The human–primate interface in the New Normal: Challenges and opportunities for primatologists in the COVID-19 era and beyond

The emergence of SARS-CoV-2 in late 2019 and human responses to the resulting COVID-19 pandemic in early 2020 have rapidly changed many aspects of human behavior, including our interactions with wildlife. In this commentary, we identify challenges and opportunities at human–primate interfaces in light of COVID-19, focusing on examples from Asia, and make recommendations for researchers working with wild primates to reduce zoonosis risk and leverage research opportunities. First, we briefly review the evidence for zoonotic origins of SARS-CoV-2 and discuss risks of zoonosis at the human–primate interface. We then identify challenges that the pandemic has caused for primates, including reduced nutrition, increased intraspecific competition, and increased poaching risk, as well as challenges facing primatologists, including lost research opportunities. Subsequently, we highlight opportunities arising from pandemic-related lockdowns and public health messaging, including opportunities to reduce the intensity of problematic human–primate interfaces, opportunities to reduce the risk of zoonosis between humans and primates, opportunities to reduce legal and illegal trade in primates, new opportunities for research on human–primate interfaces, and opportunities for community education. Finally, we recommend specific actions that primatologists should take to reduce contact and aggression between humans and primates, to reduce demand for primates as pets, to reduce risks of zoonosis in the context of field research, and to improve understanding of human–primate interfaces. Reducing the risk of zoonosis and promoting the well-being of humans and primates at our interfaces will require substantial changes from “business as usual.” We encourage primatologists to help lead the way.     

Insecticidal activity of Origanum majorana L. essential oil as anti‐cholinergic agent

The present study was focused on exploring the presence of active compounds in Origanum majorana essential oil (OmEO), and its various knock‐down effects against the rice moth, Corcyra cephalonica. GC–MS analysis detected the existence of major compounds such as monoterpenes, cis‐β‐terpineol and terpinen‐4‐ol with the total proportion of 52.16%. Fumigant toxicity against adult and larvae was calculated with an LC₅₀ value of 11.31 and 49.83 μL/L air, respectively. The contact toxicity against adult, pupa, larvae and eggs was observed with LC₅₀ value 2.54, 0.95, 2.78, and 0.49 μL/L, respectively. Furthermore, the influential repellent behavior against adults has been observed. Acetylesterase (AChE) inhibition activity of OmEO was observed against adult and larvae of C. cephalonica with an IC₅₀ value of 35.89 and 118.54 μL/mL, respectively. Moreover, computational docking study revealed the binding affinity of Cis‐β‐terpineol and terpinen‐4‐ol towards the active binding sites of AChE. On the other hand, Fluorescence‐assisted cytometry and comet assay confirmed the cytotoxic and genotoxic effect of OmEO at various concentrations on C. cephalonica. Altogether, the results showed the knock‐down effect of OmEO against C. cephalonica, and it could be a potential biocontrol measure against the stored product pest.     

Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach

Journal of Biological Physics - The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of...     

Tori lines mitigate seabird bycatch in a pelagic longline fishery

Reviews in Fish Biology and Fisheries - Albatross bycatch has been increasing over the past decade in the US tuna longline fishery of the central North Pacific. A controlled field...     

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach formulates the spike estimation as a single channel source separation problem with unknown mixing conditions. The source corresponding to the action potentials at a lower resolution is estimated at the output. Experimental studies on the spikefinder challenge dataset show that the proposed signal-to-signal conversion approach significantly outperforms state-of-the-art-methods in terms of Pearson’s correlation coefficient, Spearman’s rank correlation coefficient and yields comparable performance for the area under the receiver operating characteristics measure. We also show that the resulting system: (a) has low complexity with respect to existing supervised approaches and is reproducible; (b) is layer-wise interpretable, and (c) has the capability to generalize across different calcium indicators.