Seasonal, Age &Gender Variation of Guillain Barre Syndrome in a Tertiary Referral Center in India

Background: Seasonal variation & gender preponderance have not been adequately studied in Guillain Barre Syndrome (GBS). University of Health Sciences, Rohtak is the tertiary referral facility for a large part of North West India. We conducted a prospective study to investigate differences in GBS incidence between males and females & across different seasons of the year. Methods:65 clinically diagnosed GBS patients, aged 5 - 70 years, referred for nerve conduction, Fwave & EMG studies for 3 years. Results: 64.61% were males while 35.38% were females. Maximum patients were in the age group 5 - 20 years (46.15%). The highest incidence of GBS (41.53%) were seen in the summer months;19 (29.23%) in the spring season, 11 (16.92%) in winter season and 8 (12.30%) in rainy season. 5 patients had diarrhoea while 12 patients had flu like syndrome 1 - 2 weeks before the onset of GBS. Conclusion: The peak seasonal clustering noted by us in the summer months was consistent significantly with other Asian studies. The age and sex distribution of GBS in our series, which showed children & minor preponderance with peak incidence in 5 - 20 years age followed by another in the age group 21 - 40 years, is different from most studies which report a second peak after 50 years of age.

Magnetic Measurements of a Third Harmonic Table-Top Undulator for Free Electron Lasers

We describe the measurement studies of a third harmonic undulator.The harmonic undulator is fabricated by placing shims at suitable locations along the length of a table-top six-period planar undulator.The undulator field is measured with a Hall probe and pulsed wire bench.The pulsed wire data show good agreement with the Hall probe data.

Enhanced Field-Based Detection of Potato Blight in Complex Backgrounds Using Deep Learning

Rapid and automated identification of blight disease in potato will help farmers to apply timely remedies to protect their produce.Manual detection of blight disease can be cumbersome and may require trained experts.To overcome these issues,we present an automated system using the Mask Region-based convolutional neural network(Mask R-CNN)architecture,with residual network as the backbone network for detecting blight disease patches on potato leaves in field conditions.The approach uses transfer learning,which can generate good results even with small datasets.The model was trained on a dataset of 1423 images of potato leaves obtained from fields in different geographical locations and at different times of the day.The images were manually annotated to create over 6200 labeled patches covering diseased and healthy portions of the leaf.The Mask R-CNN model was able to correctly differentiate between the diseased patch on the potato leaf and the similar-looking background soil patches,which can confound the outcome of binary classification.To improve the detection performance,the original RGB dataset was then converted to HSL,HSV,LAB,XYZ,and YCrCb color spaces.A separate model was created for each color space and tested on 417 field-based test images.This yielded 81.4%mean average precision on the LAB model and 56.9%mean average recall on the HSL model,slightly outperforming the original RGB color space model.Manual analysis of the detection performance indicates an overall precision of 98%on leaf images in a field environment containing complex backgrounds.