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BackgroundThe See Me Smoke-Free (SMSF) mobile health (mHealth) app was developed to help women quit smoking by targeting concerns about body weight, body image, and self-efficacy through cognitive behavioral techniques and guided imagery audio files addressing smoking, diet, and physical activity. A feasibility trial found associations between SMSF usage and positive treatment outcomes. This paper reports a detailed exploration of program use among eligible individuals consenting to study participation and completing the baseline survey (participants) and ineligible or nonconsenting app installers (nonparticipants), as well as the relationship between program use and treatment outcomes. ObjectiveThe aim of this study was to determine whether (1) participants were more likely to set quit dates, be current smokers, and report higher levels of smoking at baseline than nonparticipants; (2) participants opened the app and listened to audio files more frequently than nonparticipants; and (3) participants with more app usage had a higher likelihood of self-reported smoking abstinence at follow up. MethodsThe SMSF feasibility trial was a single arm, within-subjects, prospective cohort study with assessments at baseline and 30 and 90 days post enrollment. The SMSF app was deployed on the Google Play Store for download, and basic profile characteristics were obtained for all app installers. Additional variables were assessed for study participants. Participants were prompted to use the app daily during study participation. Crude differences in baseline characteristics between trial participants and nonparticipants were evaluated using t tests (continuous variables) and Fisher exact tests (categorical variables). Exact Poisson tests were used to assess group-level differences in mean usage rates over the full study period using aggregate Google Analytics data on participation and usage. Negative binomial regression models were used to estimate associations of app usage with participant baseline characteristics after adjustment for putative confounders. Associations between app usage and self-reported smoking abstinence were assessed using separate logistic regression models for each outcome measure. ResultsParticipants (n=151) were more likely than nonparticipants (n=96) to report female gender (P

Practicing counselors will, at some point, work with clients from high-conflict separations/divorces (HCS/D); however, often they have received very little educational training for these experiences. To effectively treat these clients/client families, all counselors should have a working knowledge of the major processes and critical factors involved in these situations. This manuscript emphasizes the role of the counselor within HCS/D, seeks to give both an overview of and describe the complexities of HCS/D that counselors need to understand, and identifies areas of concern and focus for counselors functioning within distinct modalities. [ABSTRACT FROM AUTHOR]

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires. [ABSTRACT FROM AUTHOR]

Dengue transmission is determined by a complex set of interactions between the environment, Aedes aegypti mosquitoes, dengue viruses, and humans. Emergence in new geographic areas can be unpredictable, with some regions having established mosquito populations for decades without locally acquired transmission. Key factors such as mosquito longevity, temperature-driven extrinsic incubation period (EIP), and vector–human contact can strongly influence the potential for disease transmission. To assess how these factors interact at the edge of the geographical range of dengue virus transmission, we conducted mosquito sampling in multiple urban areas located throughout the Arizona–Sonora desert region during the summer rainy seasons from 2013 to 2015. Mosquito population age structure, reflecting mosquito survivorship, was measured using a combination of parity analysis and relative gene expression of an age-related gene, SCP-1. Bloodmeal analysis was conducted on field collected blood-fed mosquitoes. Site-specific temperature was used to estimate the EIP, and this predicted EIP combined with mosquito age were combined to estimate the abundance of "potential" vectors (i.e., mosquitoes old enough to survive the EIP). Comparisons were made across cities by month and year. The dengue endemic cities Hermosillo and Ciudad Obregon, both in the state of Sonora, Mexico, had higher abundance of potential vectors than non-endemic Nogales, Sonora, Mexico. Interestingly, Tucson, Arizona consistently had a higher estimated abundance of potential vectors than dengue endemic regions of Sonora, Mexico. There were no observed city-level differences in species composition of blood meals. Combined, these data offer insights into the critical factors required for dengue transmission at the ecological edge of the mosquito's range. However, further research is needed to integrate an understanding of how social and additional environmental factors constrain and enhance dengue transmission in emerging regions. [ABSTRACT FROM AUTHOR]

Specific relationships between verbal and nonverbal aspects of caregiver attention-focusing events and later verbal IQ were investigated for a risk sample of 26 very low birthweight [VLBW], preterm [PT] children. Videotaped interactions between VLBW, PT children at 2;0 and their caregivers were coded for caregiver attention-focusing speech and/or caregiver attention-focusing gestures (display, demonstration and pointing), caregiver gesture-speech combinations, and for child attention-sharing through gesture and social gaze. To investigate the specific effects of caregiver and child interactional factors, analyses statistically controlled for cognitive status. Simultaneous multiple regression analyses found that overall caregiver attention-focusing involving gesture, child attention-sharing behaviours, and cognitive status each significantly and uniquely contributed to verbal IQ at 3;0. Further analyses contrasted the contributions of caregiver gesture with relevant descriptive speech, caregiver gesture with no speech/nondescriptive speech, and caregiver pointing. Results of these analyses suggest that caregiver gesture with relevant descriptive speech makes a unique and positive contribution to later language performance.

