In response to growing demand for ecosystem‐level risk assessment in biodiversity conservation, and rapid proliferation of locally tailored protocols, the IUCN recently endorsed new Red List criteria ...as a global standard for ecosystem risk assessment. Four qualities were sought in the design of the IUCN criteria: generality; precision; realism; and simplicity. Drawing from extensive global consultation, we explore trade‐offs among these qualities when dealing with key challenges, including ecosystem classification, measuring ecosystem dynamics, degradation and collapse, and setting decision thresholds to delimit ordinal categories of threat. Experience from countries with national lists of threatened ecosystems demonstrates well‐balanced trade‐offs in current and potential applications of Red Lists of Ecosystems in legislation, policy, environmental management and education. The IUCN Red List of Ecosystems should be judged by whether it achieves conservation ends and improves natural resource management, whether its limitations are outweighed by its benefits, and whether it performs better than alternative methods. Future development of the Red List of Ecosystems will benefit from the history of the Red List of Threatened Species which was trialed and adjusted iteratively over 50 years from rudimentary beginnings. We anticipate the Red List of Ecosystems will promote policy focus on conservation outcomes in situ across whole landscapes and seascapes.
Specimen‐based data have played a central role in documenting body‐size shifts as a possible response to global warming over the last century. Identification of the drivers and patterns of these ...trends requires comparisons across taxa, often through meta‐analyses; however, a lack of repeatability within and interoperability (i.e. the potential for a dataset to be augmented for future research) among published studies is a major obstacle.
We reviewed published studies on mammal body‐size changes in the Anthropocene, focusing on those that used museum specimens to analyse body‐size trends over time in at least one species. We assessed these papers for repeatability and interoperability with the following criteria: raw data and specimen identifiers were published and accessible, measurements were unambiguously defined, and potential sex‐ and age‐based size differences among individuals were accounted for.
Most published body‐size studies have low potential for replication or augmentation; only one of 27 met all of our criteria. Although these 27 papers collectively generated an estimated 51,790 new body‐size measurements, only 1.25% (649) could be repeated or readily used in further investigations, as the remainder did not include raw data and/or specimen identifiers.
Based on these findings, we recommend the following best practices in the study of body‐size trends. First, authors should explicitly define and justify all measures of size and quantify measurement error and publish all data, including measurements and specimen catalogue numbers. In addition to complying with the fundamental scientific tenet of repeatability, this minimizes redundant handling and the concomitant risk of damage to irreplaceable and often fragile museum specimens. Second, authors should test and account for the effects of demography, as some dimensions can change throughout an individual's life. Adopting these practices will improve the quality of body‐size studies, enhance the utility of extended specimen data from natural history collections, and enable researchers to conduct more expansive investigations of size trends over time.
Abstract Shifts in mean body size coinciding with environmental change are well documented across animal species and populations, serving as a widespread and complex indicator of climate-change ...response. In mammal research, identifying and disentangling the potential drivers of these trends (e.g., thermoregulation, resource availability) is hindered by treating adult size as fixed, ignoring morphological changes that occur throughout life in many species. However, observed population-level size trends may reflect underlying shifts in age structure (i.e., change in the proportion of older, potentially larger individuals in the population). Here, we assessed the role of age structure by explicitly evaluating age as a contributor to temporal variation in skull size (a proxy for body size) in 2 carnivorans, Canadian Lynx (Lynx canadensis) and American Marten (Martes americana). Using a series of linear and nonlinear models, we tested age in years (determined by cementum-layer analysis) as a predictor of skull size alongside other factors previously proposed to be important drivers of body-size trends, including population density for lynx and growing season conditions for martens. In both species, age was a significant predictor of skull size indicating a rapid year-to-year increase in young adult size that diminished in later adulthood. However, temporal shifts in age structure alone did not explain the observed changes in size over time, indicating that age structure acts in concert with other as-yet unidentified factors to drive body-size change. By explicitly evaluating the role of age, we can both refine models of temporal body-size trends and gain insights into size change as a signal of underlying demographic shifts—such as age-specific survivorship—providing a more holistic understanding of how mammals are responding to climate change.
