Knowledge and practices regarding pesticide application and handling among farmers in selected community areas of Uttarakhand

Background: In South Asia region, India is the biggest country manufacturing pesticides for agricultural production and ranks10th in world where farmers use pesticides in agricultural area. In India, farmers have less knowledge regarding pesticide application and very rarely they get opportunity to attend formal training program regarding handling of hazardous pesticides. In developing countries, farmers have unsafe pesticide application and handling practices due to which pesticide poisoning has a major health problems among famers. Indian farmers who practice unsafe use of pesticides also experience different health problems. Hence there is a necessity to find out knowledge and practices of farmers while handling dangerous pesticides in day to day life.Methods: A quantitative research approach and cross sectional survey design was used in present study. Total of 302 farmers residing in rural area of Doiwala block were selected by using purposive sampling technique. Ethical permission was obtained from institutional ethical committee and informed consent was taken from study participants. Data were analyzed using descriptive statistics.Results: A total of 125 (41.5%) farmers were using pesticide two times in a year and 180(59.8%) farmers used it for protection of crops. It was expressed by 223 (73.8%) farmers that they read the labels on the pesticide containers before using it but only 182(60.3%) farmers followed the instructions on the label.Conclusions: Farmers did not have adequate knowledge about frequency and reasons of using pesticide in farming. Majority of the farmers did not have adequate knowledge and practices regarding use of pesticide in agricultural area.

Bio-friendly management of Guava fruit fly (Bactrocera correcta Bezzi) through wrapping technique

Fruit fly (Bactrocera correcta Bezzi) is the major pest of Guava grown in Baruipur region of West Bengal, contributing upto 90% yield loss. The present study was undertaken during 2011-12 at farmers’ field to validate the wrapping of individual fruits at tree and to standardize the wrapping material and the correct technique of wrapping. Performance of nine different types of wrapping materials (butter paper bag, polypropylene bag of 20? gauge with and without paper piece inside, non-woven poly fabric bags of white, green and blue colour with 20 gsm and 40 gsm thickness) along with two chemical approaches were studied against untreated control. Fruit fly infestation varied between 1.32 % and 17.31% in all treatments using wrapping materials and 13.14% in case of combined use of pheromone trap (Bacu lure) and Dichlorvos spray as compared to 21.71% in sole use of Dichlorvos and 66.67% in control plots. Wrapping resulted in increased weight of individual fruits (112.58 g in butter paper bag compared to 68.40 g in control). Wrapping with transparent polypropylene bags (20? gauge) with partial paper cover inside, resulted in lowest yield loss (1.66%), earlier fruit maturity, better fruit quality (in respect of colour and glossiness), highest market price (`30 per kg) and highest net profit (`1.357 lakh/ha). This material is durable enough to be reused for 4-5 times. The partial paper cover helped to prevent scorching injury to the fruit as well as to control the humidity inside the polypropylene bag

Activity controls fragility: A Random First Order Transition Theory for an active glass

How does nonequilibrium activity modify the approach to a glass? This is an important question, since many experiments reveal the near-glassy nature of the cell interior, remodelled by activity. However, different simulations of dense assemblies of active particles, parametrised by a self-propulsion force, $f_0$, and persistence time, $\tau_p$, appear to make contradictory predictions about the influence of activity on characteristic features of glass, such as fragility. This calls for a broad conceptual framework to understand active glasses; here we extend the Random First-Order Transition (RFOT) theory to a dense assembly of self-propelled particles. We compute the active contribution to the configurational entropy using an effective medium approach - that of a single particle in a caging-potential. This simple active extension of RFOT provides excellent quantitative fits to existing simulation results. We find that whereas $f_0$ always inhibits glassiness, the effect of $\tau_p$ is more subtle and depends on the microscopic details of activity. In doing so, the theory automatically resolves the apparent contradiction between the simulation models. The theory also makes several testable predictions, which we verify by both existing and new simulation data, and should be viewed as a step towards a more rigorous analytical treatment of active glass

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%

Efficient Synthesis of Homoallylic Alcohols/Amines from Allyltributylstannane and Carbonyl Compounds/Imines Using Iodine as Catalyst Under Acetic Acid–Water Medium

<div><p></p><p>This paper describes a general method for the synthesis of homoallylic alcohols and amines by nucleophilic addition reaction of allyltributylstannane to carbonyl compounds and aldimines where iodine acts as a catalyst in H₂O/acetic acid (1:1) medium. Only 10 mol% of I₂ is required for various organic transformations. By using this process, various homoallylic alcohols and amines are produced in good to excellent yields.</p></div

Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique

Rice is one of the most widely consumed foods all over the world. Various diseases and deficiency disorders impact the rice crop’s growth, thereby hampering the rice yield. Therefore, proper crop monitoring is very important for the early diagnosis of diseases or deficiency disorders. Diagnosis of diseases and disorders requires specialized manpower, which is not scalable and accessible to all farmers. To address this issue, machine learning and deep learning (DL)-driven automated systems are designed, which may help the farmers in diagnosing disease/deficiency disorders in crops so that proper care can be taken on time. Various studies have used transfer learning (TL) models in the recent past. In recent studies, further improvement in rice disease and deficiency disorder diagnosis system performance is achieved by performing the ensemble of various TL models. However, in all these DL-based studies, the segmentation of the region of interest is not done beforehand and the infected-region extraction is left for the DL model to handle automatically. Therefore, this article proposes a novel framework for the diagnosis of rice-infected leaves based on DL-based segmentation with bitwise logical AND operation and DL-based classification. The rice diseases covered in this study are bacterial leaf blight, brown spot, and leaf smut. The rice nutrient deficiencies like nitrogen (N), phosphorous (P), and potassium (K) were also included. The results of the experiment conducted on these datasets showed that the performance of DeepBatch was significantly improved as compared to the conventional technique