Modelling the impact of different irrigation regimes and mulching on strawberry crop growth and water use in the arsenic-contaminated Bengal basin

Replacement of water-intensive winter rice with strawberry (Fragaria × ananassa Duch.) may restrict groundwater extraction and improve water productivity and sustainability of agricultural production in the arsenic-contaminated Bengal basin. The potential of strawberry cultivation in terms of yield obtained and water use efficiency need to be evaluated under predominant soil types with mulch applications. Water-driven model AquaCrop was used to predict the canopy cover, soil water storage and above-ground biomass of strawberry in an arsenic-contaminated area in the Bengal basin. After successful calibration and validation over three seasons, AquaCrop was used over a range of management scenarios (nine drip-irrigation × three soil types × four mulch materials) to identify the best irrigation options for a drip-irrigated strawberry crop. The most appropriate irrigation of 176 mm for clay loam soil in lowland and 189 mm for sandy clay loam in medium land rice areas and the use of organic mulch from locally available jute agrotextile improved 1.4 times higher yield and 1.7 times higher water productivity than that of without mulch. Strawberry can be introduced as an alternative crop replacing rice in non-traditional upland and medium land areas of the arsenic-contaminated Bengal basin with 88% lower groundwater extraction load and better economic return to farmers.

Serological surveillance and clinical investigation of glanders among indigenous equines in India from 2015 to 2018

Equine glanders is an infectious and notifiable bacterial disease caused by Burkholderia mallei. The disease has been reported in South American, African and Asian countries including India. Here, we present the outcome of glanders serosurveillance carried out between January 2015 and December 2018 to know the status of equine glanders among different states in India. A total of 102,071 equid sera from 299 districts of twenty-one states and one union territory were tested for glanders. Samples were screened with Hcp1 indirect ELISA followed by confirmatory diagnosis by CFT. During this four-year surveillance, a total of 932 glanders-positive cases were detected from 120 districts of 12 states. The study also revealed increasing trend of glanders from 2016 onwards with maximum occurrence in northern India. Overall seroprevalence ranged between 0.62% (95% CI, 0.52-0.72) and 1.145% (95% CI, 1.03-1.25). Seasonal shifting from winter to summer (March to June) coincided with highest number glanders incidence with corresponding seroprevalences of 1.2% (95% CI, 1.09-1.30). The present surveillance unveils territorial ingression of glanders to six states like Jammu & Kashmir, Gujarat, Rajasthan, Madhya Pradesh, Delhi and Tamil Nadu. In addition, re-emerging cases have been reported in Maharashtra, Haryana and Punjab after a gap of 10 years. Lack of awareness, little veterinary care and unrestricted movement of equids across state borders might have led to the introduction and establishment of the infection to these states. We believe that information from this study will provide a baseline data on glanders for devising surveillance and control strategies in India. Being a zoonotic disease, the persistence of glanders poses a potential threat to occupationally exposed humans especially equine handlers and veterinarians. Therefore, targeted surveillance of human population from each glanders outbreak is also recommended.

Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.)

Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.