Bayesian semiparametric mixed effects models for meta-analysis of the literature data : An application to cadmium toxicity studies.

We propose Bayesian semiparametric mixed effects models with measurement error to analyze the literature data collected from multiple studies in a meta-analytic framework. We explore this methodology for risk assessment in cadmium toxicity studies, where the primary objective is to investigate dose-response relationships between urinary cadmium concentrations and ß2 -microglobulin. In the proposed model, a nonlinear association between exposure and response is described by a Gaussian process with shape restrictions, and study-specific random effects are modeled to have either normal or unknown distributions with Dirichlet process mixture priors. In addition, nonparametric Bayesian measurement error models are incorporated to flexibly account for the uncertainty resulting from the usage of a surrogate measurement of a true exposure. We apply the proposed model to analyze cadmium toxicity data imposing shape constraints along with measurement errors and study-specific random effects across varying characteristics, such as population gender, age, or ethnicity.

Respiratory function declines in children with asthma associated with chemical species of fine particulate matter (PM2.5) in Nagasaki, Japan.

BACKGROUND: The differential effects of PM2.5 fractions on children's lung function remain inconclusive. This study aimed to examine whether lung function in asthmatic children was associated with increased PM2.5 fractions in urban areas in Nagasaki prefecture, Japan, where the air pollution level is relatively low but influenced by transboundary air pollution. METHODS: We conducted a multiyear panel study of 73 asthmatic children (boys, 60.3%; mean age, 8.2 years) spanning spring 2014-2016 in two cities. We collected self-measured peak expiratory flow (PEF) twice a day and daily time-series data for PM2.5 total mass and its chemical species. We fitted a linear mixed effects model to examine short-term associations between PEF and PM2.5, adjusting for individual and time-varying confounders. A generalized linear mixed effects model was also used to estimate the association for worsening asthma defined by severe PEF decline. Back-trajectory and cluster analyses were used to investigate the long-range transboundary PM2.5 in the study areas. RESULTS: We found that morning PEFs were adversely associated with higher levels of sulfate (- 1.61 L/min; 95% CI: - 3.07, - 0.15) in Nagasaki city and organic carbon (OC) (- 1.02 L/min; 95% CI: - 1.94, - 0.09) in Isahaya city, per interquartile range (IQR) increase at lag1. In addition, we observed consistent findings for worsening asthma, with higher odds of severe PEF decline in the morning for sulfate (odds ratio (OR) = 2.31; 95% CI: 1.12, 4.77) and ammonium (OR = 1.73; 95% CI: 1.06, 2.84) in Nagasaki city and OC (OR = 1.51; 95% CI: 1.06, 2.15) in Isahaya city, per IQR increase at lag1. The significant chemical species were higher on days that could be largely attributed to the path of Northeast China origin (for sulfate and ammonium) or both the same path and local sources (for OC) than by other clusters. CONCLUSIONS: This study provides evidence of the differential effects of PM2.5 fractions on lung function among asthmatic children in urban areas, where the Japanese national standards of air quality have been nearly met. Continuous efforts to promote mitigation actions and public awareness of hazardous transboundary air pollution are needed to protect susceptible children with asthma.

Nonparametric Bayesian functional two-part random effects model for longitudinal semicontinuous data analysis.

Longitudinal semicontinuous data, characterized by repeated measures of a large portion of zeros and continuous positive values, are frequently encountered in many applications including biomedical, epidemiological, and social science studies. Two-part random effects models (TPREM) have been used to investigate the association between such longitudinal semicontinuous data and covariates accounting for the within-subject correlation. The existing TPREM is, however, limited to incorporate a functional covariate, which is often available in a longitudinal study. Moreover, the existing TPREM typically assumes the normality of subject-specific random effects, which can be easily violated when there exists a subgroup structure. In this article, we propose a nonparametric Bayesian functional TPREM to assess the relationship between the longitudinal semicontinuous outcome and various types of covariates including a functional covariate. The proposed model also relaxes the normality assumption for the random effects through a Dirichlet process mixture of normals, which allows for identifying an underlying subgroup structure. The methodology is illustrated through an application to social insurance expenditure data collected by the Korean Welfare Panel Study and a simulation study.

