Climatic Risks, Rice Production Losses and Risk Coping Strategies: A Case Study of a Rainfed Village in Coastal Orissa

Abiotic stresses observed in the village Kaudikol , district Cuttack, Orissa have been recorded for 8 years and analysed. Survey data collected for four years (1996-97 to 1999-2000) from the farmers of this village have been analysed to find out their livelihood system, importance of rice in this system and the coping mechanisms followed by them in case of loss to the kharif rice crop. The abiotic stresses have been found to cause flood / submergence to different degrees in 5 years, drought in 3 years and cyclone in one year out of the total 8 years, causing production losses to rice. The maximum losses to rice crop have been observed during 1999 kharif season due to cyclone. It has been found that rice is the major crop during the kharif season, covering up to 79 per cent of the total cultivated area with contribution of 21 per cent to total income. Service has been found to be the most important source of income, followed by rice and business. The average annual income of the farmers has been noticed to vary from Rs 23,329 for marginal farmers to Rs 84,072 for large farmers over the period 1997-98 to 1999-2000. Rice has been found to be the major source of income for medium than other categories of farmers. For large farmers, salaried job has been observed as the most important source of income. The marginal and small farmers have been found to compensate their rice income loss from sources like wage earnings, jute and rabi/ summer rice. Through non-farm income and crop diversification, the farmers have been able to reduce the effect of rice income losses to some extent. Therefore, more non-farm employment opportunities should be created in this area to increase income and living standard of the farmers. Development of submergence-tolerant varieties with high-yield is the priority area for rice research in this area. There is also a need to introduce crop insurance scheme for rice crop in this area.Agricultural and Food Policy,

Rice Ecosystems and Factors Affecting Varietal Adoption in Rainfed Coastal Orissa: A Multivariate Probit Analysis

Hydrology, Coastal Orissa, Rice, Probit analysis, Agricultural and Food Policy, Q15, Q16,

Pharmacotherapeutic friendly fire in the intensive care unit: high stakes seeking high calibre

Increasing numbers of patients are surviving the intensive care unit. Concordant with our shifting focus to minimizing intensive care unit-acquired morbidity, in the present issue of Critical Care Moyen, Camire, and Stelfox describe the importance of quality pharmacotherapy. They describe challenges and potential solutions to this source of iatrogenic injury in our vulnerable patients. Their article reminds us not to understate the importance of medication error, to avoid overstating the benefits of incompletely proven methods to prevent medication error, and to distinguish harmful medication errors from other types of medication error

INTEGRATED STUDY OF MODE OF ACTION OF MOCHARASA BASTI IN GRAHANI VYADHI WITH SPECIAL REFERENCE TO INFLAMMATORY BOWEL DISEASE

During medical practice physician many times come across such diseases which are difficult to treat and one of them is Grahani. All the clinical features of Grahani Vyadhi resemble with ‘inflammatory bowel disease’. It is a broad term which indicates chronic inflammatory pattern of colon and thus also includes Ulcerative colitis and Crohn’s disease. Ulcerative colitis and Crohn’s disease both involve diarrhoea, pain in abdomen, fatigue, rectal bleeding and chronic persistence of disease results into weight loss. Crohn’s disease affects small intestine, large intestine, mouth, stomach and anus as well, but in Ulcerative colitis it mainly affects colon and rectum. Inflammatory bowel syndrome may develop at any age but 15-30 years is the most common age group getting affected. The therapeutic approach towards these diseases remains symptomatic in current mainstream line of treatment and has no prompt treatment as such. According to Madhava Nidana compendia which describe Grahani vyadhi with reference to organ Grahani, where regular function of Grahani sthana (organ) gets impaired. Grahani is the prime location of Agni, Pachaka pitta dosha, and Samana vayu. According to Ayurveda clinical presentation of Grahani Vyadhi very closely depicts picture of Inflammatory Bowel Disease as mentioned above. Acharya Charaka has described Mochrasa (extract of plant Bombax ceiba Linn. rich in tannins) as one of the medication for Grahani Vyadhi. Tannin-polysaccharide complex protects ulcerated areas of colon. It has been proposed here that a Mocharasa can to be used with milk as a Basti (medicated enema) preparation in patients with IBD.

Deep Learning Models for Arrhythmia Classification Using Stacked Time-frequency Scalogram Images from ECG Signals

Electrocardiograms (ECGs), a medical monitoring technology recording cardiac activity, are widely used for diagnosing cardiac arrhythmia. The diagnosis is based on the analysis of the deformation of the signal shapes due to irregular heart rates associated with heart diseases. Due to the infeasibility of manual examination of large volumes of ECG data, this paper aims to propose an automated AI based system for ECG-based arrhythmia classification. To this front, a deep learning based solution has been proposed for ECG-based arrhythmia classification. Twelve lead electrocardiograms (ECG) of length 10 sec from 45, 152 individuals from Shaoxing People's Hospital (SPH) dataset from PhysioNet with four different types of arrhythmias were used. The sampling frequency utilized was 500 Hz. Median filtering was used to preprocess the ECG signals. For every 1 sec of ECG signal, the time-frequency (TF) scalogram was estimated and stacked row wise to obtain a single image from 12 channels, resulting in 10 stacked TF scalograms for each ECG signal. These stacked TF scalograms are fed to the pretrained convolutional neural network (CNN), 1D CNN, and 1D CNN-LSTM (Long short-term memory) models, for arrhythmia classification. The fine-tuned CNN models obtained the best test accuracy of about 98% followed by 95% test accuracy by basic CNN-LSTM in arrhythmia classification.Comment: 4 pages,1 figure

