Impact of Educational Intervention Measures on Knowledge regarding HIV/ Occupational Exposure and Post Exposure Prophylaxis among Final Year Nursing Students of a Tertiary Care Hospital in Central India

Amongst the different Health Care Personnel nurses are at a greater risk of being accidentally exposed to HIV and other Blood Borne Pathogens. The present study was conducted among 50 final year nursing students of a Medical College Hospital to assess the knowledge regarding HIV, occupational exposure and Post Exposure Prophylaxis (PEP) among the students and analyses the impact of educational intervention measures on the issues amongst the study subjects. A Pre-designed and Pre-tested semi-structured questionnaire was used to evaluate the level of knowledge before and after educational intervention sessions. Knowledge regarding risk of transmission of HIV by needle-stick injury and body fluids against which universal precautions were mandatory increased by 72% following the intervention sessions (χ2 = 53.202, p <0.001). 72% and 36% respondents correctly knew the duration within which to start PEP and the drugs available for PEP, post educational sessions 98% and 96% students were aware of it: the difference being statistically significant (χ2 = 11.294, p <0.001) and (χ2 = 37.748, p <0.001) respectively. The mean pre-intervention score was 8.32; mean post-intervention score was 14.40: statistical analysis showed the results to be significant (t= 13.857, p< 0.001). The study reflects that there is a dearth of knowledge among the study group. Incorporating the concerned issues in the academic curriculum to provide the students with adequate knowledge and information during their formative years is needed

Detection and Recognition of Badgers Using Deep Learning

This paper describes the use of two different deep-learning algorithms for object detection to recognize different badgers. We use recordings of four different badgers under varying background illuminations. In total four different object detection algorithms based on deep neural networks are compared: The single shot multi-box detector (SSD) with the Inception-V2 or MobileNet as a backbone, and the faster region-based convolutional neural network (Faster R-CNN) combined with Inception-V2 or residual networks. Furthermore, two different activation functions are compared to compute probabilities that some badger is in the detected region: the softmax and sigmoid functions. The results of all eight models show that SSD obtains higher recognition accuracies (97.8%–98.6%) than Faster R-CNN (84.8%–91.7%). However, the training time of Faster R-CNN is much shorter than that of SSD. The use of different output activation functions seems not to matter much

UEV-1 Is an Ubiquitin-Conjugating Enzyme Variant That Regulates Glutamate Receptor Trafficking in C. elegans Neurons

The regulation of AMPA-type glutamate receptor (AMPAR) membrane trafficking is a key mechanism by which neurons regulate synaptic strength and plasticity. AMPAR trafficking is modulated through a combination of receptor phosphorylation, ubiquitination, endocytosis, and recycling, yet the factors that mediate these processes are just beginning to be uncovered. Here we identify the ubiquitin-conjugating enzyme variant UEV-1 as a regulator of AMPAR trafficking in vivo. We identified mutations in uev-1 in a genetic screen for mutants with altered trafficking of the AMPAR subunit GLR-1 in C. elegans interneurons. Loss of uev-1 activity results in the accumulation of GLR-1 in elongated accretions in neuron cell bodies and along the ventral cord neurites. Mutants also have a corresponding behavioral defect—a decrease in spontaneous reversals in locomotion—consistent with diminished GLR-1 function. The localization of other synaptic proteins in uev-1-mutant interneurons appears normal, indicating that the GLR-1 trafficking defects are not due to gross deficiencies in synapse formation or overall protein trafficking. We provide evidence that GLR-1 accumulates at RAB-10-containing endosomes in uev-1 mutants, and that receptors arrive at these endosomes independent of clathrin-mediated endocytosis. UEV-1 homologs in other species bind to the ubiquitin-conjugating enzyme Ubc13 to create K63-linked polyubiquitin chains on substrate proteins. We find that whereas UEV-1 can interact with C. elegans UBC-13, global levels of K63-linked ubiquitination throughout nematodes appear to be unaffected in uev-1 mutants, even though UEV-1 is broadly expressed in most tissues. Nevertheless, ubc-13 mutants are similar in phenotype to uev-1 mutants, suggesting that the two proteins do work together to regulate GLR-1 trafficking. Our results suggest that UEV-1 could regulate a small subset of K63-linked ubiquitination events in nematodes, at least one of which is critical in regulating GLR-1 trafficking

Motion Segmentation Based on Structure-Texture Decomposition and Improved Three Frame Differencing

Part 12: Nature Inspired Flight and Robot Control - Machine VisionInternational audienceMotion segmentation from the video datasets has several important applications like traffic monitoring, action recognition, visual object tracking, and video surveillance. The proposed technique combines the structure-texture decomposition and the improved three frames differencing for motion segmentation. First, the Osher and Vese approach is employed to decompose the video frame into two components, viz., structure and texture/noise. Now, to eliminate the noise, only the structure components are employed for further steps. Subsequently, the difference between (i) the current frame and the previous frame as well as (ii) the current frame and the next frame are estimated. Next, both the difference frames are combined using pixel-wise maximum operation. Each combined difference frame is then partitioned into non-overlapping blocks, and the intensity sum as well as median of each block is computed. Successively, target objects are detected with the help of threshold and intensity median. Finally, post-processing in the form of morphology operation and connected component analysis is carried out to accurately find the foreground. Our technique has been formulated, implemented and tested on publicly available standard benchmark datasets and it is proved from performance analysis that our technique exhibit efficient outcomes than existing approaches