Understanding Pediatric Injury in Collier County, Florida: a mixed methods analysis

Background: The current trauma system in Collier County disperses injured pediatric patients to trauma centers outside the County. There is a critical gap in knowledge in the epidemiology of the County’s pediatric trauma patients. Purpose: To understand injury patterns in children ages 0-17 years in Collier County and identify challenges in transporting injured children to definitive care. Methods: This is a cross sectional, descriptive study using a sequential mixed-methods design. A thorough review of the literature and interviews of key stakeholders were conducted in August 2017. Data obtained from the interviews was used to develop a causal loop diagram using Vensim modeling (version 7.2). De-identified hospital and EMS database from January 1, 2012 to April 20, 2018 was analyzed. Descriptive statistics were calculated for age, gender, ethnicity, race, reason for transport, refusal of care, mechanism of injury, disposition and zone of injury variables. Statistical analyses were conducted using SPSS version 25 and R 3.3.1.2. Statistical significance was set at alpha \u3c0.05. Results: 5,297 records were evaluated. 95% of the pediatric EMS calls were trauma related. 90% of injured children received care at the County’s acute care hospitals. 7/10,000 children per year were trauma alerts transported out of the County. Bad weather was the main factor impeding transport outside the County. No data is available on outcomes of trauma alert patients who received care outside the County. Discussion: Our County lacks data on trauma services and outcomes for pediatric patients, despite population growth. Severe weather impedes the transfer to TCs, keeping these patients at the local hospitals. There is insufficient evidence to demonstrate that the current management of injured children in the County provides the best outcomes. This study concludes with a series of recommendations to develop a local trauma database, integrate services and mature into a robust and organized system

Seismic hazard and risk scenarios for Barcelona, Spain, using the Risk-UE vulnerability index method

The vulnerability index method, in its version developed in the framework of the European project Risk-UE, has been adapted and applied in this article, to evaluate the seismic risk for the city of Barcelona (Spain) through a GIS based tool. According to this method, which defines five damage states, the action is expressed in terms of the macroseismic intensity and the seismic quality of the buildings by means of a vulnerability index. The probabilities of damage states are obtained considering a binomial or beta-equivalent probability distribution. The most relevant seismic risk evaluation results obtained, for current buildings and monuments of Barcelona, are given in the article as scenarios of expected losses

Ground-Shaking Scenarios and Urban Risk Evaluation of Barcelona using the Risk-UE Capacity Spectrum Based Method

The Capacity Spectrum Based Method (CSBM) developed in the framework of the European project Risk-UE has been applied to evaluate the seismic risk for the city of Barcelona, Spain. Accordingly, four damage states are defined for the buildings, the action is expressed in terms of spectral values and the seismic quality of the buildings, that is, their vulnerability, is evaluated by means of capacity spectra. The probabilities of the damage states are obtained considering a lognormal probability distribution. The most relevant seismic risk evaluation results obtained for Barcelona, Spain, are given in the article as scenarios of expected losses

Lineage-based identification of cellular states and expression programs

We present a method, LineageProgram, that uses the developmental lineage relationship of observed gene expression measurements to improve the learning of developmentally relevant cellular states and expression programs. We find that incorporating lineage information allows us to significantly improve both the predictive power and interpretability of expression programs that are derived from expression measurements from in vitro differentiation experiments. The lineage tree of a differentiation experiment is a tree graph whose nodes describe all of the unique expression states in the input expression measurements, and edges describe the experimental perturbations applied to cells. Our method, LineageProgram, is based on a log-linear model with parameters that reflect changes along the lineage tree. Regularization with L1 that based methods controls the parameters in three distinct ways: the number of genes change between two cellular states, the number of unique cellular states, and the number of underlying factors responsible for changes in cell state. The model is estimated with proximal operators to quickly discover a small number of key cell states and gene sets. Comparisons with existing factorization, techniques, such as singular value decomposition and non-negative matrix factorization show that our method provides higher predictive power in held, out tests while inducing sparse and biologically relevant gene sets.National Institutes of Health (U.S.) (P01-NS055923)National Institutes of Health (U.S.) (1-UL1-RR024920

Improving the value of public RNA-seq expression data by phenotype prediction.

Publicly available genomic data are a valuable resource for studying normal human variation and disease, but these data are often not well labeled or annotated. The lack of phenotype information for public genomic data severely limits their utility for addressing targeted biological questions. We develop an in silico phenotyping approach for predicting critical missing annotation directly from genomic measurements using well-annotated genomic and phenotypic data produced by consortia like TCGA and GTEx as training data. We apply in silico phenotyping to a set of 70 000 RNA-seq samples we recently processed on a common pipeline as part of the recount2 project. We use gene expression data to build and evaluate predictors for both biological phenotypes (sex, tissue, sample source) and experimental conditions (sequencing strategy). We demonstrate how these predictions can be used to study cross-sample properties of public genomic data, select genomic projects with specific characteristics, and perform downstream analyses using predicted phenotypes. The methods to perform phenotype prediction are available in the phenopredict R package and the predictions for recount2 are available from the recount R package. With data and phenotype information available for 70,000 human samples, expression data is available for use on a scale that was not previously feasible

