Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study.

BACKGROUND: Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition. OBJECTIVE: The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD. METHODS: Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score. RESULTS: Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method's specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection. CONCLUSIONS: This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.

Quantitative performance assessment of Ultivue multiplex panels in formalin-fixed, paraffin-embedded human and murine tumor specimens.

We present a rigorous validation strategy to evaluate the performance of Ultivue multiplex immunofluorescence panels. We have quantified the accuracy and precision of four different multiplex panels (three human and one mouse) in tumor specimens with varying levels of T cell density. Our results show that Ultivue panels are typically accurate wherein the relative difference in cell proportion between a multiplex image and a 1-plex image is less than 20% for a given biomarker. Ultivue panels exhibited relatively high intra-run precision (CV ≤ 25%) and relatively low inter-run precision (CV >> 25%) which can be remedied by using local intensity thresholding to gate biomarker positivity. We also evaluated the reproducibility of cell-cell distance estimates measured from multiplex images which show high intra- and inter-run precision. We introduce a new metric, multiplex labeling efficiency, which can be used to benchmark the overall fidelity of the multiplex data across multiple batch runs. Taken together our results provide a comprehensive characterization of Ultivue panels and offer practical guidelines for analyzing multiplex images.

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using the All of Us Research Program dataset.

Postpartum depression (PPD), afflicting one in seven women, poses a major challenge in maternal health. Existing approaches to detect PPD heavily depend on in-person postpartum visits, leading to cases of the condition being overlooked and untreated. We explored the potential of consumer wearable-derived digital biomarkers for PPD recognition to address this gap. Our study demonstrated that intra-individual machine learning (ML) models developed using these digital biomarkers can discern between pre-pregnancy, pregnancy, postpartum without depression, and postpartum with depression time periods (i.e., PPD diagnosis). When evaluating variable importance, calories burned from the basal metabolic rate (calories BMR) emerged as the digital biomarker most predictive of PPD. To confirm the specificity of our method, we demonstrated that models developed in women without PPD could not accurately classify the PPD-equivalent phase. Prior depression history did not alter model efficacy for PPD recognition. Furthermore, the individualized models demonstrated superior performance compared to a conventional cohort-based model for the detection of PPD, underscoring the effectiveness of our individualized ML approach. This work establishes consumer wearables as a promising avenue for PPD identification. More importantly, it also emphasizes the utility of individualized ML model methodology, potentially transforming early disease detection strategies.

A Bayesian multi-stage modelling framework to evaluate impacts of energy development on wildlife populations: an application to greater sage-grouse (Centrocercus urophasianus).

Increased demand for domestic production of renewable energy has led to expansion of energy infrastructure across western North America. Much of the western U.S. comprises remote landscapes that are home to a variety of vegetation communities and wildlife species, including the imperiled sagebrush ecosystem and indicator species such as greater sage-grouse (Centrocercus urophasianus). Geothermal sources in particular have potential for continued development across the western U.S. but impacts to greater sage-grouse and other species are unknown. To address this information gap, we describe a novel two-pronged methodology that analyzes impacts of geothermal energy production on pattern and process of greater sage-grouse populations using (a) before-after control-impact (BACI) measures of population growth and lek absence rates and (b) concurrent-to-operation evaluations of demographic rates. Growth and absence rate analyses utilized 14 years of lek survey data collected prior (2005-2011) and concurrent (2012-2018) to geothermal operations at two sites in Nevada, USA. Demographic analyses utilized relocation data, restricted inference to concurrent years, and incorporated 17 additional control sites. Demographic results were applied to >100 potential geothermal sites distributed across the study region to generate spatially explicit predictions of unrealized population-level impacts.•State-space and generalized linear models yield estimates of population growth and lek absence rates, respectively, before and after the onset of geothermal energy production; distances ranging from 2-30 km are evaluated as alternative control-impact footprint hypotheses; this provides inference about the spatial extent as well as the magnitude of impacts associated with geothermal development.•Estimation of important population demographic rates are implemented to investigate the processes by which geothermal energy development might reduce population growth; independent estimates of confounding, environmental effects from 17 control sites are made spatially explicit within 'impact' models to establish baseline conditions otherwise masked by collinearity.•Population matrix models are built using estimates from demographic analyses to provide landscape mapping of impacts associated with potential geothermal sites.

