Recovery of the grizzly bear at the intersection of law and science

Over the last 30 years, there have been numerous legal battles over recovery of the grizzly bear. These battles have brought to fore a question central to implementation of the Act, namely is the goal of recovery to merely remediate extinction risk or to affect broader ecosystem recovery. I systematically reviewed court decisions related to the grizzly bear’s recovery plan, efforts to remove protections for grizzly bears, and challenges to logging, mining and other projects with impacts to grizzly bears. A legal challenge to the grizzly bear’s 1993 recovery plan forced the Service to develop habitat-based recovery criteria for the Greater Yellowstone Ecosystem. Legal efforts to reopen the recovery plan and expand recovery into additional areas of historic range, however, were unsuccessful, leaving the scope of recovery largely at the discretion of the U.S. Fish and Wildlife Service. Lawsuits brought by multiple conservation groups, tribes and individuals have constrained this discretion, twice stopping the agency from stripping Greater Yellowstone grizzly bears of federal protections. This has allowed the population to grow and forced consideration of the impact of removing protections for Greater Yellowstone bears on overall recovery as a requirement of any future effort to remove protections. Court decisions were issued on 65 challenges to projects impacting grizzly bear habitat, including 44 involving logging and related road construction, seven mining, four livestock grazing, two recreation, five oil and gas leasing and three road projects, leading to 11 of these projects being stopped and nine modified. Lawsuits were also filed to stop hunting in four instances, trapping in one, predator control in one and railroad mortality in two, as well as activities that disturb bears, including helicopters in two instances and snow mobile use in another, resulting in four being stopped and another 3 modified. Protection of the grizzly bear under the Endangered Species Act, along with subsequent litigation, has led to substantial changes in management of public lands in the four recovery zones with grizzly bear populations, but not elsewhere in the species’ range. Overall, the legal system is an important, but often overlooked, part of recovery of endangered species

A Wall in the Wild: The Disastrous Impacts of Trump's Border Wall on Wildlife

Trump's border wall will be a deathblow to already endangered animals on both sides of the U.S.-Mexico border. This report examines the impacts of construction of that wall on threatened and endangered species along the entirety of the nearly 2,000 miles of the border between the United States and Mexico. The wall and concurrent border-enforcement activities are a serious human-rights disaster, but the wall will also have severe impacts on wildlife and the environment, leading to direct and indirect habitat destruction. A wall will block movement of many wildlife species, precluding genetic exchange, population rescue and movement of species in response to climate change. This may very well lead to the extinction of the jaguar, ocelot, cactus ferruginous pygmy owl and other species in the United States

Extinction and the U.S. Endangered Species Act

The U.S. Endangered Species Act is one of the strongest laws of any nation for preventing species extinction, but quantifying the Act’s effectiveness has proven difficult. To provide one measure of effectiveness, we identified listed species that have gone extinct and used previously developed methods to update an estimate of the number of species extinctions prevented by the Act. To date, only four species have been confirmed extinct with another 22 possibly extinct following protection. Another 71 listed species are extinct or possibly extinct, but were last seen before protections were enacted, meaning the Act’s protections never had the opportunity to save these species. In contrast, a total of 39 species have been fully recovered, including 23 in the last 10 years. We estimate the Endangered Species Act has prevented the extinction of roughly 291 species since passage in 1973, and has to date saved more than 99% of species under its protection

Combining Multiplexed Ion Beam Imaging (MIBI) with Convolutional Neural Networks to accurately segment cells in human tissue

