Capture Heterogeneity Research Articles

MG-ASTN: Multigraph Framework With Attentive Spatial–Temporal Networks for Crowd Mobility Prediction

Predicting urban crowd patterns/flows is a challenging task due to complex spatial-temporal (ST) dependencies. In this paper, we aim to examine and report the capability and effectiveness of the current most widely used Graph Convolutional Networks (GCNs) on mobile data analysis in ST networks for crowd flow forecasting. Specifically, we propose a novel dual-stream framework leveraging Multi-Graph with Attentive Spatial-Temporal Networks (MG-ASTN) to simultaneously predict crowd in-out flow and OD flow based on the trajectory data collected by on-board devices (e.g., GPS). MG-ASTN utilizes Multi-Graph Convolutional Networks encoding non-Euclidean correlations to explore pair-wise relationships among regions. In addition, we further apply a cross-channel attention mechanism with 3D Temporal Convolutional Network to address the heterogeneity of ST features and capture more meaningful data representations for multi-task learning. In the evaluation, we conduct experiments based on 2 real-world datasets and verify most well-known state-of-the-art methods for crowd flow prediction. The results demonstrate that MG-ASTN could outperform other solutions -in-out flow prediction with lowest RMSE and MAE, and OD flow prediction beyond others in most cases, thus it has great potential in modeling the complex correlations among regions in ST networks and enabling accurate prediction in urban computing.

P02.12.B UNDERSTANDING THE ROLES OF EPHRIN A5 IN GLIOBLASTOMA AND ITS POTENTIAL AS A THERAPEUTIC TARGET

Abstract BACKGROUND Glioblastoma (GBM) is the most common and aggressive malignant primary brain cancer and is associated with very poor patient outcomes. GBM has been classified into three cell subtypes (proneural (PN), classical (CL), and mesenchymal (MES)), which have been further defined into four plastic cell states (neural-progenitor-like (NPC-like), oligodendrocyte-progenitor-like (OPC-like), astrocyte-like (AC-like), and mesenchymal-like (MES-like)). Existing treatment options have had limited success in improving overall survival, with the extreme heterogeneity present in GBM presenting a major barrier to effective therapy. Thus, there is an urgent need to develop more effective therapeutic strategies which better capture heterogeneity and delay tumour relapse. Several erythropoietin-producing human hepatocellular (Eph) receptors and ephrin ligands have been shown to be functionally overexpressed in cancer and have become attractive therapeutic targets. EphA3 has previously been shown to be a functional, tumour-specific target, frequently elevated in the MES subtype. We identified ephrin A5, the high-affinity ligand of EphA3, to be frequently co-expressed with EphA3 in GBM tumours. However, unlike EphA3, the mechanisms of ephrin A5 in GBM remain understudied. We therefore believe that ephrin A5 serves as an attractive synergistic target for the treatment of GBM. MATERIAL AND METHODS We examined the expression of ephrin A5 in a panel of GBM patient specimens. A panel of GBM tumours were also selected for immunohistochemical and spatial transcriptomics analysis. For in vitro functional studies, ephrin A5 was overexpressed in several GBM primary cell lines. Assays examining proliferation, stemness and differentiation were performed. RESULTS Expression of ephrin A5 was comparable to EphA3 in GBM. 64% of GBM tumours analysed showed higher levels of ephrin A5, relative to the highest normal brain specimen. Initial immunohistochemistry analysis suggests EphA3 and ephrin A5 to be expressed discretely on over 75% of the total tumour mass. Spatial transcriptomics analysis suggests ephrin A5 to be enriched in cells of the AC-like cell state. In vitro overexpression of ephrin A5 was shown to reduce neurosphere formation and induce differentiation of GBM cells. CONCLUSION EphA3 and ephrin A5 are overexpressed in a portion of GBM tumours. Critically, there was a strong mutually exclusive expression to different compartments of the GBM tumour, covering the vast majority of the total tumour mass. Our data suggests that ephrin A5 overexpression drives a less aggressive, more differentiated AC-like tumour phenotype. Thus, dual targeting of EphA3 and ephrin A5 in GBM could provide an opportunity to better capture tumour heterogeneity and achieve more durable therapeutic responses.

