The prediction of the chronological age of a deceased individual at time of death can provide important information in case of unidentified bodies. The methodological possibilities in these cases depend on the availability of tissues, whereby bones are preserved for a long time due to their mineralization under normal environmental conditions. Age-dependent changes in DNA methylation (DNAm) as well as the accumulation of pentosidine (Pen) and D-aspartic acid (D-Asp) could be useful molecular markers for age prediction. A combination of such molecular clocks into one age prediction model seems favorable to minimize inter- and intra-individual variation. We therefore developed (I) age prediction models based on the three molecular clocks, (II) examined the improvement of age prediction by combination, and (III) investigated if samples with signs of decomposition can also be examined using these three molecular clocks. Skull bone from deceased individuals was collected to obtain a training dataset (n = 86), and two independent test sets (without signs of decomposition: n = 44, with signs of decomposition: n = 48). DNAm of 6 CpG sites in ELOVL2, KLF14, PDE4C, RPA2, TRIM59 and ZYG11A was analyzed using massive parallel sequencing (MPS). The D-Asp and Pen contents were analyzed by high performance liquid chromatography (HPLC). Age prediction models based on ridge regression were developed resulting in mean absolute errors (MAEs)/root mean square errors (RMSE) of 5.5years /6.6 years (DNAm), 7.7 years /9.3 years (Pen) and 11.7 years /14.6 years (D-Asp) in the test set. Unsurprisingly, a general lower accuracy for the DNAm, D-Asp, and Pen models was observed in samples from decomposed bodies (MAE: 7.4-11.8 years, RMSE: 10.4-15.4 years). This reduced accuracy could be caused by multiple factors with different impact on each molecular clock. To acknowledge general changes due to decomposition, a pilot model for a possible age prediction based on the decomposed samples as training set improved the accuracy evaluated by leave-one-out-cross validation (MAE: 6.6-12 years, RMSE: 8.1-15.9 years). The combination of all three molecular age clocks did reveal comparable MAE and RMSE results to the pure analysis of the DNA methylation for the test set without signs of decomposition. However, an improvement by the combination of all three clocks was possible for the decomposed samples, reducing especially the deviation in case of outliers in samples with very high decomposition and low DNA content. The results demonstrate the general potential in a combined analysis of different molecular clocks in specific cases.

Biomechanical and orthopaedic studies frequently encounter complex datasets that encompass both circular and linear variables. In most cases (i) the circular and linear variables are considered in isolation with dependency between variables neglected and (ii) the cyclicity of the circular variables is disregarded resulting in erroneous decision making. Given the inherent characteristics of circular variables, it is imperative to adopt methods that integrate directional statistics to achieve precise modelling. This paper is motivated by the modelling of biomechanical data, that is, the fracture displacements, that is used as a measure in external fixator comparisons. We focus on a dataset, based on an Ilizarov ring fixator, comprising of six variables. A modelling framework applicable to the six-dimensional joint distribution of circular-linear data based on vine copulas is proposed. The pair-copula decomposition concept of vine copulas represents the dependence structure as a combination of circular-linear, circular-circular and linear-linear pairs modelled by their respective copulas. This framework allows us to assess the dependencies in the joint distribution as well as account for the cyclicity of the circular variables. Thus, a new approach for accurate modelling of mechanical behaviour for Ilizarov ring fixators and other data of this nature is imparted.

A multivariate spectrophotometric method is a potential approach that enables discrimination of spectra of components in complex matrices (e.g., pharmaceutical formulation) serving as a substitution method for chromatography. Four green smart multivariate spectrophotometric models were proposed and validated, including principal component regression (PCR), partial least-squares (PLS), multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural networks (ANN). The developed chemometric models were compared to resolve highly overlapping spectra of Paracetamol (PARA), Chlorpheniramine maleate (CPM), Caffeine (CAF), and Ascorbic acid (ASC). The four multivariate calibration models were assessed via recoveries percent, and root mean square error of prediction. Hence, the proposed models were efficiently applied with no need for any preliminary separation step. The models were utilized to analyze the studied components in their combined pharmaceutical formulation (Grippostad® C capsules). Analytical GREEnness Metric Approach (AGREE) and eco-scale tools were applied to assess the greenness of the established models and found to be 0.77 and 85, respectively. Moreover, the proposed models have been compared to official ones showing no considerable variations in accuracy and precision. Therefore, these models can be highly advantageous for conducting standard pharmaceutical analysis of the substances researched within product testing laboratories.

