Classification of Atrial Fibrillation using Random Forest Algorithm

The electrocardiogram is indicates the electrical activity of the heart and it can be used to detect cardiac arrhythmias. In the present work, we exhibited a methodology to classify Atrial Fibrillation (AF), Normal rhythm, and Other abnormal ECG rhythms using a machine learning algorithm by analyzing single-lead ECG signals of short duration. First, the events of ECG signals will be detected, after that morphological features and HRV features are extracted. Finally, these features are applied to the Random Forest classifier to perform classification. The Physionet challenge 2017 dataset with more than 8500 ECG recordings is used to train our model. The proposed methodology yields an F1 score of 0.86, 0.97, and 0.83 respectively in classifying AF, normal, other rhythms, and an accuracy of 0.91 after performing a 5-fold cross-validation

Crystal structure of 1-benzylsulfonyl-1,2,3,4-tetrahydroquinoline

SJ thanks the Vision Group on Science and Technology, Government of Karnataka, for the award of a major project under the CISE scheme (reference No. VGST/CISE/GRD192/2013-14). BSPM thanks Rajegowda, Department of Studies and Research in Chemistry, UCS, Tumkur University, Karnataka 572 103, India, for his support.Peer reviewedPublisher PD

A different approach to soil analysis: Indicative studies

Soil analysis is a tool that has been employed with the primary goal of providing recommendations for soil rectification, crop productivity and for soil health management. Time tested methods like ammonium acetate extraction and diethylene triamine penta acetic acid (DTPA) are commonly used for analysis of bioavailable nutrients. However, there are some limitations to these methods as both extraction fluids are buffered to neutral or near-neutral pH. Hence extracted nutrients represent a “potential or ideal-case” fertility status of soil instead of an “actual” field status. In the ‘Regular methods’, we are overlooking the role of pH, the master variable, in determining the availability of nutrients. Hence, in ‘Modified methods’, the extraction fluid is buffered to actual soil pH. Results obtained with over 150 random samples representing a range of pH, have indicated a difference in values between regular and modified extraction methods. The modified methods (MM) of ammonium acetate and DTPA extraction adjusted to soil pH were found to be better than regular method (RM) for estimation of calcium, magnesium with ammonium acetate and iron and manganese with DTPA in alkaline soils above pH 8.0. For a complete picture of soil health, productivity and fertility, microbiological and enzymatic analysis of soils were included in the present study. Soil solution equivalent medium (SSE) was found to be the appropriate culture medium for microbial counts. A linear relationship was found between urease activity and available nitrogen of soil

Vascular effects of urocortins 2 and 3 in healthy volunteers

Background: Urocortin 2 and urocortin 3 are endogenous peptides with an emerging role in cardiovascular pathophysiology. We assessed their pharmacodynamic profile and examined the role of the endothelium in mediating their vasomotor effects in vivo in man. Methods and Results: Eighteen healthy male volunteers (23±4 years) were recruited into a series of double‐blind, randomized crossover studies using bilateral forearm venous occlusion plethysmography during intra‐arterial urocortin 2 (3.6 to 120 pmol/min), urocortin 3 (1.2 to 36 nmol/min), and substance P (2 to 8 pmol/min) in the presence or absence of inhibitors of cyclooxygenase (aspirin), cytochrome P450 metabolites of arachidonic acid (fluconazole), and nitric oxide synthase (L‐NMMA). Urocortins 2 and 3 evoked arterial vasodilatation (P<0.0001) without tachyphylaxis but with a slow onset and offset of action. Inhibition of nitric oxide synthase with L‐NMMA reduced vasodilatation to substance P and urocortin 2 (P≤0.001 for both) but had little effect on urocortin 3 (P>0.05). Neither aspirin nor fluconazole affected vasodilatation induced by any of the infusions (P>0.05 for all). In the presence of all 3 inhibitors, urocortin 2– and urocortin 3–induced vasodilatation was attenuated (P<0.001 for all) to a greater extent than with L‐NMMA alone (P≤0.005). Conclusions: Urocortins 2 and 3 cause potent and prolonged arterial vasodilatation without tachyphylaxis. These vasomotor responses are at least partly mediated by endothelial nitric oxide and cytochrome P450 metabolites of arachidonic acid. The role of urocortins 2 and 3 remains to be explored in the setting of human heart failure, but they have the potential to have major therapeutic benefits