Post-recovery COVID-19 and incident heart failure in the National COVID Cohort Collaborative (N3C) study.

Cardiac involvement has been noted in COVID-19 infection. However, the relationship between post-recovery COVID-19 and development of de novo heart failure has not been investigated in a large, nationally representative population. We examined post-recovery outcomes of 587,330 patients hospitalized in the United States (257,075 with COVID-19 and 330,255 without), using data from the National COVID Cohort Collaborative study. Patients hospitalized with COVID-19 were older (51 vs. 46 years), more often male (49% vs. 42%), and less often White (61% vs. 69%). Over a median follow up of 367 days, 10,979 incident heart failure events occurred. After adjustments, COVID-19 hospitalization was associated with a 45% higher hazard of incident heart failure (hazard ratio = 1.45; 95% confidence interval: 1.39-1.51), with more pronounced associations among patients who were younger (P-interaction = 0.003), White (P-interaction = 0.005), or who had established cardiovascular disease (P-interaction = 0.005). In conclusion, COVID-19 hospitalization is associated with increased risk of incident heart failure.

Targeting Sagebrush (Artemisia Spp.) Restoration Following Wildfire with Greater Sage-Grouse (Centrocercus Urophasianus) Nest Selection and Survival Models.

Unprecedented conservation efforts for sagebrush (Artemisia spp.) ecosystems across the western United States have been catalyzed by risks from escalated wildfire activity that reduces habitat for sagebrush-obligate species such as Greater Sage-Grouse (Centrocercus urophasianus). However, post-fire restoration is challenged by spatial variation in ecosystem processes influencing resilience to disturbance and resistance to non-native invasive species, and spatial and temporal lags between slower sagebrush recovery processes and faster demographic responses of sage-grouse to loss of important habitat. Decision-support frameworks that account for these factors can help users strategically apply restoration efforts by predicting short and long-term ecological benefits of actions. Here, we developed a framework that strategically targets burned areas for restoration actions (e.g., seeding or planting sagebrush) that have the greatest potential to positively benefit sage-grouse populations through time. Specifically, we estimated sagebrush recovery following wildfire and risk of non-native annual grass invasion under four scenarios: passive recovery, grazing exclusion, active restoration with seeding, and active restoration with seedling transplants. We then applied spatial predictions of integrated nest site selection and survival models before wildfire, immediately following wildfire, and at 30 and 50 years post-wildfire based on each restoration scenario and measured changes in habitat. Application of this framework coupled with strategic planting designs aimed at developing patches of nesting habitat may help increase operational resilience for fire-impacted sagebrush ecosystems.

Characterizing Intra-Tumor and Inter-Tumor Variability of Immune Cell Infiltrates in Murine Syngeneic Tumors.

Murine tumors are indispensable model systems in preclinical immuno-oncology research. While immunologic heterogeneity is well-known to be an important factor that can influence treatment outcome, there is a severe paucity of data concerning the nature of this heterogeneity in murine tumor models. Using serial sectioning methodology combined with IHC analysis and whole-slide image analysis, the depth-dependent variation in immune-cell abundance in tumor specimens was investigated at single-cell resolution. Specifically, intra- and intertumor variability in cell density of nine immune-cell biomarkers was quantified in multiple murine tumor models. The analysis showed that intertumor variability was typically the dominant source of variation in measurements of immune-cell densities. Statistical power analysis revealed the effect of group size and variance in immune-cell density on the predictive power of detecting a statistically meaningful fold-change in immune-cell density. Intertumor variability in the ratio of immune-cell densities showed distinct patterns in select tumor models and revealed the existence of strong correlations between select biomarker pairs. Furthermore, the relative proportion of immune cells at different depths across tumor samples was preserved in some but not all tumor models, thereby revealing the existence of compositional heterogeneity. Taken together, these results reveal novel insights into the nature of immunologic heterogeneity, which is not accessible through typical omics approaches.

An orthogonal methods assessment of topical drug concentrations in skin and the impact for risk assessment in the viable epidermis.

