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The authors sought to identify the prevalence of food insecurity in a community college district during the COVID-19 pandemic. The team also investigated the association food insecurity had with academic performance. A sample of 238 individuals completed the survey, including a demographic questionnaire and the Adult Food Security Survey Module, a subset of the US Household Food Security Survey Module. The study found that 52% of the participating students reported food insecurity. Students of color were more likely to identify as food insecure. In addition, results indicate an association between being food secure and scoring a GPA of 3.5 or higher. These findings support past studies indicating that food insecurity was high in the community college population. The findings also support past results indicating a link between food security and academic performance.

This qualitative study seeks to explore teacher and administrator perceptions of teacher recruitment, retention and support within a Californian turnaround high school run since 2007 by a charter management organization referred to here as New Schools. Upon the basis of previous research that has documented statistically significant improvements in student performance at this campus since New Schools' arrival, this study uses interview data to explore teacher and administrator perceptions of the ways in which New Schools' professional capacity (particularly its practices of teacher recruitment, retention and support) help to explain this success. In short, participants attribute New Schools' success to its extensive and rigorous hiring process, competitive retention rates, high quality professional development and extensive administrative support (though the quality and rigor of such support is found to be somewhat variable).

This study examined associations among a concurrent set of social-emotional strengths and school membership across the United States (N = 1,009) and Chinese (N = 747) students aged 9 to 11 years old. Results identified significant positive relations of school membership with four social-emotional strengths (i.e., gratitude, optimism, zest, and persistence) and a higher-order latent variable (covitality) in both countries, with optimism having the strongest association in both countries. Cross-culturally, U.S. students showed a stronger gratitude association with school membership whereas Chinese students had stronger zest and persistence associations with school membership. Implications for promoting school membership across cultural contexts are discussed.

Introduction: Prior research consistently provides evidence supporting potential relationships between epistemological beliefs and learning. The current study examines the relationship between epistemological beliefs, academic motivation, and self-regulated learning strategies among different ethnic groups of American adolescents. Method: This quantitative study utilized a correlational design to examine the relationship between epistemological beliefs and self-regulated learning strategies in adolescents attending middle school. A total of 364 middle school students (6th to 8th grade) from Southern California public middle schools participated in the study. A multiple group path model was employed to analyze the student data. Results: We found that certain knowledge, omniscient authority, and innate ability beliefs about the nature of knowledge predict positive relationships with self-efficacy and intrinsic value components of motivation. Quick learning and simple knowledge beliefs predict negative relationships with self-efficacy and intrinsic-value but positive relationship with test-anxiety. Similarly, in the aspect of self-regulatory learning strategies, those who believe in the absolute nature of knowledge and the authorities tend to use cognitive and self-regulatory learning strategies more often, while those who believe in the speed of knowledge acquisition tend to employ those strategies less. Discussion and Conclusion: Findings from this study inform educators of the need to advance adolescents' epistemological beliefs for each subject (e.g., science, language arts) as a method to facilitate their motivation and self-regulated learning. We recommend that future research should include assessments of the participants' cultural orientations or the domain specificity of the epistemological beliefs, which may vary the associations of the beliefs with self-regulated learning. Additionally, future research can further investigate other potential mediators of the relationship between epistemological beliefs and self-regulatory learning.

In the United States, how to provide a high-quality education to all students has been a focal discussion, especially in urban settings. One potential solution that has emerged to confront this issue involves urban teacher residency (UTR) programs that provide innovations concerning the recruitment, preparation, and retention of teachers in high-need urban schools. In this study, we conducted a content analysis and compared steering documents of a UTR program in California with materials from a teacher-training program in Finland. Despite differences in both the legislative and local contexts under which the two programs operate, we found many similarities in both the steering documents and course offerings of these two teacher-training programs. For example, both promote aspects of social justice and are research-based. Furthermore, both offer a variety of types of courses, such as those emphasizing the pedagogical bases of education and research studies in education.

An incomplete Freund's adjuvant elicited an overt pathogenesis in vaccinated mice following the intranasal challenge of A/California/07/2009 (H1N1) virus despite the induction of a higher specific antibody titer than other adjuvanted formulations. Aluminum hydroxide adjuvants have not induced any pathogenic signs in a variety of formulations with glycolipids. A glycolipid, α-galactosyl ceramide, improved a stimulatory effect of distinct adjuvanted formulations on an anti-influenza A antibody response. In contrast to α-galactosyl ceramide, its synthetic analogue C34 was antagonistic toward a stimulatory effect of an aluminum hydroxide adjuvant on a specific antibody response. The aluminum hydroxide adjuvant alone could confer complete vaccine-induced protection against mortality as well as morbidity caused by a lethal challenge of the same strain of an influenza A virus. The research results indicated that adjuvants could reshape immune responses either to improve vaccine-induced immunity or to provoke an unexpected pathogenic consequence. On the basis of these observations, this research connotes the prominence to develop a precision adjuvant for innocuous vaccination aimed at generating a protective immunity without aberrant responses. [ABSTRACT FROM AUTHOR]

Theories of preview benefit in reading hinge on integration across saccades and the idea that preview benefit is greater the more similar the preview and target are. Schotter (2013) reported preview benefit from a synonymous preview, but it is unclear whether this effect occurs because of similarity between the preview and target (i.e., integration), or because of contextual fit of the preview--synonyms satisfy both accounts. Studies in Chinese have found evidence for preview benefit for words that are unrelated to the target, but are contextually plausible (Yang, Li, Wang, Slattery, & Rayner, 2014; Yang, Wang, Tong, & Rayner, 2012), which is incompatible with an integration account but supports a contextual fit account. Here, we used plausible and implausible unrelated previews in addition to plausible synonym, antonym, and identical previews to further investigate these accounts for readers of English. Early reading measures were shorter for all plausible preview conditions compared to the implausible preview condition. In later reading measures, a benefit for the plausible unrelated preview condition was not observed. In a second experiment, we asked questions that probed whether the reader encoded the preview or target. Readers were more likely to report the preview when they had skipped the word and not regressed to it, and when the preview was plausible. Thus, under certain circumstances, the preview word is processed to a high level of representation (i.e., semantic plausibility) regardless of its relationship to the target, but its influence on reading is relatively short-lived, being replaced by the target word, when fixated.

