Liquefied petroleum gas or biomass for cooking and effects on birth weight

BACKGROUND: Exposure during pregnancy to household air pollution caused by the burning of solid biomass fuel is associated with adverse health outcomes, including low birth weight. Whether the replacement of a biomass cookstove with a liquefied petroleum gas (LPG) cookstove would result in an increase in birth weight is unclear. METHODS: We performed a randomized, controlled trial involving pregnant women (18 to <35 years of age and at 9 to <20 weeks’ gestation as confirmed on ultrasonography) in Guatemala, India, Peru, and Rwanda. The women were assigned in a 1:1 ratio to use a free LPG cookstove and fuel (intervention group) or to continue using a biomass cookstove (control group). Birth weight, one of four prespecified primary outcomes, was the primary outcome for this report; data for the other three outcomes are not yet available. Birth weight was measured within 24 hours after birth. In addition, 24-hour personal exposures to fine particulate matter (particles with a diameter of ≤2.5 μm [PM2.5]), black carbon, and carbon monoxide were measured at baseline and twice during pregnancy. RESULTS: A total of 3200 women underwent randomization; 1593 were assigned to the intervention group, and 1607 to the control group. Uptake of the intervention was nearly complete, with traditional biomass cookstoves being used at a median rate of less than 1 day per month. After randomization, the median 24-hour personal exposure to fine particulate matter was 23.9 μg per cubic meter in the intervention group and 70.7 μg per cubic meter in the control group. Among 3061 live births, a valid birth weight was available for 94.9% of the infants born to women in the intervention group and for 92.7% of infants born to those in the control group. The mean (±SD) birth weight was 2921±474.3 g in the intervention group and 2898±467.9 g in the control group, for an adjusted mean difference of 19.6 g (95% confidence interval, −10.1 to 49.2). CONCLUSIONS: The birth weight of infants did not differ significantly between those born to women who used LPG cookstoves and those born to women who used biomass cookstoves. (Funded by the National Institutes of Health and the Bill and Melinda Gates Foundation; HAPIN ClinicalTrials.gov number, NCT02944682. opens in new tab.

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Parts based representation for pedestrian using NMF with robustness to partial occlusion

Computer Vision has seen a resurgence in the parts-based representation for objects over the past few years. The parts are usually annotated beforehand for training. We present an annotation free parts-based representation for the pedestrian using Non-Negative Matrix Factorization (NMF). We show that NMF is able to capture the wide range of pose and clothing of the pedestrians. We use a modified form of NMF i.e. NMF with sparsity constraints on the factored matrices. We also make use of Riemannian distance metric for similarity measurements in NMF space as the basis vectors generated by NMF aren't orthogonal. We show that for 1% drop in accuracy as compared to the Histogram of Oriented Gradients (HOG) representation we can achieve robustness to partial occlusion

Joint Enhancement of Multichannel SAR Data

In this paper we consider the problem of joint enhancement of multichannel Synthetic Aperture Radar (SAR) data. Previous work by Cetin and Karl introduced nonquadratic regularization methods for image enhancement using sparsity enforcing penalty terms. For multichannel data, independent enhancement of each channel is shown to degrade the relative phase information across channels that is useful for 3D reconstruction. We thus propose a method for joint enhancement of multichannel SAR data with joint sparsity constraints. We develop both a gradient-based and a Lagrange-Newton-based method for solving the joint reconstruction problem, and demonstrate the performance of the proposed methods on IFSAR height extraction problem from multi-elevation data. Keywords: Non-quadratic regularization, Sparse reconstruction, SAR, IFSAR 1

BaroSense

Traffic congestion on urban roadways is a serious problem requiring novel ways to detect and mitigate it. Determining the routes that lead to the traffic congestion segment is also vital in devising mitigation strategies. Further, crowdsourcing this information allows for use of these strategies quickly and in places where infrastructure is not available. In this work, we present an unconventional method, using the barometer sensor of mobile phones to (a) detect road traffic congestion and (b) estimate the paths that lead to the congested road segment. We make the observation that roads are not completely flat and very often, altitude varies along the road. The barometer sensor chips are sensitive enough to measure these variations and consume very little energy of the mobile phone, compared to other sensors such as the GPS or accelerometer. We devise a feature set to map the rate of change of this altitude as the user moves into activities characterized as “still” and “motion,” which are further used by the traffic congestion detection algorithm (RoadSphygmo) to classify the group of users as being in “moving,” “congestion,” &nbsp;or “stuck” states. To estimate the paths that lead to the congested road segment, we compare the user’s barometer sensor readings with a pre-stored road signature of barometer values using Dynamic Time Warping (DTW). We show that by using correlation of barometer sensor values, we can determine if users are in the same vehicle. We crowdsource this information from multiple mobile phones and use majority voting technique to improve the accuracy of traffic congestion detection and path estimation. We find a significant increase in the accuracies using crowdsourced information as compared to individual mobile phones. Further, we show that we can use barometer sensor for other applications such as bus occupancy, boarding/deboarding of a vehicle, and so on. The validation of the state determined by RoadSphygmo is done by comparing it with average GPS speed calculated during the same time period. The path estimation is validated over different intersections and considering various cases of commuter travel. The results obtained are promising and show that the traffic state determination and the estimation of the path taken by the commuter can achieve high accuracy