Structural Interventions Against Physician Burnout: Resident Schedule

Colin West and the Mayo team recently published a meta-analysis of Interventions to Prevent and Reduce Physician Burnout (Lancet 2016; 388: 2272-81) and found that only three randomized studies of structural or organizational interventions have been reported in the literature. Brian Lucas et al. examined the Effects of 2- vs 4- Week Attending Physician Inpatient Rotations on Unplanned Patient Revisits, Evaluations by Trainees, and Attending Physician Burnout (JAMA 2012). The second organizational intervention was the Healthy Work Place (HWP) study by Mark Linzer et al: Interventions to Improve Work Conditions and Clinician Burnout in Primary Care (J Gen Intern Med 2015). Third, Christopher Parshuram et al studied Patient Safety, Resident Well-Being and Continuity of Care with Different Residents Duty Schedules in the Intensive Care Unit (CMAJ 2015). The panel will discuss the findings of these four studies and what further research is needed to determine the most effective interventions for preventing and reducing physician burnout.https://openworks.mdanderson.org/hrc_burnout_presentations/1010/thumbnail.jp

Development and initial validation of the Bedside Paediatric Early Warning System score

Abstract Introduction Adverse outcomes following clinical deterioration in children admitted to hospital wards is frequently preventable. Identification of children for referral to critical care experts remains problematic. Our objective was to develop and validate a simple bedside score to quantify severity of illness in hospitalized children. Methods A case-control design was used to evaluate 11 candidate items and identify a pragmatic score for routine bedside use. Case-patients were urgently admitted to the intensive care unit (ICU). Control-patients had no 'code blue', ICU admission or care restrictions. Validation was performed using two prospectively collected datasets. Results Data from 60 case and 120 control-patients was obtained. Four out of eleven candidate-items were removed. The seven-item Bedside Paediatric Early Warning System (PEWS) score ranges from 0–26. The mean maximum scores were 10.1 in case-patients and 3.4 in control-patients. The area under the receiver operating characteristics curve was 0.91, compared with 0.84 for the retrospective nurse-rating of patient risk for near or actual cardiopulmonary arrest. At a score of 8 the sensitivity and specificity were 82% and 93%, respectively. The score increased over 24 hours preceding urgent paediatric intensive care unit (PICU) admission (P < 0.0001). In 436 urgent consultations, the Bedside PEWS score was higher in patients admitted to the ICU than patients who were not admitted (P < 0.0001). Conclusions We developed and performed the initial validation of the Bedside PEWS score. This 7-item score can quantify severity of illness in hospitalized children and identify critically ill children with at least one hours notice. Prospective validation in other populations is required before clinical application

PatchBMI-Net: Lightweight Facial Patch-based Ensemble for BMI Prediction

Due to an alarming trend related to obesity affecting 93.3 million adults in the United States alone, body mass index (BMI) and body weight have drawn significant interest in various health monitoring applications. Consequently, several studies have proposed self-diagnostic facial image-based BMI prediction methods for healthy weight monitoring. These methods have mostly used convolutional neural network (CNN) based regression baselines, such as VGG19, ResNet50, and Efficient-NetB0, for BMI prediction from facial images. However, the high computational requirement of these heavy-weight CNN models limits their deployment to resource-constrained mobile devices, thus deterring weight monitoring using smartphones. This paper aims to develop a lightweight facial patch-based ensemble (PatchBMI-Net) for BMI prediction to facilitate the deployment and weight monitoring using smartphones. Extensive experiments on BMI-annotated facial image datasets suggest that our proposed PatchBMI-Net model can obtain Mean Absolute Error (MAE) in the range [3.58, 6.51] with a size of about 3.3 million parameters. On cross-comparison with heavyweight models, such as ResNet-50 and Xception, trained for BMI prediction from facial images, our proposed PatchBMI-Net obtains equivalent MAE along with the model size reduction of about 5.4x and the average inference time reduction of about 3x when deployed on Apple-14 smartphone. Thus, demonstrating performance efficiency as well as low latency for on-device deployment and weight monitoring using smartphone applications.Comment: 7 pages,3 figure

Taxonomic studies of Eulophid parasitoids (Hymenoptera: Chalcidoidea) collected from Uttarakhand, India

A sound taxonomic knowledge base is a prerequisite for effective conservation, environment assessment, ecological research, management and sustainable use of biological resources. Parasitoids are the major component of the biocontrol, so the correct identification of the parasitoid is very important task. Eulophidae is a large family of the superfamily Chalcidoidea and comprises promising biocontrol agents for the control of insect pests causing harm to agricultural ecosystem. The present study has been done to provide the account and occurrence of 4 genera belonging to subfamily Entedoninae, Eulophinae and Tetrastichinae. Entedon costalis Dalman, Diglyphus horticola Khan, Hemiptarsenus varicornis (Girault), and Neotrichoporoides viridimaculatus (Fullaway) was collected from Uttarakhand (India) and described in detail with additional morphological characters that aids in clear identification of the parasitoids. Entedon costalis is recorded from this region with additional characters for identification