There have been consistent efforts to improve the spatiotemporal representations of biogenic/anthropogenic emission sources for photochemical transport modeling for better accuracy of local/regional air quality forecasts. While biogenic emissions, bi-directional NH 3 from fertilizer applications, and point source plume rise are dynamically coupled in the Community Multiscale Air Quality (CMAQ) "inline", there are still known meteorology-induced emissions sectors (e.g., on-road mobile sources, residential heating, and livestock waste), with little or no accounting for the meteorological impacts in the currently operational chemical and aerosol forecasts, but they are represented with static, not weather-aware annual or monthly county total emissions and standard monthly, weekly, or daily temporal allocation profiles to disaggregate them on finer timescales for the hourly air quality forecasts. It often results in poor forecasting performance due to the poor spatiotemporal representations of precursor pollutants during high ozone and PM 2.5 episodes. The main focus of this study is to develop a dynamic inline coupler within the CMAQ system for the on-road mobile emission sector that requires significant computational resources in the current modeling application. To improve their accuracy and spatiotemporal representations, we developed the inline coupler module called CMAQ-MetEmis (for meteorology-induced emission sources within CMAQ version 5.3.2 modeling system). It can dynamically estimate meteorology-induced hourly gridded on-road mobile emissions within the CMAQ, using simulated meteorology without any computational burden to the CMAQ modeling system. To understand the impacts of meteorology-driven on-road mobile emissions on local air quality, the CMAQ is applied over the continental U.S. for 2 months (January and July 2019) for two emissions scenarios, namely (a) "static" on-road vehicle emissions based on static temporal profiles and (b) inline CMAQ-MetEmis on-road vehicle emissions. Overall, the CMAQ-MetEmis coupler allows us to dynamically simulate on-road vehicle emissions from the MOtor Vehicle Emission Simulator (MOVES) on-road emission model for CMAQ, with a better spatiotemporal representation based on the simulated meteorology inputs when compared to the static scenario. The domain total of daily volatile organic compound (VOC) emissions from the inline scenario shows that the largest impacts are from the local meteorology, which is approximately 10 % lower than the ones from the static scenario. In particular, the major difference in the VOC estimates was shown over the California region. These local meteorology impacts on the on-road vehicle emissions via CMAQ-MetEmis revealed an improvement in the hourly NO 2 , daily maximum ozone, and daily average PM 2.5 patterns, with a higher agreement and correlation with daily ground observations. [ABSTRACT FROM AUTHOR]

Ambitious zero-emission vehicle (ZEV) adoption goals have been proposed to decarbonize the transportation sector, while the current market share pales in comparison. Although the distinct socio-economic characteristics of ZEV early adopters relative to mainstream car buyers are well understood, the two groups' preferences for ZEV attributes are not clear. This knowledge gap hinders the development of effective policies to achieve mass ZEV penetration goals. This paper examines consumers' preferences and willingness to pay for ZEV attributes based on 755 early adopters and 3493 mainstream consumers from the 2019 California vehicle survey data. Results show that early adopters are more sensitive to battery range, acceleration performance, home charging availability, and high occupancy vehicle lane access, while mainstream consumers attach greater importance to cost attributes (e.g., fuel and maintenance costs) and charging time. Moreover, the effects of monetary incentives are found to be significant for both groups, whereas neither early adopters nor mainstream consumers value the availability of public charging stations. The findings of this study inform targeted ZEV policymaking and marketing strategies in different adoption stages. • ZEV preferences are compared between early adopters and mainstream consumers. • Early adopters are more sensitive to battery range and acceleration performance. • Mainstream consumers attach greater importance to cost attributes and charging time. • Home charging availability significantly affects BEV utility for early adopters. • Public charging infrastructure availability plays a limited role for both groups. [ABSTRACT FROM AUTHOR]

The successful screening for possible learning disabilities (LD) is a crucial first step in the process of identifying signs of LD, gaining assistance and/or accommodations, and obtaining a more complete LD assessment. Although Latino people are the largest ethnic minority in the United States, and more specifically in California, there remains a clear need for a valid LD screening measure that is appropriate for adult Spanish speakers, particularly low-income individuals. This study evaluated the validity of three brief measures to screen for LD among low-income Spanish-speaking adults: Empire State Screen, Welfare-to-Work [WTW] 18, and MATILDA-R. The study also provides an initial estimate of LD risk in the low-income Spanish-speaking population. To estimate the predictive utility of each screening measure, 1,040 Spanish-speaking adults were administered each of the three screens and then assessed for indications of LD using multiple scoring methods (Bateria Discrepancy Diagnosis [BDD], pattern of strengths and weaknesses [PSW], and DSM-5). The translated WTW 18 Screen and the MATILDA-R appeared most promising. A culturally-sensitive, validated LD screen will help ensure that social workers and other helping professionals have access to appropriate and legally required interventions for this marginalized population. [ABSTRACT FROM AUTHOR]

413 Background: Current health information systems lack a robust framework for longitudinal integration of internal experiences and external evidence into existing workflows. The result is labor intensive, error-prone and fragmented systems plagued with process gaps. Learning health systems (LHS) can be a potential solution to address these gaps. We created and implemented a novel LHS framework in radiation oncology (RO). We hypothesize that this framework can be leveraged to identify and implement opportunities for improvement using a patient-oriented endpoint of time from radiation simulation to treatment start (RSTS). Methods: An RO-LHS was developed through a collaboration between an academic, multi-facility radiation oncology department and an industry partner (Medlever). The end-to-end clinical workflow was embedded into the Medlever platform following an interdisciplinary, iterative analysis with representatives from key teams (clerical, finance and clinical). A novel nomenclature was created to label standardized, customizable templates, which choreograph logic-driven tasks that cover all sub-steps of RSTS. The primary endpoint of RSTS was compared pre- and post-implementation, stratified by key variables, such as facility, staff member, and radiation treatment modality. Results: The iterative workflow analysis identified >700 variables and decision points necessary for >200 cancer and benign conditions routinely managed in RO. >450 treatment protocols were templated.180 logic-driven tasks were created and linked to key parameters, resulting in a choregraphed precision workflow for each patient. Using the first iteration of the build, a total of 512 patients were managed during a 50-day period (03/01/2024-04/20/2024) and were compared to 2875 patients treated prior to implementation. Post-implementation, RSTS was shortened by 36.4% (p<0.001). We identified significant differences in RSTS by radiation modalities (protons vs all others), across facilities, and by providers (p<0.01 for all). Analysis of fractional domain of RSTS, 56% of the time was driven by physician-directed tasks, while 44% was comprised of physics and dosimetry-driven work. Physician-directed tasks had the greatest heterogeneity in time to completion. The biggest variation was noted for the contouring completion task. Significant differences were identified by provider (p<0.001) in nearly each step of the process, including steps not directly tied to physician's work. Conclusions: Through an inter-disciplinary implementation of the first iteration of a LHS in our RO department, we have significantly shortened RSTS. The implementation and analysis has also enabled us to better understand underlying heterogeneities and identify further opportunities for optimization of RSTS. An LHS framework provides a generalizable solution that could be adapted in other RO clinics. [ABSTRACT FROM AUTHOR]