Enhancing the sensitivity of bacterial single-cell RNA sequencing using RamDA-seq and Cas9-based rRNA depletion

Bacterial populations exhibit heterogeneity in gene expression, which facilitates their survival and adaptation to unstable and unpredictable environments through the bet-hedging strategy. However, unraveling the rare subpopulations and heterogeneity in gene expression using population-level gene expression analysis remains a challenging task. Single-cell RNA sequencing (scRNA-seq) has the potential to identify rare subpopulations and capture heterogeneity in bacterial populations, but standard methods for scRNA-seq in bacteria are still under development, mainly due to differences in mRNA abundance and structure between eukaryotic and prokaryotic organisms. In this study, we present a hybrid approach that combines random displacement amplification sequencing (RamDA-seq) with Cas9-based rRNA depletion for scRNA-seq in bacteria. This approach allows cDNA amplification and subsequent sequencing library preparation from low-abundance bacterial RNAs. We evaluated its sequenced read proportion, gene detection sensitivity, and gene expression patterns from the dilution series of total RNA or the sorted single Escherichia coli cells. Our results demonstrated the detection of more than 1000 genes, about 24% of the genes in the E.coli genome, from single cells with less sequencing effort compared to conventional methods. We observed gene expression clusters between different cellular proliferation states or heat shock treatment. The approach demonstrated high detection sensitivity in gene expression analysis compared to current bacterial scRNA-seq methods and proved to be an invaluable tool for understanding the ecology of bacterial populations and capturing the heterogeneity of bacterial gene expression.

Trap Escape as a Driver of Capture Probability in Semiaquatic Snakes

Sampling biases resulting from capture methodology or animal behavioral responses can significantly skew our understanding of population size and structure. Behavioral responses to passive sampling with traps, such as trap-happy or trap-shy responses, can be accounted for in analyses using individual covariates but they require detailed understanding of the underlying behavioral mechanisms. We used a field experiment to investigate the impact of trap escape on capture rates and determine what traits influence trap escape rates between and within two species of semiaquatic snakes, Liodytes pygaea and Nerodia fasciata. We found that L. pygaea escaped significantly more often than did N. fasciata, smaller L. pygaea escaped at significantly higher rates than did larger conspecifics, and individual capture history significantly influenced escape rates in N. fasciata. Our findings highlight understudied sources of inter- and intraspecific capture heterogeneity in a common sampling technique, and we urge researchers to consider method-specific sampling biases when attempting to produce population parameter estimates for species that are difficult to detect.

Proteomic and functional characterization of intra-tumor heterogeneity in human endometrial cancer

SummaryEndometrial cancer is one of the most frequently diagnosed gynecological cancers worldwide, and its prevalence has increased by more than 50% over the last two decades. Despite the understanding of the major signaling pathways driving the growth and metastasis of endometrial cancer, clinical trials targeting these signals have reported poor outcomes. The heterogeneous nature of endometrial cancer is suspected to be one of the key reasons for the failure of targeted therapies. In this study, we perform a sequential window acquisition of all theoretical fragment ion spectra (SWATH)-based comparative proteomic analysis of 63 tumor biopsies collected from 20 patients and define differences in protein signature in multiple regions of the same tumor. We develop organoids from multiple biopsies collected from the same tumor and show that organoids capture heterogeneity in endometrial cancer growth. Overall, using quantitative proteomics and patient-derived organoids, we define the heterogeneous nature of endometrial cancer within a patient’s tumor.

Metabolic Tumor Volume and Total Lesion Glycolysis Can Predict Response to Very Low Dose Radiotherapy (4 Gy) in Indolent B-Cell Lymphomas

