Model Estimates Research Articles

A Statistical Evaluation of the Occurence of Meningitis in Takum, Taraba State, Nigeria

Meningitis remains a critical public health issue in Nigeria, particularly within the dry season when environmental factors such as low humidity and dust elevate transmission risks. Using historical incidence data from 2012 to 2021, this study utilizes the Autoregressive Integrated Moving Average (ARIMA) model estimate and predict the occurrence of meningitis occurrences. Findings frm the study revealed that the ARIMA(1,1,0) model emerged as the optimal fit, capturing the seasonal patterns and temporal trends in meningitis cases. This study recommends the integration of ARIMA-based forecasting into Nigeria’s public health strategies to strengthen early warning systems, optimize resource deployment, and enable more proactive responses during high-risk periods.

Markerless tracking of tumor and tissues: A motion model approach.

Respiratory motion management is essential in order to achieve high-precision radiotherapy. Markerless motion tracking of tumor can provide a non-invasive way to manage respiratory motion, thereby enhancing treatment accuracy. However, the low contrast in real-time x-ray images for image guidance limits the application of markerless tracking. We present a novel approach based on a motion model to perform markerless tracking of tumor and surrounding tissues even when they have low contrast in real-time x-ray images. A proof-of-concept validation of the method has been performed using digital and physical phantoms at breathing conditions that are significantly different than the planning stage. A motion model is first constructed by performing principal component analysis (PCA) on the planning 4DCT. During treatment, the motion of a surrogate is tracked and used as the input of the motion model, which generates a 3D real-time volume estimation. Such 3D estimation is then projected to 2D to create digitally reconstructed radiographs (DRRs). The relationships between the real-time DRRs, reference DRRs, and reference x-ray images are first established to simulate 2D real-time images from the real-time volume. The registration between the simulated 2D real-time images and real-time x-ray images corrects the initial motion model estimation to ensure the estimated volume matches the real-time condition. In digital phantom, the Dice index of pancreas was improved from 0.74 to 0.78 after correction using real-time DRRs in fully inhaled phase. Validation on lung and pancreas is performed in physical phantom with two motion traces. The surrogate-tumor relationships were intentionally altered to generate large target localization errors due to the differences in body condition between treatment planning stage and during treatment. The real-time correction for the estimated 3D real-time volume was performed using a pair of 2D x-ray images. For the deep breathing motion trace, the tumor localization mean absolute error (MAE) throughout the tracking decreases from around 3mm to less than 1mm after correction. For the shallow breathing motion trace with a 1.7mm baseline shift, the tumor localization MAE throughout the tracking decreases from around 1.5mm to less than 1mm after correction. The method combines the detailed structural information from planning 4DCT and real-time information from real-time x-ray images through a motion model. The matching between the real-time model estimation and 2D real-time images is performed in the same modality so that it can be applied to regions with low contrast in the images. The real-time images successfully corrected the initial motion model estimations in our proof-of-concept validation. This suggests the potential to perform markerless tracking in low-contrast region using a motion model.

Approximate Cross-Validated Mean Estimates for Bayesian Hierarchical Regression Models

We introduce a novel procedure for obtaining cross-validated predictive estimates for Bayesian hierarchical regression models (BHRMs). BHRMs are popular for modeling complex dependence structures (e.g., Gaussian processes and Gaussian Markov random fields) but can be computationally expensive to run. Cross-validation (CV) is, therefore, not a common practice to evaluate the predictive performance of BHRMs. Our method circumvents the need to rerun computationally costly estimation methods for each cross-validation fold and makes CV more feasible for large BHRMs. We shift the CV problem from probability-based sampling to a familiar and straightforward optimization problem by conditioning on the variance-covariance parameters. Our approximation applies to leave-one-out CV and leave-one-cluster-out CV, the latter of which is more appropriate for models with complex dependencies. In many cases, this produces estimates equivalent to full CV. We provide theoretical results, demonstrate the efficacy of our method on publicly available data and in simulations, and compare the model performance with several competing methods for CV approximation. Code and other supplementary materials available online.

Cyanobacteria's power trio: auxin, siderophores, and nitrogen fixation to foster thriving agriculture.