364 Background: Immune checkpoint inhibitors (ICIs) are increasingly used to treat advanced cancers such as lung, bladder, head and neck, colorectal cancers, and melanoma. Psychological distress may promote pro-inflammatory states which can exacerbate immune-related adverse events (irAEs) among ICI-treated cancer patients. This study assessed the impact of mood disorders (anxiety and depression diagnoses/medications before or during treatment) on the odds of irAEs among advanced cancer patients receiving ICIs, accounting for clinical characteristics and social determinants of health. Methods: This retrospective cohort study used data from a national sample of N=8,058 deceased adult patients diagnosed with advanced melanoma, lung, colorectal, head and neck, and bladder cancers from the nationwide Flatiron Health electronic health record (EHR) derived de-identified database. Diagnostic codes and medication orders were used to assess for mood disorders and auto-immune conditions documented within 2 years of receiving an ICI (irAEs). Bivariate tests and multivariable logistic regressions with interaction terms were conducted in R. Results: Accounting for age at advanced cancer diagnosis, ethnicity, socioeconomic status, baseline performance status, and number of clinic visits, female patients (aOR 1.25, 95% CI=1.07-1.46, p=0.005), patients seen in an academic practice setting only (aOR=2.16, 95% CI=1.80-2.60, p<0.001), and patients receiving combination CTLA-4 and PD-(L)1 inhibitors (aOR 1.47, 95% CI=1.11-1.95, p=0.008) had higher odds of irAEs compared with male patients, patients treated in community settings only, and patients receiving PD1/L1 inhibitors only. Non-White (aOR 0.61, 95% CI=0.49, 0.77, p<0.001), bladder (aOR 0.53, 95% CI= 0.37, 0.78, p=0.001) and colorectal cancer patients (aOR=0.55, 95% CI=0.32, 0.91, p=0.024) had lower odds of irAEs compared with White and melanoma patients. Insurance type moderated the relationship between mood disorders and IrAEs; commercially-insured ICI patients with mood disorders had higher odds of irAEs (aOR 1.65, 95% CI=1.16, 2.35, p=0.006) compared with Medicare-insured ICI patients with mood disorders. Conclusions: Social determinants of health such as insurance access may exacerbate the relationship between mood disorders and irAEs. These findings have implications for health equity and are an area warranting further study. Future analyses will also examine directionality of the associations between mood disorders and irAEs. [ABSTRACT FROM AUTHOR]

Background: A national Department of Veterans Affairs (VA) mental health (MH) quality metric tracks engagement in outpatient MH care after discharge from residential and inpatient settings, with recommendations for 2 or more visits 30 days postdischarge. Local Problem: A gap in transitioning patients from residential to outpatient MH care was identified at this site. Methods: A transition management process was developed and piloted, including a new MH Discharge Consult and an RN Transition Care Managers team. Interventions: Transition Care Managers triaged Discharge Consults, communicated with schedulers and patients pre- and postdischarge, and tracked MH engagement for 30 days postdischarge. Process, outcome, and balancing measures were developed and iteratively adjusted using Plan-Do-Study-Act (PDSA) cycles. Results: Over 55 weeks, 443 Discharge Consults were placed. There was an average 89% success rate in connecting patients with 2 or more MH visits versus 53% preintervention. Conclusions: This pilot showed promising results in improving postdischarge MH engagement with the use of PDSA cycles to collect data and refine processes. [ABSTRACT FROM AUTHOR]

The dairy industry in California emits large amounts of methane (CH 4) that contributes significantly to the state's overall Greenhouse Gas (GHG) budget. Reducing CH 4 emissions has become a key priority for dairy farms in support of California's GHG reduction goals. Anaerobic digesters designed to capture CH 4 from animal manure present a practical option for reducing CH 4 emissions, but a comprehensive evaluation of the local air quality impacts of this technology has not been previously undertaken. The simplest digester configurations decrease local emissions of volatile organic compounds (VOCs) but increase emissions of oxides of nitrogen (NOx), potentially changing local air quality. Here, we evaluate the air quality implications of widespread digester adoption in the year 2050 across the San Joaquin Valley (SJV) in central California, which is home to the highest concentration of dairy farms in the state. Changes to concentrations of air pollutants including ozone (O 3), airborne particulate matter with diameter smaller than 2.5 μm (PM 2.5), and various PM 2.5 chemical components are predicted using the UCD/CIT chemical transport model at 4 km resolution. Dairy digester adoption is evaluated within two regional energy scenarios, including a business as usual (BAU) scenario and an 80% greenhouse gas reduction (GHGAi) scenario, to consider potential changes to the chemical regime that governs formation of secondary air pollution. Concentrations are evaluated across 32 randomly selected weeks over a 10-year period from the year 2046–2055 to establish a long-term average impact in the presence of El Niño Southern Oscillation (ENSO) variability. Regional weather patterns are downscaled from Global Climate Model simulations under the RCP8.5 global scenario. The results indicate that baseline dairy emissions make minor contributions to air pollutant concentrations in 2050. Under a worst-case scenario for digester adoption, PM 2.5 concentrations would increase by 0.06 μg/m3 (current standard = 9 μg/m3), and maximum daily 8-h average (MDA8) O 3 would change by −1.0 ppb to +0.2 ppb depending on the surrounding regional energy scenario (current standard = 70 ppb). A health impact analysis shows that the widespread use of dairy digesters would result in fewer than 0.1 additional deaths per 100,000 people due to changing air pollution. For comparison, this level of mortality change is more than 100 times smaller than the risk posed by seasonal flu. Further, Environmental Justice analysis indicates that the implementation of digesters will not influence the exposure disparities among different racial groups in either the SJV or the surrounding San Francisco Bay & Sacramento area. These findings suggest that dairy digesters can be widely adopted in central California to reduce GHG emissions with minimal effect on regional air quality and public health. • Dairy digesters capture methane from farms to reduce GHG emissions. • Digesters configured to produce electricity increase local emissions of criteria pollutants. • Widespread digester adoption would have minor effects on local air quality. • Digesters do not harm public health or worsen air quality for disadvantaged communities. [ABSTRACT FROM AUTHOR]

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in P M 2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality ( O 3 and CO ) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O 3 and CO , respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly P M 2.5 and P M 10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for P M 2.5 and P M 10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily P M 2.5 and P M 10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978. [ABSTRACT FROM AUTHOR]

