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Biology subjects

Marie, Z.

Publications and source records attributed to Marie, Z..

2 recordsLinked to original sources

Smooth move: Behavioural changes in captive western lowland gorillas (Gorilla gorilla gorilla) after group split and relocation

Zoological gardens strive to prioritize excellent animal care and adhere to the highest standards of animal welfare, ensuring that the conditions of animals social and physical environment are as close as possible to those in the wild. This study investigates the effects of group split and relocation to the new enclosure on the behaviour of western lowland gorillas (Gorilla gorilla gorilla) (N = 7) living in Prague Zoo, Czech Republic. We conducted over 289 hours of behavioural observations focusing on daily activities, social interactions, and behaviours that serve as potential stress and welfare indicators. The group split led to establishment of two groups in two separate enclosures; the old enclosure consisted of a bachelor group (i.e. three males) and a new enclosure consisted of a mixed-sex group (i.e. three females and one juvenile male). The behavioural comparisons across different study periods were conducted using linear mixed models (LMMs). The changes led to an increase in time spent moving, feeding, being in social proximity, and higher rates of approaches among the gorillas, as well as to a decrease in rates of self-directed and undesirabl behaviors. Our findings indicate that the gorillas effectively adapted to the changes, most likely by relying on social support, to navigate new conditions. This study contributes to our understanding of how socio-cognitively complex species cope with necessary alterations in captive animal care programs. Furthermore, these observations may inform strategies to enhance the welfare of zoo-housed animals and to improve their captive care.

animal behavior and cognition↗

High-throughput ML-guided design of diverse single-domain antibodies against SARS-CoV-2

Treating rapidly evolving pathogenic diseases such as COVID-19 requires a therapeutic approach that accommodates the emergence of viral variants over time. Our machine learning (ML)-guided sequence design platform combines high-throughput experiments with ML to generate highly diverse single-domain antibodies (VHHs) that bind and neutralize SARS-CoV-1 and SARS-CoV-2. Crucially, the model, trained using binding data against early SARS-CoV variants, accurately captures the relationship between VHH sequence and binding activity across a broad swathe of sequence space. We discover ML-designed VHHs that exhibit considerable cross-reactivity and successfully neutralize targets not seen during training, including the Delta and Omicron BA.1 variants of SARS-CoV-2. Our ML-designed VHHs include thousands of variants 4-15 mutations from the parent sequence with significantly improved activity, demonstrating that ML-guided sequence design can successfully navigate vast regions of sequence space to unlock and future-proof potential therapeutics against rapidly evolving pathogens.

bioinformatics↗