Cyanobacteria, often overlooked in traditional agriculture, are gaining recognition for their roles in enhancing plant growth and soil health through diverse mechanisms. This review examines their multifaceted contributions to agricultural systems, highlighting their proficiency in auxin production, which promotes plant growth and development. Additionally, we examined cyanobacteria's ability to produce siderophores that enhance iron absorption and address micronutrient deficiencies, as well as their capacity for nitrogen fixation, which converts atmospheric nitrogen into a form that plants can utilize, all with the goal of reducing reliance on synthetic fertilizers. A meta-analysis of existing studies indicates significant positive effects of cyanobacteria on crop yield, although variability exists. While some research shows considerable yield increases, other studies report non-significant changes, suggesting benefits may depend on specific conditions and crop types. The overall random-effects model estimate indicates a significant aggregate effect, with a few exceptions, emphasizing the need for further research to optimize the use of cyanobacteria as biofertilizers. Although cyanobacteria-based products are limited in comparison to seaweed-derived alternatives, for instance, ongoing challenges include regulatory issues and production costs. Integrating cultivation with wastewater treatment could enhance competitiveness and viability in the agricultural market.

Abstract 4143270: PREDICTIVE MODELS AID PHYSICIAN PROGNOSTICATION: A SECONDARY ANALYSIS EVALUATING INTEGRATED MODEL AND PHYSICIAN PROGNOSTIC ESTIMATES IN PATIENTS WITH HEART FAILURE WITH REDUCED EJECTION FRACTION

Background: In recent studies from a multicenter Canadian cohort of outpatients with heart failure (HF), we found that model predictions were significantly more accurate than HF cardiologists. In this study, trying to mimic practice, we evaluated the additional predictive value and clinical impact of model predictions to refine physician estimated risk of 1-year mortality by combining model and physician estimates. Methods: We included consented consecutive HF outpatients (LVEF <40%) followed at 11 HF clinics in Canada. HF cardiologists estimated patient 1-year mortality using their clinical judgment. We calculated model predicted mortality using the Seattle HF Model (SHFM). We followed patients for at least a year to record mortality (or urgent heart transplant or ventricular assist device implant as mortality-equivalent events). Using random forest survival model and cross-validation, we compared the performance SHFM and the HF cardiologist alone, and the integrated HF cardiologist and the SHFM predictions by evaluating model discrimination (c-statistic), calibration (observed vs predicted event rate), risk reclassification and clinical net benefit analyses. Results: Among 1,643 HF patients, 1-year event rate was 9% (95%CI 8%-11%). The SHFM had the adequate discrimination (c-statistic 0.76) and excellent calibration while cardiologists showed adequate discrimination (c-statistic 0.75) and poor calibration with significant risk overestimation ( Figure 1 ). When the SHFM estimates were added physician predictions, discrimination significantly improved (0.82, 95%CI 0.78-0.86) with excellent calibration. By risk reclassification analysis, among patients with events, HF cardiologist better reclassified 44% than the SHFM or the integrated model. Among patients without event, however, HF cardiologists worse risk-classified 52% in comparison to SHFM and 71% to the integrated model. By net clinical benefit analysis ( Figure 2 ), when the decision to treat involves patients with 1-year mortality of >5%, SHFM predictions would lead to higher benefit than guiding care by physician judgement. Integrating model and HF cardiologist predictions led to minimally increased benefit in comparison to SHFM alone. Conclusions: Integrating prediction from the SHFM to physician judgment or using the SHFM alone showed superior accuracy than HF cardiologist predictions, proving that model-informed care may provide more accurate prognostic information to tailor clinical decision making.

Substantial Mercury Releases and Local Deposition from Permafrost Peatland Wildfires in Southwestern Alaska.