Over the past two decades, we have substantially increased our understanding of violence committed by individuals with mental illness, while comparatively less is known about the victimization ...experiences of this population. What has been established in the literature is that individuals with mental illness are more likely to experience victimization than the general public, and certain risk factors influence the likelihood of victimization. What remains unexplored is the possibility that a person with mental illness’ perception that mental illness is stigmatized may be significantly associated with victimization experiences. Thus, the purpose of the current study is to examine whether stigma and victimization are associated, and in what direction. In other words, does perceived stigma lead to victimization? Or does victimization lead to perceived stigma? To assess these research questions, data from the Community Outcomes of Assisted Outpatient Treatment study are used, which is a longitudinal study of individuals with serious mental illness (n = 184). A variety of methods are employed to assess the association between victimization and perceived stigma including logistic and ordinary least squares regression models. Results from the logistic regression model indicate that perceived stigma is associated with an increase in the odds that a person with mental illness will experience victimization at later follow-ups. Results from the ordinary least squares regression analysis, however, show that victimization at baseline does not predict perceived stigma at later times. Implications regarding future research and clinical practice are discussed.
To gain insight into the genomic basis of diffuse large B-cell lymphoma (DLBCL), we performed massively parallel whole-exome sequencing of 55 primary tumor samples from patients with DLBCL and ...matched normal tissue. We identified recurrent mutations in genes that are well known to be functionally relevant in DLBCL, including MYD88, CARD11, EZH2, and CREBBP. We also identified somatic mutations in genes for which a functional role in DLBCL has not been previously suspected. These genes include MEF2B, MLL2, BTG1, GNA13, ACTB, P2RY8, PCLO, and TNFRSF14. Further, we show that BCL2 mutations commonly occur in patients with BCL2/IgH rearrangements as a result of somatic hypermutation normally occurring at the IgH locus. The BCL2 point mutations are primarily synonymous, and likely caused by activation-induced cytidine deaminase–mediated somatic hypermutation, as shown by comprehensive analysis of enrichment of mutations in WRCY target motifs. Those nonsynonymous mutations that are observed tend to be found outside of the functionally important BH domains of the protein, suggesting that strong negative selection against BCL2 loss-of-function mutations is at play. Last, by using an algorithm designed to identify likely functionally relevant but infrequent mutations, we identify KRAS, BRAF, and NOTCH1 as likely drivers of DLBCL pathogenesis in some patients. Our data provide an unbiased view of the landscape of mutations in DLBCL, and this in turn may point toward new therapeutic strategies for the disease.
Over 1.3 million Californians rely on unmonitored domestic wells. Existing probability estimates of groundwater Mn concentrations, population estimates, and sociodemographic data were integrated with ...spatial data delineating domestic well communities (DWCs) to predict the probability of high Mn concentrations in extracted groundwater within DWCs in California’s Central Valley. Additional Mn concentration data of water delivered by community water systems (CWSs) were used to estimate Mn in public water supply. We estimate that 0.4% of the DWC population (2342 users) rely on groundwater with predicted Mn > 300 μg L–1. In CWSs, 2.4% of the population (904 users) served by small CWSs and 0.4% of the population (3072 users) served by medium CWS relied on drinking water with mean point-of-entry Mn concentration >300 μg L–1. Small CWSs were less likely to report Mn concentrations relative to large CWSs, yet a higher percentage of small CWSs exceed regulatory standards relative to larger systems. Modeled calculations do not reveal differences in estimated Mn concentration between groundwater from current regional domestic well depth and 33 m deeper. These analyses demonstrate the need for additional well-monitoring programs that evaluate Mn and increased access to point-of-use treatment for domestic well users disproportionately burdened by associated costs of water treatment.