Seasonality of mortality under a changing climate: a time-series analysis of mortality in Japan between 1972 and 2015.

BACKGROUND: Ambient temperature may contribute to seasonality of mortality; in particular, a warming climate is likely to influence the seasonality of mortality. However, few studies have investigated seasonality of mortality under a warming climate. METHODS: Daily mean temperature, daily counts for all-cause, circulatory, and respiratory mortality, and annual data on prefecture-specific characteristics were collected for 47 prefectures in Japan between 1972 and 2015. A quasi-Poisson regression model was used to assess the seasonal variation of mortality with a focus on its amplitude, which was quantified as the ratio of mortality estimates between the peak and trough days (peak-to-trough ratio (PTR)). We quantified the contribution of temperature to seasonality by comparing PTR before and after temperature adjustment. Associations between annual mean temperature and annual estimates of the temperature-unadjusted PTR were examined using multilevel multivariate meta-regression models controlling for prefecture-specific characteristics. RESULTS: The temperature-unadjusted PTRs for all-cause, circulatory, and respiratory mortality were 1.28 (95% confidence interval (CI): 1.27-1.30), 1.53 (95% CI: 1.50-1.55), and 1.46 (95% CI: 1.44-1.48), respectively; adjusting for temperature reduced these PTRs to 1.08 (95% CI: 1.08-1.10), 1.10 (95% CI: 1.08-1.11), and 1.35 (95% CI: 1.32-1.39), respectively. During the period of rising temperature (1.3 °C on average), decreases in the temperature-unadjusted PTRs were observed for all mortality causes except circulatory mortality. For each 1 °C increase in annual mean temperature, the temperature-unadjusted PTR for all-cause, circulatory, and respiratory mortality decreased by 0.98% (95% CI: 0.54-1.42), 1.39% (95% CI: 0.82-1.97), and 0.13% (95% CI: - 1.24 to 1.48), respectively. CONCLUSION: Seasonality of mortality is driven partly by temperature, and its amplitude may be decreasing under a warming climate.

Interactive Effect of Diurnal Temperature Range and Temperature on Mortality, Northeast Asia.

BACKGROUND: The diurnal temperature range (DTR) represents temperature variability within a day and has been reported as a potential risk factor for mortality. Previous studies attempted to identify the role of temperature in the DTR-mortality association, but results are inconclusive. The aim of this study was to investigate the interactive effect of temperature and DTR on mortality using a multicountry time series analysis. METHODS: We collected time series data for mortality and weather variables for 57 communities of three countries (Taiwan, Korea, and Japan) in Northeast Asia (1972-2012). Two-stage time series regression with a distributed lag nonlinear model and meta-analysis was used to estimate the DTR-mortality association changing over temperature strata (six strata were defined based on community-specific temperature percentiles). We first investigated the whole population and then, the subpopulations defined by temperature distribution (cold and warm regions), sex, and age group (people <65 and ≥65 years of age), separately. RESULTS: The DTR-mortality association changed over temperature strata. The relative risk (RR) of mortality for 10°C increase in DTR was larger for high-temperature strata compared with cold-temperature strata (e.g., = 1.050; 95% confidence interval [CI] = 1.040, 1.060 at extreme-hot stratum and RR = 1.040; 95% CI = 1.031, 1.050 at extreme-cold stratum); extreme-hot and -cold strata were defined as the days with daily mean temperature above 90th and below 10th percentiles each community's temperature distribution. Such increasing pattern was more pronounced in cold region and in people who were 65 years or older. CONCLUSIONS: We found evidence that the DTR-related mortality may increase as temperature increases.

Temporal Changes in Mortality Related to Extreme Temperatures for 15 Cities in Northeast Asia: Adaptation to Heat and Maladaptation to Cold.