Increasing wildfire activity at high northern latitudes has the potential to mobilize large amounts of terrestrial mercury (Hg). However, understanding implications for Hg cycling and ecosystems is hindered by sparse research on peatland wildfire Hg emissions. In this study, we used measurements of soil organic carbon (SOC) and Hg, burn depth, and environmental indices derived from satellite remote sensing to develop machine learning models for predicting Hg emissions from major wildfires in the permafrost peatland of the Yukon-Kuskokwim Delta (YKD) in southwestern Alaska. Wildfire Hg emissions during summer 2015─estimated as the product of Hg:SOC (0.38 ± 0.17 ng Hg g C1-), predicted SOC stores (mean [5th-95th] = 9.1 [5.3-11.2] kg C m-2), and burn depth (11.3 [8.2-13.9] cm)─were 556 [164-1138] kg Hg or approximately 6% of Hg emissions from wildfire activity >60°N. Modeling estimates suggest that wildfire nearly doubled summertime Hg deposition within 10 km, despite advection of more than 75% of total emissions beyond Alaska. YKD areal emissions combined with remote sensing estimates of burned area suggest that wildfire Hg emissions from northern peatlands (25.4 [14.9-33.6] Mg y-1) are an important component of the northern Hg budget. Additional research is needed to refine these estimates and understand the implications for Arctic and global Hg cycling.

Urban–rural links in relaunching left-behind places: the case of Portuguese municipalities

Abstract During the last few decades, the gap between the most prosperous metropolitan areas and the rural areas has widened, especially since the 2008 crisis. This gap has been particularly relevant in relation to population and employment. However, the bi-directional links in terms of population and employment between rural places and the most urbanized areas have been partly overlooked by the literature. At the same time, recent studies have brought the plight of “left-behind” areas to the forefront, highlighting rural as well as other types of “left-behind” places as focal points of discontent. In the context of the debate on left-behind areas, and in an attempt to address the decline in population and employment that they are experiencing, this article proposes a strategy based on the spatial interactions among the different types of territories. Specifically, we develop a spatial econometric model capable of testing all interactions among different territorial typologies based on their degree of urbanization. We apply the model to 278 continental municipalities in Portugal during the period 2010–8. The model estimates have implications for understanding the dynamics of territorial interactions in Portugal, highlighting the crucial role of semi-urban municipalities. The results reveal the positive and significant connections between change in employment in semi-urban municipalities and population change in both neighboring rural and urban municipalities. The findings suggest that semi-urban/rural diffusion should be promoted as a policy approach to left-behind rural places in Portugal.

Estimation of joint kinetics during manual material handling using inertial motion capture: a follow-up study.

Musculoskeletal models based on inertial motion capture and ground reaction force (GRF) prediction hold great potential for field-based risk assessment of manual material handling. However, previous evaluations have identified inaccuracies in the methodology?s estimation of spinal forces, while the accuracy of other key outcome variables is currently unclear. This study evaluated knee, shoulder, and L5-S1 joint reaction forces (JRFs) derived from a musculoskeletal model based on inertial motion capture and GRF prediction against a model based on simultaneously collected optical motion capture and force plate measurements. Data from 19 healthy subjects performing lifts with various horizontal locations, deposit heights and asymmetry angles were analyzed, and the consistency and absolute agreement of the model estimates statistically compared. Despite varying levels of agreement across tasks and variables, considerable absolute differences were identified for the L5-S1 axial compression (RMSE = 63.0-94.2%BW) and anteroposterior shear forces (RMSE = 40.9-80.6%BW) as well as the bilateral knee JRFs (RMSE = 78.9-117%BW). Glenohumeral JRFs and vertical GRFs exhibited the highest overall consistency (r = 0.33-0.91, median 0.78) and absolute agreement (RMSE = 7.63-34.9%BW), while the L5-S1 axial compression forces also showed decent consistency (r = 0.04-0.89, median 0.80). The findings generally align with prior evaluations, indicating persistent challenges with the accuracy of key outcome variables. While the modelling framework shows promise, further development of the methodology is encouraged to enhance its applicability in ergonomic evaluations.

The Impact of Non-Normality on Causal Effect Estimates in Structural Equation Models with Ordered Categorical Variables

Multilevel structural equation models (SEM) are widely used for estimating causal effects in social and behavioral sciences. However, the impact of non-normality on these estimates, particularly with ordered categorical variables, remains unclear. This study investigates how non-normality affects causal effect estimation in path and multigroup SEMs with ordered categorical variables. Using the Vine-to-Anything approach, we simulated multivariate distributions by independently varying marginals and copulas. We compared maximum likelihood (ML), robust ML, and diagonally weighted least squares (DWLS) estimators across various non-normal conditions in three simulation studies. Results show that bias in effect estimates critically depends on the combination of copulas and marginal distributions in treatment and control groups. DWLS generally outperformed ML, especially for average treatment effects and interaction effects. However, all methods were sensitive to distributional misspecifications. These findings highlight the importance of considering distributional assumptions when using ordered categorical data in causal inference studies.

