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Background NOAA’s Hazard Mapping System (HMS) smoke product comprises smoke plumes digitised from satellite imagery. Recent studies have used HMS as a proxy for surface smoke presence. Aims We compare HMS with airport observations, air quality station measurements and model estimates of near-surface smoke. Methods We quantify the agreement in numbers of smoke days and trends, regional discrepancies in levels of near-surface smoke fine particulate matter (PM2.5) within HMS polygons, and separation of total PM2.5 on smoke and non-smoke days across the contiguous US and Alaska from 2010 to 2021. Key results We find large overestimates in HMS-derived smoke days and trends if we include light smoke plumes in the HMS smoke day definition. Outside the western US and Alaska, near-surface smoke PM2.5 within areas of HMS smoke plumes is low and almost indistinguishable across density categories, likely indicating frequent smoke aloft. Conclusions Compared with airport, Environmental Protection Agency (EPA) and model-derived estimates, HMS most closely reflects surface smoke in the Pacific and Mountain regions and Alaska when smoke days are defined using only heavy plumes or both medium and heavy plumes. Implications We recommend careful consideration of biases in the HMS smoke product for air quality and public health assessments of fires.

Purpose: The direction of causality between job satisfaction and job performance (known as the holy grail of industrial psychologists) is undetermined and related research findings in different organizational contexts are mixed. Based on the ample literature, mainly from Western countries, on the relationship between job satisfaction and job performance, a non-recursive bow pattern model was utilized to investigate the direct relationship between these two variables in an Asia-Pacific higher education system. Design/methodology/approach: This study is quantitative in approach and survey in design. Additionally, to meet the statistical requirements of non-recursive bow pattern analysis, the authors added welfare as a theory-driven instrumental variable to introduce exogenous variability. Using the efficient partial least squares (PLSe2) estimator, the authors fitted the model to the data collected from 2008 academics affiliated with Malaysian public universities and polytechnics. Findings: The results showed that while job satisfaction is considerably influenced by welfare, it is not a significant predictor of job performance directly. In addition, a meaningful positive correlation between the disturbance terms of job satisfaction and job performance was observed, suggesting the existence of other factors that could increase both job satisfaction and job performance. The findings' theoretical and practical implications are discussed, and a list of theory-driven evidenced-based policies in this regard is provided. Originality/value: This is the first study to test a non-recursive bow pattern model and examine the holy grail of industrial psychology based on the PLSe2 methodology, as a parametric approach to partial least squares (PLS), in a higher education context. This study also provides higher education researchers with the advantages of the PLSe2 method, especially in causal-predictive modeling, in the context of applied higher education research.

Drug-induced kidney injury (DIKI) is a cause of drug development failure. Dogs represent a common non-rodent animal model in pre-clinical safety studies; however, biomarker assays for detecting nephrotoxicity in dogs are limited. To identify novel proteins and gain insight into the molecular mechanisms involved in DIKI, we developed an assay to evaluate proteomic changes associated with DIKI in male beagle dogs that received nephrotoxic doses of tobramycin for 10 consecutive days. Label-free quantitative discovery proteomics analysis on representative kidney cortex tissues collected on Day 11 showed that the tobramycin-induced kidney injury led to a significant differential regulation of 94 proteins mostly associated with mechanisms of nephrotoxicity such as oxidative stress and proteasome degradation. For verification of the proteomic results, we developed a multiplex peptide-centric immunoaffinity liquid chromatography tandem mass spectrometry assay (IA LC-MS/MS) to evaluate the association of eight DIKI protein biomarker candidates using kidney cortices collected on Day 11 and urine samples collected on Days -4, 1, 3, 7 and 10. The results showed that most biomarkers evaluated were detected in the kidney cortices and their expression profile in tissue aligned with the label-free data. Cystatin C was the most consistent marker regardless of the magnitude of the renal injury while fatty acid-binding protein-4 (FABP4) and kidney injury molecule-1 (KIM-1) were the most affected biomarkers in response to moderate proximal tubular injury in absence of changes in serum-based concentrations of blood urea nitrogen or creatinine. In the urine, clusterin is considered the most consistent biomarker regardless of the magnitude and time of the renal injury. To our knowledge, this is the most comprehensive multiplex assay for the quantitative analysis of mechanism-based proximal tubular injury biomarkers in dogs.

Meta-analytic models for dependent effect sizes have grown increasingly sophisticated over the last few decades, which has created challenges for a priori power calculations. We introduce power approximations for tests of average effect sizes based upon several common approaches for handling dependent effect sizes. In a Monte Carlo simulation, we show that the new power formulas can accurately approximate the true power of meta-analytic models for dependent effect sizes. Lastly, we investigate the Type I error rate and power for several common models, finding that tests using robust variance estimation provide better Type I error calibration than tests with model-based variance estimation. We consider implications for practice with respect to selecting a working model and an inferential approach.

