Developing speed-related safety performance indicators from floating car data

In the road traffic safety domain there is a need for using proactive (non-crash-based) indicators, known as safety performance indicators (SPIs). Traffic speed based on big data (floating car data [FCD]) could help develop network-wide SPIs, but related knowledge and experience are insufficient so far. The authors attempted to fill this gap by using nationwide Italian FCD to develop speed-related SPIs and validating their relationship to crashes to see their potential explanatory value. The authors calculated the coefficient of variance (CV), congestion index (CI), and the number of incidents as candidate SPIs. For validation, the authors used linear correlation, crash frequency model, and ranking consistency. Incidents turned out to be the best SPI, especially for motorways

Predictors of Daily Mobility of Adults in Peri-Urban South India

Daily mobility, an important aspect of environmental exposures and health behavior, has mainly been investigated in high-income countries. We aimed to identify the main dimensions of mobility and investigate their individual, contextual, and external predictors among men and women living in a peri-urban area of South India. We used 192 global positioning system (GPS)-recorded mobility tracks from 47 participants (24 women, 23 men) from the Cardiovascular Health effects of Air pollution in Telangana, India (CHAI) project (mean: 4.1 days/person). The mean age was 44 (standard deviation: 14) years. Half of the population was illiterate and 55% was in unskilled manual employment, mostly agriculture-related. Sex was the largest determinant of mobility. During daytime, time spent at home averaged 13.4 (3.7) h for women and 9.4 (4.2) h for men. Women's activity spaces were smaller and more circular than men's. A principal component analysis identified three main mobility dimensions related to the size of the activity space, the mobility in/around the residence, and mobility inside the village, explaining 86% (women) and 61% (men) of the total variability in mobility. Age, socioeconomic status, and urbanicity were associated with all three dimensions. Our results have multiple potential applications for improved assessment of environmental exposures and their effects on health

MicroRNA-155 is induced during the macrophage inflammatory response

The mammalian inflammatory response to infection involves the induction of several hundred genes, a process that must be carefully regulated to achieve pathogen clearance and prevent the consequences of unregulated expression, such as cancer. Recently, microRNAs (miRNAs) have emerged as a class of gene expression regulators that has also been linked to cancer. However, the relationship between inflammation, innate immunity, and miRNA expression is just beginning to be explored. In the present study, we use microarray technology to identify miRNAs induced in primary murine macrophages after exposure to polyriboinosinic:polyribocytidylic acid or the cytokine IFN-{beta}. miR-155 was the only miRNA of those tested that was substantially up-regulated by both stimuli. It also was induced by several Toll-like receptor ligands through myeloid differentiation factor 88- or TRIF-dependent pathways, whereas up-regulation by IFNs was shown to involve TNF-{alpha} autocrine signaling. Pharmacological inhibition of the kinase JNK blocked induction of miR-155 in response to either polyriboinosinic:polyribocytidylic acid or TNF-{alpha}, suggesting that miR-155-inducing signals use the JNK pathway. Together, these findings characterize miR-155 as a common target of a broad range of inflammatory mediators. Importantly, because miR-155 is known to function as an oncogene, these observations identify a potential link between inflammation and cancer

Development of land-use regression models for fine particles and black carbon in peri-urban South India

Land-use regression (LUR) has been used to model local spatial variability of particulate matter in cities of high-income countries. Performance of LUR models is unknown in less urbanized areas of low-/middle-income countries (LMICs) experiencing complex sources of ambient air pollution and which typically have limited land use data. To address these concerns, we developed LUR models using satellite imagery (e.g., vegetation, urbanicity) and manually-collected data from a comprehensive built-environment survey (e.g., roads, industries, non-residential places) for a peri-urban area outside Hyderabad, India. As part of the CHAI (Cardiovascular Health effects of Air pollution in Telangana, India) project, concentrations of fine particulate matter (PM2.5) and black carbon were measured over two seasons at 23 sites. Annual mean (sd) was 34.1 (3.2) mug/m(3) for PM2.5 and 2.7 (0.5) mug/m(3) for black carbon. The LUR model for annual black carbon explained 78% of total variance and included both local-scale (energy supply places) and regional-scale (roads) predictors. Explained variance was 58% for annual PM2.5 and the included predictors were only regional (urbanicity, vegetation). During leave-one-out cross-validation and cross-holdout validation, only the black carbon model showed consistent performance. The LUR model for black carbon explained a substantial proportion of the spatial variability that could not be captured by simpler interpolation technique (ordinary kriging). This is the first study to develop a LUR model for ambient concentrations of PM2.5 and black carbon in a non-urban area of LMICs, supporting the applicability of the LUR approach in such settings. Our results provide insights on the added value of manually-collected built-environment data to improve the performance of LUR models in settings with limited data availability. For both pollutants, LUR models predicted substantial within-village variability, an important feature for future epidemiological studies