Systemic toxicity assessments for oral or parenteral drugs often utilize the concentration of drug in plasma to enable safety margin calculations for human risk assessment. For topical drugs, there is no standard method for measuring drug concentrations in the stratum basale of the viable epidermis. This is particularly important since the superficial part of the epidermis, the stratum corneum (SC), is nonviable and where most of a topically applied drug remains, never penetrating deeper into the skin. We investigated the relative concentrations of a prototype kinase inhibitor using punch biopsy, laser capture microdissection, and imaging mass spectrometry methods in the SC, stratum basale, and dermis of minipig skin following topical application as a cream formulation. The results highlight the value of laser capture microdissection and mass spectrometry imaging in quantifying the large difference in drug concentration across the skin and even within the epidermis, and supports use of these methods for threshold-based toxicity risk assessments in specific anatomic locations of the skin, like of the stratum basale.

Nesting, brood rearing, and summer habitat selection by translocated greater sage-grouse in North Dakota, USA.

Human enterprise has led to large-scale changes in landscapes and altered wildlife population distribution and abundance, necessitating efficient and effective conservation strategies for impacted species. Greater sage-grouse (Centrocercus urophasianus; hereafter sage-grouse) are a widespread sagebrush (Artemisia spp.) obligate species that has experienced population declines since the mid-1900s resulting from habitat loss and expansion of anthropogenic features into sagebrush ecosystems. Habitat loss is especially evident in North Dakota, USA, on the northeastern fringe of sage-grouse' distribution, where a remnant population remains despite recent development of energy-related infrastructure. Resource managers in this region have determined a need to augment sage-grouse populations using translocation techniques that can be important management tools for countering species decline from range contraction. Although translocations are a common tool for wildlife management, very little research has evaluated habitat following translocation, to track individual behaviors such as habitat selection and fidelity to the release site, which can help inform habitat requirements to guide selection of future release sites. We provide an example where locations from previously released radio-marked sage-grouse are used in a resource selection function framework to evaluate habitat selection following translocation and identify areas of seasonal habitat to inform habitat management and potential restoration needs. We also evaluated possible changes in seasonal habitat since the late 1980s using spatial data provided by the Rangeland Analysis Platform coupled with resource selection modeling results. Our results serve as critical baseline information for habitat used by translocated individuals across life stages in this study area, and will inform future evaluations of population performance and potential for long-term recovery.

Wildfire and the ecological niche: Diminishing habitat suitability for an indicator species within semi-arid ecosystems.

Globally accelerating frequency and extent of wildfire threatens the persistence of specialist wildlife species through direct loss of habitat and indirect facilitation of exotic invasive species. Habitat specialists may be especially prone to rapidly changing environmental conditions because their ability to adapt lags behind the rate of habitat alteration. As a result, these populations may become increasingly susceptible to ecological traps by returning to suboptimal breeding habitats that were dramatically altered by disturbance. We demonstrate a multistage modeling approach that integrates habitat selection and survival during the key nesting life-stage of a bird species of high conservation concern, the greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse). We applied these spatially explicit models to a spatiotemporally robust dataset of sage-grouse nest locations and fates across wildfire-altered sagebrush ecosystems of the Great Basin ecoregion, western United States. Female sage-grouse exhibited intricate habitat selection patterns that varied across regional gradients of ecological productivity among sagebrush communities, but often selected nest sites that disproportionately resulted in nest failure. For example, 23% of nests occurred in wildfire-affected habitats characterized by reduced sagebrush cover and greater composition of invasive annual grasses. We found survival of nests was negatively associated with wildfire-affected areas, but positively associated with higher elevations with increased ruggedness and overall shrub cover. Strong site fidelity likely drove sage-grouse to continue nesting in habitats degraded by wildfire. Hence, increasing frequency and extent of wildfire may contribute disproportionately to reduced reproductive success by creating ecological traps that act as population sinks. Identifying such habitat mismatches between selection and survival facilitates deeper understanding of the mechanisms driving reduced geographic niche space and population decline at broad spatiotemporal scales, while guiding management actions to areas that would be most beneficial to the species.

Territoriality drives preemptive habitat selection in recovering wolves: Implications for carnivore conservation.

