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This article builds upon a proiminent definition of construct validity that focuses on variation in attributes causing variation in measurement outcomes. This article synthesizes the defintion and uses Rasch measurement modeling to explicate a modified conceptualization of construct validity for assessments of developmental attributes. If attributes are conceived as developmental, hypotheses about how new knowledge builds cumulatively upon the cognitive capacity afforded by prior knowledge can be developed. This "cumulative ordering" of knowledge required to accomplish test items constitutes evidence of a specific form of construct validity. Examples of cumulative ordering appear in the extant literature, but they are rare and confined to the early literature. Furthermore, cumulative ordering has never been explicated, especially its relationship to construct validity. This article describes three of the most complete examples of cumulative ordering in the literature. These examples are used to synthesize a method for assessing cumulative ordering, in which the Rasch model is used to assess the progression of item difficulties which are, in turn, used to review developmental theories and hypotheses, and the tests themselves. We discuss how this conceptualization of construct validity can lead to a more direct relationship between developmental theories and tests which, for practitioners, should result in a clearer understanding of what tests results actually mean. Finally, we discuss how cumulative ordering can be used to facilitate decisions about consequential validity.

Abstract Background Redox imbalance and inflammation have been proposed as the principal mechanisms of damage in the auditory system, resulting in functional alterations and hearing loss. Microglia and astrocytes play a crucial role in mediating oxidative/inflammatory injury in the central nervous system; however, the role of glial cells in the auditory damage is still elusive. Objectives Here we investigated glial-mediated responses to toxic injury in peripheral and central structures of the auditory pathway, i.e., the cochlea and the auditory cortex (ACx), in rats exposed to styrene, a volatile compound with well-known oto/neurotoxic properties. Methods Male adult Wistar rats were treated with styrene (400 mg/kg daily for 3 weeks, 5/days a week). Electrophysiological, morphological, immunofluorescence and molecular analyses were performed in both the cochlea and the ACx to evaluate the mechanisms underlying styrene-induced oto/neurotoxicity in the auditory system. Results We showed that the oto/neurotoxic insult induced by styrene increases oxidative stress in both cochlea and ACx. This was associated with macrophages and glial cell activation, increased expression of inflammatory markers (i.e., pro-inflammatory cytokines and chemokine receptors) and alterations in connexin (Cxs) and pannexin (Panx) expression, likely responsible for dysregulation of the microglia/astrocyte network. Specifically, we found downregulation of Cx26 and Cx30 in the cochlea, and high level of Cx43 and Panx1 in the ACx. Conclusions Collectively, our results provide novel evidence on the role of immune and glial cell activation in the oxidative/inflammatory damage induced by styrene in the auditory system at both peripheral and central levels, also involving alterations of gap junction networks. Our data suggest that targeting glial cells and connexin/pannexin expression might be useful to attenuate oxidative/inflammatory damage in the auditory system.

In September 2020, a global aerosol forecasting model was implemented as an ensemble member of the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Global Ensemble Forecasting System (GEFS) v12.0.1 (hereafter referred to as “GEFS-Aerosols”). In this study, GEFS-Aerosols simulation results from 1 September 2019 to 30 September 2020 were evaluated using an aerosol budget analysis. These results were compared with results from other global models as well as reanalysis data. From this analysis, the global average lifetimes of black carbon (BC), organic carbon (OC), dust, sea salt, and sulfate are 4.06, 4.29, 4.59, 0.34, and 3.3 d, respectively, with the annual average loads of 0.14, 1.29, 4.52, 6.80, and 0.51 Tg. Compared with the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System–Goddard Chemistry Aerosol and Radiation Transport (GEOS4-GOCART) model, the aerosols in GEFS-Aerosols have a relatively short lifetime because of the faster removal processes in GEFS-Aerosols. Meanwhile, in GEFS-Aerosols, aerosol emissions are the determining factor for the mass and composition of aerosols in the atmosphere. The size (bin) distribution of aerosol emissions is as important as its total emissions, especially in simulations of dust and sea salt. Moreover, most importantly, the strong monthly and interannual variations in natural sources of aerosols in GEFS-Aerosols suggest that improving the accuracy of the prognostic concentrations of aerosols is important for applying aerosol feedback to weather and climate predictions.

Belatacept is a novel T-cell costimulation blockade agent that has unresolved controversy in lung transplant recipients. Belatacept has been recognized as a calcineurin sparing agent for solid organ transplant recipients after reported success in renal transplant patients, despite limited evidence in other transplant recipients. We present the first case of a lung transplant recipient receiving Belatacept, in combination with low dose calcineurin inhibitor, who developed progressive multifocal leukoencephalopathy. While Belatacept without calcineurin inhibitor has been associated with increased risk of acute rejection in solid organ transplant recipients, its infectious risk profile in combination with calcineurin inhibitor remains unclear.

