Map of the study sites.

<p>The left panel shows the location of Bangladesh in South Asia (the Indian subcontinent) and the right panel highlights the location of Dhaka and Matlab, the study sites in Bangladesh.</p

Cross wavelet coherence of global and local climatic time series with cholera in Dhaka by serotype O1 and O139.

<p>(A) Dipole mode index (DMI) and cholera O1; (B) DMI and cholera O139; (C) Nino3 and cholera O1; (D) Nino3 and cholera O139; (E) Sea surface temperature (SST) in the Bay of Bengal (°C) and cholera O1; (F) SST in the Bay of Bengal (°C) and cholera O139; (G) Rainfall (mm) and cholera O1; (H) Rainfall (mm) and cholera O139; (I) Temperature (°C) and cholera O1; (J) Temperature (°C) and cholera O139; (K) River level (m) and cholera O1; (L) River level (m) and cholera O139. Time series span from January 1983 to December 2008.</p

Cross wavelet analysis (CWA, coherence [C] and phase [P]) of sea surface temperature (SST) in the Bay of Bengal (°C) with local rainfall (mm) and temperature (°C).

<p>(A) CWAC of SST in the Bay of Bengal and Dhaka rainfall; (B) CWAC of SST in the Bay of Bengal and Matlab R; (C) CWAP of SST in the Bay of Bengal and Dhaka rainfall; (D) CWAP of SST in the Bay of Bengal and Matlab rainfall; (E) CWAC of SST in the Bay of Bengal and Dhaka temperature; (F) CWAC of SST in the Bay of Bengal and Matlab temperature; (G) CWAP of SST in the Bay of Bengal and Dhaka temperature; (H) CWAP of SST in the Bay of Bengal and Matlab temperature. In cross wavelet phase plots, colors correspond to different lags between the variability in the two series for a given time and frequency, measured in angles from -PI to PI. A value of PI corresponds to a lag of 16 months. Dhaka time series span from January 1983 to December 2008. Matlab time series span from November 1981 to December 2008.</p

Monthly time series data for Dhaka (January 1983–December 2008).

<p>(a) Total cholera cases; (b) Monthly rainfall (mm); (c) Monthly average of daily maximum temperature (°C); (d) Monthly average of daily mean river level (m) of Brigonga river (the data for 2008 for Millbarrak in Dhaka was missing).</p

Monthly time series data for Matlab (November 1981–December 2008).

<p>(a) Total cholera cases; (b) Monthly rainfall (mm); (c) Monthly average of daily maximum temperature (°C); (d) Monthly average of daily mean river level (m) of Danagoda river (the data for 2008 and before January 1983 for Matlab Bazar was missing).</p

Cross wavelet coherence of global and local climatic time series with cholera in Matlab by serotype O1 and O139.

<p>(A) Dipole mode index (DMI) and cholera O1; (B) DMI and cholera O139; (C) Nino3 and cholera O1; (D) Nino3 and cholera O139; (E) Sea surface temperature (SST) in the Bay of Bengal (°C) and cholera O1; (F) SST in the Bay of Bengal (°C) and cholera O139; (G) Rainfall (mm) and cholera O1; (H) Rainfall (mm) and cholera O139; (I) Temperature (°C) and cholera O1; (J) Temperature (°C) and cholera O139; (K) River level (m) and cholera O1; (L) River level (m) and cholera O139. Time series span from November 1981 to December 2008, with the exception of river level that begins in January 1983.</p

Monthly time series data for global climatic indices (November 1981–December 2008).

<p>(a) Sea surface temperature (SST) in the Bay of Bengal (°C); (b) Nino3; (c) Dipole mode index (DMI).</p

Personal exposure to household air pollution and lung function in rural Bangladesh: A population-based cross-sectional study

We assessed whether personal exposure to household air pollution [PM2.5 and black carbon (BC)] is associated with lung functions (FEV1, FVC, and their ratio) in non-smoking adults in rural Bangladesh. We measured personal exposure to PM2.5 using gravimetric analysis of PM2.5 mass and BC by reflectance measurement between April 2016 and June 2019. The average 24-hour PM2.5 and BC concentration was 141.0μgm−3 and 13.8μgm−3 for females, and 91.7 μgm−3 and 10.1 μgm−3 for males, respectively. A 1 μgm−3 increase in PM2.5 resulted in a 0.02 ml reduction in FEV1, 0.43 ml reduction in FVC, and 0.004% reduction in FEV1/FVC. We also found a similar inverse relationship between BC and lung functions (9.6 ml decrease in FEV1 and 18.5 ml decrease in FVC per 1μgm−3 increase in BC). A higher proportion of non-smoking biomass fuel users (50.1% of the females and 46.7% of the males) had restrictive patterns of lung function abnormalities, which need further exploration.</p

Association between the 10q24.32 genotyped variants and arsenical skin lesion risk and SNP-arsenic interaction estimates.

a<p>MA, minor allele.</p>b<p>Each Logistic Regression model includes one SNP, adjusting for age and sex.</p>c<p>The ROADTRIPS case-control test does not allow multivariate modeling (i.e., no adjustments), but accounts for cryptic relatedness.</p>d<p>Interaction P-values are from mixed linear models that account for relatedness among subjects. Interactions are on the additive scale and are calculated using data on 69 cases and incident 700 controls.</p

Multivariate associations between arsenic metabolites and genotyped SNPs in the 10q24.32 region showing the strongest univariate associations with DMA% and MMA% (n = 1,313).

<p>Regression models including all five SNPs in a single model, adjusting for age, sex, and water arsenic. All regressions were mixed models carried out using EMMAX.</p>a<p>MA, minor allele.</p>b<p>MAF, minor allele frequency.</p