Option Pricing Based on REGARCH Model with High-Frequency Information

As a prevalent tool for hedging risk, the trading volume of options has been growing increasingly in the derivatives market. Precision in the estimation of volatility leads to accurate option pricing. Since volatility is time-varying and has a clustering effect, GARCH class of volatility models is effective in modeling volatility precisely. This paper utilizes the realized EGARCH (REGARCH) model combined with Monte Carlo simulation to investigate the role of high-frequency information in option pricing. The parameter estimates of the REGARCH model are obtained via joint maximum likelihood estimation using observations on returns and realized measure. Applying the model to S&P options market, the empirical results show that the REGARCH model that using high-frequency data is more efficient than the model that only use daily closing prices, including the EGARCH, NGARCH and GJR-GARCH models. This paper demonstrates that incorporating realized measures into volatility models can improve the accuracy of option pricing. The REGARCH model contained more intraday trading information from high-frequency data, can measure the additional risk premiums and specific volatility shocks.

Value Chain Analysis of Haricot Bean in East Shewa Zone, Oromia Regional State, Ethiopia

The key objectives of the study were identifying haricot bean value chain actors, their functions and relationship among them, examining actor’s performance along the chain and analyzing factors affecting supply to the market and factors affecting market outlet choice in the study area. Both primary and secondary sources were used for data collection. The primary data was collected through interviewing 180 sample household farmers and 40 traders using semi-structured questionnaires. STATA version 15 Software was used for data analyzing. As the descriptive result indicate that, on average farmers allocate 0.72ha of land for haricot bean production, from which 21.72qt/ha of output gained. In the value chain of haricot bean input suppliers, farmers, collectors, traders, transporters and consumers are the major value chain actors who perform various value chain. From the study result, six marketing channels of haricot bean are exhibited in the study areas. Estimates of the multiple linear regression models indicate that the level of haricot bean supplied to the market is determined by educational level of household, haricot bean farming experience, land allocated for haricot bean, market price of haricot bean, TLU, access to credit, and access to market information. Therefore, policy makers should focus more on the abovementioned variables in the future intervention to increase volume of haricot bean supplied to the market. As the multivariate probit model result indicated that the market outlet choice of rural assemblers was positively and significantly influenced by distance to the nearest market center, distance to all weather road, and educational level whereas it’s negatively and significantly influenced by output level where as selling direct to consumers outlet choice was negatively and significantly affected by sex of household, educational level, farm experience of household in haricot bean, and output level. Urban traders/wholesalers market outlet was positively and significantly affected by sex of household, educational level, haricot bean farm experience, access to credit, access to market information, membership of cooperative, and output level whereas it’s negatively influenced by distance to the nearest market center, distance to all weather road and extension service. Based on the findings of this study, some relevant implications can be drawn that can assist to design appropriate intervention mechanisms to improve market outlets choice of haricot bean farmers in the study area.

Global sensitivity analysis of Open Systems Pharmacology Suite physiologically based pharmacokinetic models.

Sensitivity analyses are important components of physiologically based pharmacokinetic (PBPK) model development and are required by regulatory agencies for PBPK submissions. They assess the impact of parametric uncertainty and variability on model estimates, aid model optimization by identifying parameters requiring calibration, and enable the testing of assumptions within PBPK models. One-at-a-time (OAT) sensitivity analyses quantify the impact on a model output in response to changes in a single parameter while holding others fixed. Global sensitivity analysis (GSA) methods provide more comprehensive assessments by accounting for changes in all uncertain or variable parameters, though at a higher computational cost. This tutorial article presents a software package for conducting both OAT and GSA of PBPK models built in the Open Systems Pharmacology (OSP) Suite. The tool is accessible through either an R script or a graphical user interface, and the outputs consist of sensitivity metrics of pharmacokinetic (PK) parameters, such as Cmax and AUC, evaluated with respect to model input parameters. Results are formatted according to regulatory standards. The OAT analysis methods comprise two-way local sensitivity analyses and probabilistic uncertainty analyses, whereas the GSA methods include the Morris, Sobol, and EFAST methods. These analyses can be conducted on single PBPK models or pairs of models for the evaluation of the sensitivity of PK parameter ratiosin drug-drug interaction studies. The practical application of the package is demonstrated through three illustrative case studies.

