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The reuse of wastewater for beneficial purposes (e.g. irrigation) can boost water sustainability, but may have positive or negative impacts on the environment. In some drought-prone or over-allocated river basins, wastewater is an important component of stream flows and may buffer river ecosystems from drought. In these locations, wastewater may have a net positive impact on river ecosystems and its capture and reuse would be harmful to these ecosystems. In other locations, wastewater may have a net negative impact if the quality of the wastewater (i.e. concentrations of contaminants and nutrients) is lower than that of the receiving water body. In this study, we quantified the effects of wastewater on fish and macroinvertebrate communities in the Red River, Oklahoma. We investigated two competing hypotheses. First, we hypothesized that if wastewater flows buffer aquatic communities from drought, then measures of biodiversity should be higher at sampling sites near wastewater outfalls than at sites not near wastewater outfalls. Second, we hypothesized that if wastewater outfalls negatively impact water quality or homogenize the flow regime, then measures of biodiversity should be lower at sites near wastewater outfalls. To test these competing hypotheses, we calculated a suite of biodiversity indices at 320 biological sampling sites in the Red River Basin and analyzed for significant differences in communities based on proximity to wastewater outfalls. We found that proximity to upstream wastewater outfalls had significant positive impacts on macroinvertebrate community biodiversity, but we did not find any significant relationships with fish community diversity. Findings from this study provide insight to management decisions on wastewater reuse initiatives in beneficial subbasins while minimizing harmful ecological impacts. [ABSTRACT FROM AUTHOR]

Mathematical modeling is a powerful method to understand how biological systems work. By creating a mathematical model of a given phenomenon one can investigate which model assumptions are needed to explain the phenomenon and which assumptions can be omitted. Creating an appropriate mathematical model (or a set of models) for a given biological system is an art, and classical textbooks on mathematical modeling in biology go into great detail in discussing how mathematical models can be understood via analytical and numerical analyses. In the last few decades mathematical modeling in biology has grown in size and complexity, and along with this growth new tools for the analysis of mathematical models and/or comparing models to data have been proposed. Examples of tools include methods of sensitivity analyses, methods for comparing alternative models to data based on AIC/BIC/etc.), and mixed-effect-based fitting of models to data. I argue that the use of many of these "toolbox" approaches for the analysis of mathematical models has negatively impacted the basic philosophical principle of the modeling--to understand what the model does and why it does what it does. I provide several examples of limitations of these toolbox-based approaches and how they hamper generation of insights about the system in question. I also argue that while we should learn new ways to automate mathematical modeling-based analyses of biological phenomena, we should aim beyond a mechanical use of such methods and bring back intuitive insights into model functioning, by remembering that after all, modeling is an art and not simply engineering. [ABSTRACT FROM AUTHOR]

Mathematical modeling is a powerful method to understand how biological systems work. By creating a mathematical model of a given phenomenon one can investigate which model assumptions are needed to explain the phenomenon and which assumptions can be omitted. Creating an appropriate mathematical model (or a set of models) for a given biological system is an art, and classical textbooks on mathematical modeling in biology go into great detail in discussing how mathematical models can be understood via analytical and numerical analyses. In the last few decades mathematical modeling in biology has grown in size and complexity, and along with this growth new tools for the analysis of mathematical models and/or comparing models to data have been proposed. Examples of tools include methods of sensitivity analyses, methods for comparing alternative models to data (based on AIC/BIC/etc.), and mixed-effect-based fitting of models to data. I argue that the use of many of these “toolbox” approaches for the analysis of mathematical models has negatively impacted the basic philosophical principle of the modeling—to understand what the model does and why it does what it does. I provide several examples of limitations of these toolbox-based approaches and how they hamper generation of insights about the system in question. I also argue that while we should learn new ways to automate mathematical modeling-based analyses of biological phenomena, we should aim beyond a mechanical use of such methods and bring back intuitive insights into model functioning, by remembering that after all, modeling is an art and not simply engineering. “Getting something for nothing is impossible; there is always a price to pay.” Louis Gross.“There is not such a thing as a free lunch.”

