Macrophomina phaseolina (Tassi) Goid remains the prevailing causal agent of charcoal rot disease that significantly suppresses the yield of a variety of oilseed crops. Its wide host range and ability to survive under arid conditions, coupled with the ineffective use of fungicides against it, have spurred scientific endeavours for alternative avenues to control this phytopathogen. Hence, the present study aimed to provide empirical evidence of the efficacy of three fungal isolates (T2, T10 and T12) of Trichoderma harzianum as biological control agents against charcoal rot in soybean (Glycine max L.). The results of the in vitro studies revealed that all three fungal isolates significantly inhibited the growth of M. phaseolina phytopathogen, with T12 showing considerably higher inhibition effect than T2 and T10 isolates. T12 inhibited the growth of M. phaseolina in the dual culture (72.31%) and volatile production (63.36%) assays, and the hyperparasitism test indicated cell lysis following the interactions with T12 mycelia. T12 isolate was mostly effective in field experiments, observable in the attained minimum plant disease indices both in the soil incorporation (11.98%) and seed inoculation (5.55%) treatments, in comparison to isolates T2 and T10. Moreover, the stem and root lengths, as well as the seed weight, were considerably increased, as compared to the control. Hence, the findings reported in the present study supported the applicability of T12 isolate as possible alternative to fungicides for the control of charcoal rot in soybean.

Antimicrobial Peptides (AMPs) have been considered as potential alternatives for infection therapeutics since antibiotic resistance has been raised as a global problem. The AMPs are a group of natural peptides that play a crucial role in the immune system in various organisms AMPs have features such as a short length and efficiency against microbes. Importantly, they have represented low toxicity in mammals which makes them potential candidates for peptide-based drugs. Nevertheless, the discovery of AMPs is accompanied by several issues which are associated with labour-intensive and time-consuming wet-lab experiments. During the last decades, numerous studies have been conducted on the investigation of AMPs, either natural or synthetic type, and relevant data are recently available in many databases. Through the advancement of computational methods, a great number of AMP data are obtained from publicly accessible databanks, which are valuable resources for mining patterns to design new models for AMP prediction. However, due to the current flaws in assessing computational methods, more interrogations are warranted for accurate evaluation/analysis. Considering the diversity of AMPs and newly reported ones, an improvement in Machine Learning algorithms are crucial. In this review, we aim to provide valuable information about different types of AMPs, their mechanism of action and a landscape of current databases and computational tools as resources to collect AMPs and beneficial tools for the prediction and design of a computational model for new active AMPs.

Brown spot caused by Bipolaris oryzae is an important rice disease in Southern coast of Caspian Sea, the major rice growing region in Iran. A total of 45 Trichoderma isolates were obtained from rice paddy fields in Golestan and Mazandaran provinces which belonged to Trichoderma harzianum, T. virens and T. atroviride species. Initially, they were screened against B. oryzae by antagonism tests including dual culture, volatile and nonvolatile metabolites and hyperparasitism. Results showed that Trichoderma isolates can significantly inhibit mycelium growth of pathogen in vitro by producing volatile and nonvolatile metabolites Light microscopic observations showed no evidence of mycoparasitic behaviour of the tested isolates of Trichoderma spp. such as coiling around the B. oryzae. According to in vitro experiments, Trichoderma isolates were selected in order to evaluate their efficacy in controlling brown spot in glasshouse using seed treatment and foliar spray methods. Concerning the glasshouse tests, two strains of T. harzianum significantly controlled the disease and one strain of T. atroviride increased the seedling growth. It is the first time that the biological control of rice brown spot and increase of seedling growth with Trichoderma species have been studied in Iran

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Early prediction of pathogen infestation is a key factor to reduce the disease spread in plants. Macrophomina phaseolina (Tassi) Goid, as one of the main causes of charcoal rot disease, suppresses the plant productivity significantly. Charcoal rot disease is one of the most severe threats to soybean productivity. Prediction of this disease in soybeans is very tedious and non-practical using traditional approaches. Machine learning (ML) techniques have recently gained substantial traction across numerous domains. ML methods can be applied to detect plant diseases, prior to the full appearance of symptoms. In this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants. A hybrid set of physiological and morphological features were suggested as inputs to the ML models. All developed ML models were performed better than 90% in terms of accuracy. Gradient Tree Boosting (GBT) was the best performing classifier which obtained 96.25% and 97.33% in terms of sensitivity and specificity. Our findings supported the applicability of ML especially GBT for charcoal rot disease prediction in a real environment. Moreover, our analysis demonstrated the importance of including physiological featured in the learning. The collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code.