Effects of a liquefied petroleum gas stove intervention on gestational blood pressure: Intention-to-treat and exposure-response findings from the HAPIN trial

BACKGROUND: Approximately 3 to 4 billion people worldwide are exposed to household air pollution, which has been associated with increased blood pressure (BP) in pregnant women in some studies. METHODS: We recruited 3195 pregnant women in Guatemala, India, Peru, and Rwanda and randomly assigned them to intervention or control groups. The intervention group received a gas stove and fuel during pregnancy, while the controls continued cooking with solid fuels. We measured BP and personal exposure to PM RESULTS: Median 24-hour PM CONCLUSIONS: In intention-to-treat, we found higher gestational BP in the intervention group compared with controls, contrary to expected. In exposure-response analyses, we found a slight increase in BP with higher exposure, but it was not statistically significant. Overall, an intervention with gas stoves did not markedly affect gestational BP

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.Comment: Accepted by AAAI 2023, appendix included. 9 pages + 5 pages appendix, 12 figures, in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23), Feb 202

Modeling the Impact of an Indoor Air Filter on Air Pollution Exposure Reduction and Associated Mortality in Urban Delhi Household.

Indoor exposure to fine particulate matter (PM2.5) is a prominent health concern. However, few studies have examined the effectiveness of long-term use of indoor air filters for reduction of PM2.5 exposure and associated decrease in adverse health impacts in urban India. We conducted 20 simulations of yearlong personal exposure to PM2.5 in urban Delhi using the National Institute of Standards and Technology's CONTAM program (NIST, Gaithersburg, MD, USA). Simulation scenarios were developed to examine different air filter efficiencies, use schedules, and the influence of a smoker at home. We quantified associated mortality reductions with Household Air Pollution Intervention Tool (HAPIT, University of California, Berkeley, CA, USA). Without an air filter, we estimated an annual mean PM2.5 personal exposure of 103 µg/m3 (95% Confidence Interval (CI): 93, 112) and 137 µg/m3 (95% CI: 125, 149) for households without and with a smoker, respectively. All day use of a high-efficiency particle air (HEPA) filter would reduce personal PM2.5 exposure to 29 µg/m3 and 30 µg/m3, respectively. The reduced personal PM2.5 exposure from air filter use is associated with 8-37% reduction in mortality attributable to PM2.5 pollution in Delhi. The findings of this study indicate that air filter may provide significant improvements in indoor air quality and result in health benefits