Background: Multiplexed imaging is a rapidly growing field that promises to substantially increase the number of proteins that can be imaged simultaneously. We have developed Multiplexed Ion Beam Imaging by Time of Flight (MIBI-TOF), which uses elemental reporters conjugated to primary antibodies that are then quantified using a time of flight mass-spectrometer. This technique allows for more than 40 distinct proteins to visualized at once in the same clinical samples. This has already yielded significant insights into the interactions and relationships between the many different immune cell populations present in the tumor microenvironment. However, one of the remaining challenges in analyzing such data is accurately determining target protein expression values for each cell in the image. This requires the precise delineation of boundaries between cells that are often tightly packed next to one another. Current methods to address this challenge largely rely on DNA intensity to make these splits, and are thus mostly limited to nuclear segmentation. Methods: We have developed a novel convolutional neural network to perform whole-cell segmentation from multiplexed imaging data. Rather than relying only on DNA signal, we use a panel of morphological markers. Our method integrates the information from these distinct proteins, allowing it to segment large cancer cells, small lymphocytes, and normal epithelium at the same time without requiring fine-tuning or manual adjustment. Results: By combining our novel imaging platform with new computational tools, we are able to achieve extremely accurate segmentation of whole cells in tissue. Our approach compares favorably with many of the currently used tools for segmentation. We show that our improvements in accuracy come both from our novel imaging approach as well as algorithmic advances. We perform significantly better than traditional machine learning algorithms trained on the same dataset. Additionally, we show that our algorithm can be trained to identify cells across a range of cancer histologies and disease grades. Conclusions: We have developed a robust and accurate approach to whole-cell segmentation in human tissues. We show the superiority over this method over current state of the art algorithms. The accurate segmentation generated by our approach will enable the analysis of complex tissue architectures with highly overlapping cell types, and will help to advance our understanding of the interactions between cell types in the diseased state

Increased expression of programmed death ligand 1 (PD-L1) in human pituitary tumors

PURPOSE: Subsets of pituitary tumors exhibit an aggressive clinical courses and recur despite surgery, radiation, and chemotherapy. Because modulation of the immune response through inhibition of T-cell checkpoints has led to durable clinical responses in multiple malignancies, we explored whether pituitary adenomas express immune-related biomarkers that could suggest suitability for immunotherapy. Specifically, programmed death ligand 1 (PD-L1) has emerged as a potential biomarker whose expression may portend more favorable responses to immune checkpoint blockade therapies. We thus investigated the expression of PD-L1 in pituitary adenomas. METHODS: PD-L1 RNA and protein expression were evaluated in 48 pituitary tumors, including functioning and non-functioning adenomas as well as atypical and recurrent tumors. Tumor infiltrating lymphocyte populations were also assessed by immunohistochemistry. RESULTS: Pituitary tumors express variable levels of PD-L1 transcript and protein. PD-L1 RNA and protein expression were significantly increased in functioning (growth hormone and prolactin-expressing) pituitary adenomas compared to non-functioning (null cell and silent gonadotroph) adenomas. Moreover, primary pituitary adenomas harbored higher levels of PD-L1 mRNA compared to recurrent tumors. Tumor infiltrating lymphocytes were observed in all pituitary tumors and were positively correlated with increased PD-L1 expression, particularly in the functional subtypes. CONCLUSIONS: Human pituitary adenomas harbor PD-L1 across subtypes, with significantly higher expression in functioning adenomas compared to non-functioning adenomas. This expression is accompanied by the presence of tumor infiltrating lymphocytes. These findings suggest the existence of an immune response to pituitary tumors and raise the possibility of considering checkpoint blockade immunotherapy in cases refractory to conventional management

DeepCell Kiosk: scaling deep learning–enabled cellular image analysis with Kubernetes

Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 10⁶ 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console; a persistent deployment is available at https://deepcell.org/

DeepCell Kiosk: scaling deep learning–enabled cellular image analysis with Kubernetes

Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 10⁶ 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console; a persistent deployment is available at https://deepcell.org/

The call of the emperor penguin: Legal responses to species threatened by climate change

Species extinction risk is accelerating due to anthropogenic climate change, making it urgent to protect vulnerable species through legal frameworks in order to facilitate conservation actions that help mitigate risk. Here, we discuss fundamental concepts for assessing climate change risks to species using the example of the emperor penguin (Aptenodytes forsteri), currently being considered for protection under the US Endangered Species Act (ESA). This species forms colonies on Antarctic sea ice, which is projected to significantly decline due to ongoing greenhouse gas (GHG) emissions. We project the dynamics of all known emperor penguin colonies under different GHG emission scenarios using a climate-dependent meta-population model including the effects of extreme climate events based on the observational satellite record of colonies. Assessments for listing species under the ESA require information about how species resiliency, redundancy and representation (3Rs) will be affected by threats within the foreseeable future. Our results show that if sea ice declines at the rate projected by climate models under current energy system trends and policies, the 3Rs would be dramatically reduced and almost all colonies would become quasi-extinct by 2100. We conclude that the species should be listed as threatened under the ESA