Physics-guided fault diagnosis method for proton exchange membrane fuel cells based on LSTM neural network

In this paper, we propose a novel hybrid method for fault diagnosis of Proton Exchange Membrane Fuel Cells (PEMFCs) based on the combination of a physics-based model and a long short-term memory (LSTM) neural network. By incorporating the physics-based model in the fault diagnosis algorithm, we can access to several process variables not directly measured through sensors but related to the state of the PEMFC stack. The model estimates are subsequently combined with signals measured on the PEMFC stack and inputted to a LSTM neural network. The performance of the physics-guided LSTM is evaluated on an extensive dataset comprising a thousand hours of operation of a PEMFC stack under dynamic load profiles, proving the enhanced capability of the proposed fault diagnosis method in capturing complex fault patterns. Furthermore, the effectiveness of the proposed method in dealing with PEMFC aging is tested by using data from the initial phase of stack operation for algorithm training and reserving the data from the aged stack operation for the testing phase. The experimental results reveal that the proposed physics-guided LSTM method allows for a significant amelioration over purely data-driven LSTM.

Dynamics trajectory of patient-reported quality of life and its associated risk factors among hepatocellular carcinoma patients receiving immune checkpoint inhibitors: a prospective cohort study

ObjectiveWe aimed to characterize quality of life (QOL) trajectories among patients with intermediate and advanced hepatocellular carcinoma patients treated with immunotherapy.MethodsBarcelona Clinic Liver Cancer (BCLC) stage B-C HCC patients receiving immunotherapy at Guangxi Medical University Cancer Hospital were included. Trajectories of QOL, assessed using the Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep) questionnaire, were identified through iterative estimations of group-based trajectory models. Associations with trajectory group membership were analyzed using multivariable multinomial logistic regression.ResultsThree trajectory groups were identified (n=156): excellent (35.3%), poor (43.6%), and deteriorating (21.1%) QOL. The deteriorating trajectory group reported a mean QOL score of 124.79 (95% CI, 116.58–133.00), but then declined significantly at month-2 (estimated QOL score 98.67 [95% CI, 84.33–113.00]), and the lowest mean score is reached at month-6 (estimated QOL score 16.58 [95% CI, 0–46.07]). Factors associated with membership to the deteriorating group included no drinking (odds ratio [OR] vs yes [95% CI], 3.70 [1.28–11.11]), no received radiotherapy (OR vs yes [95% CI], 8.33 [1.41–50.00]), diabetes (OR vs no [95% CI], 6.83 [1.57–29.73]), and extrahepatic metastasis (OR vs no [95% CI], 3.08 [1.07–8.87]). Factors associated with membership to the poor group also included body mass index ≤24.0 kg/m2 (OR vs no [95% CI], 4.49 [1.65–12.22]).ConclusionsThis latent-class analysis identified a high-risk cluster of patients with severe, persistent post-immunotherapy QOL deterioration. Screening relevant patient-level characteristics may inform tailored interventions to mitigate the detrimental impact of immunotherapy and preserve QOL.

Changes in water regime in the high-mountain region of the Terek River (North Caucasus) in connection with climate change and degradation of glaciation