There is a great emphasis on the importance of serving communities and the need to study servant leadership in recent higher education literature. Yet, academics' community citizenship behavior has not been examined through applied research. To address these issues, our study aims at investigating the effects of two dimensions of servant leadership (behaving ethically and helping subordinates grow and succeed) on job satisfaction and community citizenship behavior. The context of our multi-level study is the Malaysian higher education. To estimate our theoretical model, we employed the EQS 6.4 statistical package and analyzed the data collected from 1876 lecturers from 120 departments using a multi-level structural equation modeling (MSEM) methodology. Our results revealed two completely different model structures at both lecturer and department levels. More specifically, while both the predictors were useful for explaining the outcome variables at the lecturer level, we observed that only behaving ethically was a significant predictor of community citizenship behavior at the department level. We discuss the findings in detail and elaborate on the implications of the findings with respect to policy and practice, theory, and methodology. We also address limitations and make recommendations for future research.

Religion and Myth in the Marvel Cinematic Universe. By Michael D. Nichols. (Jefferson: McFarland & Company, Inc., Publishers, 2021. Pp. ix + 208, preface, acknowledgments, introduction, chapter notes, filmography, bibliography, [...]

Job satisfaction is the focus of this study, given its strategic importance, both to generate differentiation and competitive advantages, and to promote better and more sustainable results in organizations. Guided by the Affective Events Theory (AET), the interest is in analyzing the direct and indirect effects of supervisory support and involvement on job satisfaction and highlighting the implications of the academics' emotions for sustainability-related policies and practices in academic environments. The context selected is the higher education (HE) sector in Malaysia for science disciplines, due to its socio-economic relevance and its direct link to the Fourth Industrial Revolution (IR4), as well as to the Sustainable Development Goals (SDGs). A sample of 579 scientists were selected randomly and the analysis was carried out based on partial least squares structural equation modeling (PLS-SEM) methodology. Our results supported the hypotheses postulated in the study and especially highlighted the effects of supervisory support and positive affect on job satisfaction, as well as the effect of interpersonal conflict on negative affect. In terms of implications, policy makers at ministry level are advised to consider introducing directives which promote a climate of emotional safety and trust in academic institutions, to achieve the SDGs more effectively and efficiently. At the university level, and given the relevance of the observed effects, academic leaders are recommended to contemplate improving university environments in some way that reduces levels of negative work events (interpersonal conflict in our case). This is crucial as the negative work events can trigger negative emotions, which in turn cause scientists' job dissatisfaction.

Sample size and statistical power are important factors to consider when planning a research synthesis. Power analysis methods have been developed for fixed effect or random effects models, but until recently these methods were limited to simple data structures with a single, independent effect per study. Recent work has provided power approximation formulas for meta‐analyses involving studies with multiple, dependent effect size estimates, which are common in syntheses of social science research. Prior work focused on developing and validating the approximations but did not address the practice challenges encountered in applying them for purposes of planning a synthesis involving dependent effect sizes. We aim to facilitate the application of these recent developments by providing practical guidance on how to conduct power analysis for planning a meta‐analysis of dependent effect sizes and by introducing a new R package, POMADE, designed for this purpose. We present a comprehensive overview of resources for finding information about the study design features and model parameters needed to conduct power analysis, along with detailed worked examples using the POMADE package. For presenting power analysis findings, we emphasize graphical tools that can depict power under a range of plausible assumptions and introduce a novel plot, the traffic light power plot, for conveying the degree of certainty in one's assumptions. [ABSTRACT FROM AUTHOR]

THOUGH STUART HALL DEFENDS POPULAR REPRESENTATION as an important terrain of political struggle, he also argues that images of difference are dominated by 'racialized regimes of representation' manifest in stereotypes [...]