Understanding how the temperature-mortality association worldwide changes over time is crucial to addressing questions of human adaptation under climate change. Previous studies investigated the temporal changes in the association over a few discrete time frames or assumed a linear change. Also, most studies focused on attenuation of heat-related mortality and studied the United States or Europe. This research examined continuous temporal changes (potentially nonlinear) in mortality related to extreme temperature (both heat and cold) for 15 cities in Northeast Asia (1972-2009). We used a generalized linear model with splines to simultaneously capture 2 types of nonlinearity: nonlinear association between temperature and mortality and nonlinear change over time in the association. We combined city-specific results to generate country-specific results using Bayesian hierarchical modeling. Cold-related mortality remained roughly constant over decades and slightly increased in the late 2000s, with a larger increase for cardiorespiratory deaths than for deaths from other causes. Heat-related mortality rates have decreased continuously over time, with more substantial decrease in earlier decades, for older populations and for cardiorespiratory deaths. Our findings suggest that future assessment of health effects of climate change should account for the continuous changes in temperature-related health risk and variations by factors such as age, cause of death, and location.

Monte Carlo simulation-based estimation for the minimum mortality temperature in temperature-mortality association study.

BACKGROUND: Rich literature has reported that there exists a nonlinear association between temperature and mortality. One important feature in the temperature-mortality association is the minimum mortality temperature (MMT). The commonly used approach for estimating the MMT is to determine the MMT as the temperature at which mortality is minimized in the estimated temperature-mortality association curve. Also, an approximate bootstrap approach was proposed to calculate the standard errors and the confidence interval for the MMT. However, the statistical properties of these methods were not fully studied. METHODS: Our research assessed the statistical properties of the previously proposed methods in various types of the temperature-mortality association. We also suggested an alternative approach to provide a point and an interval estimates for the MMT, which improve upon the previous approach if some prior knowledge is available on the MMT. We compare the previous and alternative methods through a simulation study and an application. In addition, as the MMT is often used as a reference temperature to calculate the cold- and heat-related relative risk (RR), we examined how the uncertainty in the MMT affects the estimation of the RRs. RESULTS: The previously proposed method of estimating the MMT as a point (indicated as Argmin2) may increase bias or mean squared error in some types of temperature-mortality association. The approximate bootstrap method to calculate the confidence interval (indicated as Empirical1) performs properly achieving near 95% coverage but the length can be unnecessarily extremely large in some types of the association. We showed that an alternative approach (indicated as Empirical2), which can be applied if some prior knowledge is available on the MMT, works better reducing the bias and the mean squared error in point estimation and achieving near 95% coverage while shortening the length of the interval estimates. CONCLUSIONS: The Monte Carlo simulation-based approach to estimate the MMT either as a point or as an interval was shown to perform well particularly when some prior knowledge is incorporated to reduce the uncertainty. The MMT uncertainty also can affect the estimation for the MMT-referenced RR and ignoring the MMT uncertainty in the RR estimation may lead to invalid results with respect to the bias in point estimation and the coverage in interval estimation.

Identifying Sensitive Periods for Alcohol Use: The Roles of Timing and Chronicity of Child Physical Abuse.

BACKGROUND: Childhood maltreatment, particularly physical abuse (PA), increases the risk of alcohol use during young adulthood. Although prior research underscores the importance of examining the roles of PA-event characteristics such as timing of and chronicity of PA in initiating and maintaining alcohol use, few studies have explored the risk of developing alcohol use based on the timing and chronicity of PA. METHODS: Using a community sample of 300 young adults (ages 18 to 25), this study examined how variations in timing and chronicity of PA relate to 4 distinct drinking behaviors including drinking frequency, binge drinking, alcohol-related problems, and alcohol use disorder (AUD) in the past 12 months. We controlled for sociodemographic information, other types of maltreatment, and common risk factors for alcohol use, such as psychological distress, parental alcoholism, and peer alcohol use in all analyses. This study used person-centered and developmental-stage-based characterizations of PA timing and chronicity to explore the relationship between timing and chronicity of PA and later drinking behaviors. RESULTS: Overall, individuals who were physically abused, particularly during adolescence, and who chronically experienced PA, reported higher levels of monthly drinking frequency and more pathological drinking behaviors such as binge drinking, alcohol-related problems, and AUD. CONCLUSIONS: This study describes the specific roles of timing and chronicity of PA in understanding the increased vulnerability to alcohol use among victims of PA. Our findings suggest that PA during adolescence and chronic PA are related to problematic drinking behaviors in young adulthood.

Mortality related to extreme temperature for 15 cities in northeast Asia.