In this study, we adapted the ECOMAG model of the runoff formation for analysis of the Terek River basin using comprehensive hydrometeorological information as well as data on soils, landscape, and glaciation. To take account of regional characteristics of the glaciation, the additional ice module was used with the model. This improvement has resulted in a satisfactory agreement between the modeled runoff hydrographs and the observed ones. In our simulations we used the updated glacier cover predictions from the- global glaciological model GloGEMflowdebris together with regional climate projections from the CORDEX experiment to determine possible future changes in the Terek River flow in the 21st century. The results show that the runoff will change between −2% and +5% according to the RCP2.6 scenario, and from −8% to +14% in the RCP8.5 scenario. The directedness of the runoff changes in particular subbasins of the River will essentially depend on the altitude position of the snow and glacier feeding zones, that is responsible for the intensity of their degradation. Thus, in the RCP8.5 scenario, the flow of the Chegem River will begin to decrease significantly in the second half of the 21st century. In contrast, the predicted increasing of the runoff in Malka and Baksan rivers, which are primarily fed by meltwater from glaciers and snow on Elbrus and other high-mountain zones, is expected to be continued until the end of the century. But this increase may be caused only by a growth of a part of the snowmelt feeding due to greater winter precipitation. The model estimates confirm the present-day observed trends within the intra-annual runoff distribution, demonstrating the earlier start of the spring flood, a decrease in summer runoff volumes and then its increase in the autumn months. The results of the research may be used for more efficient management of water resources in the North Caucasus in the future, including electricity generation and water supply.

Quantifying residents' exposure to agricultural pesticides using new geospatial approaches

A better understanding of environmental exposure to agricultural pesticides is crucial for public health, regulatory and management purposes. Residents in close vicinity to agricultural fields are likely to be more exposed to pesticides. In that context, an innovative geospatial approach for mapping estimates of agricultural pesticide exposure was developed in this study.Data on pesticide application rates, high-resolution annual datasets of the geographic distribution of crops, and the 100x100m grid population dataset were utilized to complete this analysis in Wallonia (Belgium) over the period 2015-2019. Pesticide exposure metrics were estimated using a buffer-based exposure model by conducting neighborhood analysis within a 1000m buffer radius. Subsequently, a population weighted method was used to ‘up-scale’ the exposure data to administrative levels. In a limited validation effort, model estimates were compared with pesticide measurements in air collected at 12 stations during the period 2015-2016.The results present the first modeling map of environmental exposure to agricultural pesticides for the whole of the Walloon region. The northern part of the Sambre-Meuse axis demonstrates more intensive agriculture and the highest pesticide weighted exposure indices. The majority of the population resides near agricultural areas, with only 4% living in regions that lack crops within a 1000m radius, primarily in the central areas of large cities. A positive trend association between pesticide measures in air and the index was observed at the different stations, nevertheless further validation efforts are needed to accurately compare the same active ingredients.This work gives a valuable basis for research and environmental health actions in Belgium. Maps highlight areas where human biomonitoring and epidemiological studies should be implemented to investigate the impact of potential environmental exposure to pesticides. This information helps policy-makers to take measures to control and reduce the load of agricultural pesticides in the environment.

A Method for the Analysis of Data About Dyadic Relationships

Psychological research in many subfields focuses on dyads because they influence individual cognitions, emotions, and behavior. However, the measurement of relationship characteristics has been elusive. Although direct observational methods have been hailed as the gold standard, they have some disadvantages, such as their high cost and external validity. We propose a factor-analytic method, specifically the bifactor model, using self-report data from each individual of a dyad. This method has been applied to many contexts but not yet to dyadic data. We show that the information that can be gleaned from these data is relatively low-cost but rich in terms of the insight it yields about the nature of the dyadic relationship. We demonstrate two different applications of the bifactor model to data from married couples.•The bifactor model can be used to identify the individual-specific and dyadic aspects of data reported by the members of a dyad.•The estimates of the bifactor model of dyadic data can yield information about hierarchical aspects of a relationship, which are not directly assessed.

Association of the Life's Essential 8 cardiovascular health score with periodontitis among US adults.