Circulating microRNAs are associated with Paroxysmal or Persistent Atrial Fibrillation

Introduction: Novel methods of identifying individuals at risk for atrial fibrillation (AF) are needed. MicroRNAs (MiRNAs) regulate gene expression in a number of cardiovascular diseases, including AF. It is unknown, however, if key circulating, cardiac-specific miRNAs differ between individuals with paroxysmal or persistent AF and those in sinus rhythm. Methods: 17 individuals with a history of AF were recruited prior to catheter ablation. 24 hospitalized patients in normal sinus rhythm and no history of AF comprised the control group. 94 plasma miRNAs were selected based on a priori associations with processes implicated in AF for evaluation using the TaqMan miRNA expression profiling system. Results:We found that miRNA expression differed by at least 2-fold for 14 miRNAs, including several previously implicated in cardiovascular remodeling and disease (Figure 1). Levels of miR-7, miR-208, and miR-302b were statistically significantly up- or down-regulated in AF patients relative to controls (p Application: Although power was limited by the modest sample size, these data support the rationale for using circulating miRNA as AF biomarkers. Moreover, since miRNA can modulate disease pathways, miRNA-based therapeutics would theoretically enable targeting of novel gene regulatory pathways implicated in AF in a unique and powerful manner. Next Steps/ Future: Further investigations involving well-characterized, large samples from longitudinal studies with standardized miRNA assessment and evaluation for AF are required to validate the observed associations

Identification of microRNAs with regulatory potential using a matched microRNA-mRNA time-course data

Over the past decade, a class of small RNA molecules called microRNAs (miRNAs) has been shown to regulate gene expression at the post-transcription stage. While early work focused on the identification of miRNAs using a combination of experimental and computational techniques, subsequent studies have focused on identification of miRNA-target mRNA pairs as each miRNA can have hundreds of mRNA targets. The experimental validation of some miRNAs as oncogenic has provided further motivation for research in this area. In this article we propose an odds-ratio (OR) statistic for identification of regulatory miRNAs. It is based on integrative analysis of matched miRNA and mRNA time-course microarray data. The OR-statistic was used for (i) identification of miRNAs with regulatory potential, (ii) identification of miRNA-target mRNA pairs and (iii) identification of time lags between changes in miRNA expression and those of its target mRNAs. We applied the OR-statistic to a cancer data set and identified a small set of miRNAs that were negatively correlated to mRNAs. A literature survey revealed that some of the miRNAs that were predicted to be regulatory, were indeed oncogenic or tumor suppressors. Finally, some of the predicted miRNA targets have been shown to be experimentally valid

Development of an open-source, flexible framework for complex inter-institutional disparate data sharing and collaboration

Clinical information, “-omic” datasets, and tissue samples are difficult to harmonize and manage for data mining. We have developed a platform for storing clinical research data while providing access to associated data from other information stores. Data on 34 metrics from 11,000 neuroblastoma patients were instantiated into a database. The Django web framework was used to create a model for rapid development of tools and views with a front-end interface for generating complex queries. Working with Nationwide Children’s Hospital, we can now consume their tissue inventory data through an API. The end-user sees the number of patients who both match their search and have tissue available. Since initial implementation, the current tasks revolve around developing a governance structure and the necessary data use agreements. Efforts now are to (1) update the data with 5000 more patients, and (2) link to genomic data stores, facilitating disparate data acquisition for research studies

Segmental chromosomal alterations have prognostic impact in neuroblastoma: a report from the INRG project