Numerous studies have established the role of very low dose radiotherapy (VLDRT), only 4 Gy in 2 fractions, in the management of indolent non-Hodgkin's lymphoma (NHL). While objective response rates to VLDRT are excellent, there are no widely accepted biomarkers of the depth and the durability of response after VLDRT. Radiosensitivity to VLDRT is not clearly linked to clinical features, such as tumor grade, histology, prior treatments or [18F]-FDG-PET standardized uptake values (SUV). Our group has recently demonstrated that pre-treatment tumor size greater than 6 cm may predict suboptimal response to VLDRT; however, this remains an imperfect predictor, and the majority of patients selected for VLDRT have lesions smaller than 6 cm. [18F]-FDG-PET metrics like baseline metabolic tumor volume (MTV) and total lesion glycolysis (TLG) are shown to stratify patient response to chemotherapy for some NHLs, but neither has been shown to predict radiotherapy response to VLDRT. Therefore, we aimed to determine if these features could predict response to VLDRT in indolent B-cell NHLs.Between 2005 and 2018, 250 patients with follicular or marginal zone lymphoma were locally irradiated to 299 sites using 2 Gy x2. Response was assessed within 1.5-6 months of VLDRT (n=231), and local failure (LF) was assessed over a median follow-up of 29 months. Out of the 299 treated sites, 254 (85%) had a corresponding [18F]-FDG-PET/CT scan performed prior to radiotherapy. PET scans were imported into MIM, a radiotherapy contouring and planning software program, and pre-radiotherapy disease was manually defined. A uniform expansion of 0.5 cm was performed to define a larger region of interest containing the contoured lesion but accounting for technical variability in defining the tumor edges. Of the 254 lesions, 207 (81%) had a maximum SUV (SUVmax) greater than 2.5 and were considered for further analysis. PET parameters were obtained using an SUV of 2.5 as a cutoff for MTV (MTV2.5) and TLG (TLG2.5), which capture lesion bulk, and using 41% of the SUVmax as a cutoff for MTV (MTV41%) and TLG (TLG41%), which capture the most metabolically active parts of a lesion. Maximum lesion size was analyzed using 4 cm and 6 cm as predetermined cutoffs. Lesion response was analyzed using multivariate logistic LASSO regression to account for predictor multicollinearity, and receiver operating characteristic curve analysis to obtain estimates of the area under the curve (AUC). To analyze time to LF, we performed Cox proportional hazards multivariate regression using stepwise selection. PET parameters were divided in quartiles to aid in interpretability. Estimates are shown with 95% confidence intervals.A complete response (CR) was seen in 156 (68%) lesions and LF was seen in 81 (27%) lesions after VLDRT. Using size alone had similar predictive value for response (AUC: 0.57(0.49-0.65) compared to MTV2.5 (AUC:0.66 (0.57-0.74)), MTV41% (AUC:0.64 (0.56-0.73)), TLG2.5 (AUC:0.64 (0.54-0.74)), and TLG41% (AUC:0.64 (0.54,0.75)). Through LASSO logistic regression, we determined that only MTV41% > 75 th percentile was associated with lower odds of CR (odds ratio (OR):0.21 (0.08-0.53)) versus size ≥4 (OR: 0.94 (0.32, 2.73) ) with no interaction noted. Likewise, in a multivariate model of LF, TLG2.5> 75 th percentile was shown to be a better predictor of worse LF (HR: 2.44 (1.32, 4.52)) than size when considering lesions < 6 cm.In stratifying response and local control for patients receiving VLDRT for indolent lymphomas, MTV and TLG may aid in patient selection, especially for lesions smaller than 6 cm. As PET-based radiotherapy planning is routinely performed in the treatment of indolent lymphomas, these parameters are easily attained after contouring and may have immediate clinical utility. These findings may also be applicable to radiotherapy response with higher doses, and the incorporation of [18F]-FDG-PET metrics into treatment decisions is an area of active research. It remains to be seen in ongoing work whether higher-order PET radiomic features (which for instance capture heterogeneity of tumor glucose metabolism) can further improve the accuracy of [18F]-FDG-PET-based radiotherapy outcome prediction. DisclosuresMayerhoefer: Siemens: Other: Speaker Honoraria; General Electric: Other: Speaker Honoraria; Bristol Myers Squibb: Other: Speaker Honoraria.

Survival Rates and Capture Heterogeneity of Bottlenose Dolphins (Tursiops truncatus) in the Shannon Estuary, Ireland

Adult survival is arguably the most important demographic parameter for long-lived species as it has a large impact on population growth, and it can be estimated for cetacean populations using natural markings and mark-recapture (MR) modelling. Here we describe a 26-year study of a genetically discrete, resident population of bottlenose dolphins in the Shannon Estuary, Ireland, conducted by an NGO using multiple platforms. We estimated survival rates (SRs) using Cormack-Jolly-Seber models and explored the effects of variable survey effort, multiple researchers, and changes in camera equipment as well as capture heterogeneity induced by changes in marks and site fidelity variation, all common issues affecting longitudinal dolphin studies. The mean adult SR was 0.94 (±0.001 SD) and thus comparable to the estimates reported for other bottlenose dolphin populations. Capture heterogeneity through variation in mark severity was confirmed, with higher capture probabilities for well-marked individuals than for poorly marked individuals and a “transience” effect being detected for less well-marked individuals with 43% only recorded once. Likewise, both SR and capture probabilities were comparatively low for individuals with low site fidelity to the Shannon Estuary, and SR of these individuals additionally decreased even further toward the end of the study, reflecting a terminal bias. This bias was attributed to non-random temporal migration, and, together with high encounter rates in Brandon Bay, supported the hypothesis of range expansion. Our results highlight the importance of consistent and geographically homogenous survey effort and support the differentiation of individuals according to their distinctiveness to avoid biased survival estimates.