In the presence of aggregation-prone proteins, the cytosol and endoplasmic reticulum (ER) undergo a dramatic shift in their respective redox status, with the cytosol becoming more oxidized and the ER ...more reducing. However, whether and how changes in the cellular redox status may affect protein aggregation is unknown. Here, we show that C. elegans loss-of-function mutants for the glutathione reductase gsr-1 gene enhance the deleterious phenotypes of heterologous human, as well as endogenous worm aggregation-prone proteins. These effects are phenocopied by the GSH-depleting agent diethyl maleate. Additionally, gsr-1 mutants abolish the nuclear translocation of HLH-30/TFEB transcription factor, a key inducer of autophagy, and strongly impair the degradation of the autophagy substrate p62/SQST-1::GFP, revealing glutathione reductase may have a role in the clearance of protein aggregates by autophagy. Blocking autophagy in gsr-1 worms expressing aggregation-prone proteins results in strong synthetic developmental phenotypes and lethality, supporting the physiological importance of glutathione reductase in the regulation of misfolded protein clearance. Furthermore, impairing redox homeostasis in both yeast and mammalian cells induces toxicity phenotypes associated with protein aggregation. Together, our data reveal that glutathione redox homeostasis may be central to proteostasis maintenance through autophagy regulation.
Objectives
To evaluate a deep learning model for automated and interpretable classification of central canal stenosis, neural foraminal stenosis, and facet arthropathy from lumbar spine MRI.
Methods
...T2-weighted axial MRI studies of the lumbar spine acquired between 2008 and 2019 were retrospectively selected (
n
= 200) and graded for central canal stenosis, neural foraminal stenosis, and facet arthropathy. Studies were partitioned into patient-level train (
n
= 150), validation (
n
= 20), and test (
n
= 30) splits. V-Net models were first trained to segment the dural sac and the intervertebral disk, and localize facet and foramen using geometric rules. Subsequently, Big Transfer (BiT) models were trained for downstream classification tasks. An interpretable model for central canal stenosis was also trained using a decision tree classifier. Evaluation metrics included linearly weighted Cohen’s kappa score for multi-grade classification and area under the receiver operator characteristic curve (AUROC) for binarized classification.
Results
Segmentation of the dural sac and intervertebral disk achieved Dice scores of 0.93 and 0.94. Localization of foramen and facet achieved intersection over union of 0.72 and 0.83. Multi-class grading of central canal stenosis achieved a kappa score of 0.54. The interpretable decision tree classifier had a kappa score of 0.80. Pairwise agreement between readers (R1, R2), (R1, R3), and (R2, R3) was 0.86, 0.80, and 0.74. Binary classification of neural foraminal stenosis and facet arthropathy achieved AUROCs of 0.92 and 0.93.
Conclusion
Deep learning systems can be performant as well as interpretable for automated evaluation of lumbar spine MRI including classification of central canal stenosis, neural foraminal stenosis, and facet arthropathy.
Key Points
• Interpretable deep-learning systems can be developed for the evaluation of clinical lumbar spine MRI. Multi-grade classification of central canal stenosis with a kappa of 0.80 was comparable to inter-reader agreement scores (0.74, 0.80, 0.86). Binary classification of neural foraminal stenosis and facet arthropathy achieved favorable and accurate AUROCs of 0.92 and 0.93, respectively.
• While existing deep-learning systems are opaque, leading to clinical deployment challenges, the proposed system is accurate as well as interpretable, providing valuable information to a radiologist in clinical practice.
General methods to achieve better physical insight about nanoparticle aggregation and assembly are needed because of the potential role of aggregation in a wide range of materials, environmental, and ...biological outcomes. Scanning electron microscopy (SEM) is fast and affordable compared to transmission electron microscopy, but SEM micrographs lack contrast and resolution due to lower beam energy, topographic contrast, edge effects, and charging. We present a new segmentation algorithm called SEMseg that is robust to the challenges inherent in SEM micrograph analysis and demonstrate its utility for analyzing gold (Au) nanorod aggregates. SEMseg not only supports nanoparticle size analysis for dispersed nanoparticles, but also discriminates between nanoparticles within an aggregate. We compare our algorithm to those incorporated into the commonly used software ImageJ and demonstrate improved segmentation of aggregate structures. New physical insight about aggregation is demonstrated by the introduction of an order parameter describing side-by-side structure in nanoparticle aggregates. We also present the segmentation and fitting algorithms included in SEMseg within a user-friendly graphical user interface. The resulting code is provided with an open-source interface to provide quantitative image processing tools for researchers to characterize both dispersed nanoparticles and nanoparticle assemblies in SEM micrographs with high throughput.
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