Background: In the INRG dataset, the hypothesis that any segmental chromosomal alteration might be of prognostic impact in neuroblastoma without MYCN amplification (MNA) was tested. Methods: The presence of any segmental chromosomal alteration (chromosome 1p deletion, 11q deletion and/or chromosome 17q gain) defined a segmental genomic profile. Only tumours with a confirmed unaltered status for all three chromosome arms were considered as having no segmental chromosomal alterations. Results: Among the 8800 patients in the INRG database, a genomic type could be attributed for 505 patients without MNA: 397 cases had a segmental genomic type, whereas 108 cases had an absence of any segmental alteration. A segmental genomic type was more frequent in patients >18 months and in stage 4 disease (P<0.0001). In univariate analysis, 11q deletion, 17q gain and a segmental genomic type were associated with a poorer event-free survival (EFS) (P<0.0001, P=0.0002 and P<0.0001, respectively). In multivariate analysis modelling EFS, the parameters age, stage and a segmental genomic type were retained in the model, whereas the individual genetic markers were not (P<0.0001 and RR=2.56; P=0.0002 and RR=1.8; P=0.01 and RR=1.7, respectively). Conclusion: A segmental genomic profile, rather than the single genetic markers, adds prognostic information to the clinical markers age and stage in neuroblastoma patients without MNA, underlining the importance of pangenomic studies

PENGARUH BAYANGAN TERHADAP OUTPUT TEGANGAN DAN KUAT ARUS PADA PEMBANGKIT LISTRIK TENAGA SURYA (PLTS)

Electrical energy is a very important requirement for the community, along with the development of the era and technological advances that are urgently needed, the need for electrical energy is very large, while the source of electricity that is currently being used still uses energy derived from fossil fuels. As we know that the source of energy derived from fossils is very limited, therefore other energy sources are sought or we are more familiar with renewable energy, one of which is the energy source that comes from the sun, which is better known as solar cell. The electricity from this solar cell is very dependent on sunlight which must illuminate the solar panels so that solar energy can be converted into electrical energy. The output from these solar panels is in the form of voltage and electric current. Some factors that can affect the amount of output or output voltage of electric current in PLTS is, temperature, shadow, (cloud condition, and surrounding environment), and wind speed. Therefore, the purpose of this research is to find out how much the shadow effect on the output voltage and electric current produced by PLTS. It is expected that this research can increase the knowledge of energy derived from the sun in this case the solar cell and know the effect of the shadow on the output voltage and electric current from solar panels.The results showed that there was a shadow effect on voltage reduction and current strength in the PLTS system, namely the 10% shadow area and 12.44 volt DC solar panel current and 2.54 amperage, 100% area covering the voltage output panel and the current of solar panels 12.10 volt DC and 0.22 amperage. The area of the shadow that covers the solar panel affects the output voltage and the strong current of the battery that is the area of the shadow 10% voltage and strong current battery 12.35 volt DC and 18.54 amper, 100% area cover the output panel voltage and strong current battery 11.90 volt DC and 13.85 amperes The shadow area covering the solar panels influences the output voltage and current strength of the inverter, namely the area of the shadow 10% voltage and 226.4 volt AC inverter current and 0.97 amperage, 100% covering the output voltage panel and 220.2 volt AC and 0.66 amperage current. Keywords: Electrical energy, Solar cell, Shadow (cloud condition

Circulating Cell and Plasma microRNA Profiles Differ between Non-ST-Segment and ST-Segment-Elevation Myocardial Infarction

BACKGROUND: Differences in plasma and whole blood expression microRNAs (miRNAs) in patients with an acute coronary syndrome (ACS) have been determined in both in vitro and in vivo studies. Although most circulating miRNAs are located in the cellular components of whole blood, little is known about the miRNA profiles of whole blood subcomponents, including plasma, platelets and leukocytes in patients with myocardial ischemia. METHODS: Thirteen patients with a ST-segment-elevation (STEMI) or non-ST-segment elevation (NSTEMI) myocardial infarction were identified in the University of Massachusetts Medical Center Emergency Department (ED) or cardiac catheterization laboratory between February and June of 2012. Whole blood was obtained from arterial blood samples at the time of cardiac catheterization and cell-specific miRNA profiling was performed. Expression of 343 miRNAs was quantified from whole blood, plasma, platelets, and peripheral blood mononuclear cells using a high-throughput, quantitative Real-Time polymerase-chain reaction system (qRT-PCR). RESULTS: MiRNAs associated with STEMI as compared to NSTEMI patients included miR-25-3p, miR-221-3p, and miR-374b-5p. MiRNA 30d-5p was associated with plasma, platelets, and leukocytes in both STEMI and NSTEMI patients; miRNAs 221-3p and 483-5p were correlated with plasma and platelets only in NSTEMI patients. CONCLUSIONS: Cell-specific miRNA profiles differed between patients with STEMI and NSTEMI. The miRNA distribution is also unique amongst plasma, platelets, and leukocytes in patients with ischemic heart disease or ACS. Our findings suggest unique miRNA profiles among the circulating subcomponents in patients presenting with myocardial ischemia