Increases in wildfire activity across the Western US pose a significant public health threat. While there is evidence that wildfire smoke is detrimental for respiratory health, the impacts on cardiovascular health remain unclear. This study evaluates the association between fine particulate matter (PM2.5) from wildfire smoke and unscheduled cardiorespiratory hospital visits in California during the 2004–2009 wildfire seasons. We estimate daily mean wildfire‐specific PM2.5 with Goddard Earth Observing System‐Chem, a global three‐dimensional model of atmospheric chemistry, with wildfire emissions estimates from the Global Fire Emissions Database. We defined a "smoke event day" as cumulative 0‐1‐day lag wildfire‐specific PM2.5 ≥ 98th percentile of cumulative 0–1 lag day wildfire PM2.5. Associations between exposure and outcomes are estimated using negative binomial regression. Results indicate that smoke event days are associated with a 3.3% (95% CI: [0.4%, 6.3%]) increase in visits for all respiratory diseases and a 10.3% (95% CI: [2.3%, 19.0%]) increase for asthma specifically. Stratifying by age, we found the largest effect for asthma among children ages 0–5 years. We observed no significant association between exposure and overall cardiovascular disease, but stratified analyses revealed increases in visits for all cardiovascular, ischemic heart disease, and heart failure among non‐Hispanic white individuals and those older than 65 years. Further, we found a significant interaction between smoke event days and daily average temperature for all cardiovascular disease visits, suggesting that days with high wildfire PM2.5 concentrations and high temperatures may pose greater risk for cardiovascular disease. These results suggest substantial increases in adverse outcomes from wildfire smoke exposure and indicate the need for improved prevention strategies and adaptations to protect vulnerable populations. Plain Language Summary: Due to continued climate change, wildfire activity has increased in recent years and poses a significant public health threat. In this study, we investigated the impact of increased wildfire smoke exposure on cardiovascular and respiratory emergency department (ED) visits. We found that smoke events are linked to a > 3% increase of respiratory ED visits with a > 10% increase for asthma specifically, with the largest effect seen in children 0–5 years of age. We did not find an increase in cardiovascular visits for the entire population, but we did observe significant increases in several cardiovascular outcomes for individuals 65 years of age and older as well as for non‐Hispanic white individuals. Key Points: Consecutive days of wildfire smoke PM2.5 exposure can significantly impact respiratory and cardiovascular health outcomesIschemic heart disease and failure highest in adults 65+; heart failure elevated in non‐Hispanic white and non‐Hispanic Black populationsWhen combined with increase in temperature, wildfire smoke exposure is associated with increased risk of cardiovascular disease [ABSTRACT FROM AUTHOR]

Objective: To operationalize an intersectionality framework using a novel statistical approach and with these efforts, improve the estimation of disparities in access (i.e., wait time to treatment entry) to opioid use disorder (OUD) treatment beyond race. Data source Sample of 941,286 treatment episodes collected in 2015–2017 in the United States from the Treatment Episodes Data Survey (TEDS‐A) and a subset from California (n = 188,637) and Maryland (n = 184,276), states with the largest sample of episodes. Study design This retrospective subgroup analysis used a two‐step approach called virtual twins. In Step 1, we trained a classification model that gives the probability of waiting (1 day or more). In Step 2, we identified subgroups with a higher probability of differences due to race. We tested three classification models for Step 1 and identified the model with the best estimation. Data collection: Client data were collected by states during personal interviews at admission and discharge. Principal findings: Random forest was the most accurate model for the first step of subgroup analysis. We found large variation across states in racial disparities. Stratified analysis of two states with the largest samples showed critical factors that augmented disparities beyond race. In California, factors such as service setting, referral source, and homelessness defined the subgroup most vulnerable to racial disparities. In Maryland, service setting, prior episodes, receipt of medication‐assisted opioid treatment, and primary drug use frequency augmented disparities beyond race. The identified subgroups had significantly larger racial disparities. Conclusions: The methodology used in this study enabled a nuanced understanding of the complexities in disparities research. We found state and service factors that intersected with race and augmented disparities in wait time. Findings can help decision makers target modifiable factors that make subgroups vulnerable to waiting longer to enter treatment. [ABSTRACT FROM AUTHOR]

Mitigation of greenhouse gas emissions from agriculture requires an understanding of spatial‐temporal dynamics of nitrous oxide (N2O) emissions. Process‐based models can quantify N2O emissions from agricultural soils but have rarely been applied to regions with highly diverse agriculture. In this study, a process‐based biogeochemical model, DeNitrification‐DeComposition (DNDC), was applied to quantify spatial‐temporal dynamics of direct N2O emissions from California cropland employing a wide range of cropping systems. DNDC simulated direct N2O emissions from nitrogen (N) inputs through applications of synthetic fertilizers and crop residues during 2000–2015 by linking the model with a spatial‐temporal differentiated database containing data on weather, crop areas, soil properties, and management. Simulated direct N2O emissions ranged from 3,830 to 7,875 tonnes N2O‐N yr−1, representing 0.73%–1.21% of the N inputs. N2O emission rates were higher for hay and field crops and lower for orchard and vineyard. State cropland total N2O emissions showed a decreasing trend primarily driven by reductions of cropland area and N inputs, the trend toward growing more orchard, and changes in irrigation. Annual direct N2O emissions declined by 47% from 2000 to 2015. Simulations showed N2O emission variations could be explained not only by cropland area and N fertilizer inputs but also climate, soil properties, and management besides N fertilization. The detailed spatial‐temporal emission dynamics and driving factors provide knowledge toward effective N2O mitigation and highlight the importance of coupling process‐based models with high‐resolution data for characterizing the spatial‐temporal variability of N2O emissions in regions with diverse croplands. Plain Language Summary: Nitrous oxide (N2O) is the third most important greenhouse gas and an important ozone‐depleting substance. It is challenging to quantify N2O emissions from croplands in regions with highly diverse agriculture. In this study, a process‐based biogeochemical model, DeNitrification‐DeComposition (DNDC), was applied to quantify spatial‐temporal dynamics of direct N2O emissions from California cropland, which has a wide range of cropping systems. DNDC simulated direct N2O emissions from nitrogen (N) inputs from synthetic fertilizers and crop residues during 2000–2015. Simulated direct N2O emissions ranged from 3,830 to 7,875 tonnes N yr−1, representing 0.73%–1.21% of the N inputs from synthetic fertilizers and crop residues. Simulated annual direct N2O emissions from California cropland declined by 47% from 2000 to 2015. This decreasing trend could be explained by reductions of cropland area and associated N inputs, the trend toward growing more orchard crops, and changes in irrigation. This study characterized spatial‐temporal dynamics of N2O emissions from California cropland, and provided knowledge toward effective N2O mitigation. It also highlights the importance of coupling process‐based models with high‐resolution data to quantify the spatial‐temporal variability of N2O emissions in regions with complicated agricultural systems. Key Points: Direct nitrous oxide (N2O) emission from N inputs of fertilizers and residues ranged from 3,830 to 7,875 ton N yr−1 from 2000 to 2015 in California croplandSimulated annual direct N2O emission decreased by 47% from 2000 to 2015, primarily driven by changes in land use and farming managementCoupling model with environmental and activity database improved characterization of direct N2O emissions from California cropland [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to investigate how to improve the effectiveness of public participation in public infrastructure megaprojects (PIMs). Conflicts among stakeholders and uncertainty disrupt the success of PIMs when public participation is ineffective. Design/methodology/approach: Secondhand data are collected to study the effectiveness of public participation in the Beijing–Shenyang High-Speed Rail (China) and the California High-Speed Rail (USA). The employed research method is an inductive case study. Findings: Ineffective public participation can cause schedule and cost overruns and increase uncertainty in PIMs. The ambiguity of meaningful public participation, ineffective participatory approaches and overburden of public participation in environmental impact assessment are the causes of ineffective public participation in PIMs. Research limitations/implications: Public participation has become an essential part in PIMs management. This study looks at the understanding of the relationship between public participation and the success of PIMs. Practical implications: Legislative bodies should perfect the laws to guarantee meaningful public participation. Lead agencies should adopt additional effective participatory approaches to solicit public comments and identify critical voices. A dispute solution mechanism is necessary to solve public participation disputes in PIMs in practice. Originality/value: Stakeholder and uncertainty management are important to the success of PIMs. This study reveals methods to improve the effectiveness of public participation to alleviate the conflicts among stakeholders and reduce uncertainty in PIMs. [ABSTRACT FROM AUTHOR]