Since species' traits are closely linked to ecosystem functioning, the species versus functional diversity relationship (the SD–FD relationship) is considered a valuable indicator of ecosystem health. However, the extent to which the SD–FD relationship varies among disturbed versus undisturbed subtropical forests and whether the conclusion depends on the spatial scale of the observation or vegetation layer remains unclear. In this study, using plots of 100 m2, 400 m2 and 2000 m2, we gathered species and functional diversity data for woody and non-woody vegetation from replicate sites of undisturbed mature forests versus disturbed shrubland and plantations from subtropical China. Our analyses indicated that the species richness versus functional richness relationship, as well as the species evenness versus functional evenness relationship, varies markedly among disturbed versus undisturbed forests and woody versus non-woody layers. Scale-dependent variations in the SD–FD relationship were evident mainly in the woody layers or evenness component. Additional analyses revealed that disturbance-dependent variation in the community-level functional redundancy and species-level functional uniqueness can partly explain the observed variation in the SD–FD relationships. Overall, our study demonstrated that the SD–FD relationships are positive, but the relationship's slope varies with contexts, partly due to variations in community-level functional redundancy and species-level functional uniqueness. [ABSTRACT FROM AUTHOR]

Background: Accurate and reliable prediction of changes in the severity of cerebellar ataxia (CA) will be necessary for trials of disease-modifying therapies. Cerebellar dysarthria (CD) is a common feature of CA. This study demonstrated that objective acoustic measures were more sensitive than perceptive analysis through the Scale for the Assessment and Rating of Ataxia (SARA) in assessing the progression of CD, within a time window of two years (mean). Method: Thirty-seven people with CA were tested at baseline (time point 1, TP1) and two years later (time point 2, TP2). A machine-learning framework with a robust three-step feature selection criterion and a Bayesian data-driven clustering technique based on the multivariate mixture extension of the generalized linear mixed model (GLMM) was used. The outcomes included two (time and cepstral-based) objective speech parameters recorded at TP1 and TP2. Subject testing involved dynamic prediction and was conducted using samples from the posterior distributions of parameter estimates and random effects. This study further employed the penalized expected deviance (PED) criterion for model comparison and the selection of the number of groups in the clustering procedure. Results: First, the selected objective speech metrics in the individual patients showed a significant worsening of the speech impairment (p $PED_{Fea1+Fea2}=5175$ ; number of groups = 2) performed more reliably than the univariate models ( $PED_{Fea1}=4225$ , $PED_{Fea2}=3850$ ; number of groups = 2) in discriminating the CA patients. Fourth, the individual-level predictions of the change in profiles of the objective measures over time were performed for the testing data. Conclusion: Such a framework using objective speech metrics indeed holds promise to predict the rate of clinical progression of CD in individuals with CA.

Highlights • Species richness and functional richness relationship varies with microhabitat. • Species evenness and functional evenness relationship varies with disturbance type. • Functional trait dispersion explains the variation in the species and functional diversity relationship. • Landscape spatial heterogeneity should be considered in understanding the functioning of a natural landscape. Abstract Spatial heterogeneity in site conditions and differing disturbance types are common features of natural landscapes. We asked: does the relationship between species diversity and functional trait diversity (SD-FD relationship) of plant communities vary among habitats experiencing different types of disturbance and among microhabitats (i.e., landscape position) within a landscape? We quantified the relationship between species richness and functional richness (FRic) and between species evenness and functional evenness (FEve) of riparian plant communities along small headwater streams at five spatially adjacent microhabitats in two types of disturbed (wildfire and clearcut logging with buffer) and reference habitats (unlogged mature forest) in northwestern Ontario, Canada. We found significant dependence of species richness vs. FRic relationship on microhabitat, but not on disturbance type. Species evenness vs. FEve relationship varied slightly among reference and wildfire sites, but not among microhabitats. A significant variation in functional trait dispersion (FDis) among microhabitats offered a mechanistic explanation to the observed variation in the SD-FD relationship. A very weak effect of disturbance type on the SD-FD relationship indicates that clearcut with buffer retention may emulate riparian plant composition created by wildfire. However, the microhabitat dependent variation in the SD-FD relationship indicates that the same range of species diversity may refer to different ranges of functional trait diversity depending on microhabitat. We suggest that microhabitat plays a stronger role than disturbance type in trait dispersion and ultimately modifies the SD-FD relationship in our studied communities. This result highlights the role of spatial environmental heterogeneity of a landscape and different dimensions of species diversity and functional trait diversity, e.g. richness and evenness, in understanding the functioning of a natural landscape. [ABSTRACT FROM AUTHOR]