Interventions to improve sanitation for preventing diarrhoea

BACKGROUND: Diarrhoea is a major contributor to the global disease burden, particularly amongst children under five years in low- and middle-income countries (LMICs). As many of the infectious agents associated with diarrhoea are transmitted through faeces, sanitation interventions to safely contain and manage human faeces have the potential to reduce exposure and diarrhoeal disease. OBJECTIVES: To assess the effectiveness of sanitation interventions for preventing diarrhoeal disease, alone or in combination with other WASH interventions. SEARCH METHODS: We searched the Cochrane Infectious Diseases Group Specialized Register, CENTRAL, MEDLINE, Embase, LILACS, and Chinese language databases available under the China National Knowledge Infrastructure (CNKI-CAJ). We also searched the metaRegister of Controlled Trials (mRCT) and conference proceedings, contacted researchers, and searched references of included studies. The last search date was 16 February 2022. SELECTION CRITERIA: We included randomized controlled trials (RCTs), quasi-RCTs, non-randomized controlled trials (NRCTs), controlled before-and-after studies (CBAs), and matched cohort studies of interventions aimed at introducing or expanding the coverage and/or use of sanitation facilities in children and adults in any country or population. Our primary outcome of interest was diarrhoea and secondary outcomes included dysentery (bloody diarrhoea), persistent diarrhoea, hospital or clinical visits for diarrhoea, mortality, and adverse events. We included sanitation interventions whether they were conducted independently or in combination with other interventions. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed eligible studies, extracted relevant data, assessed risk of bias, and assessed the certainty of evidence using the GRADE approach. We used meta-analyses to estimate pooled measures of effect, described results narratively, and investigated potential sources of heterogeneity using subgroup analyses. MAIN RESULTS: Fifty-one studies met our inclusion criteria, with a total of 238,535 participants. Of these, 50 studies had sufficient information to be included in quantitative meta-analysis, including 17 cluster-RCTs and 33 studies with non-randomized study designs (20 NRCTs, one CBA, and 12 matched cohort studies). Most were conducted in LMICs and 86% were conducted in whole or part in rural areas. Studies covered three broad types of interventions: (1) providing access to any sanitation facility to participants without existing access practising open defecation, (2) improving participants' existing sanitation facility, or (3) behaviour change messaging to improve sanitation access or practices without providing hardware or subsidy, although many studies overlapped multiple categories. There was substantial heterogeneity amongst individual study results for all types of interventions. Providing access to any sanitation facility Providing access to sanitation facilities was evaluated in seven cluster-RCTs, and may reduce diarrhoea prevalence in all age groups (risk ratio (RR) 0.89, 95% confidence interval (CI) 0.73 to 1.08; 7 trials, 40,129 participants, low-certainty evidence). In children under five years, access may have little or no effect on diarrhoea prevalence (RR 0.98, 95% CI 0.83 to 1.16, 4 trials, 16,215 participants, low-certainty evidence). Additional analysis in non-randomized studies was generally consistent with these findings. Pooled estimates across randomized and non-randomized studies provided similar protective estimates (all ages: RR 0.79, 95% CI 0.66 to 0.94; 15 studies, 73,511 participants; children < 5 years: RR 0.83, 95% CI 0.68 to 1.02; 11 studies, 25,614 participants).  Sanitation facility improvement Interventions designed to improve existing sanitation facilities were evaluated in three cluster-RCTs in children under five and may reduce diarrhoea prevalence (RR 0.85, 95% CI 0.69 to 1.06; 3 trials, 14,900 participants, low-certainty evidence). However, some of these interventions, such as sewerage connection, are not easily randomized. Non-randomized studies across participants of all ages provided estimates that improving sanitation facilities may reduce diarrhoea, but may be subject to confounding (RR 0.61, 95% CI 0.50 to 0.74; 23 studies, 117,639 participants, low-certainty evidence). Pooled estimates across randomized and non-randomized studies provided similar protective estimates (all ages: RR 0.65, 95% CI 0.55 to 0.78; 26 studies, 132,539 participants; children < 5 years: RR 0.70, 95% CI 0.54 to 0.91, 12 studies, 23,353 participants).  Behaviour change messaging only (no hardware or subsidy provided) Strategies to promote behaviour change to construct, upgrade, or use sanitation facilities were evaluated in seven cluster-RCTs in children under five, and probably reduce diarrhoea prevalence (RR 0.82, 95% CI 0.69 to 0.98; 7 studies, 28,909 participants, moderate-certainty evidence). Additional analysis from two non-randomized studies found no effect, though with very high uncertainty. Pooled estimates across randomized and non-randomized studies provided similar protective estimates (RR 0.85, 95% CI 0.73 to 1.01; 9 studies, 31,080 participants). No studies measured the effects of this type of intervention in older populations.  Any sanitation intervention A pooled analysis of cluster-RCTs across all sanitation interventions demonstrated that the interventions may reduce diarrhoea prevalence in all ages (RR 0.85, 95% CI 0.76 to 0.95, 17 trials, 83,938 participants, low-certainty evidence) and children under five (RR 0.87, 95% CI 0.77 to 0.97; 14 trials, 60,024 participants, low-certainty evidence). Non-randomized comparisons also demonstrated a protective effect, but may be subject to confounding. Pooled estimates across randomized and non-randomized studies provided similar protective estimates (all ages: RR 0.74, 95% CI 0.67 to 0.82; 50 studies, 237,130 participants; children < 5 years: RR 0.80, 95% CI 0.71 to 0.89; 32 studies, 80,047 participants). In subgroup analysis, there was some evidence of larger effects in studies with increased coverage amongst all participants (75% or higher coverage levels) and also some evidence that the effect decreased over longer follow-up times for children under five years. There was limited evidence on other outcomes. However, there was some evidence that any sanitation intervention was protective against dysentery (RR 0.74, 95% CI 0.54 to 1.00; 5 studies, 34,025 participants) and persistent diarrhoea (RR 0.57, 95% CI 0.43 to 0.75; 2 studies, 2665 participants), but not against clinic visits for diarrhoea (RR 0.86, 95% CI 0.44 to 1.67; 2 studies, 3720 participants) or all-cause mortality (RR 0.99, 95% CI 0.89 to1.09; 7 studies, 46,123 participants). AUTHORS' CONCLUSIONS: There is evidence that sanitation interventions are effective at preventing diarrhoea, both for young children and all age populations. The actual level of effectiveness, however, varies by type of intervention and setting. There is a need for research to better understand the factors that influence effectiveness

Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): Emissions of particulate matter and sulfur dioxide from vehicles and brick kilns and their impacts on air quality in the Kathmandu Valley, Nepal