The central issue for successful implementation of a multimodal approach is the development of appropriate crash frequency models that can jointly estimate the crash risk of different mode users. This study proposed two multivariate spatial-temporal models to analyze 6 years of modal crash data from counties in California: one with fixed time trend applied to all modes, the other with mode-varying time trend coefficients. These models were compared with three other multivariate models from past studies. The major objective was to examine the benefits of the newly proposed models that have substantially increased computational cost, because both dimensions of time and space, and their interaction, are considered. The model fitness results from penalized criterion revealed that the proposed models significantly improved the model fitting by pooling strength from neighboring sites, consideration of time trend, as well as their interactions. The model with mode-varying time coefficients was observed to be superior in terms of prediction accuracy. The posterior deviance was observed to be the governing factor for overall fit and consistently showed a strong positive correlation between model fit and prediction accuracy. Overall, the study is anticipated to enhance the understanding of safety impacts from the interaction of various modes. [ABSTRACT FROM AUTHOR]

The Argentine ant, Linepithema humile (Mayr), poses a significant economic threat to citrus production in southern California. Broad-spectrum insecticide sprays provide poor ant control and present a hazard to human and environmental health. Liquid sucrose bait infused with a low concentration of insecticide is an effective alternative treatment but current approaches require considerable economic investment in plastic dispensers and continual maintenance. To produce a baiting product for control of L. humile suitable for large-scale application, a biodegradable, broadcastable alginate hydrogel for delivery of aqueous low-dose thiamethoxam sucrose bait was developed and evaluated in replicated field trials in commercial citrus groves. Ant activity was significantly reduced in hydrogel-treated trees. Peak L. humile suppression was achieved 48 h following the final hydrogel disbursement, with an estimated 91% reduction in activity from baseline estimates and a 17-fold lower activity in treated trees in comparison with untreated trees. Significant residual activity of the hydrogel treatments was recorded, with a nearly 70% reduction from pre-treatment levels persisting at least 3 weeks after the last application. We conclude that alginate hydrogels can provide excellent control of L. humile while deploying 99.99% less insecticide into orchards than commercial barrier spray treatments. [ABSTRACT FROM AUTHOR]

Objective: This study evaluated whether emergency department (ED) patient presentations for problems related to mental and substance use disorders could be validly monitored by a syndromic surveillance system that uses chief complaints to identify mental disorders.Methods: The study used syndromic surveillance data on 146,315 ED visits to participating Fresno County, California, hospitals between January 1 and December 31, 2013. Free-text patient chief complaints are automatically classified into syndromes based on the developer's algorithms. Agreement was assessed between the algorithm's syndrome classification of mental health and substance abuse (MHSA) disorders and ICD-9-CM discharge diagnostic codes. Diagnosis and ED utilization patterns among patients with at least one visit with an MHSA syndrome classification were also examined.Results: Approximately 8% of ED visits during the study period received an MHSA syndrome classification. Overall agreement between MHSA syndrome classification and psychiatric- or substance use-related ICD-9 discharge diagnoses was high (κ=.92, 95% confidence interval=.91-.92). Sensitivity (100%) and specificity (98.6%) of the MHSA syndrome classification were also very high. MHSA syndrome-classified patients exhibited high levels of health care and morbidity burden compared with other patients.Conclusions: ED chief complaints can be utilized to reliably and validly ascertain the incidence of patient presentations for mental and substance use disorders in contexts in which discharge diagnoses are not routinely available. Wider adoption of MHSA-related syndrome algorithms by syndromic surveillance systems could be valuable for public mental health surveillance, service delivery, and resource planning efforts. [ABSTRACT FROM AUTHOR]

Atmospheric mercury (Hg) poses human health and ecological risks once deposited and bio-accumulated through food chains. Source contribution analysis of Hg deposition is essential to formulating emission control strategies to alleviate the adverse impact of Hg release from anthropogenic sources. In this study, a Hg version of California Puff Dispersion Modeling (denoted as CALPUFF-Hg) system with added Hg environmental processes was implemented to simulate the Hg concentration and deposition in the central region of the Pearl River Delta (cPRD) at 1 km × 1 km resolution. The contributions of eight source sectors to Hg deposition were evaluated. Model results indicated that the emission from cement production was the largest contributor to Hg deposition, accounting for 13.0%, followed by coal-fired power plants (6.5%), non-ferrous metal smelting (5.4%), iron and steel production (3.5%), and municipal solid waste incineration (3.4%). The point sources that released a higher fraction of gaseous oxidized mercury, such as cement production and municipal solid waste incineration, were the most significant contributors to local deposition. In this intensive industrialized region, large point sources contributed 67–94% of total Hg deposition of 6 receptors which were the nearest grid-cells from top five Hg emitters of the domain and the largest municipal solid waste incinerator in Guangzhou. Based on the source apportionment results, cement production and the rapidly growing municipal solid waste incineration are identified as priority sectors for Hg emission control in the cPRD region. Image 1 • A modified version of CALPUFF is developed for simulating Hg chemical transport. • Hg deposition near point sources and its contribution are evaluated using CALPUFF-Hg. • Contributions of Hg emission from multiple point sources to the deposition were assessed. • Cement and waste incineration are of greatest concern causing Hg deposition in PRD region. A modified version of CALPUFF was developed for simulating the Hg pollution near point sources and assessing the source contribution of deposition at a higher resolution. [ABSTRACT FROM AUTHOR]

This study investigates critical thinking (CT) studies in e-learning settings to provide a comprehensive analysis of CT studies. Research indexed in the Web of Science database which includes major peer-reviewed social sciences citation index journals in the e-learning field from January 2006 to November 2017 was searched for a systematic review and analysis. A total of 42 studies were selected and synthesized. The distribution of publishing years, geographical areas, education sectors, publications, disciplines, research methods, activities, CT measurement instruments, teaching instruction, the CT roles applied in the studies, and the experiment durations were reviewed. The results showed that most CT studies were conducted in nontraditional teaching environments using strategies such as online discussion or concept mapping; the most used quantitative method was the California critical thinking skills test (CCTST), whereas the most frequently adopted qualitative method was the practical inquiry model of cognitive presence (PIMCP). The common method to enhance students' CT was online synchronous or asynchronous discussion, and the role that CT played in these studies was as a dependent variable for evaluating whether the designed e-learning instruction(s) can cultivate CT. This study concludes that with active learning instruction and experienced tutor facilitation, students' CT could be enhanced in a variety of e-learning environments. [ABSTRACT FROM AUTHOR]

