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BackgroundIt is well established that females are more susceptible to the toxic effects of alcohol, although the exact mechanisms are still poorly understood. Previous studies noted that alcohol reduces the expression of mitogen-activated protein kinase phosphatase 1 (MKP1), a negative regulator of mitogen-activated protein kinases (MAPK) in the liver. However, the role of hepatocyte- specific MKP1 in the pathogenesis of alcohol-associated liver disease (ALD) remains uncharacterized. This study aimed to evaluate the role of hepatocyte-specific MKP1 in the susceptibility and sexual dimorphism in alcohol-induced liver injury.MethodsC57Bl/6 mice were used in an intragastric ethanol feeding model of alcohol-associated steatohepatitis (ASH). Hepatocyte-specific Mkp1-/- knockout and (Mkp1+/+ “f/f” male and female mice were subjected to the NIAAA chronic plus binge model. Primary mouse hepatocytes were used for in vitro studies. Liver RNA sequencing was performed on an Illumina NextSeq 500. Liver injury was evaluated by plasma alanine transaminase (ALT), hepatic ER stress and inflammation markers. Statistical analysis was carried out using ANOVA and the unpaired Student’s t-test.ResultsASH was associated with the severe injury accompanied by increased endoplasmic reticulum (ER) stress and significant downregulation of Dusp1 mRNA expression. In vitro, ethanol treatment resulted in a time-dependent decrease in Dusp1 mRNA and protein expression in primary hepatocytes in both males and females; however, this effect was significantly more pronounced in hepatocytes from females. In vivo, female mice developed more liver injury in a chronic plus binge model which was accompanied by a significant decrease in liver Dusp1 mRNA expression. In comparison, liver Dusp1 was not changed in male mice, while they developed milder injury to alcohol. Mkp1 deletion in hepatocytes led to increased alcohol induced liver injury, ER stress and inflammation in both sexes.ConclusionHepatocyte Mkp1 plays a significant role in alcohol induced liver injury. Alcohol downregulates Mkp1 expression in hepatocytes in a sex dependent manner and could play a role in sexual dimorphism in increased female susceptibility to alcohol.

The extent to which teachers make changes in classroom seating reflects, in part, the degree to which they value promoting positive peer relationships in the classroom. We assessed the frequency with which teachers made both minor (i.e., involving only 2-3 students) and major (involving half or more students in the class) changes in classroom seating. We tested whether the frequency of seating changes was linked to teachers' beliefs about promoting positive peer relationships, their attunement to child- or peer-reports of peer victimization, and their concern about bullying at the school. Participants were 37 fourth-grade teachers and their students (N = 677). The frequency of major seating changes was negatively associated with teachers' peer-focused classroom seating (PFCS) beliefs and to their attunement to student peer victimization. Minor seating changes were positively associated with PFCS beliefs for teachers with low or average attunement to peer victimization; however, teachers highly attuned to peer victimization made fewer minor seating changes regardless of their PFCS beliefs. Implications for research and practice are discussed.

In recent decades, advances in the flexibility and complexity of hydrologic models have enhanced their utility in scientific studies and practice alike. However, the increasing complexity of these tools leads to a number of challenges, including steep learning curves for new users and issues regarding the reproducibility of modelling studies. Here, we present the RavenR package, an R package that leverages the power of scripting to both enhance the usability of the Raven hydrologic modelling framework and provide complementary analyses that are useful for modellers. The RavenR package contains functions that may be useful in each step of the model-building process, particularly for preparing input files and analyzing model outputs. The utility of the RavenR package is demonstrated with the presentation of six use cases for a model of the Liard River basin in Canada. These use cases provide examples of visually reviewing the model configuration, preparing input files for observation and forcing data, simplifying the model discretization, performing realism checks on the model output, and evaluating the performance of the model. All of the use cases are fully reproducible, with additional reproducible examples of RavenR functions included with the package distribution itself. It is anticipated that the RavenR package will continue to evolve with the Raven project and will provide a useful tool to new and experienced users of Raven alike.

A blended model structure has emerged as an alternative to the traditional representation of model structure in a hydrologic model, in which multiple algorithmic choices are used to represent some hydrologic process within a model, and are combined within a single model run using a weighted average of process fluxes. This approach has been shown to improve overall model performance, as well as provide an efficient way to test multiple model structures. We propose that a blended model may also be at least a partial solution to the calls for a more robust Community Hydrologic Model, which can mitigate the need for developing new hydrologic models for each catchment and application. We develop an updated version of the blended model configuration that defines the suite of all possible hydrologic process options in the blended model. Configuration development was guided by model performance for more than 30 different discrete model configurations across 12 Model Parameter Estimation Experiment (MOPEX) catchments. Improvements to the blended model include the introduction of blended potential melt and potential evapotranspiration as new process groups, inclusion of nonblended structural changes, and a revision of the process options within each existing group. This leads to a very high-performing model with a mean calibration Kling–Gupta efficiency (KGE) score of 0.90 and mean validation KGE score of 0.80 across all 12 MOPEX catchments, a substantial improvement in model performance relative to the initial version. We tested for overfitting of models and found little statistical evidence that increasing the complexity of blended models reduces validation performance. We then selected the preferred model configuration as Version 2 of the blended model and tested it with 24 independent catchments against the original configuration. This test showed a statistically significant improvement or statistically similar performance in 22 of the 24 catchments in calibration and 21 of the 24 catchments in validation. The results also suggested a greater improvement in drier catchments. Version 2 of the blended model is robust across a range of catchments without the need for adjusting its flexible model structure and may be useful in future hydrology studies and applications alike. [ABSTRACT FROM AUTHOR]

pyDarwin is an open‐source Python package for nonlinear mixed‐effect model selection. pyDarwin combines machine‐learning algorithms and NONMEM to perform a global search for the optimal model in a user‐defined model search space. Compared with traditional stepwise search, pyDarwin provides an efficient platform for conducting an objective, robust, less labor‐intensive model selection process without compromising model interpretability. In this tutorial, we will begin by introducing the essential components and concepts within the package. Subsequently, we will provide an overview of the pyDarwin modeling workflow and the necessary files needed for model selection. To illustrate the entire process, we will conclude with an example utilizing quetiapine clinical data. [ABSTRACT FROM AUTHOR]