Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation

Human BRCA1-BARD1 ubiquitin ligase activity counters chromatin barriers to DNA resection

The opposing activities of 53BP1 and BRCA1 influence pathway choice of DNA double-strand break repair. How BRCA1 counters the inhibitory effect of 53BP1 on DNA resection and homologous recombination is unknown. Here we identify the site of BRCA1-BARD1 required for priming ubiquitin transfer from E2~ubiquitin. We demonstrate that BRCA1-BARD1’s ubiquitin ligase activity is required for repositioning 53BP1 on damaged chromatin. We confirm H2A ubiquitylation by BRCA1-BARD1 and show that an H2A-ubiquitin fusion protein promotes DNA resection and repair in BARD1 deficient cells. We show BRCA1-BARD1 function in homologous recombination requires the chromatin remodeler SMARCAD1. SMARCAD1 binding to H2A-ubiquitin, optimal localization to sites of damage and activity in DNA repair requires its ubiquitin-binding CUE domains. SMARCAD1 is required for 53BP1 repositioning and the need for SMARCAD1 in Olaparib or camptothecin resistance is alleviated by 53BP1 loss. Thus BRCA1- BARD1 ligase activity and subsequent SMARCAD1-dependent chromatin remodeling are critical regulators of DNA repair

Perspectives in machine learning for wildlife conservation

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.publishe