Field Efficacy of Different IPM Modules for Management of Aphid on Cumin (Cuminum Cyminum L.) Under Semi-Arid Conditions

Aphid, Myzus persicae (Sulzer) and Aphis gossypii Glower (Hemiptera: Aphididae) are the two major species damaging cumin crop throughout the cumin growing states of India. Its infestation initiated on cumin crops at 20 days after seed germination with very low incidence and became abundant at 70-80 day after seed germination during second week of January till February when crops are in full grown stage. Since cumin is most important seed spice crop, having huge nutritional, medicinal and aromatic properties, and its seed and essential oil is exporting in many countries over the world, hence quality production is the major challenge for the growers. The multiple sprays of chemical pesticides with heavy doses for pest management induce resistance in aphids, harmful to natural enemies and also enhance the pesticide residue level in the produce (seed). With this approach, the present study was conducted for two consecutive years to evaluate the cumulative effect of botanicals, cultural and physical methods, bio-pesticides, and bio-control agents and insecticides in the form of IPM module for the management of aphid and relative safety to natural enemies in cumin under field conditions. In this study, amongst eight different IPM modules evaluated against aphid, module M-5 consisted by seed treatment with imidacloprid 600FS @ 3ml/kg seed followed by foliar spray of ker plant extract @ 10ml/litre, Verticillium lecanii (1x108cfu/g) @ 6g/litre, and fipronil 5%SC @ 0.035% at definite interval secured efficient management of aphids (>92% mortality) with relative safety to natural enemies and minimized pesticide residue in the seeds. The highest seed yield (934 kg/ha), test weight (5.24 g), essential oil (5.98%), economics (Rs.1.08 Lakh) and B: C ratio (3.69:1) was obtained by the application of IPM modules M-5. Similarly, the higher seed yield 1072.5 kg per hectare (21.19% appreciation over farmers practices) was also recorded in field validation study under non replicated field in large area, proved its effectiveness by the farmers. Consequently, it recommends that IPM module M-5 consisted with botanical product, entomopathogenic fungi and insecticides exhibited sustainable aphid management module for cumin, comparatively safer to natural enemies and lower residue level in the seeds.

Residue level and health risk assessment of organophosphorus pesticides in country bean and bitter gourd collected from Cumilla, Bangladesh

Organophosphorus pesticides are one of the most commonly used pesticide classes in agriculture in the management of insect pests due to their high efficacy. The extensive use of organophosphorus pesticides can contaminate both the atmosphere and food, which may lead to health problems. In this study, the residue level of organophosphorus pesticides, and their health risk assessment was evaluated on country bean and bitter gourd. Out of forty analysed samples of the country bean, two samples contained multiple residues, seven samples contained single residue. On the other hand, out of forty samples of bitter gourd, one sample contained multiple residues and three samples contained single residue. Among the organophosphorus pesticides, diazinon was the most frequently detected organophosphorus pesticide in country bean and bitter gourd. The highest short-term risks or acute risk (aHI) was below 1.83%, and the highest long-term risk or chronic risk (HQs) was below 10.46% for the country bean and bitter gourd, respectively. Short- and long-term risk assessment results showed that the intake risks of country bean and bitter gourd were acceptable. The health hazard results showed that country bean and bitter gourd consumption in Cumilla, Bangladesh does not pose an acute or chronic risk to human health. However, a routine monitoring system must be established to control the contamination of vegetables with pesticides.

ME-ACP: Multi-view Neural Networks with Ensemble Model for Identification of Anticancer Peptides

Cancer remains one of the most threatening diseases, which kills millions of lives every year. As a promising perspective for cancer treatments, anticancer peptides (ACPs) overcome a lot of disadvantages of traditional treatments. However, it is time-consuming and expensive to identify ACPs through conventional experiments. Hence, it is urgent and necessary to develop highly effective approaches to accurately identify ACPs in large amounts of protein sequences. In this work, we proposed a novel and effective method named ME-ACP which employed multi-view neural networks with ensemble model to identify ACPs. Firstly, we employed residue level and peptide level features preliminarily with ensemble models based on lightGBMs. Then, the outputs of lightGBM classifiers were fed into a hybrid deep neural network (HDNN) to identify ACPs. The experiments on independent test datasets demonstrated that ME-ACP achieved competitive performance on common evaluation metrics.

DomBpred: protein domain boundary predictor using inter-residue distance and domain-residue level clustering

Domain boundary prediction is one of the most important problems in the study of protein structure and function, especially for large proteins. At present, most domain boundary prediction methods have low accuracy and limitations in dealing with multi-domain proteins. In this study, we develop a sequence-based protein domain boundary predictor, named DomBpred. In DomBpred, the input sequence is firstly classified as either a single-domain protein or a multi-domain protein through a designed effective sequence metric based on a constructed single-domain sequence library. For the multi-domain protein, a domain-residue level clustering algorithm inspired by Ising model is proposed to cluster the spatially close residues according inter-residue distance. The unclassified residues and the residues at the edge of the cluster are then tuned by the secondary structure to form potential cut points. Finally, a domain boundary scoring function is proposed to recursively evaluate the potential cut points to generate the domain boundary. DomBpred is tested on a large-scale test set of FUpred comprising 2549 proteins. Experimental results show that DomBpred better performs than the state-of-the-art methods in classifying whether protein sequences are composed by single or multiple domains, and the Matthew's correlation coefficient is 0.882. Moreover, on 849 multi-domain proteins, the domain boundary distance and normalised domain overlap scores of DomBpred are 0.523 and 0.824, respectively, which are 5.0% and 4.2% higher than those of the best comparison method, respectively. Comparison with other methods on the given test set shows that DomBpred outperforms most state-of-the-art sequence-based methods and even achieves better results than the top-level template-based method.