BACKGROUND: Multisite time-series studies for temperature-related mortality have been conducted mainly in the United States and Europe, but are lacking in Asia. This multisite time-series study examined mortality related to extreme temperatures (both cold and hot) in Northeast Asia, focusing on 15 cities of 3 high-income countries. METHODS: This study includes 3 cities in Taiwan for 1994-2007, 6 cities in Korea for 1992-2010, and 6 cities in Japan for 1972-2009. We used 2-stage Bayesian hierarchical Poisson semiparametric regression to model the nonlinear relationship between temperature and mortality, providing city-specific and country-wide estimates for cold and heat effects. Various exposure time frames, age groups, and causes of death were considered. RESULTS: Cold effects had longer time lags (5-11 days) than heat effects, which were immediate (1-3 days). Cold effects were larger for cities in Taiwan, whereas heat effects were larger for cities in Korea and Japan. Patterns of increasing effects with age were observed in both cold and heat effects. Both cold and heat effects were larger for cardiorespiratory mortality than for other causes of death. Several city characteristics related to weather or air pollution were associated with both cold and heat effects. CONCLUSIONS: Mortality increased with either cold or hot temperature in urban populations of high-income countries in Northeast Asia, with spatial variations of effects among cities and countries. Findings suggest that climate factors are major contributors to the spatial heterogeneity of effects in this region, although further research is merited to identify other factors as determinants of variability.

Change in employment status and its causal effect on suicidal ideation and depressive symptoms: A marginal structural model with machine learning algorithms.

OBJECTIVE: This study aimed to assess the causal effect of a change in employment status on suicidal ideation and depressive symptoms by applying marginal structural models (MSM) with machine-learning (ML) algorithms. METHODS: We analyzed data from the 8-15th waves (2013-2020) of the Korean Welfare Panel Study, a nationally representative longitudinal dataset. Our analysis included 13 294 observations from 3621 participants who had standard employment at baseline (2013-2019). Based on employment status at follow-up year (2014-2020), respondents were classified into two groups: (i) maintained standard employment (reference group), (ii) changed to non-standard employment. Suicidal ideation during the past year and depressive symptoms during the past week were assessed through self-report questionnaire. To apply the ML algorithms to the MSM, we conducted eight ML algorithms to build the propensity score indicating a change in employment status. Then, we applied the MSM to examine the causal effect by using inverse probability weights calculated based on the propensity score from ML algorithms. RESULTS: The random forest algorithm performed best among all algorithms, showing the highest area under the curve 0.702, 95% confidence interval (CI) 0.686-0.718. In the MSM with the random forest algorithm, workers who changed from standard to non-standard employment were 2.07 times more likely to report suicidal ideation compared to those who maintained standard employment (95% CI 1.16-3.70). A similar trend was observed in the analysis of depressive symptoms. CONCLUSIONS: This study found that a change in employment status could lead to a higher risk of suicidal ideation and depressive symptoms.

Impulsivity and substance use in young adulthood.

BACKGROUND AND OBJECTIVES: Although the significant role of impulsive personality on substance use has been widely documented, distinct aspects of impulsivity and their relation to substance use during young adulthood have received scant attention. We sought to fill this research gap and explored impulsivity subtraits in relation to hazardous drinking and illicit substance use in young adulthood. METHODS: The present study used cross-sectional survey data collected from a community sample of 257 young adults aged 18-25 (53% female). Data were primarily analyzed by three different types of regression models. RESULTS: We found impulsivity to be more strongly associated with illicit substance use than hazardous drinking during young adulthood. Among the four impulsivity subtraits examined, lack of premeditation (p < .01) and sensation seeking (p < .05) were significantly related to illicit substance use in young adulthood. CONCLUSIONS AND SIGNIFICANCE: These findings suggest that each impulsivity subtrait might play different roles in illicit substance use during young adulthood. For young adults, assessment of differential patterns of impulsive personality in individuals with substance use problems may have important implication for treatment strategies.

Effects of fresh and aged vehicular exhaust emissions on breathing pattern and cellular responses--pilot single vehicle study.

