Introduction:Expansion in road network, motorization, and urbanization in the country has been accompanied by a rise in road accidents leading to road traffic injuries (RTIs). Today RTIs are one of the leading causes of deaths, disabilities, and hospitalizations with severe socioeconomic costs across the world.Objectives:The following study analyses the: Age and sex distribution of injured in road traffic accidents (RTAs).Circumstances leading to RTA.Pattern and severity of injuries sustained in RTAs cases. Design:Retrospective record-based study.Materials and Methods:The aim of this study was to audit retrospectively the circumstances, severity, and pattern of injury sustained by vehicle occupants presenting to the Saraswathi Institute of Medical Sciences (SIMS) hospital Hapur, for a period of one year. Data were collected using the case sheets of 347 patients from the medical records section of hospital and analyzed using SPSS computer software version 16.0. Results are interpreted in terms of percentage, mean, chi-square, and z-test.Results:The pattern and severity of injuries sustained by 347 vehicle occupants admitted to the emergency department of SIMS, Hapur were retrospectively documented. Male victims 258 (74.35%) were more commonly involved than females 89 (25.65%) and majority of victims 141 (40.63%) were in age group of 20-30 years. Urban victims 222 (64.00%) outnumbered rural. The most frequently injured body regions were the extremities 499 (53.54%), followed by maxillofacial180(19.31%).. Out of total 802 external injuries, the most common type of injury was lacerations 307 (38.28%), abrasions 306 (38.15%)and followed by bruises 154 (19.20%). Multiple external injuries were more common on upper limb 216 (26.93%), lower limbs 210 (26.18%) and face 170 (21.20%), while crush injuries were more predominently seen in both the limbs. While laceration were common on face 120 (38.83%). Injuries to the chest 19 (2.36%), abdomen13 (1.61%), and spine 11 (1.36%) were seen in roughy equal proprotion of victims. The bones on right side 55 (55.55%) were more commonly fractured which is statistically significant. Skull injuries were mostly found on frontal 77 (47.53%), followed by parietal bone 33 (20.37%), mostly on right side. Conclusion: RTAs constitute a major public health problem in our setting. Urgent preventive measures targeting at reducing the occurrence of RTAs are necessary to reduce the morbidity and mortality resulting from these injuries.

The advent of multitemporal high resolution data, like the Copernicus Sentinel-2, has enhanced significantly the potential of monitoring the earth's surface and environmental dynamics. In this paper, we present a novel deep learning framework for urban change detection which combines state-ofthe-art fully convolutional networks (similar to U-Net) for feature representation and powerful recurrent networks (such as LSTMs) for temporal modeling. We report our results on the recently publicly available bi-temporal Onera Satellite Change Detection (OSCD) Sentinel-2 dataset, enhancing the temporal information with additional images of the same region on different dates. Moreover, we evaluate the performance of the recurrent networks as well as the use of the additional dates on the unseen test-set using an ensemble crossvalidation strategy. All the developed models during the validation phase have scored an overall accuracy of more than 95%, while the use of LSTMs and further temporal information, boost the F1 rate of the change class by an additional 1.5%.