Multistressor studies were performed in five regions of the United States to assess the role of pesticides as stressors affecting invertebrate communities in wadable streams. Pesticides and other chemical and physical stressors were measured in 75 to 99 streams per region for 4 weeks, after which invertebrate communities were surveyed (435 total sites). Pesticides were sampled weekly in filtered water, and once in bed sediment. The role of pesticides as a stressor to invertebrate communities was assessed by evaluating multiple lines of evidence: toxicity predictions based on measured pesticide concentrations, multivariate models and other statistical analyses, and previously published mesocosm experiments. Toxicity predictions using benchmarks and species sensitivity distributions and statistical correlations suggested that pesticides were present at high enough concentrations to adversely affect invertebrate communities at the regional scale. Two undirected techniques-boosted regression tree models and distance-based linear models-identified which pesticides were predictors of (respectively) invertebrate metrics and community composition. To put insecticides in context with known, influential covariates of invertebrate response, generalized additive models were used to identify which individual pesticide(s) were important predictors of invertebrate community condition in each region, after accounting for natural covariates. Four insecticides were identified as stressors to invertebrate communities at the regional scale: bifenthrin, chlordane, fipronil and its degradates, and imidacloprid. Fipronil was particularly important in the Southeast region, and imidacloprid, bifenthrin, and chlordane were important in multiple regions. For imidacloprid, bifenthrin, and fipronil, toxicity predictions were supported by mesocosm experiments that demonstrated adverse effects on naïve aquatic communities when dosed under controlled conditions. These multiple lines of evidence do not prove causality-which is challenging in the field under multistressor conditions-but they make a strong case for the role of insecticides as stressors adversely affecting invertebrate communities in streams within the five sampled regions.

During COVID-19 pandemic, artificial neural network (ANN) systems have been providing aid for clinical decisions. However, to achieve optimal results, these models should link multiple clinical data points to simple models. This study aimed to model the in-hospital mortality and mechanical ventilation risk using a two step approach combining clinical variables and ANN-analyzed lung inflammation data.
A data set of 4317 COVID-19 hospitalized patients, including 266 patients requiring mechanical ventilation, was analyzed. Demographic and clinical data (including the length of hospital stay and mortality) and chest computed tomography (CT) data were collected. Lung involvement was analyzed using a trained ANN. The combined data were then analyzed using unadjusted and multivariate Cox proportional hazards models.
Overall in-hospital mortality associated with ANN-assigned percentage of the lung involvement (hazard ratio [HR]: 5.72, 95% confidence interval [CI]: 4.4-7.43, p < 0.001 for the patients with >50% of lung tissue affected by COVID-19 pneumonia), age category (HR: 5.34, 95% CI: 3.32-8.59 for cases >80 years, p < 0.001), procalcitonin (HR: 2.1, 95% CI: 1.59-2.76, p < 0.001, C-reactive protein level (CRP) (HR: 2.11, 95% CI: 1.25-3.56, p = 0.004), glomerular filtration rate (eGFR) (HR: 1.82, 95% CI: 1.37-2.42, p < 0.001) and troponin (HR: 2.14, 95% CI: 1.69-2.72, p < 0.001). Furthermore, the risk of mechanical ventilation is also associated with ANN-based percentage of lung inflammation (HR: 13.2, 95% CI: 8.65-20.4, p < 0.001 for patients with >50% involvement), age, procalcitonin (HR: 1.91, 95% CI: 1.14-3.2, p = 0.14, eGFR (HR: 1.82, 95% CI: 1.2-2.74, p = 0.004) and clinical variables, including diabetes (HR: 2.5, 95% CI: 1.91-3.27, p < 0.001), cardiovascular and cerebrovascular disease (HR: 3.16, 95% CI: 2.38-4.2, p < 0.001) and chronic pulmonary disease (HR: 2.31, 95% CI: 1.44-3.7, p < 0.001).
ANN-based lung tissue involvement is the strongest predictor of unfavorable outcomes in COVID-19 and represents a valuable support tool for clinical decisions.