According to the ideal-free distribution (IFD), individuals within a population are free to select habitats that maximize their chances of success. Assuming knowledge of habitat quality, the IFD predicts that average fitness will be approximately equal among individuals and between habitats, while density varies, implying that habitat selection will be density dependent. Populations are often assumed to follow an IFD, although this assumption is rarely tested with empirical data, and may be incorrect when territoriality indicates habitat selection tactics that deviate from the IFD (e.g. ideal-despotic distribution or ideal-preemptive distribution). When territoriality influences habitat selection, species' density will not directly reflect components of fitness such as reproductive success or survival. In such cases, assuming an IFD can lead to false conclusions about habitat quality. We tested theoretical models of density-dependent habitat selection on a species known to exhibit territorial behaviour in order to determine whether commonly applied habitat models are appropriate under these circumstances. We combined long-term radiotelemetry and census data from grey wolves Canis lupus in the Upper Peninsula of Michigan, USA to relate spatiotemporal variability in wolf density to underlying classifications of habitat within a hierarchical state-space modelling framework. We then iteratively applied isodar analysis to evaluate which distribution of habitat selection best described this recolonizing wolf population. The wolf population in our study expanded by >1,000% during our study (~50 to >600 individuals), and density-dependent habitat selection was most consistent with the ideal-preemptive distribution, as opposed to the ideal-free or ideal-despotic alternatives. Population density of terrestrial carnivores may not be positively correlated with the fitness value of their habitats, and density-dependent habitat selection patterns may help to explain complex predator-prey dynamics and cascading indirect effects. Source-sink population dynamics appear likely when species exhibit rapid growth and occupy interspersed habitats of contrasting quality. These conditions are likely and have implications for large carnivores in many systems, such as areas in North America and Europe where large predator species are currently recolonizing their former ranges.

Comparative nonclinical assessments of the biosimilar PF-06410293 and originator adalimumab.

Adalimumab, a recombinant fully human monoclonal antibody targeting tumor necrosis factor (TNF), is approved in the United States and Europe to treat various inflammatory and autoimmune indications. Biosimilars are approved biologics highly similar, but not identical, to approved biotherapeutics. To support clinical development of PF-06410293, an adalimumab biosimilar, nonclinical studies evaluated the structural, functional, toxicologic, and toxicokinetic similarity to originator adalimumab sourced from the United States (adalimumab-US) and European Union (adalimumab-EU). Structural similarity was assessed by peptide mapping. Biologic activity was measured via inhibition of TNF-induced apoptosis and Fc-based functionality assessments. In vivo nonclinical similarity was evaluated in a toxicity study in cynomolgus monkeys administered subcutaneous PF-06410293 or adalimumab-EU (0 or 157 mg/kg/week). Peptide mapping demonstrated PF-06410293, adalimumab-US, and adalimumab-EU had identical amino acid sequences. Comparative functional and binding assessments were similar. Effects of PF-06410293 and adalimumab-EU were similar and limited to pharmacologically mediated decreased cellularity of lymphoid follicles and germinal centers in spleen. Toxicokinetics were similar; maximum plasma concentration and area-under-the-concentration-time curve ratio of PF-06410293:adalimumab-EU ranged from 1.0 to 1.2. These studies supported PF-06410293 entry into clinical development. Many regulatory agencies now only request nonclinical in vivo testing if there is residual uncertainty regarding biosimilarity after in vitro analytical studies.

Territorial landscapes: incorporating density-dependence into wolf habitat selection studies.

Habitat selection is a process that spans space, time and individual life histories. Ecological analyses of animal distributions and preferences are most accurate when they account for inherent dynamics of the habitat selection process. Strong territoriality can constrain perception of habitat availability by individual animals or groups attempting to colonize or establish new territory. Because habitat selection is a function of habitat availability, broad-scale changes in habitat availability or occupancy can drive density-dependent habitat functional responses. We investigated density-dependent habitat selection over a 19-year period of grey wolf (Canis lupus) recovery in Michigan, USA, using a generalized linear mixed model framework to develop a resource selection probability function (RSPF) with habitat coefficients conditioned on random effects for wolf packs and random year intercepts. In addition, we allowed habitat coefficients to vary as interactions with increasing wolf density over space and time. Results indicated that pack presence was driven by factors representing topography, human development, winter prey availability, forest structure, roads, streams and snow. Importantly, responses to many of these predictors were density-dependent. Spatio-temporal dynamics and population changes can cause considerable variation in wildlife-habitat relationships, possibly confounding interpretation of conventional habitat selection models. By incorporating territoriality into an RSPF analysis, we determined that wolves' habitat use in Michigan shifted over time, for example, exhibiting declining responses to winter prey indices and switching from positive to negative responses with respect to stream densities. We consider this an important example of a habitat functional response in wolves, driven by colonization, density-dependence and changes in occupancy during a time period of range expansion and population increase.