Objective Data in electronic health records (EHRs) is being increasingly leveraged for secondary uses, ranging from biomedical association studies to comparative effectiveness. To perform studies at scale and transfer knowledge from one institution to another in a meaningful way, we need to harmonize the phenotypes in such systems. Traditionally, this has been accomplished through expert specification of phenotypes via standardized terminologies, such as billing codes. However, this approach may be biased by the experience and expectations of the experts, as well as the vocabulary used to describe such patients. The goal of this work is to develop a data-driven strategy to 1) infer phenotypic topics within patient populations and 2) assess the degree to which such topics facilitate a mapping across populations in disparate healthcare systems. Methods We adapt a generative topic modeling strategy, based on latent Dirichlet allocation, to infer phenotypic topics. We utilize a variance analysis to assess the projection of a patient population from one healthcare system onto the topics learned from another system. The consistency of learned phenotypic topics was evaluated using 1) the similarity of topics, 2) the stability of a patient population across topics, and 3) the transferability of a topic across sites. We evaluated our approaches using four months of inpatient data from two geographically distinct healthcare systems: 1) Northwestern Memorial Hospital (NMH) and 2) Vanderbilt University Medical Center (VUMC). Results The method learned 25 phenotypic topics from each healthcare system. The average cosine similarity between matched topics across the two sites was 0.39, a remarkably high value given the very high dimensionality of the feature space. The average stability of VUMC and NMH patients across the topics of two sites was 0.988 and 0.812, respectively, as measured by the Pearson correlation coefficient. Also the VUMC and NMH topics have smaller variance of characterizing patient population of two sites than standard clinical terminologies (e.g., ICD9), suggesting they may be more reliably transferred across hospital systems. Conclusions Phenotypic topics learned from EHR data can be more stable and transferable than billing codes for characterizing the general status of a patient population. This suggests that EHR-based research may be able to leverage such phenotypic topics as variables when pooling patient populations in predictive models.

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We define the crossing graph of a given embedded graph (such as a road network) to be a graph with a vertex for each edge of the embedding, with two crossing graph vertices adjacent when the corresponding two edges of the embedding cross each other. In this paper, we study the sparsity properties of crossing graphs of real-world road networks. We show that, in large road networks (the Urban Road Network Dataset), the crossing graphs have connected components that are primarily trees, and that the remaining non-tree components are typically sparse (technically, that they have bounded degeneracy). We prove theoretically that when an embedded graph has a sparse crossing graph, it has other desirable properties that lead to fast algorithms for shortest paths and other algorithms important in geographic information systems. Notably, these graphs have polynomial expansion, meaning that they and all their subgraphs have small separators.

Connected Component Labeling (CCL) is an important step in pattern recognition and image processing. It assigns labels to the pixels such that adjacent pixels sharing the same features are assigned the same label. Typically, CCL requires several passes over the data. We focus on two-pass technique where each pixel is given a provisional label in the first pass whereas an actual label is assigned in the second pass. We present a scalable parallel two-pass CCL algorithm, called PAREMSP, which employs a scan strategy and the best union-find technique called REMSP, which uses REM's algorithm for storing label equivalence information of pixels in a 2-D image. In the first pass, we divide the image among threads and each thread runs the scan phase along with REMSP simultaneously. In the second phase, we assign the final labels to the pixels. As REMSP is easily parallelizable, we use the parallel version of REMSP for merging the pixels on the boundary. Our experiments show the scalability of PAREMSP achieving speedups up to $20.1$ using $24$ cores on shared memory architecture using OpenMP for an image of size $465.20$ MB. We find that our proposed parallel algorithm achieves linear scaling for a large resolution fixed problem size as the number of processing elements are increased. Additionally, the parallel algorithm does not make use of any hardware specific routines, and thus is highly portable.Comment: Parallel & Distributed Processing Symposium Workshops (IPDPSW), 201

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Aim: The objective of this study was to determine the possible extent of soil contamination at different public places with Toxocara species eggs. Materials and Methods: A Total of 327 samples of soil were collected and examined from different locations which are of public health importance like public parks, playgrounds, door mat dusts, Sidewalks or road sides, in Bareilly, Uttar Pradesh, to establish the prevalence of Toxocara eggs. Samples were also categorised in to sandy type (225) and clay type (102) which were examined by Dunsmore modified technique. Results: 42 samples out of 327 (12.84%) were found to be contaminated with the Toxocara spp.eggs and public parks were more contaminated than the other sites we studied. Clay type soil samples were found to be more contaminated than sandy type with a prevalence of 17.64%. Conclusions: The prevalence of this zoonotic parasite in soil has implications for the spread of human disease in these areas. The authors believe that this may constitute a significant health risk, particularly to children. [Vet World 2013; 6(2.000): 87-90