Objectives: This study sought to identify challenges and protective factors for resilience related to the process of aging among older Chinese immigrants in the United States. Methods: This study used qualitative methods and involved 24 in-depth interviews with Chinese immigrants aged 65 or older in Los Angeles. Content analysis was employed to analyze qualitative data. Results: Three major themes emerged regarding challenges older Chinese immigrants encountered in aging in the United States: language barriers, loneliness and social isolation, and insufficient use of social services. Four themes were identified regarding resilience protective factors: acceptance and optimism; independence and autonomy; informal social support; and use of the formal social welfare system. Conclusion: This study provides several implications for future human services to build relational and societal resilience and enhance Chinese older immigrants' personal resilience. Cultural strengths should be taken into consideration by practitioners and policy makers. [ABSTRACT FROM AUTHOR]

• The models are applicable to various engineer fields. • The relationships between the UCS and other mechanical indices. • Generalized function for capturing the effect of curing time. Given the wide application of lime stabilized loess in road base and subbase, subgrade, building foundations and slopes, a set of generalized and accurate mechanical estimation models of lime stabilized loess is essential for quality control. In this research, three modified models were proposed to capture the effects of lime content and compactness on the unconfined compressive strength (UCS), California bearing ratio (CBR), resilience modulus (M R), internal friction angle (φ), cohesion (c) and compression coefficient (a 1-2) of lime stabilized loess at both 7-day and 28-day curing periods. Compared with the logarithmic model, the hyperbolic model can more reasonably characterize the effect of the curing period on these mechanical indices. Taking those factors together, a set of three-factor estimation models based on both 7-day and 28-day references were developed and show high reliability. Moreover, the establishment of relationships between UCS and CBR , M R , φ and c provides a shortcut for the mechanical property estimation of lime stabilized loess. Finally, based on multiple sets of UCS data sourced from numerous papers, a hyperbolic model with a wider application range was established to link normalized UCS to curing time. The proposed estimation models can be adopted for design checking and quality evaluation of lime stabilized loess applied in various engineering fields. [ABSTRACT FROM AUTHOR]

Wildfires burn more than 7 million acres in the United States annually, according to the US Forest Service. Little is known about which subpopulations are more vulnerable to health risks from wildfire smoke, including those associated with fine particulate matter. We estimated exposure to fine particles specifically from wildfires, as well as the associations between the presence of wildfire-specific fine particles and the amount of hospital admissions for respiratory causes among subpopulations older than 65 years of age in the western United States (2004-2009). Compared with other populations, higher fractions of persons who were black, lived in urban counties, and lived in California were exposed to more than 1 smoke wave (high-pollution episodes from wildfire smoke). The risks of respiratory admissions on smoke-wave days compared with non-smoke-wave days increased 10.4% (95% confidence interval: 1.9, 19.6) for women and 21.7% (95% confidence interval: 0.4, 47.3) for blacks. Our findings suggest that increased risks of respiratory admissions from wildfire smoke was significantly higher for women than for men (10.4% vs. 3.7%), blacks than whites (21.7% vs. 6.9%), and, although associations were not statistically different, people in lower-education counties than higher-educated counties (12.7% vs. 6.1%). Our study raised important environmental justice issues that can inform public health programs and wildfire management. As climate change increases the frequency and intensity of wildfires, evidence on vulnerable subpopulations can inform disaster preparedness and the understanding of climate change consequences. [ABSTRACT FROM AUTHOR]

Purpose: To develop an intratumor partitioning framework for identifying high-risk subregions from (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer.Methods and Materials: In this institutional review board-approved retrospective study, we analyzed the pretreatment FDG-PET and CT scans of 44 lung cancer patients treated with radiation therapy. A novel, intratumor partitioning method was developed, based on a 2-stage clustering process: first at the patient level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP).Results: Three spatially distinct subregions were identified within each tumor that were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI of 0.66-0.67. When restricting the analysis to patients with stage III disease (n=32), the same subregion achieved an even higher CI of 0.75 (hazard ratio 3.93, log-rank P=.002) for predicting OS, and a CI of 0.76 (hazard ratio 4.84, log-rank P=.002) for predicting OFP. In comparison, conventional imaging markers, including tumor volume, maximum standardized uptake value, and metabolic tumor volume using threshold of 50% standardized uptake value maximum, were not predictive of OS or OFP, with CI mostly below 0.60 (log-rank P>.05).Conclusion: We propose a robust intratumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types. [ABSTRACT FROM AUTHOR]

BACKGROUND: Organophosphorous (OP) pesticides are associated with reduced fetal growth in animals, but human studies are inconsistent. OBJECTIVES: We pooled data from four cohorts to examine associations of prenatal OP exposure with birth weight (n = 1,169), length (n = 1,152), and head circumference (n = 1,143). METHODS: Data were from the CHAMACOS, HOME, Columbia, and Mount Sinai birth cohorts. Concentrations of three diethyl phosphate (ΣDEP) and three dimethyl phosphate (ΣDMP) metabolites of OP pesticides [summed to six dialkyl phosphates (ΣDAPs)] were measured in maternal urine. Linear regression and mixed-effects models were used to examine associations with birth outcomes. RESULTS: We found no significant associations of ΣDEP, ΣDMP, or ΣDAPs with birth weight, length, or head circumference overall. However, among non-Hispanic black women, increasing urinary ΣDAP and ΣDMP concentrations were associated with decreased birth length (β = -0.4 cm; 95% CI: -0.9, 0.0 and β = -0.4 cm; 95% CI: -0.8, 0.0, respectively, for each 10-fold increase in metabolite concentration). Among infants with the PON1[sub 192RR] genotype, ΣDAP and ΣDMP were negatively associated with length (β = -0.4 cm; 95% CI: -0.9, 0.0 and β = -0.5 cm; 95% CI:-0.9, -0.1). CONCLUSIONS: This study confirms previously reported associations of prenatal OP exposure among black women with decreased infant size at birth, but finds no evidence of smaller birth weight, length, or head circumference among whites or Hispanics. Contrary to our hypothesis, we found stronger inverse associations of DAPs and birth outcome in infants with the less susceptible PON1[sub 192RR] genotype. The large pooled data set facilitated exploration of interactions by race/ethnicity and PON1 genotype, but was limited by differences in study populations. [ABSTRACT FROM AUTHOR]