Thinning is commonly applied to increase the tree growth in forest stands by improving the availability of water, light, and nutrients. However, thinning also can increase soil evaporation and intensify wind penetration into residual stands, potentially increasing moisture stress and wind damage. To strengthen our understanding of tree-level responses to thinning, we used long-term measurements from three controlled, replicated thinning experiments for four commercially important softwood species in North America, including the shade-intolerant loblolly pine ( Pinus taeda L.), moderately shade-tolerant Douglas-fir ( Pseudotsuga menziesii Mirbel), and shade-tolerant red spruce ( Picea rubens Sarg.) and balsam fir ( Abies balsamea L.). The objectives of this study were to assess the long-term (13–24 years) pattern of individual-tree growth and survival after a variety of commercial thinning treatments. Our results showed that on average tree volume growth was 31% higher in thinned stands relative to unthinned stands irrespective of species and tree size. However, the rate of growth decreased over time following thinning for loblolly pine and Douglas-fir, while a curvilinear relationship was observed for red spruce and balsam fir. Tree size was important only for loblolly pine where growth increased linearly with the size of residual trees. Tree survival was also higher in thinned stands than unthinned stands across all species in the long-term, but a significant initial decrease in survival was found in balsam fir and red spruce immediately after thinning due primarily to windthrow and breakage. Stand relative age and total basal area at time of thinning were negatively related with growth for all tree species, which may indicate that the trees examined in this study had reached their maximum growth potential or had a period of suppression prior to thinning. The relatively minor influence (i.e., 5% of total R 2 ) of thinning intensity on growth may suggest that the timing of thinning (i.e., age of trees when thinned) and stand characteristics (species, tree age, and stand basal area) were more important in promoting individual-tree growth. However, a heavier intensity of thinning increased survival of loblolly pine and Douglas-fir trees. Overall, our results indicated that thinning can increase tree growth and survival across species of varying shade tolerance. To ensure the maximum benefits of thinning, the timing and intensity of the treatment needs to be adjusted for species characteristics, stand structure, and tree age. [ABSTRACT FROM AUTHOR]

Recent prospective studies have shown that dysregulation of the immune system may precede the development of B‐cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case–control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01–15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor‐2 (FGF‐2 p = 7.2 × 10−4) and transforming growth factor alpha (TGF‐α, p = 6.5 × 10−5) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF‐2 (p = 7.8 × 10−7), TGF‐α (p = 4.08 × 10−5), fractalkine (p = 1.12 × 10−3), monocyte chemotactic protein‐3 (p = 1.36 × 10−4), macrophage inflammatory protein 1‐alpha (p = 4.6 × 10−4) and vascular endothelial growth factor (p = 4.23 × 10−5). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case‐only analyses showed that Granulocyte‐macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth‐factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL. [ABSTRACT FROM AUTHOR]

Traditionally, growth patterns are described as constant throughout life using von Bertalanffy's equation. However, a change in growth due to a reallocation of energy during an individual's lifespan is to be expected. Following this hypothesis, back-calculation measurements obtained from SoleMon survey data were used to fit and compare monophasic and biphasic growth curves for common sole in the northern and central Adriatic Sea. Moreover, individual variability in growth was considered through nonlinear mixed-effect models in which the individual parameters were considered as a random effect. The analyses performed in this study revealed systematic age-specific discrepancies in the monophasic curve and demonstrated that the fit of the biphasic curve was superior (Δ AIC: 329; Δ BIC: 310), confirming the theory that growth in size would decrease as a consequence of reproductive effort. Finally, since common sole is routinely assessed using models that rely on growth to derive assessment estimates and related management reference points, a stock assessment simulation was performed to compare the two growth alternatives. The results showed how the biphasic alternative was preferable to the conventional alternative and how the use of the monophasic pattern would result in an overly optimistic view of stock status (+40% in SSB/SSB target and −35% in F/F target compared to the biphasic pattern), thereby increasing the risk of overfishing. [ABSTRACT FROM AUTHOR]