The National Oceanic and Atmospheric Administration (NOAA)'s National Weather Service (NWS) is on its way to deploying various operational prediction applications using the Unified Forecast System (https://ufscommunity.org/, last access: 18 June 2022), a community-based coupled, comprehensive Earth modeling system. An aerosol model component developed in collaboration between the Global Systems Laboratory, Chemical Science Laboratory, Air Resources Laboratory, and Environmental Modeling Center (GSL, CSL, ARL, EMC) was coupled online with the FV3 Global Forecast System (FV3GFS) using the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software framework. This aerosol prediction system replaced the NEMS GFS Aerosol Component version 2 (NGACv2) system in the National Center for Environment Prediction (NCEP) production suite in September 2020 as one of the ensemble members of the Global Ensemble Forecast System (GEFS), dubbed GEFS-Aerosols v1. The aerosol component of atmospheric composition in the GEFS is based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). GEFS-Aerosols includes bulk modules from the Goddard Chemistry Aerosol Radiation and Transport model (GOCART). Additionally, the biomass burning plume rise module from High-Resolution Rapid Refresh (HRRR)-Smoke based on WRF-Chem was implemented. The GOCART dust scheme was replaced by the FENGSHA dust scheme (developed by ARL). The Blended Global Biomass Burning Emissions Product (GBBEPx version 3) provides biomass burning emission and fire radiative power (FRP) data. The global anthropogenic emission inventories are derived from the Community Emissions Data System (CEDS). All sub-grid-scale transport and deposition are handled inside the atmospheric physics routines, which required consistent implementation of positive definite tracer transport and wet scavenging in the physics parameterizations used by the NCEP's operational FV3GFS. This paper describes the details of GEFS-Aerosols model development and evaluation of real-time and retrospective runs using different observations from in situ measurement and satellite and aircraft data. GEFS-Aerosols predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational NGACv2 system with the fundamental updates (e.g., dust and fire emission) in the atmospheric and chemical transport model.

A key feature of the polytomous Rasch model is that it entails a single classification process into one of a set of ordered response categories. The first aim of this article is to make explicit two processes in rubric designs that bring about such classifications, and identify implications of the distinction between them. The first process involves judgements of similarity, typified by judgements that a performance is similar to a category description. The second process involves judgements of difference. It is typified by judgements that a performance is better than a category description. In this article, each process is explicated using a hypothetical context in which assessors compare performances with exemplars. The second aim is to demonstrate that, for reporting purposes, judgements of similarity are recommended, particularly when results are interpreted by persons with limited knowledge of the Rasch model and its parameters. In judgements of similarity, category descriptions are located at the peak of each category probability curve, at the point of highest probability in each the category, with thresholds located between them. This is easier to interpret than judgements of difference, where category descriptions are located at the thresholds, and where the point of highest probability has no material reference. This is explicated using a simple real-world example. Implications for rubric design are discussed, as well as implications for related types of instruments, such as attitudinal measures and pairwise comparisons.

Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to re-grid, combine, overwrite, subset, and scale emissions from different inventories through a configuration file and with no change to the model source code. The configuration file also maps emissions to model species with appropriate units. HEMCO can operate in offline stand-alone mode, but more importantly it provides an online facility for models to compute emissions at runtime. HEMCO complies with the Earth System Modeling Framework (ESMF) for portability across models. We present a new version here, HEMCO 3.0, that features an improved three-layer architecture to facilitate implementation into any atmospheric model and improved capability for calculating emissions at any model resolution including multiscale and unstructured grids. The three-layer architecture of HEMCO 3.0 includes (1) the Data Input Layer that reads the configuration file and accesses the HEMCO library of emission inventories and other environmental data, (2) the HEMCO Core that computes emissions on the user-selected HEMCO grid, and (3) the Model Interface Layer that re-grids (if needed) and serves the data to the atmospheric model and also serves model data to the HEMCO Core for computing emissions dependent on model state (such as from dust or vegetation). The HEMCO Core is common to the implementation in all models, while the Data Input Layer and the Model Interface Layer are adaptable to the model environment. Default versions of the Data Input Layer and Model Interface Layer enable straightforward implementation of HEMCO in any simple model architecture, and options are available to disable features such as re-gridding that may be done by independent couplers in more complex architectures. The HEMCO library of emission inventories and algorithms is continuously enriched through user contributions so that new inventories can be immediately shared across models. HEMCO can also serve as a general data broker for models to process input data not only for emissions but for any gridded environmental datasets. We describe existing implementations of HEMCO 3.0 in (1) the GEOS-Chem “Classic” chemical transport model with shared-memory infrastructure, (2) the high-performance GEOS-Chem (GCHP) model with distributed-memory architecture, (3) the NASA GEOS Earth System Model (GEOS ESM), (4) the Weather Research and Forecasting model with GEOS-Chem (WRF-GC), (5) the Community Earth System Model Version 2 (CESM2), and (6) the NOAA Global Ensemble Forecast System – Aerosols (GEFS-Aerosols), as well as the planned implementation in the NOAA Unified Forecast System (UFS). Implementation of HEMCO in CESM2 contributes to the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) by providing a common emissions infrastructure to support different simulations of atmospheric chemistry across scales.