Multiscale extended finite element method for deformable fractured porous media

Deformable fractured porous media appear in many geoscience applications. While the extended finite element method (XFEM) has been successfully developed within the computational mechanics community for accurate modeling of deformation, its application in geoscientific applications is not straightforward. This is mainly due to the fact that subsurface formations are heterogeneous and span large length scales with many fractures at different scales. To resolve this limitation, in this work, we propose a novel multiscale formulation for XFEM, based on locally computed enriched basis functions. The local multiscale basis functions capture heterogeneity of th e porous rock properties, and discontinuities introduced by the fractures. In order to preserve accuracy of these basis functions, reduced-dimensional boundary conditions are set as localization condition. Using these multiscale bases, a multiscale coarse-scale system is then governed algebraically and solved. The coarse scale system entails no enrichment due to the fractures. Such formulation allows for significant computational cost reduction, at the same time, it preserves the accuracy of the discrete displacement vector space. The coarse-scale solution is finally interpolated back to the fine scale system, using the same multiscale basis functions. The proposed multiscale XFEM (MS-XFEM) is also integrated within a two-stage algebraic iterative solver, through which error reduction to any desired level can be achieved. Several proof-of-concept numerical tests are presented to assess the performance of the developed method. It is shown that the MS-XFEM is accurate, when compared with the fine-scale reference XFEM solutions. At the same time, it is significantly more efficient than the XFEM on fine-scale resolution, as it significantly reduces the size of the linear systems. As such, it develops a promising scalable XFEM method for large-scale heavily fractured porous media.

Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art.

Normal tissue complication probability (NTCP) models that were formulated in the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) are one of the pillars in support of everyday’s clinical radiation oncology. Because of steady therapeutic refinements and the availability of cutting-edge technical solutions, the ceiling of organs-at-risk-sparing has been reached for photon-based intensity modulated radiotherapy (IMRT). The possibility to capture heterogeneity of patients and tissues in the prediction of toxicity is still an unmet need in modern radiation therapy. Potentially, a major step towards a wider therapeutic index could be obtained from refined assessment of radiation-induced morbidity at an individual level. The rising integration of quantitative imaging and machine learning applications into radiation oncology workflow offers an unprecedented opportunity to further explore the biologic interplay underlying the normal tissue response to radiation. Based on these premises, in this review we focused on the current-state-of-the-art on the use of radiomics for the prediction of toxicity in the field of head and neck, lung, breast and prostate radiotherapy.

Expanding the feasibility of fish and wildlife assessments with close‐kin mark–recapture

AbstractClose‐kin mark–recapture (CKMR) is a powerful new method for the assessment of fish and wildlife population dynamics. Unlike traditional mark–recapture techniques, the use of kinship as an identifying mark is robust to many forms of capture heterogeneity including variation in gear efficiency and tagging‐based effects such as loss and differential mortality. In addition, close‐kin methods can be applied to a wider range of sampling designs than traditional methods (e.g., single‐occasion surveys and lethal capture), can provide retrospective historical abundance estimates, and can produce survival estimates from as few as two sampling occasions. We evaluated the ability of CKMR to provide estimates of abundance and adult survival and then compared results to those from traditional mark–recapture. This analysis incorporated data from a three‐year study of lake resident brook trout (Salvelinus fontinalis) where individuals were both physically (PIT) tagged and genotyped for 44 de novo developed microsatellites with high throughput sequencing. Traditional mark–recapture estimates were derived using Pollock’s Robust Design, relying upon three primary open sampling occasions and four secondary closed occasions. We found that close‐kin methods produced contemporary estimates of adult abundance and survival that were similar to those produced by traditional mark–recapture in both magnitude and precision. Furthermore, CKMR provided abundance estimates for multiple years prior to sampling and, when restricted to data from a single year, still produced reliable abundance estimates for at least one and as many as three years. Retrospective abundance estimates corresponded with those from a separate historical two‐sample mark–recapture dataset. This study provides support for the use of CKMR as a robust and sampling‐efficient alternative to traditional mark–recapture methods of assessing population parameters.

Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture-recapture data.

Population dynamic models combine density dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density dependence. This is typically addressed using state‐space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture–recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state‐space models. We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state‐space models for density‐dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model for a closed population, using a conditional multinomial likelihood, followed by a Horvitz–Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R‐package VGAM, for different parameter specifications. The methods were then applied to a real data set of gray‐sided voles Myodes rufocanus from Northern Norway. We found that density‐dependence estimation was improved when explicitly modeling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.

Clinical Relevance of an Amplicon-Based Liquid Biopsy for Detecting ALK and ROS1 Fusion and Resistance Mutations in Patients With Non-Small-Cell Lung Cancer.

Liquid biopsy specimen genomic profiling is integrated in non-small-cell lung cancer (NSCLC) guidelines; however, data on the clinical relevance for ALK /ROS1 alterations are scarce. We evaluated the clinical utility of a targeted amplicon-based assay in a large prospective cohort of patients with ALK/ROS1-positive NSCLC and its impact on outcomes. Patients with advanced ALK/ROS1-positive NSCLC were prospectively enrolled in the study by researchers at eight French institutions. Plasma samples were analyzed using InVisionFirst-Lung and correlated with clinical outcomes. Of the 128 patients included in the study, 101 were positive for ALK and 27 for ROS1 alterations. Blood samples (N = 405) were collected from 29 patients naïve for treatment with tyrosine kinase inhibitors (TKI) or from 375 patients under treatment, including 105 samples collected at disease progression (PD). Sensitivity was 67% (n = 18 of 27) for ALK/ROS1 fusion detection. Higher detection was observed for ALK fusions at TKI failure (n = 33 of 74; 46%) versus in patients with therapeutic response (n = 12 of 109; 11%). ALK-resistance mutations were detected in 22% patients (n = 16 of 74) overall; 43% of the total ALK-resistance mutations identified occurred after next-generation TKI therapy. ALK G1202R was the most common mutation detected (n = 7 of 16). Heterogeneity of resistance was observed. ROS1 G2032R resistance was detected in 30% (n = 3 of 10). The absence of circulating tumor DNA mutations at TKI failure was associated with prolonged median overall survival (105.7 months). Complex ALK-resistance mutations correlated with poor overall survival (median, 26.9 months v NR for single mutation; P = .003) and progression-free survival to subsequent therapy (median 1.7 v 6.3 months; P = .003). Next-generation, targeted, amplicon-based sequencing for liquid biopsy specimen profiling provides clinically relevant detection of ALK/ROS1 fusions in TKI-naïve patients and allows for the identification of resistance mutations in patients treated with TKIs. Liquid biopsy specimens from patients treated with TKIs may affect clinical outcomes and capture heterogeneity of TKI resistance, supporting their role in selecting sequential therapy.

Meta analysis of binary data with excessive zeros in two-arm trials

We present a novel Bayesian approach to random effects meta analysis of binary data with excessive zeros in two-arm trials. We discuss the development of likelihood accounting for excessive zeros, the prior, and the posterior distributions of parameters of interest. Dirichlet process prior is used to account for the heterogeneity among studies. A zero inflated binomial model with excessive zero parameters were used to account for excessive zeros in treatment and control arms. We then define a modified unconditional odds ratio accounting for excessive zeros in two arms. The Bayesian inference is carried out using Markov chain Monte Carlo (MCMC) sampling techniques. We illustrate the approach using data available in published literature on myocardial infarction and death from cardiovascular causes. Bayesian approaches presented here use all the data, including the studies with zero events and capture heterogeneity among study effects, and produce interpretable estimates of overall and study-level odds-ratios, over the commonly used frequentist’s approaches. Results from the data analysis and the model selection also indicate that the proposed Bayesian method, while accounting for zero events, adjusts for excessive zeros and provides better fit to the data resulting in the estimates of overall odds-ratio and study-level odds-ratios that are based on the totality of the information.