The study presented here is a laboratory pilot study using diluted car exhaust from a single vehicle to assess differences in toxicological response between primary emissions and secondary products resulting from atmospheric photochemical reactions of gas phase compounds with O3, OH and other radicals. Sprague Dawley rats were exposed for 5 h to either filtered room air (sham) or one of two different atmospheres: (i) diluted car exhaust (P)+Mt. Saint Helens Ash (MSHA); (ii) P+MSHA+secondary organic aerosol (SOA, formed during simulated photochemical aging of diluted exhaust). Primary and secondary gases were removed using a nonselective diffusion denuder. Continuous respiratory data was collected during the exposure, and bronchoalveolar lavage (BAL) and complete blood counts (CBC) were performed 24 h after exposure. ANOVA models were used to assess the exposure effect and to compare those effects across different exposure types. Total average exposures were 363 ± 66 µg/m³ P+MSHA and 212 ± 95 µg/m³ P+MSHA+SOA. For both exposures, we observed decreases in breathing rate, tidal and minute volumes (TV, MV) and peak and median flows (PIF, PEF and EF50) along with increases in breathing cycle times (Ti, Te) compared to sham. These results indicate that the animals are changing their breathing pattern with these test atmospheres. Exposure to P+MSHA+SOA produced significant increases in total cells, macrophages and neutrophils in the BAL and in vivo chemiluminescence of the lung. There were no significant differences in CBC parameters. Our data suggest that simulated atmospheric photochemistry, producing SOA in the P+MSHA+SOA exposures, enhanced the toxicity of vehicular emissions.

Assessing seasonality and the role of its potential drivers in environmental epidemiology: a tutorial.

Several methods have been used to assess the seasonality of health outcomes in epidemiological studies. However, little information is available on the methods to study the changes in seasonality before and after adjusting for environmental or other known seasonally varying factors. Such investigations will help us understand the role of these factors in seasonal variation in health outcomes and further identify currently unknown or unmeasured risk factors. This tutorial illustrates a statistical procedure for examining the seasonality of health outcomes and their changes, after adjusting for potential environmental drivers by assessing and comparing shape, timings and size. We recommend a three-step procedure, each carried out and compared before and after adjustment: (i) inspecting the fitted seasonal curve to determine the broad shape of seasonality; (ii) identifying the peak and trough of seasonality to determine the timings of seasonality; and (iii) estimating the peak-to-trough ratio and attributable fraction to measure the size of seasonality. Reporting changes in these features on adjusting for potential drivers allows readers to understand their role in seasonality and the nature of any residual seasonal pattern. Furthermore, the proposed approach can be extended to other health outcomes and environmental drivers.

Seasonal variation in mortality and the role of temperature: a multi-country multi-city study.

BACKGROUND: Although seasonal variations in mortality have been recognized for millennia, the role of temperature remains unclear. We aimed to assess seasonal variation in mortality and to examine the contribution of temperature. METHODS: We compiled daily data on all-cause, cardiovascular and respiratory mortality, temperature and indicators on location-specific characteristics from 719 locations in tropical, dry, temperate and continental climate zones. We fitted time-series regression models to estimate the amplitude of seasonal variation in mortality on a daily basis, defined as the peak-to-trough ratio (PTR) of maximum mortality estimates to minimum mortality estimates at day of year. Meta-analysis was used to summarize location-specific estimates for each climate zone. We estimated the PTR with and without temperature adjustment, with the differences representing the seasonal effect attributable to temperature. We also evaluated the effect of location-specific characteristics on the PTR across locations by using meta-regression models. RESULTS: Seasonality estimates and responses to temperature adjustment varied across locations. The unadjusted PTR for all-cause mortality was 1.05 [95% confidence interval (CI): 1.00-1.11] in the tropical zone and 1.23 (95% CI: 1.20-1.25) in the temperate zone; adjusting for temperature reduced the estimates to 1.02 (95% CI: 0.95-1.09) and 1.10 (95% CI: 1.07-1.12), respectively. Furthermore, the unadjusted PTR was positively associated with average mean temperature. CONCLUSIONS: This study suggests that seasonality of mortality is importantly driven by temperature, most evidently in temperate/continental climate zones, and that warmer locations show stronger seasonal variations in mortality, which is related to a stronger effect of temperature.