Molecular Profiling of Ulcerative Colitis Subjects from the TURANDOT Trial Reveals Novel Pharmacodynamic/Efficacy Biomarkers.

BACKGROUND AND AIMS: To define pharmacodynamic and efficacy biomarkers in ulcerative colitis [UC] patients treated with PF-00547659, an anti-human mucosal addressin cell adhesion molecule-1 [MAdCAM-1] monoclonal antibody, in the TURANDOT study. METHODS: Transcriptome, proteome and immunohistochemistry data were generated in peripheral blood and intestinal biopsies from 357 subjects in the TURANDOT study. RESULTS: In peripheral blood, C-C motif chemokine receptor 9 [CCR9] gene expression demonstrated a dose-dependent increase relative to placebo, but in inflamed intestinal biopsies CCR9 gene expression decreased with increasing PF-00547659 dose. Statistical models incorporating the full RNA transcriptome in inflamed intestinal biopsies showed significant ability to assess response and remission status. Oncostatin M [OSM] gene expression in inflamed intestinal biopsies demonstrated significant associations with, and good accuracy for, efficacy, and this observation was confirmed in independent published studies in which UC patients were treated with infliximab or vedolizumab. Compared with the placebo group, intestinal T-regulatory cells demonstrated a significant increase in the intermediate 22.5-mg dose cohort, but not in the 225-mg cohort. CONCLUSIONS: CCR9 and OSM are implicated as novel pharmacodynamic and efficacy biomarkers. These findings occur amid coordinated transcriptional changes that enable the definition of surrogate efficacy biomarkers based on inflamed biopsy or blood transcriptomics data.ClinicalTrials.gov identifierNCT01620255.

Quantitative biodistribution of biotherapeutics at whole body, organ and cellular levels by autoradiography.

AIM: Tools for mapping and quantifying monoclonal antibody (mAb) and peptide biotherapeutics distribumtion were evaluated by comparing data from three independent methods conducted at the whole body, organ or tissue, and cellular levels. MATERIALS & METHODS: [3H]-mAb1 and [3H]-peptide A were administered intravenously to rats followed by quantitative whole-body autoradiography, kidney macro-autoradiography and micro-autoradiography. RESULTS: [3H]-mAb1 and [3H]-peptide A concentrations were measured in anatomical regions ranging from whole body to whole organ to sub-organ level, such as the kidney glomerulus, with increasing resolution. The tissue/blood [3H]-mAb1 concentrations in selected kidney microenvironments were comparable among the three quantitative methods. CONCLUSION: Quantitative whole-body autoradiography, tissue macro-autoradiography and micro-autoradiography all provide useful tools for quantifying the concentrations of biotherapeutics at different anatomical levels in tissues, facilitating better predictions of efficacy and toxicity.

A k-mer based transcriptomics approach for antisense drug discovery targeting the Ewing's family of tumors.

Ewing's sarcoma treatment failures are associated with high mortality indicating a need for new therapeutic approaches. We used a k-mer counting approach to identify cancer-specific mRNA transcripts in 3 Ewing's Family Tumor (EFT) cell lines not found in the normal human transcriptome. Phosphorodiamidate morpholino oligomers targeting six EFT-specific transcripts were evaluated for cytotoxicity in TC-32 and CHLA-10 EFT lines and in HEK293 renal epithelial control cells. Average morpholino efficacy (EC50) was 0.66 ± 0.13 in TC-32, 0.25 ± 0.14 in CHLA-10 and 3.07 ± 5.02 µM in HEK293 control cells (ANOVA p < 0.01). Synergy was observed for a cocktail of 12 morpholinos at low dose (0.3 µM) in TC-32 cells, but not in CHLA-10 cells. Paired synergy was also observed in both EFT cell lines when the PHGDH pre-mRNA transcript was targeted in combination with XAGE1B or CYP4F22 transcripts. Antagonism was observed when CCND1 was targeted with XAGE1B or CYP4F22, or when IGFBP-2 was targeted with CCND1 or RBM11. This transcriptome profiling approach is highly effective for cancer drug discovery, as it identified new EWS-specific target genes (e.g. CYP4F22, RBM11 and IGBP-2), and predicted effective antisense agents (EC50 < 1 µM) that demonstrate both synergy and antagonism in combination therapy.