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

Rank of Correlation Coefficient as a Comparable Measure for Biological Significance of Gene Coexpression

Information regarding gene coexpression is useful to predict gene function. Several databases have been constructed for gene coexpression in model organisms based on a large amount of publicly available gene expression data measured by GeneChip platforms. In these databases, Pearson's correlation coefficients (PCCs) of gene expression patterns are widely used as a measure of gene coexpression. Although the coexpression measure or GeneChip summarization method affects the performance of the gene coexpression database, previous studies for these calculation procedures were tested with only a small number of samples and a particular species. To evaluate the effectiveness of coexpression measures, assessments with large-scale microarray data are required. We first examined characteristics of PCC and found that the optimal PCC threshold to retrieve functionally related genes was affected by the method of gene expression database construction and the target gene function. In addition, we found that this problem could be overcome when we used correlation ranks instead of correlation values. This observation was evaluated by large-scale gene expression data for four species: Arabidopsis, human, mouse and rat

COXPRESdb: a database of coexpressed gene networks in mammals

A database of coexpressed gene sets can provide valuable information for a wide variety of experimental designs, such as targeting of genes for functional identification, gene regulation and/or protein–protein interactions. Coexpressed gene databases derived from publicly available GeneChip data are widely used in Arabidopsis research, but platforms that examine coexpression for higher mammals are rather limited. Therefore, we have constructed a new database, COXPRESdb (coexpressed gene database) (http://coxpresdb.hgc.jp), for coexpressed gene lists and networks in human and mouse. Coexpression data could be calculated for 19 777 and 21 036 genes in human and mouse, respectively, by using the GeneChip data in NCBI GEO. COXPRESdb enables analysis of the four types of coexpression networks: (i) highly coexpressed genes for every gene, (ii) genes with the same GO annotation, (iii) genes expressed in the same tissue and (iv) user-defined gene sets. When the networks became too big for the static picture on the web in GO networks or in tissue networks, we used Google Maps API to visualize them interactively. COXPRESdb also provides a view to compare the human and mouse coexpression patterns to estimate the conservation between the two species

Bioterrorism-Related Anthrax: International Response by the Centers for Disease Control and Prevention

After reports of the intentional release of Bacillus anthracis in the United States, epidemiologists, laboratorians, and clinicians around the world were called upon to respond to widespread political and public concerns. To respond to inquiries from other countries regarding anthrax and bioterrorism, the Centers for Disease Control and Prevention established an international team in its Emergency Operations Center. From October 12, 2001, to January 2, 2002, this team received 130 requests from 70 countries and 2 territories. Requests originated from ministries of health, international organizations, and physicians and included subjects ranging from laboratory procedures and clinical evaluations to assessments of environmental and occupational health risks. The information and technical support provided by the international team helped allay fears, prevent unnecessary antibiotic treatment, and enhance laboratory-based surveillance for bioterrorism events worldwide

Gene expression anti-profiles as a basis for accurate universal cancer signatures

Background: Early screening for cancer is arguably one of the greatest public health advances over the last fifty years. However, many cancer screening tests are invasive (digital rectal exams), expensive (mammograms, imaging) or both (colonoscopies). This has spurred growing interest in developing genomic signatures that can be used for cancer diagnosis and prognosis. However, progress has been slowed by heterogeneity in cancer profiles and the lack of effective computational prediction tools for this type of data. Results: We developed anti-profiles as a first step towards translating experimental findings suggesting that stochastic across-sample hyper-variability in the expression of specific genes is a stable and general property of cancer into predictive and diagnostic signatures. Using single-chip microarray normalization and quality assessment methods, we developed an anti-profile for colon cancer in tissue biopsy samples. To demonstrate the translational potential of our findings, we applied the signature developed in the tissue samples, without any further retraining or normalization, to screen patients for colon cancer based on genomic measurements from peripheral blood in an independent study (AUC of 0.89). This method achieved higher accuracy than the signature underlying commercially available peripheral blood screening tests for colon cancer (AUC of 0.81). We also confirmed the existence of hyper-variable genes across a range of cancer types and found that a significant proportion of tissue-specific genes are hyper-variable in cancer. Based on these observations, we developed a universal cancer anti-profile that accurately distinguishes cancer from normal regardless of tissue type (ten-fold cross-validation AUC > 0.92). Conclusions: We have introduced anti-profiles as a new approach for developing cancer genomic signatures that specifically takes advantage of gene expression heterogeneity. We have demonstrated that anti-profiles can be successfully applied to develop peripheral-blood based diagnostics for cancer and used anti-profiles to develop a highly accurate universal cancer signature. By using single-chip normalization and quality assessment methods, no further retraining of signatures developed by the anti-profile approach would be required before their application in clinical settings. Our results suggest that anti-profiles may be used to develop inexpensive and non-invasive universal cancer screening tests.https://doi.org/10.1186/1471-2105-13-27