Cochlear redox unbalance is the main mechanism of damage involved in the pathogenesis of noise-induced-hearing loss. Indeed, the increased free radical production, in conjunction with a reduced efficacy of the endogenous antioxidant system, plays a key role in cochlear damage induced by noise exposure. For this reason, several studies focused on the possibility to use exogenous antioxidant to prevent or attenuate noise-induce injury. Thus, several antioxidant molecules, alone or in combination with other compounds, have been tested in both experimental and clinical settings. In our findings, we tested the protective effects of several antioxidant enzymes, spanning from organic compounds to natural compounds, such as nutraceuticals of polyphenols. In this review, we summarize and discuss the strengths and weaknesses of antioxidant supplementation focusing on polyphenols, Q-Ter, the soluble form of CoQ10, Vitamin E and N-acetil-cysteine, which showed great otoprotective effects in different animal models of noise induced hearing loss and which has been proposed in clinical trials.

Air quality forecasting system is an essential tool widely used by environmental managers to mitigate adverse health effects of air pollutants. This work presents the latest development of the next generation regional air quality model (AQM) forecast system within the Unified Forecast System (UFS) framework in the National Oceanic and Atmospheric Administration (NOAA). The UFS air quality model incorporates the U.S. Environmental Protection Agency (EPA)'s Community Multiscale Air Quality (CMAQ) model as its main chemistry component. In this system, CMAQ is integrated as a column model to solve gas and aerosol chemistry while the transport of chemical species is processed by UFS. The current AQM version 7 (AQMv7) is coupled with an earlier version of CMAQ (version 5.2.1). Here we describe the development of the updated AQMv7 by coupling to a 'state-of-the-science' CMAQ version 5.4. The updates include improvements in gas and aerosol chemistry, dry deposition processes, and structural changes to the Input/Output (IO) interface, enhancing both computational efficiency and the representation of air-surface exchange processes. A simulation was conducted for the period of August 2023 to assess the effects of these updates on the forecast performance of ozone (O3) and fine particulate matter (PM2.5), two major air pollutants over the continental United States (CONUS). The results show that the updated model demonstrates a significantly enhanced capability in simulating O3 over the CONUS by reducing the positive bias during both day and night, leading to a reduction of the mean bias by 50 % and 72 % for hourly and the maximum daily 8-hour average O3, respectively. Spatially, the updated model lowers the positive bias of hourly O3 in all of the ten EPA regions, particularly within the Great Plains. Similarly, the updates induce uniformly lower fine particulate matter (PM2.5) concentrations across the CONUS domain, reducing the positive bias in the northeast and central Great Plain and exacerbating the negative bias in the west and south. The updated model does not improve model performance for PM2.5 in the vicinity of fire emission sources as compared to AQMv7, thus indicating a focal point of model uncertainty and needed improvement. Despite these challenges, the study highlights the importance of the ongoing refinements for reliable air quality predictions from the UFS-AQM model, which is the future replacement of NOAA's current operational air quality forecast system. [ABSTRACT FROM AUTHOR]

This article demonstrates that the Rasch model cannot reveal systematic differential item functioning (DIF) in single tests. The person total score is the sufficient statistic for the person parameter estimate, eliminating the possibility for residuals at the test level. An alternative approach is to use subset DIF analysis to search for DIF in item subsets that form the components of the broader latent trait. In this methodology, person parameter estimates are initially calculated using all test items. Then, in separate analyses, these person estimates are compared to the observed means in each subset, and the residuals assessed. As such, this methodology tests the assumption that the person locations in each factor group are invariant across subsets. The first objective is to demonstrate that in single tests differences in factor groups will appear as differences in the mean person estimates and the distributions of these estimates. The second objective is to demonstrate how subset DIF analysis reveals differences between person estimates and the observed means in subsets. Implications for practitioners are discussed.