Accidental and Programmed Cell Death in Investigative and Toxicologic Pathology.

Cellular development and homeostasis are regulated via programmed cell death (PCD; apoptosis), which is a genetically regulated cellular process. Accidental cell death (ACD; necrosis) can be triggered by chemical, physical, or mechanical stress. Necrosis is the presence of dead tissues or cells in a living organism regardless of the initiating process and can be observed in infectious and non-infectious diseases and toxicities. This article describes tissue-based immunohistotechnical protocols used for assessing PCD and necrosis in formalin-fixed tissues obtained from preclinical species used in investigative and toxicologic pathology. Two commonly employed protocols for the identification of PCD and necrosis are described in this article: immunohistochemistry (IHC) for cleaved caspase 3, and terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick-end labeling (TUNEL). TUNEL has been used to detect DNA fragmentation by labeling the terminal ends of nucleic acids in necrotic and apoptotic cells. © 2018 by John Wiley & Sons, Inc.

Spatially varying density dependence drives a shifting mosaic of survival in a recovering apex predator (Canis lupus).

Understanding landscape patterns in mortality risk is crucial for promoting recovery of threatened and endangered species. Humans affect mortality risk in large carnivores such as wolves (Canis lupus), but spatiotemporally varying density dependence can significantly influence the landscape of survival. This potentially occurs when density varies spatially and risk is unevenly distributed. We quantified spatiotemporal sources of variation in survival rates of gray wolves (C. lupus) during a 21-year period of population recovery in the Upper Peninsula of Michigan, USA. We focused on mapping risk across time using Cox Proportional Hazards (CPH) models with time-dependent covariates, thus exploring a shifting mosaic of survival. Extended CPH models and time-dependent covariates revealed influences of seasonality, density dependence and experience, as well as individual-level factors and landscape predictors of risk. We used results to predict the shifting landscape of risk at the beginning, middle, and end of the wolf recovery time series. Survival rates varied spatially and declined over time. Long-term change was density-dependent, with landscape predictors such as agricultural land cover and edge densities contributing negatively to survival. Survival also varied seasonally and depended on individual experience, sex, and resident versus transient status. The shifting landscape of survival suggested that increasing density contributed to greater potential for human conflict and wolf mortality risk. Long-term spatial variation in key population vital rates is largely unquantified in many threatened, endangered, and recovering species. Variation in risk may indicate potential for source-sink population dynamics, especially where individuals preemptively occupy suitable territories, which forces new individuals into riskier habitat types as density increases. We encourage managers to explore relationships between adult survival and localized changes in population density. Density-dependent risk maps can identify increasing conflict areas or potential habitat sinks which may persist due to high recruitment in adjacent habitats.

Patterns of divergence across the geographic and genomic landscape of a butterfly hybrid zone associated with a climatic gradient.

Hybrid zones are a valuable tool for studying the process of speciation and for identifying the genomic regions undergoing divergence and the ecological (extrinsic) and nonecological (intrinsic) factors involved. Here, we explored the genomic and geographic landscape of divergence in a hybrid zone between Papilio glaucus and Papilio canadensis. Using a genome scan of 28,417 ddRAD SNPs, we identified genomic regions under possible selection and examined their distribution in the context of previously identified candidate genes for ecological adaptations. We showed that differentiation was genomewide, including multiple candidate genes for ecological adaptations, particularly those involved in seasonal adaptation and host plant detoxification. The Z chromosome and four autosomes showed a disproportionate amount of differentiation, suggesting genes on these chromosomes play a potential role in reproductive isolation. Cline analyses of significantly differentiated genomic SNPs, and of species-diagnostic genetic markers, showed a high degree of geographic coincidence (81%) and concordance (80%) and were associated with the geographic distribution of a climate-mediated developmental threshold (length of the growing season). A relatively large proportion (1.3%) of the outliers for divergent selection were not associated with candidate genes for ecological adaptations and may reflect the presence of previously unrecognized intrinsic barriers between these species. These results suggest that exogenous (climate-mediated) and endogenous (unknown) clines may have become coupled and act together to reinforce reproductive isolation. This approach of assessing divergence across both the genomic and geographic landscape can provide insight about the interplay between the genetic architecture of reproductive isolation and endogenous and exogenous selection.