The critically endangered Malayan tiger (Panthera tigris jacksoni), with an estimated population of less than 200 individuals left in isolated rainforest habitats in Malaysia, is in an intermediate population crash leading to extinction in the next decade. The population has decreased significantly by illegal poaching, environmental perturbation, roadkill, and being captured during human–wildlife conflicts. Forty-five or more individuals were extracted from the wild (four animals captured due to conflict, one death due to canine distemper, one roadkilled, and 39 poached) in the 12 years between 2008–2019. The Malayan tigers are the first wildlife species to test positive for COVID-19 and are subject to the Canine Distemper Virus. These anthropogenic disturbances (poaching and human–tiger conflict) and environmental perturbation (decreasing habitat coverage and quality) have long been identified as impending extinction factors. Roadkill and infectious diseases have emerged recently as new confounding factors threatening Malayan tiger extinction in the near future. Peninsular Malaysia has an existing Malayan tiger conservation management plan; however, to enhance the protection and conservation of Malayan tigers from potential extinction, the authority should reassess the existing legislation, regulation, and management plan and realign them to prevent further population decline, and to better enable preparedness and readiness for the ongoing pandemic and future threats.

Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics.

Taste perception is an essential function that provides valuable dietary and sensory information, which is crucial for the survival of animals. Studies into the evolution of the sweet taste receptor gene (TAS1R2) are scarce, especially for Bornean endemic primates such as Nasalis larvatus (proboscis monkey), Pongo pygmaeus (Bornean orangutan), and Hylobates muelleri (Muller’s Bornean gibbon). Primates are the perfect taxa to study as they are diverse dietary feeders, comprising specialist folivores, frugivores, gummivores, herbivores, and omnivores. We constructed phylogenetic trees of the TAS1R2 gene for 20 species of anthropoid primates using four different methods (neighbor-joining, maximum parsimony, maximum-likelihood, and Bayesian) and also established the time divergence of the phylogeny. The phylogeny successfully separated the primates into their taxonomic groups as well as by their dietary preferences. Of note, the reviewed time of divergence estimation for the primate speciation pattern in this study was more recent than the previously published estimates. It is believed that this difference may be due to environmental changes, such as food scarcity and climate change, during the late Miocene epoch, which forced primates to change their dietary preferences. These findings provide a starting point for further investigation.