The local Dirichlet process.

As a generalization of the Dirichlet process (DP) to allow predictor dependence, we propose a local Dirichlet process (lDP). The lDP provides a prior distribution for a collection of random probability measures indexed by predictors. This is accomplished by assigning stick-breaking weights and atoms to random locations in a predictor space. The probability measure at a given predictor value is then formulated using the weights and atoms located in a neighborhood about that predictor value. This construction results in a marginal DP prior for the random measure at any specific predictor value. Dependence is induced through local sharing of random components. Theoretical properties are considered and a blocked Gibbs sampler is proposed for posterior computation in lDP mixture models. The methods are illustrated using simulated examples and an epidemiologic application.

Mosquito abundance in relation to extremely high temperatures in urban and rural areas of Incheon Metropolitan City, South Korea from 2015 to 2020: an observational study.

BACKGROUND: Despite concerns regarding increasingly frequent and intense heat waves due to global warming, there is still a lack of information on the effects of extremely high temperatures on the adult abundance of mosquito species that are known to transmit vector-borne diseases. This study aimed to evaluate the effects of extremely high temperatures on the abundance of mosquitoes by analyzing time series data for temperature and mosquito abundance in Incheon Metropolitan City (IMC), Republic of Korea, for the period from 2015 to 2020. METHODS: A generalized linear model with Poisson distribution and overdispersion was used to model the nonlinear association between temperature and mosquito count for the whole study area and for its constituent urban and rural regions. The association parameters were pooled using multivariate meta-regression. The temperature-mosquito abundance curve was estimated from the pooled estimates, and the ambient temperature at which mosquito populations reached maximum abundance (TMA) was estimated using a Monte Carlo simulation method. To quantify the effect of extremely high temperatures on mosquito abundance, we estimated the mosquito abundance ratio (AR) at the 99th temperature percentile (AR99th) against the TMA. RESULTS: Culex pipiens was the most common mosquito species (51.7%) in the urban region of the IMC, while mosquitoes of the genus Aedes (Ochlerotatus) were the most common in the rural region (47.8%). Mosquito abundance reached a maximum at 23.5 °C for Cx. pipiens and 26.4 °C for Aedes vexans. Exposure to extremely high temperatures reduced the abundance of Cx. pipiens mosquitoes {AR99th 0.34 [95% confidence interval (CI) 0.21-0.54]} to a greater extent than that of Anopheles spp. [AR99th 0.64 (95% CI 0.40-1.03)]. When stratified by region, Ae. vexans and Ochlerotatus koreicus mosquitoes showed higher TMA and a smaller reduction in abundance at extreme heat in urban Incheon than in Ganghwa, suggesting that urban mosquitoes can thrive at extremely high temperatures as they adapt to urban thermal environments. CONCLUSIONS: We confirmed that the temperature-related abundance of the adult mosquitoes was species and location specific. Tailoring measures for mosquito prevention and control according to mosquito species and anticipated extreme temperature conditions would help to improve the effectiveness of mosquito-borne disease control programs.

Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach.

OBJECTIVES: This study was conducted to examine gender differences in under-reporting hiring discrimination by building a prediction model for workers who responded "not applicable (NA)" to a question about hiring discrimination despite being eligible to answer. METHODS: Using data from 3,576 wage workers in the seventh wave (2004) of the Korea Labor and Income Panel Study, we trained and tested 9 machine learning algorithms using "yes" or "no" responses regarding the lifetime experience of hiring discrimination. We then applied the best-performing model to estimate the prevalence of experiencing hiring discrimination among those who answered "NA." Under-reporting of hiring discrimination was calculated by comparing the prevalence of hiring discrimination between the "yes" or "no" group and the "NA" group. RESULTS: Based on the predictions from the random forest model, we found that 58.8% of the "NA" group were predicted to have experienced hiring discrimination, while 19.7% of the "yes" or "no" group reported hiring discrimination. Among the "NA" group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively. CONCLUSIONS: This study introduces a methodological strategy for epidemiologic studies to address the under-reporting of discrimination by applying machine learning algorithms.

Early Chronic Memantine Treatment-Induced Transcriptomic Changes in Wild-Type and Shank2-Mutant Mice.