Multi-modal latent factor exploration of atrophy, cognitive and tau heterogeneity in Alzheimer’s disease

Individuals with Alzheimer’s disease (AD) dementia exhibit significant heterogeneity across clinical symptoms, atrophy patterns, and spatial distribution of Tau deposition. Most previous studies of AD heterogeneity have focused on atypical clinical subtypes, defined subtypes with a single modality, or restricted their analyses to a priori brain regions and cognitive tests. Here, we considered a data-driven hierarchical Bayesian model to identify latent factors from atrophy patterns and cognitive deficits simultaneously, thus exploiting the rich dimensionality within each modality. Unlike most previous studies, our model allows each factor to be expressed to varying degrees within an individual, in order to reflect potential multiple co-existing pathologies.By applying our model to ADNI-GO/2 AD dementia participants, we found three atrophy-cognitive factors. The first factor was associated with medial temporal lobe atrophy, episodic memory deficits and disorientation to time/place (“MTL-Memory”). The second factor was associated with lateral temporal atrophy and language deficits (“Lateral Temporal-Language”). The third factor was associated with atrophy in posterior bilateral cortex, and visuospatial executive function deficits (“Posterior Cortical-Executive”). While the MTL-Memory and Posterior Cortical-Executive factors were discussed in previous literature, the Lateral Temporal-Language factor is novel and emerged only by considering atrophy and cognition jointly. Several analyses were performed to ensure generalizability, replicability and stability of the estimated factors. First, the factors generalized to new participants within a 10-fold cross-validation of ADNI-GO/2 AD dementia participants. Second, the factors were replicated in an independent ADNI-1 AD dementia cohort. Third, factor loadings of ADNI-GO/2 AD dementia participants were longitudinally stable, suggesting that these factors capture heterogeneity across patients, rather than longitudinal disease progression. Fourth, the model outperformed canonical correlation analysis at capturing associations between atrophy patterns and cognitive deficits.To explore the influence of the factors early in the disease process, factor loadings were estimated in ADNI-GO/2 mild cognitively impaired (MCI) participants. Although the associations between the atrophy patterns and cognitive profiles were weak in MCI compared to AD, we found that factor loadings were associated with inter-individual regional variation in Tau uptake. Taken together, these results suggest that distinct atrophy-cognitive patterns exist in typical Alzheimer’s disease, and are associated with distinct patterns of Tau depositions before clinical dementia emerges.

Decline in abundance and apparent survival rates of fin whales (Balaenoptera physalus) in the northern Gulf of St. Lawrence.

Estimates of abundance and survivorship provide quantifiable measures to monitor populations and to define and understand their conservation status. This study investigated changes in abundance and survival rates of fin whales (Balaenoptera physalus) in the northern Gulf of St. Lawrence in the context of anthropogenic pressures and changing environmental conditions. A long‐term data set, consisting of 35 years of photo‐identification surveys and comprising more than 5,000 identifications of 507 individuals, formed the basis of this mark–recapture study. Based on model selection using corrected Akaike Information Criterion, the most parsimonious Cormack–Jolly–Seber model included a linear temporal trend in noncalf apparent survival rates with a sharp decline in the last 5 years of the study and a median survival rate of 0.946 (95% confidence interval (CI) 0.910–0.967). To account for capture heterogeneity due to divergent patterns of site fidelity, agglomerative hierarchical cluster analysis was employed to categorize individuals based on their annual and survey site fidelity indices. However, the negative trend in survivorship remained and was corroborated by a significant decline in the estimated super‐population size from 335 (95% CI 321–348) individuals in 2004–2010 to 291 (95% CI 270–312) individuals in 2010–2016. Concurrently, a negative trend was estimated in recruitment to the population, supported by a sharp decrease in the number of observed calves. Ship strikes and changes in prey availability are potential drivers of the observed decline in fin whale abundance. The combination of clustering methods with mark–recapture represents a flexible way to investigate the effects of site fidelity on demographic variables and is broadly applicable to other individual‐based studies.

Low-risk population size estimates in the presence of capture heterogeneity

SUMMARY Population estimation methods are used for estimating the size of a population from samples of individuals. In many applications, the probability of being observed in the sample varies across individuals, resulting in sampling bias. We show that in this setting, estimators of the population size have high and sometimes infinite risk, leading to large uncertainty in the population size. As an alternative, we propose estimating the population of individuals with observation probability exceeding a small threshold. We show that estimators of this quantity have lower risk than estimators of the total population size. The proposed approach is shown empirically to result in large reductions in mean squared error in a common model for capture-recapture population estimation with heterogeneous capture probabilities.