BACKGROUND: Organophosphorus pesticides (OPs) are used in agriculture worldwide. Residential use was common in the United States before 2001. Objectives: We conducted a pooled analysis of four birth cohorts (children's centers; n = 936) to evaluate associations of prenatal exposure to OPs with child development at 24 months. METHODS: Using general linear models, we computed site-specific and pooled estimates of the association of total dialkyl (ΣDAP), diethyl (ΣDEP), and dimethylphosphate (Σ) metabolite concentrations in maternal prenatal urine with mental and psychomotor development indices (MDI/PDI) and evaluated heterogeneity by children's center, race/ethnicity, and PON1 genotype. RESULTS: There was significant heterogeneity in the center-specific estimates of association for ΣDAP and ΣDMP and the MDI (p = 0.09, and p = 0.05, respectively), as well as heterogeneity in the race/ethnicity-specific estimates for ΣDAP (p = 0.06) and ΣDMP (p = 0.02) and the MDI. Strong MDI associations in the CHAMACOS population per 10-fold increase in ΣDAP (β = -4.17; 95% CI: -7.00, -1.33) and ΣDMP (β = -3.64; 95% CI: -5.97, -1.32) were influential, as were associations among Hispanics (β per 10-fold increase in ΣDAP = -2.91; 95% CI: -4.71, -1.12). We generally found stronger negative associations of ΣDAP and ΣDEP with the 24-month MDI for carriers of the 192Q PON1 allele, particularly among blacks and Hispanics. CONCLUSIONS: Data pooling was complicated by center-related differences in subject characteristics, eligibility, and changes in regulations governing residential use of OPs during the study periods. Pooled summary estimates of prenatal exposure to OPs and neurodevelopment should be interpreted with caution because of significant heterogeneity in associations by center, race/ethnicity, and PON1 genotype. Subgroups with unique exposure profiles or susceptibilities may be at higher risk for adverse neurodevelopment following prenatal exposure. [ABSTRACT FROM AUTHOR]

Bone cancer pain is severe pain with complicated pathogenesis and remains challenging to treat. The ability to understand the phenotype of pain is critical for developing an effective care plan. Large-scale electronic health record (EHR) data can be leveraged to identify different patterns or trajectories of pain. According to the Dynamic Symptoms Model (DSM), cancer pain can be influenced by demographic, clinical, and social or environmental factors. Data presenting some of these factors have been collected in EHRs. Therefore, this study aimed to EHR data to identify specific pain trajectories of bone cancer patients and then extract predictors for each pain trajectory. This secondary data research used data from the Veterans Affairs healthcare system. The EHR data were included in-patients with bone cancer diagnosed between 2000 and 2019. Their first two years of pain data were used to identify different pain trajectories using growth mixture modeling (GMM). Guided by the DSM model, we included demographics (e.g., sex, race), clinical parameters (e.g., cancer therapies, the use of pain medication), and social factors (e.g., marital status or religion) to predict individual pain trajectories. A total of 201 bone cancer patient data were included in this study. The majority of patients were male (n = 193) and White (n = 145). The mean age at diagnosis was 62.1 years (SD = 14.9, range =20 - 92). The mean pain score was 3.5 (SD = 2.2, range:0-10). Two distinct pain trajectory patterns were identified: stable/consistent mild pain (60.7%) and decreasing from moderate to mild pain (7.8%), and consistent moderate pain (39.3%). Decision tree, a machine learning technique, indicated that race, marital status, opioid pain medication use, and smoking or drinking status were important to classify bone cancer patients to their pain trajectories with an accuracy of 70.3%. The findings can support personalized pain care by helping patients understand their potential pain trajectories and facilitating patient and clinician communications in discussing pain care plans. Future studies can consider including more comprehensive factors to improve the accuracy of model prediction The innovation of this study is to demonstrate an approach to developing patient-centered pain management by using EHR data and machine learning techniques. [ABSTRACT FROM AUTHOR]

Background The White House, the American Heart Association, the Agency for Healthcare Research and Quality, and the National Heart, Lung and Blood Institute have all recently acknowledged the need to disaggregate Asian American subgroups to better understand this heterogeneous racial group. This study aims to assess racial/ethnic differences in relative contribution of risk factors of gestational diabetes mellitus ( GDM) among Asian subgroups ( Asian Indian, Chinese, Filipino, Japanese, Korean, and Vietnamese), Hispanics, non-Hispanic blacks, and non-Hispanic whites. Methods Pregnant women in 2007-2012 were identified through California state birth certificate records and linked to the electronic health records in a large mixed-payer ambulatory care organisation in Northern California ( n = 24 195). Relative risk and population attributable fraction ( PAF) for specific racial/ethnic groups were calculated to assess the contributions of advanced maternal age, overweight/obesity ( Centers for Disease Control and Prevention (CDC) standards and World Health Organization ( WHO)/ American Diabetes Association ( ADA) body mass index cut-offs for Asians), family history of type 2 diabetes, and foreign-born status. Results GDM was most prevalent among Asian Indians (19.3%). Relative risks were similar across all race/ethnic groups. Advanced maternal age had higher PAFs in non- Hispanic whites (22.5%) and Hispanics (22.7%). Meanwhile family history ( Asian Indians 22.6%, Chinese 22.9%) and foreign-borne status ( Chinese 40.2%, Filipinos 30.2%) had higher PAFs in Asian subgroups. Overweight/obesity was the most important GDM risk factor for non- Hispanic whites, Hispanics, Asian Indians, and Filipinos when the WHO/ ADA cut-off points were applied. Advanced maternal age was the only risk factor studied that was modified by race/ethnicity, with non- Hispanic white and Hispanic women being more adversely affected than other racial/ethnic groups. Conclusions Overweight/obesity, advanced maternal age, family history of type 2 diabetes, and foreign-borne status are important risk factors for GDM. The relative contributions of these risk factors differ by race/ethnicity, mainly due to differences in population prevalence of these risk factors. [ABSTRACT FROM AUTHOR]

Background Regional referral systems are considered important for children hospitalized for surgery, but there is little information on existing systems. Objectives To examine geographic variations in anesthetic caseloads in California for surgical inpatients ≤6 years and to evaluate the feasibility of regionalizing anesthetic care. Methods We reviewed California's unmasked patient discharge database between 2000 and 2009 to determine surgical procedures, dates, and inpatient anesthetic caseloads. Hospitals were classified as urban or rural and were further stratified as low, intermediate, high, and very high volume. Results We reviewed 257 541 anesthetic cases from 402 hospitals. Seventeen California Children's Services ( CCS) hospitals conducted about two-thirds of all inpatient anesthetics; 385 non- CCS hospitals accounted for the rest. Urban hospitals comprised 82% of low- and intermediate-volume centers ( n = 297) and 100% of the high- and very high-volume centers ( n = 41). Ninety percent ( n = 361) of hospitals performed <100 cases annually. Although potentially lower risk procedures such as appendectomies were the most frequent in urban low- and intermediate-volume hospitals, fairly complex neurosurgical and general surgeries were also performed. The median distance from urban lower-volume hospitals to the nearest high- or very high-volume center was 12 miles. Up to 98% ( n = 40 316) of inpatient anesthetics at low- or intermediate-volume centers could have been transferred to higher-volume centers within 25 miles of smaller centers. Conclusions Many urban California hospitals maintained low annual inpatient anesthetic caseloads for children ≤6 years while conducting potentially more complex procedures. Further efforts are necessary to define the scope of pediatric anesthetic care at urban low- and intermediate-volume hospitals in California. [ABSTRACT FROM AUTHOR]

