Pest management through Bacillus thuringiensis (Bt) in a tea-silkworm ecosystem: status and potential prospects

Bacillus thuringiensis (Bt) is a soil bacterium that forms spores containing crystals comprising one or more Cry or Cyt proteins having potential and specific insecticidal activity. Different strains of Bt produce different types of toxins, affecting a narrow taxonomic group of insects. Therefore, it is used in non-chemical pest management, including inherent pest resistance through GM crops. The specificity of action of Bt toxins reduces the concern of adverse effects on non-target species, a concern which remains with chemical insecticides as well. To make use of Bt more sustainable, new strains expressing novel toxins are actively being sought globally. Since Bt is successfully used against many pests including the lepidopteran pests in different crop groups, the insecticidal activity against Samia cynthia (Drury) (Eri silkworm) and Antheraea assamensis Helfer (Muga silkworm) becomes a concern in the state of Assam in India which is a predominantly tea- and silk-producing zone. Though Bt can be used as an effective non-chemical approach for pest management for tea pests in the same geographical region, yet, it may potentially affect the silk industry which depends on silkworm. There is a need to identify the potentially lethal impact (through evaluating their mortality potential) of local Bt strains on key silkworm species in North Eastern India. This will allow the use of existing Bt for which the silkworms have natural resistance. Through this review, the authors aim to highlight recent progress in the use of Bt and its insecticidal toxins in tea pest control and the potential sensitivity for tea- and silk-producing zone of Assam in India.

     

Synthesis of silver nanoparticles employing Polyalthia longifolia leaf extract and their in vitro antifungal activity against phytopathogen

The P. longifolia mediated silver (PL-AgNPs) nanoparticles are very stable and efficient. UV–Vis spectroscopy, dynamic light scattering (DLS), X-ray diffraction (XRD), transmission electron microscope (TEM), scanning electron microscope (SEM), and energy dispersive X-ray spectroscopy (EDX) were used to characterize the produced AgNPs. UV–Vis analysis showed a characteristic peak at 435 nm corresponding to surface plasmon resonance. The synthesis process was spectrophotometrically optimized for various parameters. After optimization, highly stable AgNPs were prepared using 3.0 ml of P. longifolia leaf extract, pH 7.0, 1.0 mM AgNO₃, and 60 °C. The zeta potential was measured by DLS, which showed ?20.8 mV and the PDI value was 5.42. TEM and SEM analysis shows a spherical shape of the synthesized nanoparticles, and the size was measured between 10 and 40 nm. EDX analysis showed intense peaks from silver and oxygen and small peaks from various metal atoms such as Na, P, S and Al indicating their presence in trace amounts. The average size of the PL-AgNPs was 14 nm. The phytochemical analysis shows that the presence of alkaloids, essential oils and saponins seems to be responsible for the synthesis of nanoparticles. PL-AgNPs were further investigated for their antifungal activity against Alternaria alternata. The minimum inhibitory concentration (MIC), minimum fungicidal concentration (MFC) and effect of nanoparticles on cytomorphology of A. alternata have also been reported. Biosynthesized nanoparticles have proven to be inexpensive, environmentally friendly, stable, easily reproducible, and highly effective against plant-pathogenic fungi.     

Deadlock-free scheduling of an automated manufacturing system using an enhanced colored time resource Petri-net model-based evolutionary endosymbiotic learning automata approach

Deadlock-free scheduling of parts is vital for increasing the utilization of an Automated Manufacturing System (AMS). An existing literature survey has identified the role of an effective modeling methodology for AMS in ensuring the appropriate scheduling of the parts on the available resources. In this paper, a new modeling methodology termed as Extended Color Time Net of Set of Simple Sequential Process with Resources (ECTS³PR) has been presented that efficiently handles dynamic behavior of the manufacturing system. The model is subsequently utilized to obtain a deadlock-free schedule with minimized makespan using a new Evolutionary Endosymbiotic Learning Automata (EELA) algorithm. The ECTS³PR model, which can easily handle various relations and structural interactions, proves to be very helpful in measuring and managing system performances. The novel algorithm EELA has the merits of both endosymbiotic systems and learning automata. The proposed algorithm performs better than various benchmark strategies available in the literature. Extensive experiments have been performed to examine the effectiveness of the proposed methodology, and the results obtained over different data sets of varying dimensions authenticate the performance claim. Superiority of the proposed approach has been validated by defining a new performance index termed as the ‘makespan index’ (MI), whereas the ANOVA analysis reveals the robustness of the algorithm.