Estimation of Population Size When Capture Probability Depends on Individual States

We develop a multi-state model to estimate the size of a closed population from capture–recapture studies. We consider the case where capture–recapture data are not of a simple binary form, but where the state of an individual is also recorded upon every capture as a discrete variable. The proposed multi-state model can be regarded as a generalisation of the commonly applied set of closed population models to a multi-state form. The model allows for heterogeneity within the capture probabilities associated with each state while also permitting individuals to move between the different discrete states. A closed-form expression for the likelihood is presented in terms of a set of sufficient statistics. The link between existing models for capture heterogeneity is established, and simulation is used to show that the estimate of population size can be biased when movement between states is not accounted for. The proposed unconditional approach is also compared to a conditional approach to assess estimation bias. The model derived in this paper is motivated by a real ecological data set on great crested newts, Triturus cristatus. Supplementary materials accompanying this paper appear online.

Utility of the bucket cable trap to capture American black bears

Most American black bear (Ursus americanus) population studies involving live capture have used foot-hold restraints or barrel and culvert traps, but new capture methods, including the bucket cable trap, are increasingly being used by wildlife management agencies and researchers. Although the bucket cable trap has been used to capture black bears and grizzly bears (U. arctos) in the United States and Canada, quantitative assessments of its capture efficiency, injury rates, and capture biases are lacking. We addressed this gap in knowledge using a camera-trap-based study of bucket-cable-trap capture methodology. Between 12 May and 12 August 2015, we placed remotely triggered cameras at active bucket-trap sites throughout southeastern Oklahoma, USA. During 1,285 camera-trap-nights, we recorded 711 black bear visitation events and 106 successful captures. Of the 402 visitation events in which the trap was active, 26.3% resulted in a successful capture. Incidental captures were limited to northern raccoons (Procyon lotor). Sex, previous capture, and mass characteristics appeared to affect the capture process, indicating that it is important to keep capture heterogeneity in mind when characterizing population demographics and calculating abundance using this capture method. © 2018 The Wildlife Society.

Partial stratification in two‐sample capture–recapture experiments

Capture heterogeneity is known to cause bias in estimates of abundance in capture–recapture experiments. This heterogeneity is often related to observable fixed characteristics of the animals such as sex. If this information can be observed for each handled animal at both sample occasions, then it is straightforward to stratify (e.g., by sex) and obtain stratum‐specific estimates. However, in many fishery experiments, it is difficult to sex all captured fish because morphological differences are slight or because of logistic constraints. In these cases, a subsample of the captured fish at each sample occasion is selected, and additional and often more costly measurements are made, such as sex determination through sacrificing the fish. Our data now consist of two types of marked animals: animals whose value of the stratification variable is unknown, and subsamples at each occasion where the value of the stratification variables are determined. In this paper, we develop and apply new methods for these types of experiments. Furthermore, given the relative costs of sampling for a simple capture and for processing the subsample, optimal allocation of effort for a given cost can be determined. We also develop methods to account for additional information (e.g., prior information about the sex ratio) and for supplemental continuous covariates such as length. These methods are applied to a problem of estimating the size of the walleye population in Mille Lacs Lake, MN.

XPAT: a toolkit to conduct cross-platform association studies with heterogeneous sequencing datasets.

High-throughput sequencing data are increasingly being made available to the research community for secondary analyses, providing new opportunities for large-scale association studies. However, heterogeneity in target capture and sequencing technologies often introduce strong technological stratification biases that overwhelm subtle signals of association in studies of complex traits. Here, we introduce the Cross-Platform Association Toolkit, XPAT, which provides a suite of tools designed to support and conduct large-scale association studies with heterogeneous sequencing datasets. XPAT includes tools to support cross-platform aware variant calling, quality control filtering, gene-based association testing and rare variant effect size estimation. To evaluate the performance of XPAT, we conducted case-control association studies for three diseases, including 783 breast cancer cases, 272 ovarian cancer cases, 205 Crohn disease cases and 3507 shared controls (including 1722 females) using sequencing data from multiple sources. XPAT greatly reduced Type I error inflation in the case-control analyses, while replicating many previously identified disease–gene associations. We also show that association tests conducted with XPAT using cross-platform data have comparable performance to tests using matched platform data. XPAT enables new association studies that combine existing sequencing datasets to identify genetic loci associated with common diseases and other complex traits.