Search for: All records

A longstanding challenge in biology is accurately analyzing images acquired using microscopy. Recently, machine learning (ML) approaches have facilitated detailed quantification of images that were refractile to traditional computation methods. Here, we detail a method for measuring pigments in the complex-mosaic adult Drosophila eye using high-resolution photographs and the pixel classifier ilastik [1]. We compare our results to analyses focused on pigment biochemistry and subjective interpretation, demonstrating general overlap, while highlighting the inverse relationship between accuracy and high-throughput capability of each approach. Notably, no coding experience is necessary for image analysis and pigment quantification. When considering time, resolution, and accuracy, our view is that ML-based image analysis is the preferred method. 

Sustainable development requires an accelerated transition toward renewable energy. In particular, substantially scaling up solar photovoltaics (PV) adoption is a crucial component of reducing the impacts of climate change and promoting sustainable development. However, it is challenging to convince local governments to take action. This study uses a combination of propensity score matching (PSM) and difference-in-differences (DID) models to assess the effectiveness of a voluntary environmental program (VEP) called SolSmart that targets local governments to engage in solar-friendly practices to promote the local solar PV market in the United States. Via specific designation requirements and technical assistance, SolSmart simplifies the process of acting on interest in being solar friendly, has a wide coverage of basic solar-friendly actions with flexible implementation, and motivates completion with multiple levels of designation. We find that a local government’s participation in SolSmart is associated with an increased installed capacity of 18 to 19%/mo or with less statistical significance, an increased number of installations of 17%/mo in its jurisdiction. However, SolSmart has not shown a statistically significant impact on soft cost reductions to date. In evaluating the impact of the SolSmart program, this study improves our understanding of the causation between a VEP that encourages solar-friendly local government practices and multiple solar market outcomes. VEPs may be able to promote shifts toward sustainable development at the local level. Our findings have several implications for the design of VEPs that promote local sustainability. 

ABSTRACT We present here the complete genomes of eight phages that infect Paenibacillus larvae , the causative agent of American foulbrood in honeybees. Phage PBL1c was originally isolated in 1984 from a P. larvae lysogen, while the remaining phages were isolated in 2014 from bee debris, honeycomb, and lysogens from three states in the USA.