We use the Weather Research and Forecasting with Chemistry (WRF-Chem) model with new implementations of GOES-16 wildfire emissions and plume rise based on fire radiative power (FRP) to interpret aerosol observations during the 2019 NASA-NOAA FIREX-AQ field campaign and perform model evaluations. We compare simulated aerosol concentrations and optical properties against observations of black carbon aerosol from the NOAA Single Particle Soot Photometer (NOAA-SP2), organic aerosol from the CU High-Resolution Aerosol Mass Spectrometer (HR-AMS), and aerosol backscatter coefficients from the high-spectral-resolution lidar (HSRL) system. This study focuses on the Williams Flats fire in Washington, which was repeatedly sampled during four science flights by the NASA DC-8 (3–8 August 2019). The emissions and plume-rise methodologies are implemented following NOAA's operational High-Resolution Rapid Refresh coupled with Smoke (HRRR-Smoke) forecasting model. In addition, new GOES-16 FRP-based diurnal cycle functions are developed and incorporated into WRF-Chem. The FIREX-AQ observations represented a diverse set of sampled environments ranging from fresh/aged smoke from the Williams Flats fire to remnants of plumes transported over long distances. The Williams Flats fire resulted in significant aerosol enhancements during 3–8 August 2019, which were substantially underestimated by the standard version of WRF-Chem. The simulated black carbon (BC) and organic carbon (OC) concentrations increased between a factor of 92–125 (BC) and a factor of 28–78 (OC) with the new implementation compared to the standard WRF-Chem version. These increases resulted in better agreement with the FIREX-AQ airborne observations for BC and OC concentrations (particularly for fresh smoke sampling phases) and aerosol backscatter coefficients. The model still showed a low bias in simulating the aerosol loadings observed in aged plumes from Williams Flats. WRF-Chem with the FRP-based plume rise simulated similar plume heights to the standard plume-rise model in WRF-Chem. The simulated plume heights (for both versions) compared well with estimated plume heights using the HSRL measurements. Therefore, the better agreement with observations was mainly driven by the higher emissions in the FRP-based version. The model evaluations also highlighted the importance of accurately accounting for the wildfire diurnal cycle and including adequate representation of the underlying chemical mechanisms, both of which could significantly impact model forecasting performance. [ABSTRACT FROM AUTHOR]

We use the Weather Research and Forecasting with Chemistry (WRF-Chem) model with new implementations of GOES-16 fire radiative power (FRP) based wildfire emissions and plume-rise to interpret aerosol observations during the 2019 NASA-NOAA FIREX-AQ field campaign and perform model evaluations. We compare simulated aerosol concentrations and optical properties against observations of black carbon aerosol from the NOAA Single Particle Soot Photometer (NOAA-SP2), organic aerosol from the CU High Resolution Aerosol Mass Spectrometer (HR36 AMS) and aerosol backscatter coefficients from the High Spectral Resolution Lidar (HSRL) system. This study focuses on the Williams Flats fire in Washington, which was repeatedly sampled during four science flights by the NASA DC-8 (August 3 – August 8, 2019). The emissions and plume-rise methodologies are implemented following NOAA’s operational High Resolution Rapid Refresh coupled with Smoke (HRRR-Smoke) forecasting model. In addition, new GOES-16 FRP based diurnal cycle functions are developed and incorporated in WRF-Chem. The FIREX-AQ observations represented a diverse set of sampled environments ranging from fresh/aged smoke from the Williams Flats fire to remnants of plumes transported over long distances. The Williams Flats fire resulted in significant aerosol enhancements during August 3- 8, 2019, which were substantially underestimated by the standard version of WRF-Chem. The simulated BC and OC concentrations increased between 92 – 125 times (BC) and 28-78 times (OC) with the new implementation compared to the standard WRF-Chem version. These increases resulted in better agreement with the FIREX-AQ airborne observations for BC and OC concentrations (particularly for fresh smoke sampling phases) and aerosol backscatter coefficients. The model still showed a low bias in simulating the aerosol loadings observed in aged plumes from Williams Flats. WRF-Chem with the FRP-based plumerise simulated similar plume heights to the standard plumerise model in WRF-52 Chem. The simulated plume heights (for both versions) compared well with estimated plume heights using the HSRL measurements. Therefore, the improvements in the model simulation were mainly driven by the higher emissions in the FRP55 based version. The model evaluations also highlighted the importance of accurately accounting for the wildfire diurnal cycle and including adequate representation of the underlying chemical mechanisms, both of which could significantly impact model forecasting performance. [ABSTRACT FROM AUTHOR]

Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 0·71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50·2% exceed this threshold for suitability in at least one 5 × 5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify. Author summary: As of 2018, it was unknown if onchocerciasis transmission occurred among approximately 2 400 implementation units (IUs; typically, second administrative-level units, such as districts) considered potentially endemic. These IUs have either never been surveyed for onchocerciasis or historical data are not sufficient to define contemporary endemicity status. Given the large number of IUs for which baseline data collection is likely required to achieve continental elimination, there is a need to prioritise areas for surveys to ensure that those suitable for endemic transmission, and therefore potentially eligible for mass drug administration, are able to initiate interventions as soon as possible. We used boosted regression trees to predict environmental suitability for onchocerciasis, with corresponding measures of uncertainty. We summarized the fine scale spatial predictions at the IU level by using receiver operating characteristic (ROC) curve analysis to identify a threshold that maximized agreement with the occurrence locations to identify IUs that may warrant prioritisation for mapping surveys. This analysis suggests that approximately half of the IUs considered for surveys could be classified as environmentally suitable for onchocerciasis. In order to develop an elimination strategy, many national onchocerciasis elimination programmes (NOEPs) need a mechanism to synthesise historical data to define priority areas for surveys. [ABSTRACT FROM AUTHOR]