THE EFFECT OF CONTRAST ENHANCEMENT ON EPIPHYTE SEGMENTATION USING GENERATIVE NETWORK

The performance of the deep learning-based image segmentation is highly dependent on two major factors as follows: 1) The organization and structure of the architecture used to train the model and 2) The quality of input data used to train the model. The input image quality and the variety of training samples are highly influencing the features derived by the deep learning filters for segmentation. This study focus on the effect of image quality of a natural dataset of epiphytes captured using Unmanned Aerial Vehicles (UAV), while segmenting the epiphytes from other background vegetation. The dataset used in this work is highly challenging in terms of pixel overlap between target and background to be segmented, the occupancy of target in the image and shadows from nearby vegetation. The proposed study used four different contrast enhancement techniques to improve the image quality of low contrast images from the epiphyte dataset. The enhanced dataset with four different methods were used to train five different segmentation models. The segmentation performances of four different models are reported using structural similarity index (SSIM) and intersection over union (IoU) score. The study shows that the epiphyte segmentation performance is highly influenced by the input image quality and recommendations are given based on four different techniques for experts to work with segmentation with natural datasets like epiphytes. The study also reported that the occupancy of the target epiphyte and vegetation highly influence the performance of the segmentation model

CD11b suppresses TLR activation of nonclassical monocytes to reduce primary graft dysfunction after lung transplantation

Primary graft dysfunction (PGD) is the leading cause of postoperative mortality in lung transplant recipients and the most important risk factor for development of chronic lung allograft dysfunction. The mechanistic basis for the variability in the incidence and severity of PGD between lung transplant recipients is not known. Using a murine orthotopic vascularized lung transplant model, we found that redundant activation of Toll-like receptors 2 and 4 (TLR2 and -4) on nonclassical monocytes activates MyD88, inducing the release of the neutrophil attractant chemokine CXCL2. Deletion of Itgam (encodes CD11b) in nonclassical monocytes enhanced their production of CXCL2 and worsened PGD, while a CD11b agonist, leukadherin-1, administered only to the donor lung prior to lung transplantation, abrogated CXCL2 production and PGD. The damage-associated molecular pattern molecule HMGB1 was increased in peripheral blood samples from patients undergoing lung transplantation after reperfusion and induced CXCL2 production in nonclassical monocytes via TLR4/MyD88. An inhibitor of HMGB1 administered to the donor and recipient prior to lung transplantation attenuated PGD. Our findings suggest that CD11b acts as a molecular brake to prevent neutrophil recruitment by nonclassical monocytes following lung transplantation, revealing an attractive therapeutic target in the donor lung to prevent PGD in lung transplant recipients

INFLUENCE OF ADDITIONAL SPECTRAL BANDS FOR EPIPHYTE SEGMENTATION ON DRU-NET

Dense Residual U-Net (DRU-Net) is a neural network used for image segmentation. It is based on the U-Net architecture and isa combination of modified ResNet as the encoder and modified DenseNet as the decoder blocks. DRU-Net captures both the local and contextual information. Previous studies on DRU-Net have not tested the influence of the spectral resolution of the images. In an earlier study, the DRU-Net was trained with grayscale images for epiphyte segmentation. The network trained and tested with grayscale images underperformed while varying the illumination and occupancy of the target in the frame. In this study, the same network was trained and tested with RGB images for assessing the increase in overall learning. The performance of the network in segmenting epiphytes under conditions such as good/poor illumination and high/low target occupancy was analyzed. Dice and Jaccard scores were used as evaluation metrics. The DRU-Net model trained with RGB images had an improvement of 20% over the grayscale model in both average Dice and average Jaccard scores of the target class. Based on the higher Dice and Jaccard scores, adding additional spectral information improves DRU-Net learning. The increased computation time required for training DRU-Net with RGB images will result in better output. This model could be further used for identifying multiple epiphytes in images with poor illumination and different occupancy conditions

System for Water Quality Monitoring and Distribution

Water plays a vital role in the creation of human being and other natural phenomena. More than 80% of the resources is surrounded by water but in that only 20% is good for consumption others are fully polluted and contaminated. Now a days water is more polluted, and even supplied in a very lesser level so to check and monitor the quality of the water we mainly using a number of sensors are used to monitor the water’s quality and distribute it to the less fortunate. The quality of the water is affected by several parameters. Water is provided from difference resources like lake, pond, well, ground water, oceans etc.so these waters are not good for consumption Therefore, our goal is to assess the water’s quality while keeping an eye on the flow and level of the water. It is intended to use a variety of cutting-edge devices to check various water quality system parameters

P04-41. Kinetics of Antibody Neutralization and Viral Evolution Following Envelope Vaccination in SIV-infected Rhesus Monkeys

Poster presentation. Conclusion: Our results indicate that env vaccination is associated with an accelerated development of autologous neutralizing antibodies. These antibodies were focused at least in part on the V1 region of Env, since there was selective pressure in this region of the envelope for the evolution of mutational changes