Summary: We used an osteopenic adult ovariectomized (OVX) rat model to evaluate various sequential treatments for osteoporosis, using FDA-approved agents with complementary tissue-level mechanisms of action. Sequential treatment for 3 months each with alendronate (Aln), followed by PTH, followed by resumption of Aln, created the highest trabecular bone mass, best microarchitecture, and highest bone strength. Introduction: Individual agents used to treat human osteoporosis reduce fracture risk by ∼50-60 %. As agents that act with complementary mechanisms are available, sequential therapies that mix antiresorptive and anabolic agents could improve fracture risk reduction, when compared with monotherapies. Methods: We evaluated bone mass, bone microarchitecture, and bone strength in adult OVX, osteopenic rats, during different sequences of vehicle (Veh), parathyroid hormone (PTH), Aln, or raloxifene (Ral) in three 90- day treatment periods, over 9 months. Differences among groups were evaluated. The interrelationships of bone mass and microarchitecture endpoints and their relationship to bone strength were studied. Results: Estrogen deficiency caused bone loss. OVX rats treated with Aln monotherapy had significantly better bone mass, microarchitecture, and bone strength than untreated OVX rats. Rats treated with an Aln drug holiday had bone mass and microarchitecture similar to the Aln monotherapy group but with significantly lower bone strength. PTH-treated rats had markedly higher bone endpoints, but all were lost after PTH withdrawal without follow-up treatment. Rats treated with PTH followed by Aln had better bone endpoints than those treated with Aln monotherapy, PTH monotherapy, or an Aln holiday. Rats treated initially with Aln or Ral, then switched to PTH, also had better bone endpoints, than monotherapy treatment. Rats treated with Aln, then PTH, and returned to Aln had the highest values for all endpoints. Conclusion: Our data indicate that antiresorptive therapy can be coupled with an anabolic agent, to produce and maintain better bone mass, microarchitecture, and strength than can be achieved with any monotherapy. [ABSTRACT FROM AUTHOR]

SUMMARY A series of hybrid and cyclic loading tests were conducted on a three-story single-bay full-scale buckling-restrained braced frame (BRBF) at the Taiwan National Center for Research on Earthquake Engineering in 2010. Six buckling-restrained braces (BRBs) including two thin BRBs and four end-slotted BRBs, all using welded end connection details, were installed in the frame specimen. The BRBF was designed to sustain a design basis earthquake in Los Angeles. In the first hybrid test, the maximum inter-story drift reached nearly 0.030 rad in the second story and one of the thin BRBs in the first story locally bulged and fractured subsequently before the test ended. After replacing the BRBs in the first story with a new pair, a second hybrid test with the same but reversed direction ground motion was applied. The maximum inter-story drifts reached more than 0.030 rad and some cracks were found on the gusset welds in the second story. The frame responses were satisfactorily predicted by both OpenSees and PISA3D analytical models. The cyclic loading test with triangular lateral force distribution was conducted right after the second hybrid test. The maximum inter-story drift reached 0.032, 0.031, and 0.008 rad for the first to the third story, respectively. This paper then presents the findings on the local bulging failure of the steel casing by using cyclic test results of two thin BRB specimens. It is found that the steel casing bulging resistance can be computed from an equivalent beam model constructed from the steel core plate width and restraining concrete thickness. This paper concludes with the recommendations on the seismic design of thin BRB steel casings against local bulging failure. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article discusses a study which examined the impact of federal and state policy changes on California hospitals. Data on structures, processes and outcomes of care were collected before and after policy changes. The study confirmed that mandatory reporting had intended and unintended consequences, highlighting the importance of technology in preventing infections.

A novel pattern-based model evaluation technique is proposed and demonstrated for air quality models (AQMs) driven by meteorological model (MM) output. The evaluation technique is applied directly to the MM output; however, it is ultimately used to gauge the performance of the driven AQM. This evaluation of AQM performance based on MM performance is a major advance over traditional evaluation methods. First, meteorological cluster analysis is used to assign the days of a historical measurement period among a small number of weather patterns having distinct air quality characteristics. The clustering algorithm groups days sharing similar empirical orthogonal function (EOF) representations of their measurements. In this study, EOF analysis is used to extract space-time patterns in the surface wind field reflecting both synoptic and mesoscale influences. Second, simulated wind fields are classified among the determined weather patterns using the measurement-derived EOFs. For a given period, the level of agreement between the observation-based clustering labels and the simulation-based classification labels is used to assess the validity of the simulation results. Mismatches occurring between the two sets of labels for a given period imply inaccurately simulated conditions. Moreover, the specific nature of a mismatch can help to diagnose the downstream effects of improperly simulated meteorological fields on AQM performance. This pattern-based model evaluation technique was applied to extended simulations of fine particulate matter (PM2.5) covering two winter seasons for the San Francisco Bay Area of California. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Despite the documented utility of regionalized systems of pediatric specialty care, little is known about the actual use of such systems in total populations of chronically ill children. The objective of this study was to evaluate variations and trends in regional patterns of specialty care hospitalization for children with chronic illness in California. METHODS: Using California's Office of Statewide Health Planning and Development unmasked discharge data set between 1999 and 2007, we performed a retrospective, total-population analysis of variations in specialty care hospitalization for children with chronic illness in California. The main outcome measure was the use of pediatric specialty care centers for hospitalization of children with a chronic condition in California. RESULTS: Analysis of 2 170 102 pediatric discharges revealed that 41% had a chronic condition, and 44% of these were discharged from specialty care centers. Specialty care hospitalization varied by county and type of condition. Multivariate analyses associated increased specialty care center use with public insurance and high pediatric specialty care bed supply. Decreased use of regionalized care was seen for adolescent patients, black, non-Hispanic children, and children who resided in zip codes of low income or were located farther from a regional center of care. CONCLUSIONS: Significant variation exists in specialty care hospitalization among chronically ill children in California. These findings suggest a need for greater scrutiny of clinical practices and child health policies that shape patterns of hospitalization of children with serious chronic disease. [ABSTRACT FROM AUTHOR]

We model and analyze E. & J. Gallo Winery's Modesto, California cellar tank piping network to determine the routing path for wine through the cellar tank piping network that minimizes wine damage and optimizes cellar tank and piping network resources. Our two-phase approach first explored an optimization-based solution methodology. Next, computational issues associated with the size of Gallo's piping network led us to develop a heuristic-based solution approach capable of routing wine flows in seconds. Finally, we motivate some extensions to this research in order to fully adapt our research to the specific challenges faced at Gallo's Modesto wine production facility. [ABSTRACT FROM AUTHOR]