When studying health risks across a large geographic region such as a state or province, researchers often assume that finer-resolution data on health outcomes and risk factors will improve inferences by avoiding ecological bias and other issues associated with geographic aggregation. However, coarser-resolution data (e.g., at the town or county-level) are more commonly publicly available and packaged for easier access, allowing for rapid analyses. The advantages and limitations of using finer-resolution data, which may improve precision at the cost of time spent gaining access and processing data, have not been considered in detail to date. We systematically examine the implications of conducting town-level mixed-effect regression analyses versus census-tract-level analyses to study sociodemographic predictors of COVID-19 in Massachusetts. In a series of negative binomial regressions, we vary the spatial resolution of the outcome, the resolution of variable selection, and the resolution of the random effect to allow for more direct comparison across models. We find stability in some estimates across scenarios, changes in magnitude, direction, and significance in others, and tighter confidence intervals on the census-tract level. Conclusions regarding sociodemographic predictors are robust when regions of high concentration remain consistent across town and census-tract resolutions. Inferences about high-risk populations may be misleading if derived from town- or county-resolution data, especially for covariates that capture small subgroups (e.g., small racial minority populations) or are geographically concentrated or skewed (e.g., % college students). Our analysis can help inform more rapid and efficient use of public health data by identifying when finer-resolution data are truly most informative, or when coarser-resolution data may be misleading. [ABSTRACT FROM AUTHOR]

The nonlinear mixed effects models are commonly used modeling techniques in the pharmaceutical research as they enable the characterization of the individual profiles together with the population to which the individuals belong. To ensure a correct use of them is fundamental to provide powerful diagnostic tools that are able to evaluate the predictive performance of the models. The visual predictive check (VPC) is a commonly used tool that helps the user to check by visual inspection if the model is able to reproduce the variability and the main trend of the observed data. However, the simulation from the model is not always trivial, for example, when using models with time-varying input function (IF). In this class of models, there is a potential mismatch between each set of simulated parameters and the associated individual IF which can cause an incorrect profile simulation. We introduce a refinement of the VPC by taking in consideration a correlation term (the Mahalanobis or normalized Euclidean distance) that helps the association of the correct IF with the individual set of simulated parameters. We investigate and compare its performance with the standard VPC in models of the glucose and insulin system applied on real and simulated data and in a simulated pharmacokinetic/pharmacodynamic (PK/PD) example. The newly proposed VPC performance appears to be better with respect to the standard VPC especially for the models with big variability in the IF where the probability of simulating incorrect profiles is higher. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) are a group of pollutants with multiple variants classified as carcinogenic. The Occupational Safety and Health Administration (OSHA) provided access to two PAH exposure databanks of United States workplace compliance testing data collected between 1979 and 2010. Mixed-effects logistic models were used to predict the exceedance fraction (EF), i.e., the probability of exceeding OSHA's Permissible Exposure Limit (PEL = 0.2 mg/m3) for PAHs based on industry and occupation. Measurements of coal tar pitch volatiles were used as a surrogate for PAHs. Time, databank, occupation, and industry were included as fixed-effects while an identifier for the compliance inspection number was included as a random effect. Analyses involved 2,509 full-shift personal measurements. Results showed that the majority of industries had an estimated EF < 0.5, although several industries, including Standardized Industry Classification codes 1623 (Water, Sewer, Pipeline, and Communication and Powerline Construction), 1711 (Plumbing, Heating, and Air-Conditioning), 2824 (Manmade Organic Fibres), 3496 (Misc. Fabricated Wire products), and 5812 (Eating Places), and Major group's 13 (Oil and Gas Extraction) and 30 (Rubber and Miscellaneous Plastic Products), were estimated to have more than an 80% likelihood of exceeding the PEL. There was an inverse temporal trend of exceeding the PEL, with lower risk in most recent years, albeit not statistically significant. Similar results were shown when incorporating occupation, but varied depending on the occupation as the majority of industries predicted at the administrative level, e.g., managers, had an estimated EF < 0.5 while at the minimally skilled/laborer level there was a substantial increase in the estimated EF. These statistical models allow the prediction of PAH exposure risk through individual occupational histories and will be used to create a job-exposure matrix for use in a population-based case-control study exploring PAH exposure and breast cancer risk. [ABSTRACT FROM AUTHOR]