Air pollution is one of the most pressing environmental issues in the Kathmandu Valley, where the capital city of Nepal is located. We estimated emissions from two of the major source types in the valley (vehicles and brick kilns) and analyzed the corresponding impacts on regional air quality. First, we estimated the on-road vehicle emissions in the valley using the International Vehicle Emissions (IVE) model with local emissions factors and the latest available data for vehicle registration. We also identified the locations of the brick kilns in the Kathmandu Valley and developed an emissions inventory for these kilns using emissions factors measured during the Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE) field campaign in April 2015. Our results indicate that the commonly used global emissions inventory, the Hemispheric Transport of Air Pollution (HTAP_v2.2), underestimates particulate matter emissions from vehicles in the Kathmandu Valley by a factor greater than 100. HTAP_v2.2 does not include the brick sector and we found that our sulfur dioxide (SO2) emissions estimates from brick kilns are comparable to 70 % of the total SO2 emissions considered in HTAP_v2.2. Next, we simulated air quality using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for April 2015 based on three different emissions scenarios: HTAP only, HTAP with updated vehicle emissions, and HTAP with both updated vehicle and brick kilns emissions. Comparisons between simulated results and observations indicate that the model underestimates observed surface elemental carbon (EC) and SO2 concentrations under all emissions scenarios. However, our updated estimates of vehicle emissions significantly reduced model bias for EC, while updated emissions from brick kilns improved model performance in simulating SO2. These results highlight the importance of improving local emissions estimates for air quality modeling. We further find that model overestimation of surface wind leads to underestimated air pollutant concentrations in the Kathmandu Valley. Future work should focus on improving local emissions estimates for other major and underrepresented sources (e.g., crop residue burning and garbage burning) with a high spatial resolution, as well as the model\u27s boundary layer representation, to capture strong spatial gradients of air pollutant concentrations

LPG stove and fuel intervention among pregnant women reduce fine particle air pollution exposures in three countries: Pilot results from the HAPIN trial

The Household Air Pollution Intervention Network trial is a multi-country study on the effects of a liquefied petroleum gas (LPG) stove and fuel distribution intervention on women's and children's health. There is limited data on exposure reductions achieved by switching from solid to clean cooking fuels in rural settings across multiple countries. As formative research in 2017, we recruited pregnant women and characterized the impact of the intervention on personal exposures and kitchen levels of fine particulate matter (PM2.5) in Guatemala, India, and Rwanda. Forty pregnant women were enrolled in each site. We measured cooking area concentrations of and personal exposures to PM2.5 for 24 or 48 h using gravimetric-based PM2.5 samplers at baseline and two follow-ups over two months after delivery of an LPG cookstove and free fuel supply. Mixed models were used to estimate PM2.5 reductions. Median kitchen PM2.5 concentrations were 296 μg/m3 at baseline (interquartile range, IQR: 158-507), 24 μg/m3 at first follow-up (IQR: 18-37), and 23 μg/m3 at second follow-up (IQR: 14-37). Median personal exposures to PM2.5 were 134 μg/m3 at baseline (IQR: 71-224), 35 μg/m3 at first follow-up (IQR: 23-51), and 32 μg/m3 at second follow-up (IQR: 23-47). Overall, the LPG intervention was associated with a 92% (95% confidence interval (CI): 90-94%) reduction in kitchen PM2.5 concentrations and a 74% (95% CI: 70-79%) reduction in personal PM2.5 exposures. Results were similar for each site. CONCLUSIONS: The intervention was associated with substantial reductions in kitchen and personal PM2.5 overall and in all sites. Results suggest LPG interventions in these rural settings may lower exposures to the WHO annual interim target-1 of 35 μg/m3. The range of exposure contrasts falls on steep sections of estimated exposure-response curves for birthweight, blood pressure, and acute lower respiratory infections, implying potentially important health benefits when transitioning from solid fuels to LPG

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

A pre-trained conditional transformer for Target-specific De Novo Molecular Generation

In the scope of drug discovery, the molecular design aims to identify novel compounds from the chemical space where the potential drug-like molecules are estimated to be in the order of 1060–10100. Since this search task is computationally intractable due to the unbounded search space, deep learning draws a lot of attention as a new way of generating unseen molecules. As we seek compounds with specific target proteins, we propose a Transformer-based deep model for de novo target specific molecular design. The proposed method is capable of gen erating both drug-like compounds (without specified targets) and target-specific compounds. The latter are generated by enforcing different keys and values of the multi-head attention for each target. In this way, we allow the generation of SMILES strings to be conditional on the specified target. Experimental results demonstrate that our method is capable of generating both valid drug-like compounds and target-specific compounds. Moreover, the sampled compounds from conditional model largely occupy the real target-specific molecules’ chemical space and also cover a significant fraction of novel compounds.In the scope of drug discovery, the molecular design aims to identify novel compounds from the chemical space where the potential drug-like molecules are estimated to be in the order of 1060–10100. Since this search task is computationally intractable due to the unbounded search space, deep learning draws a lot of attention as a new way of generating unseen molecules. As we seek compounds with specific target proteins, we propose a Transformer-based deep model for de novo target specific molecular design. The proposed method is capable of gen erating both drug-like compounds (without specified targets) and target-specific compounds. The latter are generated by enforcing different keys and values of the multi-head attention for each target. In this way, we allow the generation of SMILES strings to be conditional on the specified target. Experimental results demonstrate that our method is capable of generating both valid drug-like compounds and target-specific compounds. Moreover, the sampled compounds from conditional model largely occupy the real target-specific molecules’ chemical space and also cover a significant fraction of novel compounds