Recent studies have shown that student behavioral engagement is malleable. For example, these studies have reported that students who feel emotionally supported by their teachers experience higher levels of behavioral engagement. The authors contributed to this research by investigating behavioral disengagement among innocent bystanders during classroom management events. Participants included 528 Year 7 students from Melbourne, Australia, who completed 3 questionnaire measures. Results revealed that personal responsibility is inversely related to behavioral disengagement during classroom management events. This relationship is moderated by teacher aggressive tendencies, with higher levels of teacher aggression leading to higher levels of behavioral disengagement. However, the Johnson-Neyman technique analysis revealed that the moderating influence of teacher aggressive tendencies was only statistically significant equal to or below the 76th percentile of the distribution of the variable. Implications regarding the influence of the teacher--student relationship on behavioral disengagement are discussed.

Background: Considerable research has investigated the deleterious effects of teachers responding aggressively to students who misbehave, but the mechanism underlying this dysfunctional behaviour remains unknown. Aims: This study investigated whether the mechanism underlying teacher aggression follows I[superscript 3] theory or General Aggression Model (GAM) metatheory of human aggression. I[superscript 3] theory explains exceptional, catastrophic events of human aggression, whereas the GAM explains common human aggression behaviours. Sample: A total of 249 Australian teachers participated in this study, including 142 primary school teachers (Mdn [age] = 35-39 years; Mdn [years teaching] = 10-14 years; 84% female) and 107 secondary school teachers (Mdn [age] = 45-49 years; Mdn [years teaching] = 15-19 years; 65% female). Methods: Participants completed four online self-report questionnaires, which assessed caregiving responsiveness, trait self-control, misbehaviour provocation, and teacher aggression. Results: Analyses revealed that the GAM most accurately captures the mechanism underlying teacher aggression, with lower caregiving responsiveness appearing to indirectly lead to teacher aggression via higher misbehaviour provocation and lower trait self-control in serial, controlling for gender, age, years teaching, and current role (primary, secondary). Conclusions: This study indicates that teacher aggression proceeds from "the person in the situation." Specifically, lower caregiving responsiveness appears to negatively shape a teacher's affective, cognitive, and arousal states, which influence how they perceive and interpret student misbehaviour. These internal states, in turn, appear to negatively influence appraisal and decision processes, leading to immediate appraisal and impulsive actions. These results raise the possibility that teacher aggression is a form of countertransference.

This article builds upon a proiminent definition of construct validity that focuses on variation in attributes causing variation in measurement outcomes. This article synthesizes the defintion and uses Rasch measurement modeling to explicate a modified conceptualization of construct validity for assessments of developmental attributes. If attributes are conceived as developmental, hypotheses about how new knowledge builds cumulatively upon the cognitive capacity afforded by prior knowledge can be developed. This cumulative ordering of knowledge required to accomplish test items constitutes evidence of a specific form of construct validity. Examples of cumulative ordering appear in the extant literature, but they are rare and confined to the early literature. Furthermore, cumulative ordering has never been explicated, especially its relationship to construct validity. This article describes three of the most complete examples of cumulative ordering in the literature. These examples are used to synthesize a method for assessing cumulative ordering, in which the Rasch model is used to assess the progression of item difficulties which are, in turn, used to review developmental theories and hypotheses, and the tests themselves. We discuss how this conceptualization of construct validity can lead to a more direct relationship between developmental theories and tests which, for practitioners, should result in a clearer understanding of what tests results actually mean. Finally, we discuss how cumulative ordering can be used to facilitate decisions about consequential validity. [ABSTRACT FROM AUTHOR]

Abstract Vitreomacular adhesion (VMA) represents a prognostic biomarker in the management of exudative macular disease using anti-vascular endothelial growth factor (VEGF) agents. However, manual evaluation of VMA in 3D optical coherence tomography (OCT) is laborious and data on its impact on therapy of retinal vein occlusion (RVO) are limited. The aim of this study was to (1) develop a fully automated segmentation algorithm for the posterior vitreous boundary and (2) to study the effect of VMA on anti-VEGF therapy for RVO. A combined machine learning/graph cut segmentation algorithm for the posterior vitreous boundary was designed and evaluated. 391 patients with central/branch RVO under standardized ranibizumab treatment for 6/12 months were included in a systematic post-hoc analysis. VMA (70%) was automatically differentiated from non-VMA (30%) using the developed method combined with unsupervised clustering. In this proof-of-principle study, eyes with VMA showed larger BCVA gains than non-VMA eyes (BRVO: 15 ± 12 vs. 11 ± 11 letters, p = 0.02; CRVO: 18 ± 14 vs. 9 ± 13 letters, p