Purpose: Despite the many benefits of percutaneous thoracic sympathectomy, it also has serious complications such as pneumothorax. This study was conducted in order to determine the safe percutaneous entering window and angles for the needle during T2 and T3 thoracic sympathectomy avoiding pneumothorax. Methods: Transverse section of CT images that crosses at the middle of the T2 or T3 vertebral body was selected. Medial and lateral imaginary lines were drawn from the dorsoventrally midpoint on the lateral surface of the vertebral body (v) to the skin. The medial one was drawn to the skin medially as much as possible tangent to the vertebral body (vM). The lateral one was drawn to the skin tangent to parietal pleura (vL). c was defined as the point where the midsagittal line meets the skin. The distance cM and cL, the angle aM and aL made between the midsagittal line and vM or vL lines were measured. To determine the relations between patients' covariates and measured data, mixed-effect population analysis was performed for the cL, aL, and vL. Results: In males, the mean values of cL were 85.3 and 79.2 mm for T2 and T3, respectively. In females, they were 71.5 and 63.7 mm for T2 and T3, respectively. Population analysis revealed that cL was best described with age, weight, gender covariates, and interindividual variability. The aL was best described with BMI and gender covariates. Conclusions: The covariates' relationship and interindividual variability resulting from the mixed-effect analysis enhanced individual prediction for safe widows. [ABSTRACT FROM AUTHOR]

Introduction Preclinical concentration–effect (pharmacokinetic–pharmacodynamic, PKPD) modeling has successfully quantified QT effects of several drugs known for significant QT prolongation. This study investigated its sensitivity for detecting small magnitudes of QT-prolongation in a typical preclinical cardiovascular (CV) safety study in the conscious telemetered dog (crossover study in 4–8 animals receiving a vehicle and three dose levels). Results were compared with conventional statistical analysis (analysis of covariance, ANCOVA). Methods A PKPD model predicting individual QTc was first developed from vehicle arms of 28 typical CV studies and one positive control study (sotalol). The model quantified between-animal, inter-occasion and within-animal variability and described QTc over 24 h as a function of circadian variation and drug concentration. This “true” model was used to repeatedly (n = 500) simulate studies with typical drug-induced QTc prolongation (∆QTc) of 1 to 12 ms at high-dose peak concentrations. Simulated studies were re-analyzed by both PKPD analysis (with varying complexity) and ANCOVA. Sensitivity (power) was calculated as the percentage of studies in which a significant (α = 0.05) drug effect was found. One simulation scenario did not include a concentration–effect relationship and served to investigate false-positive rates. Exposure–effect relationships were derived from both PKPD analysis (linear concentration–effect) and ANCOVA (linear trend test for dose) and compared. Results PKPD analysis/ANCOVA had a sensitivity of 80% to detect the effects of 7/13 ms (n = 4), 5/10 ms (n = 6) and 4.5/8 ms (n = 8), respectively. The false-positive rate was much higher using ANCOVA (40%) compared to PKPD analysis (1%). Typical drug effects were more precisely predicted using estimated concentration–effect slopes (± 1.5–2.8 ms) than dose–effect slopes (± 3.3–3.7 ms). Discussion Preclinical PKPD analysis can increase the confidence in the quantification of small QTc effects and potentially allow reducing the number of animals while maintaining the required study sensitivity. This underscores the value of PKPD modeling in preclinical safety testing. [ABSTRACT FROM AUTHOR]