To investigate whether cardiovascular health (CVH) is associated with periodontitis. We used data from the 2009 to 2014 National Health and Nutrition Examination Survey. We quantified CVH using Life's Essential 8, which includes four health behaviours (diet, smoking, physical activity and sleep) and four health factors (body mass index, blood cholesterol, glucose and pressure). We categorized scores as low (0-49), moderate (50-79) and high (80-100). We calculated subscores of health behaviours and factors and categorized them as low, moderate and high. We used logistic regression to assess the association of CVH with periodontitis, adjusting for age, gender, race/ethnicity, education, poverty index, marital status and health insurance. We computed odds ratios (ORs) and 95% confidence intervals (CIs). This study included 9296 adults ≥30 years old. Multivariable-adjusted models showed that subjects with moderate (OR, 0.62; 95% CI: 0.52-0.74) or high (OR, 0.43; 95% CI: 0.33-0.57) CVH had significantly lower odds of periodontitis compared to those with low CVH. These results were consistent in the health behaviours model, but the estimates in the health factors model were not significant. Improving CVH may help prevent periodontitis. Longitudinal studies are needed to confirm our findings.

Estimating Demographic Parameters for Bearded Seals, Erignathus barbatus, in Alaska Using Close-Kin Mark-Recapture Methods.

Reliable estimates of population abundance and demographics are essential for managing harvested species. Ice-associated phocids, "ice seals," are a vital resource for subsistence-dependent coastal Native communities in western and northern Alaska, USA. In 2012, the Beringia distinct population segment of the bearded seal, Erignathus barbatus nauticus, was listed as "threatened" under the US Endangered Species Act requiring greater scrutiny for management assessments. We sought to estimate requisite population parameters from harvested seals by using close-kin mark-recapture (CKMR) methods, the first such application for marine mammals. Samples from 1758 bearded seals harvested by Bering, Chukchi, and Beaufort Sea communities during 1998-2020 were genotyped, genetically sexed, and aged by tooth annuli. After rigorous quality control, kin relationships were established for 1484 seals including two parent-offspring pairs (POPs) and 25 potential second-order kin pairs. Most of the second-order kin were half-sibling pairs (HSPs), but four were potential grandparent-grandchild pairs (GGPs). There were no full sibling pairs, suggesting a lack of mate fidelity. Mitochondrial DNA analysis identified 17 potential HSPs as paternally related, providing substantial evidence of persistent heterogeneity in reproductive success among adult males. The statistical CKMR model incorporates probabilities associated with POPs, HSPs, and GGPs and assumes known ages and a stable population. Our top model accommodates heterogeneity in adult male breeding success and yields an abundance estimate of ~409,000 with a coefficient of variation (CV) = 0.35, which is substantially greater than the "non-heterogeneity" model estimate of ~232,000 (CV = 0.21), an important difference for managing a harvested species. Using CKMR methods with harvested species provides estimates of abundance with the added opportunity to acquire information about adult survival, fecundity, and breeding success that could be applied to other species of concern, marine and terrestrial.

Toxicity of selected pharmaceuticals and their mixtures to the aquatic indicators Daphnia magna and Aliivibrio fischeri.

Despite the benefits derived from the use of pharmaceuticals, these compounds are currently considered contaminants of emerging concern because of their presence and persistence in the environment. This study aimed to determine the toxicity of 27 pharmaceuticals and the interaction effects of binary mixtures of selected compounds towards two model organisms: the microcrustacean Daphnia magna and the bacterium Aliivibrio fischeri (Microtox test). Six compounds, namely polymyxin B, polymyxin E, fluoxetine, diphenhydramine, clenbuterol and ketoprofen exhibited moderate toxicity towards D. magna. Additionally, three compounds (cefotaxime, polymyxin B, polymyxin E) also showed a moderate toxic effect on A. fischeri. The comparison of such results with model estimations showed inaccuracy in the predicted data, highlighting the relevance of experimental ecotoxicological assays. The assayed mixtures contained four selected drugs of high-hazard according to their reported concentrations in wastewater and surface water (diphenhydramine, trimethoprim, ketoprofen, and fluoxetine); data revealed interactions only in the fluoxetine-containing mixtures for D. magna, while all mixtures showed interactions (mostly synergistic) for Microtox. Chronic effects on the reproduction of D. magna were observed after exposure to fluoxetine and diphenhydramine, although higher sensitivity was determined for the latter, while the mixture of these compounds (which showed acute synergy in both models) also affected the reproduction patterns. Nonetheless, all the effects described at the acute or chronic level (for individual compounds or mixtures) were determined at concentrations higher than commonly reported at environmental levels. This work provides valuable ecotoxicological information for the risk assessment of pharmaceuticals and their mixtures in the environment.