Piper betle is a dioecious pan-Asiatic plant having cultural and medicinal uses. It belongs to the family Piperaceae and is a native of the tropics although it is also cultivated in subtropical areas. Flowering in P. betle occurs only in tropical regions. Due to lack of inductive floral cycles the plant remains in its vegetative state in the subtropics. Therefore, due to lack of flowering, gender distinction cannot be made the in the subtropics. Gender distinction in P. betle in vegetative state can be made using Direct Analysis in Real Time Mass Spectroscopy (DARTMS), a robust high-throughput method. DARTMS analysis of leaf samples of two male and six female plants showed characteristic differences in the spectra between male and female plants. Semi-quantitative differences in some of the identified peaks in male and female landraces showed gender-based differences in metabolites. Cluster analysis using the peaks at m/z 151, 193, 235 and 252 showed two distinct clusters of male and female landraces. It appears that male and female plants besides having flowers of different sexes also have characteristic differences in the metabolites representing two metabolic types.

Protein–protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

Climate change, along with current agricultural practices, is going to pose a significant challenge for futurefood security, especially in developing countries. Orphan crops can help mitigate this threat due to theirinherent properties of stress tolerance and nutrition content. Industrialization of agriculture has left these minorcrops behind in terms of domestication. As a result, the potential of these crops is underutilized. These cropscan be a game-changer in the long term if necessary steps are taken to improve the quality as well as quantity ofyield. Concerted efforts by many groups around the world have been taken for research and development ofthese crops. Besides, the unique properties of these crops have caught the media attention, which hails thesecrops as superfoods. Favourable government policies to promote these crops can help in the large-scaleadoption of these crops by the farming community. Besides, the stress-resilience of these crops can help boostthe sustainability of agriculture and ensure food security for future generations.