Increasing impacts of wildfires on Western US air quality highlights the need for forecasts of smoke emissions based on dynamic modeled wildfires. This work utilizes knowledge of weather, fuels, topography, and firefighting, combined with machine learning and other statistical methods, to generate 1‐ and 2‐day forecasts of fire radiative energy (FRE). The models are trained on data covering 2019 and 2021 and evaluated on data for 2020. For the 1‐day (2‐day) forecasts, the random forest model shows the most skill, explaining 48% (25%) of the variance in observed daily FRE when trained on all available predictors compared to the 2% (<0%) of variance explained by persistence for the extreme fire year of 2020. The random forest model also shows improved skill in forecasting day‐to‐day increases and decreases in FRE, with 28% (39%) of observed increase (decrease) days predicted, and increase (decrease) days are identified with 62% (60%) accuracy. Error in the random forest increases with FRE, and the random forest tends toward persistence under severe fire weather. Sensitivity analysis shows that near‐surface weather and the latest observed FRE contribute the most to the skill of the model. When the random forest model was trained on subsets of the training data produced by agencies (e.g., the Canadian or US Forest Services), comparable if not better performance was achieved (1‐day R2 = 0.39–0.48, 2‐day R2 = 0.13–0.34). FRE is used to compute emissions, so these results demonstrate potential for improved fire emissions forecasts for air quality models. Plain Language Summary: Increasing wildfire smoke is undoing decades of air quality progress, yet air quality forecasts often miss the most intense smoke events. This is because forecasted smoke is released at constant rates whereas the rate of smoke release from real wildfires varies in time. In this work we teach a machine learning algorithm to predict the daily change in fire heat output, a quantity that is used to calculate wildfire emissions. The machine learning algorithm uses information regarding weather, fuel moisture and amount, and firefighting efforts to make its predictions. We also tried to predict the daily change in fire heat output using only weather information but found the machine learning method to be more successful. Many federal agencies have their own ways of tracking fire weather and fuel moisture, and in this paper, we show that we can apply machine learning to the data from any of several agencies and get the same level of forecasting skill. Key Points: Random forest models trained on weather, fuel, and firefighting data surpass persistence and weather‐based methods to predict fire energyRandom forest models beat persistence across states, for most days in the 2020 fire season, and across levels of fire severityFire weather and latest fire energy predictors add most skill to the random forest; models using agency weather data perform similarly [ABSTRACT FROM AUTHOR]

Immunization of volunteers under chloroquine prophylaxis by bites of Plasmodium falciparum sporozoite (PfSPZ)-infected mosquitoes induces > 90% protection against controlled human malaria infection (CHMI). We studied intradermal immunization with cryopreserved, infectious PfSPZ in volunteers taking chloroquine (PfSPZ chemoprophylaxis vaccine [CVac]). Vaccine groups 1 and 3 received 3× monthly immunizations with 7.5 × 10(4) PfSPZ. Control groups 2 and 4 received normal saline. Groups 1 and 2 underwent CHMI (#1) by mosquito bite 60 days after the third immunization. Groups 3 and 4 were boosted 168 days after the third immunization and underwent CHMI (#2) 137 days later. Vaccinees (11/20, 55%) and controls (6/10, 60%) had the same percentage of mild to moderate solicited adverse events. After CHMI #1, 8/10 vaccinees (group 1) and 5/5 controls (group 2) became parasitemic by microscopy; the two negatives were positive by quantitative real-time polymerase chain reaction (qPCR). After CHMI #2, all vaccinees in group 3 and controls in group 4 were parasitemic by qPCR. Vaccinees showed weak antibody and no detectable cellular immune responses. Intradermal immunization with up to 3 × 10(5) PfSPZ-CVac was safe, but induced only minimal immune responses and no sterile protection against Pf CHMI.

Many modern school systems require children to assume sedentary positions for extended periods of time, and the increasing use of classroom-based technology adds to the number of hours seated. With the incidence of musculoskeletal pain reported not only in adult populations but increasingly in young people, an understanding of spinal health could be essential for students. An extensive database search yielded 19 studies featuring postural interventions in elementary schools which were synthesised for this review. Results suggest that although approaches to promoting postural health in primary schools vary, studies can be compared in terms of their impetus (e.g. low back pain in students, increasing classroom technology), aim and methodologies. Trends in the literature include delivery by posture experts (as opposed to homeroom teachers), examination of both sitting and moving postures in children and the impact of computer use on musculoskeletal heath. Much of the literature relies largely on self-report data; assessment instruments are wide-ranging. Recommendations for future research on posture interventions for elementary school children include gathering more data about participants' perceptions of interventions, employing cohesive data collection techniques to allow for more robust comparisons of studies' findings and incorporating more content about proprioception and kinaesthetic awareness in treatments.

Job satisfaction is the focus of this study, given its strategic importance, both to generate differentiation and competitive advantages, and to promote better and more sustainable results in organizations. Guided by the Affective Events Theory (AET), the interest is in analyzing the direct and indirect effects of supervisory support and involvement on job satisfaction and highlighting the implications of the academics’ emotions for sustainability-related policies and practices in academic environments. The context selected is the higher education (HE) sector in Malaysia for science disciplines, due to its socio-economic relevance and its direct link to the Fourth Industrial Revolution (IR4), as well as to the Sustainable Development Goals (SDGs). A sample of 579 scientists were selected randomly and the analysis was carried out based on partial least squares structural equation modeling (PLS-SEM) methodology. Our results supported the hypotheses postulated in the study and especially highlighted the effects of supervisory support and positive affect on job satisfaction, as well as the effect of interpersonal conflict on negative affect. In terms of implications, policy makers at ministry level are advised to consider introducing directives which promote a climate of emotional safety and trust in academic institutions, to achieve the SDGs more effectively and efficiently. At the university level, and given the relevance of the observed effects, academic leaders are recommended to contemplate improving university environments in some way that reduces levels of negative work events (interpersonal conflict in our case). This is crucial as the negative work events can trigger negative emotions, which in turn cause scientists’ job dissatisfaction.