Summary Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular due to their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (though richer than standard meta-analysis, comparing only two treatments) and researchers often choose study arms based upon which treatments emerge as superior in previous trials. In this paper, we summarize existing hierarchical Bayesian methods for MTCs with a single outcome, and introduce novel Bayesian approaches for multiple outcomes simultaneously, rather than in separate MTC analyses. We do this by incorporating partially observed data and its correlation structure between outcomes through contrast- and arm-based parameterizations that consider any unobserved treatment arms as missing data to be imputed. We also extend the model to apply to all types of generalized linear model outcomes, such as count or continuous responses. We offer a simulation study under various missingness mechanisms (e.g., MCAR, MAR, and MNAR) providing evidence that our models outperform existing models in terms of bias, MSE, and coverage probability, then illustrate our methods with a real MTC dataset. We close with a discussion of our results, several contentious issues in MTC analysis, and a few avenues for future methodological development.

Objective Manganese (Mn), an established neurotoxicant, is a common component of welding fume. The neurological phenotype associated with welding exposures has not been well described. Prior epidemiologic evidence linking occupational welding to parkinsonism is mixed, and remains controversial. Methods This was a cross-sectional and nested case–control study to investigate the prevalence and phenotype of parkinsonism among 811 shipyard and fabrication welders recruited from trade unions. Two reference groups included 59 non-welder trade workers and 118 newly diagnosed, untreated idiopathic PD patients. Study subjects were examined by a movement disorders specialist using the Unified Parkinson Disease Rating Scale motor subsection 3 (UPDRS3). Parkinsonism cases were defined as welders with UPDRS3 score ≥15. Normal was defined as UPDRS3 < 6. Exposure was classified as intensity adjusted, cumulative years of welding. Adjusted prevalence ratios for parkinsonism were calculated in relation to quartiles of welding years. Results The overall prevalence estimate of parkinsonism was 15.6% in welding exposed workers compared to 0% in the reference group. Among welders, we observed a U-shaped dose–response relation between weighted welding exposure-years and parkinsonism. UPDRS3 scores for most domains were similar between welders and newly diagnosed idiopathic Parkinson disease (PD) patients, except for greater frequency of rest tremor and asymmetry in PD patients. Conclusion This work-site based study among welders demonstrates a high prevalence of parkinsonism compared to nonwelding-exposed workers and a clinical phenotype that overlaps substantially with PD.

Collective variable (CV) or order parameter based enhanced sampling algorithms have achieved great success due to their ability to efficiently explore the rough potential energy landscapes of complex systems. However, the degeneracy of microscopic configurations, originating from the orthogonal space perpendicular to the CVs, is likely to shadow "hidden barriers" and greatly reduce the efficiency of CV-based sampling. Here we demonstrate that systematic machine learning CV, through enhanced sampling, can iteratively lift such degeneracies on the fly. We introduce an active learning scheme that consists of a parametric CV learner based on deep neural network and a CV-based enhanced sampler. Our active enhanced sampling algorithm is capable of identifying the least informative regions based on a historical sample, forming a positive feedback loop between the CV learner and sampler. This approach is able to globally preserve kinetic characteristics by incrementally enhancing both sample completeness and CV quality.

Permanganate (Mn(VII)) has been widely applied as an oxidant in water treatment plants. However, compared with ozone, Fenton, and other advanced oxidation processes, the reaction rates of some trace organic contaminants (TrOCs) with Mn(VII) are relatively low. Therefore, further studies on the strategies for enhancing the oxidation of organic contaminants by Mn(VII) are valuable. In this work, 2,2,6,6-tetramethylpiperidine-N-oxyl (TEMPO), as an electron shuttle, enhanced Mn(VII) oxidation toward various TrOCs (i.e., bisphenol A (BPA), phenol, estrone, sulfisoxazole, etc.). TEMPO sped up the oxidative kinetics of BPA by Mn(VII) greatly, and this enhancement was observed at a wide pH range of 4.0−11.0. The exact mechanism of TEMPO in Mn(VII) oxidation was described briefly as follows: (i) TEMPO was oxidized by Mn(VII) to its oxoammonium cation (TEMPO + ) by electron transfer, which was the reactive species responsible for the accelerated degradation of TrOCs and (ii) TEMPO + could decompose TrOCs rapidly with itself back to TEMPO or TEMPOH (TEMPO hydroxylamine). To further illustrate the interaction between TEMPO and target TrOCs, we explored the transformation pathways of BPA in Mn(VII)/ TEMPO oxidation. Compared to Mn(VII) alone, adding TEMPO into the Mn(VII) solution significantly suppressed BPA's selfcoupling and promoted hydroxylation, ring-opening, and decarboxylation. Moreover, the Mn(VII)/TEMPO system was promising for the abatement of TrOCs in real waters for humic acid, and ubiquitous cations/anions had no adverse or even beneficial impact on the Mn(VII)/TEMPO system.

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