To determine the effect size of observed factors considering trigger factors based on parallel-serial models and to explore how multiple factors can be related to the result of complex events for low-probability events with binary outcomes.
A low-probability event with a true binary outcome can be explained by a trigger factor. The models were based on the parallel-serial connection of switches; causal factors, including trigger factors, were simplified as switches. Effect size values of an observed factor for an outcome were calculated as SAR = (Pe-Pn)/(Pe + Pn), where Pe and Pn represent percentages in the exposed and nonexposed groups, respectively, and SAR represents standardized absolute risk. The influence of trigger factors is eliminated by SAR. Actual data were collected to obtain a deeper understanding of the system.
SAR values of < 0.25, 0.25-0.50, and > 0.50 indicate low, medium, and high effect sizes, respectively. The system of data visualization based on the parallel-serial connection model revealed that at least 7 predictors with SAR > 0.50, including a trigger factor, were needed to predict schizophrenia. The SAR of the HLADQB1*03 gene was 0.22 for schizophrenia.
It is likely that the trigger factors and observed factors had a cumulative effect, as indicated by the parallel-serial connection model for binary outcomes. SAR may allow better evaluation of the effect size of a factor in complex events by eliminating the influence of trigger factors. The efficiency and efficacy of observational research could be increased if we are able to clarify how multiple factors can be related to a result in a pragmatic manner.

Child maltreatment (CM) in one generation can predict CM in the next generation, a concept known as intergenerational continuity. Yet, the form taken by the intergenerational continuity of CM remains unclear and fathers are mostly absent from this literature. This longitudinal study aimed to document patterns of intergenerational continuity of substantiated CM, on the maternal and paternal sides, by examining the presence of: homotypical CM, which is the same type of CM in both generations; and heterotypical CM, which is different CM types in both generations. The study included all children substantiated for CM with the Centre Jeunesse de Montréal between 1 January 2003, and 31 December 2020, with at least one parent who was also reported to that agency during their childhood (n = 5861 children). The cohort was extracted using clinical administrative data, and logistic regression models were tested with the children\'s CM types as the dependent variables. Homotypical continuity was found for: (1) physical abuse on the paternal side; (2) sexual abuse on the maternal side; and (3) exposure to domestic violence on the maternal side. Heterotypical continuity was also prevalent, but to a lesser extent. Interventions helping maltreated parents overcome their traumatic past are essential to foster intergenerational resilience.

Formal volunteering holds great importance for the recipients of volunteer services, individuals who volunteer, and the wider society. However, how much recent birth cohorts volunteer in middle and late adulthood compared with earlier birth cohorts is not well understood. Even less well-known are the age and cohort trends in informal helping provided to friends and neighbors in later adulthood. Using longitudinal data from the Health and Retirement Study, we estimated age and cohort trends in formal volunteering and informal helping from 1998 to 2018 for a wide range of birth cohorts born between 1909 and 1958. We used multivariate, multilevel models based on Bayesian generalized modeling methods to estimate the probabilities of volunteering and informal helping simultaneously in a single model. Despite having advantages in human and health capital, recent birth cohorts showed volunteering levels in late adulthood that are similar to those of their predecessors. Moreover, more recent birth cohorts were consistently less engaged in informal helping than earlier birth cohorts throughout the observation period. More research is needed to illuminate the sociocultural drivers of changes in helping behaviors and overall prosocial and civic engagement.

The BGLR-R package implements various types of single-trait shrinkage/variable selection Bayesian regressions. The package was first released in 2014, since then it has become a software very often used in genomic studies. We recently develop functionality for multitrait models. The implementation allows users to include an arbitrary number of random-effects terms. For each set of predictors, users can choose diffuse, Gaussian, and Gaussian-spike-slab multivariate priors. Unlike other software packages for multitrait genomic regressions, BGLR offers many specifications for (co)variance parameters (unstructured, diagonal, factor analytic, and recursive). Samples from the posterior distribution of the models implemented in the multitrait function are generated using a Gibbs sampler, which is implemented by combining code written in the R and C programming languages. In this article, we provide an overview of the models and methods implemented BGLR\'s multitrait function, present examples that illustrate the use of the package, and benchmark the performance of the software.

This study was designed to examine the classification accuracy of the Erdodi Index (EI-5), a novel method for aggregating validity indicators that takes into account both the number and extent of performance validity test (PVT) failures. Archival data were collected from a mixed clinical/forensic sample of 452 adults referred for neuropsychological assessment. The classification accuracy of the EI-5 was evaluated against established free-standing PVTs. The EI-5 achieved a good combination of sensitivity (.65) and specificity (.97), correctly classifying 92% of the sample. Its classification accuracy was comparable with that of another free-standing PVT. An indeterminate range between Pass and Fail emerged as a legitimate third outcome of performance validity assessment, indicating that the underlying construct is an inherently continuous variable. Results support the use of the EI model as a practical and psychometrically sound method of aggregating multiple embedded PVTs into a single-number summary of performance validity. Combining free-standing PVTs with the EI-5 resulted in a better separation between credible and non-credible profiles, demonstrating incremental validity. Findings are consistent with recent endorsements of a three-way outcome for PVTs (Pass, Borderline, and Fail).