Screening of Comprehensive Panel of Cultivated and Wild Vigna Species for Resistance to Pulse Beetle, Callosobruchus chinensis L.

Pulses are a key source of dietary proteins in human nutrition. Despite several efforts to increase the production, various constraints, such as biotic and abiotic factors, threaten pulse production by various means. Bruchids (Callosobruchus spp.) are the serious issue of concern, particularly in storage conditions. Understanding host-plant resistance at morphological, biochemical and molecular levels is the best way to minimize yield losses. The 117 mungbean (Vigna radiata L. Wilczek) genotypes, including endemic wild relatives, were screened for resistance against Callosobruchus chinensis; among them, two genotypes, PRR 2008-2 and PRR 2008-2-sel, which belong to V. umbellata (Thumb.), were identified as highly resistant. The expression of antioxidants in susceptible and resistant genotypes revealed that the activity of phenylalanine ammonia lyase (PAL) was upregulated in the highly resistant wild Vigna species and lower in the cultivated susceptible genotypes, along with other biomarkers. Further, the SCoT-based genotyping revealed SCoT-30 (200 bp), SCoT-31 (1200 bp) and SCoT-32 (300 bp) as unique amplicons, which might be useful for developing the novel ricebean-based SCAR markers to accelerate the molecular breeding programme.

Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications.

The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE's efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.

Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications.

The application of the Internet of Things (IoT) in wireless sensor networks (WSNs) poses serious challenges in preserving network longevity since the IoT necessitates a considerable amount of energy usage for sensing, processing, and data communication. As a result, there are several conventional algorithms that aim to enhance the performance of WSN networks by incorporating various optimization strategies. These algorithms primarily focus on the network layer by developing routing protocols to perform reliable communication in an energy-efficient manner, thus leading to an enhanced network life. For increasing the network lifetime in WSNs, clustering has been widely accepted as an important method that groups sensor nodes (SNs) into clusters. Additionally, numerous researchers have been focusing on devising various methods to increase the network lifetime. The prime factor that helps to maximize the network lifetime is the minimization of energy consumption. The authors of this paper propose a multi-objective optimization approach. It selects the optimal route for transmitting packets from source to sink or the base station (BS). The proposed model employs a two-step approach. The first step employs a trust model to select the cluster heads (CHs) that manage the data communication between the BS and nodes in the cluster. Further, a novel hybrid algorithm, combining a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA), is proposed to determine the routes for data transmission. To validate the efficacy of the proposed hybrid algorithm, named PSOGA, simulations were conducted and the results were compared with the existing LEACH method and PSO, with a random route selection for five different cases. The obtained results establish the efficiency of the proposed approach, as it outperforms existing methods with increased energy efficiency, increased network throughput, high packet delivery rate, and high residual energy throughout the entire iterations.

Skin banking at a regional burns centre-The way forward.

In India approximately 1 million people get burnt every year and most of them are from the lower or middle income strata. Therefore it is obligatory to find out an economic way of treatment for the affected populace. Since use of human skin allograft is the gold standard for the treatment of burn wound, in-house skin banking for a burn unit hospital is prerequisite to make the treatment procedure affordable. Although, there was one skin bank at India till 2009, but it was difficult for a single bank to cover the entire country's need. Looking at the necessities, National Burns Centre (a tertiary burn care centre) along with Rotary International and Euro Skin Bank collaborated and developed an effective cadaveric skin banking model in Mumbai, Maharashtra in 2009. Initial two to three years were formation phase; by the year 2013 the entire system was organized and started running full fledged. The model has also been replicated in other states of India to accommodate the large burn population of the country. This paper therefore, gives a step by step account of how the bank evolved and its present status.

Exposure to Phlebotomus argentipes (Diptera, Psychodidae, Phlebotominae) sand flies in rural areas of Bihar, India: the role of housing conditions.

BACKGROUND: Visceral Leishmaniasis (VL) is a vector-borne infectious disease, caused by the protozoan Leishmania donovani, which is transmitted by phlebotomine sand flies. In an earlier study in Bihar, India, we found an association between incidence of VL and housing conditions. In the current study we investigated the influence of housing structure and conditions in and around the house on the indoor abundance of Phlebotomus argentipes, the vector of VL in this area. METHODS: In each of 50 study villages in Muzaffarpur district, we randomly selected 10 houses. Light traps were installed in each house for one night during three annual peaks of sand fly density over two successive years. Sand flies captured were morphologically identified and segregated by species, sex and feeding status. Data on housing conditions and socio-economic status were also collected. We fitted a linear mixed-effects regression model with log-transformed P. argentipes counts as outcome variable and village as random effect. RESULTS: P. argentipes was found in all but four of the 500 households. There was considerable variability between the years and the seasons. On bivariate analysis, housing structure, dampness of the floor, keeping animals inside, presence of animal dung around the house, and socio-economic status were all significantly associated with sand fly density. Highest sand fly densities were observed in thatched houses. In the multivariate model only the housing structure and socio-economic status remained significant. CONCLUSIONS: Better housing conditions are associated with lower sand fly densities, independent of other socio-economic conditions. However, in this area in Bihar even in the better-built houses sand flies are present.

Chemically synthesized graphene for electrochemical biosensing.

We report a new chemical method for synthesis of graphene with good yield. Graphene obtained by this chemical route was subjected to electrochemical characterization using two different redox materials for their suitability in electrochemical biosensing applications. The synthesized graphene was used for the detection of neurotransmitters like dopamine and serotonin. The electrodes exhibited 20% +/- 5% (N = 5) decrease in their signal after forty five days storage in the laboratory atmosphere. High stability of chemically synthesized graphene as electrochemical biosensor is presented in this work.

Seasonal variation in the prevalence of sand flies infected with Leishmania donovani.

BACKGROUND: Visceral Leishmaniasis (VL) is a life threatening neglected infectious disease in the Indian subcontinent, transmitted by the bite of female sand flies. Estimation of the infectivity in the vector population, collected in different seasons, may be useful to better understanding the transmission dynamics of VL as well as to plan vector control measures. METHODOLOGY: We collected sand flies from highly endemic regions of Bihar state, India for one year over three seasons. The species of the sand flies were confirmed by species-specific PCR-RFLP. Leishmania donovani infection was investigated in 1397 female Phlebotomus argentipes using PCR, targeting the Leishmania specific minicircle of the kDNA region. Further, the parasitic load in the infected sand flies was measured using quantitative PCR. CONCLUSION: Though sand flies were most abundant in the rainy season, the highest rate of infection was detected in the winter season with 2.84% sand flies infected followed by the summer and rainy seasons respectively. This study can help in vector elimination programmes and to reduce disease transmission.