. The flexibility of the Poisson-Tweedie family enables a direct fitting of emerging features of large expression profiles, such as heavy-tails or zero-inflation, without the need to alter a single configuration parameter. We provide a software package for R called tweeDEseq implementing a new test for differential expression based on the Poisson-Tweedie family. Using simulations on synthetic and real RNA-seq data we show that tweeDEseq yields P-values that are equally or more accurate than competing methods under different configuration parameters. By surveying the tiny fraction of sex-specific gene expression changes in human lymphoblastoid cell lines, we also show that tweeDEseq accurately detects differentially expressed genes in a real large RNA-seq data set with improved performance and reproducibility over the previously compared methodologies. Finally, we compared the results with those obtained from microarrays in order to check for reproducibility., Conclusions: RNA-seq data with many replicates leads to a handful of count data distributions which can be accurately estimated with the statistical model illustrated in this paper. This method provides a better fit to the underlying biological variability; this may be critical when comparing groups of RNA-seq samples with markedly different count data distributions. The tweeDEseq package forms part of the Bioconductor project and it is available for download at http://www.bioconductor.org.

Background: Copy number variants (CNV) are a potentially important component of the genetic contribution to risk of common complex diseases. Analysis of the association between CNVs and disease requires that uncertainty in CNV copy-number calls, which can be substantial, be taken into account; failure to consider this uncertainty can lead to biased results. Therefore, there is a need to develop and use appropriate statistical tools. To address this issue, we have developed CNVassoc, an R package for carrying out association analysis of common copy number variants in population-based studies. This package includes functions for testing for association with different classes of response variables (e.g. class status, censored data, counts) under a series of study designs (case-control, cohort, etc) and inheritance models, adjusting for covariates. The package includes functions for inferring copy number (CNV genotype calling), but can also accept copy number data generated by other algorithms (e.g. CANARY, CGHcall, IMPUTE)., Results: Here we present a new R package, CNVassoc, that can deal with different types of CNV arising from different platforms such as MLPA o aCGH. Through a real data example we illustrate that our method is able to incorporate uncertainty in the association process. We also show how our package can also be useful when analyzing imputed data when analyzing imputed SNPs. Through a simulation study we show that CNVassoc outperforms CNVtools in terms of computing time as well as in convergence failure rate., Conclusions: We provide a package that outperforms the existing ones in terms of modelling flexibility, power, convergence rate, ease of covariate adjustment, and requirements for sample size and signal quality. Therefore, we offer CNVassoc as a method for routine use in CNV association studies.