Listening to ecosystems: data-rich acoustic monitoring through landscape-scale sensor networks

Ecologists have many ways to measure and monitor ecosystems, each of which can reveal details about the processes unfolding therein. Acoustic recording combined with machine learning methods for species detection can provide remote, automated monitoring of species richness and relative abundance. Such recordings also open a window into how species behave and compete for niche space in the sensory environment. These opportunities are associated with new challenges: the volume and velocity of such data require new approaches to species identification and visualization. Here we introduce a newly-initiated acoustic monitoring network across the subtropical island of Okinawa, Japan, as part of the broader OKEON (Okinawa Environmental Observation Network) project. Our aim is to monitor the acoustic environment of Okinawa’s ecosystems and use these space–time data to better understand ecosystem dynamics. We present a pilot study based on recordings from five field sites conducted over a one-month period in the summer. Our results provide a proof of concept for automated species identification on Okinawa, and reveal patterns of biogenic vs. anthropogenic noise across the landscape. In particular, we found correlations between forest land cover and detection rates of two culturally important species in the island soundscape: the Okinawa Rail and Ruddy Kingfisher. Among the soundscape indices we examined, NDSI, Acoustic Diversity and the Bioacoustic Index showed both diurnal patterns and differences among sites. Our results highlight the potential utility of remote acoustic monitoring practices that, in combination with other methods can provide a holistic picture of biodiversity. We intend this project as an open resource, and wish to extend an invitation to researchers interested in scientific collaboration

Autonomously Improving Systems in Industry: A Systematic Literature Review

A significant amount of research effort is put into studying machine learning (ML) and deep learning (DL) technologies. Real-world ML applications help companies to improve products and automate tasks such as classification, image recognition and automation. However, a traditional “fixed” approach where the system is frozen before deployment leads to a sub-optimal system performance. Systems autonomously experimenting with and improving their own behavior and performance could improve business outcomes but we need to know how this could actually work in practice. While there is some research on autonomously improving systems, the focus on the concepts and theoretical algorithms. However, less research is focused on empirical industry validation of the proposed theory. Empirical validations are usually done through simulations or by using synthetic or manually alteration of datasets. The contribution of this paper is twofold. First, we conduct a systematic literature review in which we focus on papers describing industrial deployments of autonomously improving systems and their real-world applications. Secondly, we identify open research questions and derive a model that classifies the level of autonomy based on our findings in the literature review

Proof-of-Concept Gene Editing for the Murine Model of Inducible Arginase-1 Deficiency

Arginase-1 deficiency in humans is a rare genetic disorder of metabolism resulting from a loss of arginase-1, leading to impaired ureagenesis, hyperargininemia and neurological deficits. Previously, we generated a tamoxifen-inducible arginase-1 deficient mouse model harboring a deletion of Arg1 exons 7 and 8 that leads to similar biochemical defects, along with a wasting phenotype and death within two weeks. Here, we report a strategy utilizing the Clustered, Regularly Interspaced, Short Palindromic Repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system in conjunction with piggyBac technology to target and reincorporate exons 7 and 8 at the specific Arg1 locus in attempts to restore the function of arginase-1 in induced pluripotent stem cell (iPSC)-derived hepatocyte-like cells (iHLCs) and macrophages in vitro. While successful gene targeted repair was achieved, minimal urea cycle function was observed in the targeted iHLCs compared to adult hepatocytes likely due to inadequate maturation of the cells. On the other hand, iPSC-derived macrophages expressed substantial amounts of "repaired" arginase. Our studies provide proof-of-concept for gene-editing at the Arg1 locus and highlight the challenges that lie ahead to restore sufficient liver-based urea cycle function in patients with urea cycle disorders