Shank2 is an excitatory postsynaptic scaffolding protein strongly implicated in autism spectrum disorders (ASDs). Shank2-mutant mice with a homozygous deletion of exons 6 and 7 (Shank2-KO mice) show decreased NMDA receptor (NMDAR) function and autistic-like behaviors at juvenile [∼postnatal day (P21)] and adult (>P56) stages that are rescued by NMDAR activation. However, at ∼P14, these mice show the opposite change - increased NMDAR function; moreover, suppression of NMDAR activity with early, chronic memantine treatment during P7-21 prevents NMDAR hypofunction and autistic-like behaviors at later (∼P21 and >P56) stages. To better understand the mechanisms underlying this rescue, we performed RNA-Seq gene-set enrichment analysis of forebrain transcriptomes from wild-type (WT) and Shank2-KO juvenile (P25) mice treated early and chronically (P7-21) with vehicle or memantine. Vehicle-treated Shank2-KO mice showed upregulation of synapse-related genes and downregulation of ribosome- and mitochondria-related genes compared with vehicle-treated WT mice. They also showed a transcriptomic pattern largely opposite that observed in ASD (reverse-ASD pattern), based on ASD-related/risk genes and cell-type-specific genes. In memantine-treated Shank2-KO mice, chromatin-related genes were upregulated; mitochondria, extracellular matrix (ECM), and actin-related genes were downregulated; and the reverse-ASD pattern was weakened compared with that in vehicle-treated Shank2-KO mice. In WT mice, memantine treatment, which does not alter NMDAR function, upregulated synaptic genes and downregulated ECM genes; memantine-treated WT mice also exhibited a reverse-ASD pattern. Therefore, early chronic treatment of Shank2-KO mice with memantine alters expression of chromatin, mitochondria, ECM, actin, and ASD-related genes.

Robust and distributed neural representation of action values.

Studies in rats, monkeys, and humans have found action-value signals in multiple regions of the brain. These findings suggest that action-value signals encoded in these brain structures bias choices toward higher expected rewards. However, previous estimates of action-value signals might have been inflated by serial correlations in neural activity and also by activity related to other decision variables. Here, we applied several statistical tests based on permutation and surrogate data to analyze neural activity recorded from the striatum, frontal cortex, and hippocampus. The results show that previously identified action-value signals in these brain areas cannot be entirely accounted for by concurrent serial correlations in neural activity and action value. We also found that neural activity related to action value is intermixed with signals related to other decision variables. Our findings provide strong evidence for broadly distributed neural signals related to action value throughout the brain.

Geographical Variations of the Minimum Mortality Temperature at a Global Scale: A Multicountry Study.

BACKGROUND: Minimum mortality temperature (MMT) is an important indicator to assess the temperature-mortality association, indicating long-term adaptation to local climate. Limited evidence about the geographical variability of the MMT is available at a global scale. METHODS: We collected data from 658 communities in 43 countries under different climates. We estimated temperature-mortality associations to derive the MMT for each community using Poisson regression with distributed lag nonlinear models. We investigated the variation in MMT by climatic zone using a mixed-effects meta-analysis and explored the association with climatic and socioeconomic indicators. RESULTS: The geographical distribution of MMTs varied considerably by country between 14.2 and 31.1 °C decreasing by latitude. For climatic zones, the MMTs increased from alpine (13.0 °C) to continental (19.3 °C), temperate (21.7 °C), arid (24.5 °C), and tropical (26.5 °C). The MMT percentiles (MMTPs) corresponding to the MMTs decreased from temperate (79.5th) to continental (75.4th), arid (68.0th), tropical (58.5th), and alpine (41.4th). The MMTs indreased by 0.8 °C for a 1 °C rise in a community's annual mean temperature, and by 1 °C for a 1 °C rise in its SD. While the MMTP decreased by 0.3 centile points for a 1 °C rise in a community's annual mean temperature and by 1.3 for a 1 °C rise in its SD. CONCLUSIONS: The geographical distribution of the MMTs and MMTPs is driven mainly by the mean annual temperature, which seems to be a valuable indicator of overall adaptation across populations. Our results suggest that populations have adapted to the average temperature, although there is still more room for adaptation.