CR1 Knops blood group alleles are not associated with severe malaria in the Gambia

The Knops blood group antigen erythrocyte polymorphisms have been associated with reduced falciparum malaria-based in vitro rosette formation (putative malaria virulence factor). Having previously identified single-nucleotide polymorphisms (SNPs) in the human complement receptor 1 (CR1/CD35) gene underlying the Knops antithetical antigens Sl1/Sl2 and McC(a)/McC(b), we have now performed genotype comparisons to test associations between these two molecular variants and severe malaria in West African children living in the Gambia. While SNPs associated with Sl:2 and McC(b+) were equally distributed among malaria-infected children with severe malaria and control children not infected with malaria parasites, high allele frequencies for Sl 2 (0.800, 1,365/1,706) and McC(b) (0.385, 658/1706) were observed. Further, when compared to the Sl 1/McC(a) allele observed in all populations, the African Sl 2/McC(b) allele appears to have evolved as a result of positive selection (modified Nei-Gojobori test Ka-Ks/s.e.=1.77, P-value <0.05). Given the role of CR1 in host defense, our findings suggest that Sl 2 and McC(b) have arisen to confer a selective advantage against infectious disease that, in view of these case-control study data, was not solely Plasmodium falciparum malaria. Factors underlying the lack of association between Sl 2 and McC(b) with severe malaria may involve variation in CR1 expression levels

Dhaka city water logging hazards: area identification and vulnerability assessment through GIS-remote sensing techniques

Water logging is one of the most detrimental phenomena continuing to burden Dhaka dwellers. This study aims to spatio-temporarily identify the water logging hazard zones within Dhaka Metropolitan area and assess the extent of their water logging susceptibility based on informal settlements, built-up areas, and demographical characteristics. The study utilizes integrated geographic information system (GIS)-remote sensing (RS) methods, using the Normalized Difference Vegetation Water and Moisture Index, distance buffer zone from drainage streams, and built-up distributions to identify waterlogged zones with a temporal extent, incorporating social and infrastructural attributes to evaluate water logging effects. These indicators were integrated into an overlay GIS method to measure the vulnerability level across Dhaka city areas. The findings reveal that south and south-western parts of Dhaka were more susceptible to water logging hazards. Almost 35% of Dhaka belongs to the high/very highly vulnerable zone. Greater number of slum households were found within high to very high water logging vulnerable zones and approximately 70% of them are poorly structured. The built-up areas were observed to be increased toward the northern part of Dhaka and were exposed to severe water logging issues. The overall findings reveal the spatio-temporal distribution of the water logging vulnerabilities across the city as well as its impact on the social indicators. An integrated approach is necessary for future development plans to mitigate the risk of water logging

A velocity map ion imaging study of difluorobenzene-water complexes: Binding energies and recoil distributions

Susan M. Bellm, Rebecca J. Moulds, Matthew P. van Leeuwen, and Warren D. Lawranc

Soil Moisture to Runoff (SM2R): A Data-Driven Model for Runoff Estimation Across Poorly Gauged Asian Water Towers Based on Soil Moisture Dynamics

Data Availability Statement: RA5L reanalysis data (Muñoz Sabater, 2019) are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.68d2bb30 . Data sets from the HWSD (Fischer et al., 2008) are accessed at https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ . Glacier elevation changes (Berthier et al., 2021) are provided at https://doi.org/10.6096/13 , and the RGI 6.0 glacier mask (RGI Consortium, 2017) can be accessed at https://doi.org/10.7265/4m1f-gd79 . Percentages of persistent snow cover in each water tower (Immerzeel et al., 2019) are provided at https://doi.org/10.5281/zenodo.3521933 . Soil moisture estimated from GLDAS NOAH [Beaudoing and Rodell, 2020; Rodell et al., 2004] and CLSM [B. Li et al., 2019; B. Li et al., 2020] land surface models can be accessed at https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_M_2.1/summary and https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_DA1_D_2.2/summary , respectively. Precipitation estimated from the IMERG product (Huffman et al., 2019) can be accessed at https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_06/summary . Runoff estimation results of this study (Li & Long, 2023) are available at https://doi.org/10.5281/zenodo.7505876 .Supporting Information is available online at https://doi.org/10.1029/2022WR033597 .Copyright © 2022 The Author(s) and American Geophysical Union. Almost 2 billion people depend on freshwater provided by the Asian water towers, yet long-term runoff estimation is challenging in this high-mountain region with a harsh environment and scarce observations. Most hydrologic models rely on observed runoff for calibration, and have limited applicability in the poorly gauged Asian water towers. To overcome such limitations, here we propose a novel data-driven model, SM2R (Soil Moisture to Runoff), to simulate monthly runoff based on soil moisture dynamics using reanalysis forcing data. The SM2R model was applied and examined in 20 drainage basins across seven Asian water towers during the past four decades of 1981–2020. Without invoking any observations for calibration, the overall good performance of SM2R-derived runoff (correlation coefficient ≥0.74 and normalized root mean square error ≤0.22 compared to observed runoff at 20 gauges) suggests considerable potential for runoff simulation in poorly gauged basins. Even though the SM2R model is forced by ERA5-Land (ERA5L) reanalysis data, it largely outperforms the ERA5L-estimated runoff across the seven Asian water towers, particularly in basins with widely distributed glaciers and frozen soil. The SM2R approach is highly promising for constraining hydrologic variables from soil moisture information. Our results provide valuable insights for not only long-term runoff estimation over key Asian basins, but also understanding hydrologic processes across poorly gauged regions globally.Second Tibetan Plateau Scientific Expedition and Research program. Grant Number: 2019QZKK0105 National Natural Science Foundation of China. Grant Number: 92047301,92047203 UK Research and Innovation. Grant Number: MR/V022008/

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy

A methodology to downscale water demand data with application to the Andean region (Ecuador, Peru, Bolivia, Chile)

Mountainous regions are a hotspot for water scarcity and anthropogenic pressure on water resources. Substantial uncertainty surrounds projections of future climate and water availability. Furthermore, quantitative and distributed data on water demand are generally scarce, dispersed, and highly heterogeneous. This forms a major bottleneck to studying water resources issues and developing strategies to improve water resource management. Here we present a methodology to produce and evaluate high-resolution gridded maps of anthropogenic surface water demand with application to the Andean region. These data are disaggregated according to the major types of water demand: domestic users, irrigated area, and hydropower. This dataset was built by homogenizing, integrating, and interpolating data obtained from various national institutions in charge of water resource management as well as relevant global datasets. The maps can be used to research anthropogenic impacts on water resources, and to guide regional decision-making in regions such as the Andes

Stellar jitter from variable gravitational redshift: implications for RV confirmation of habitable exoplanets

A variation of gravitational redshift, arising from stellar radius fluctuations, will introduce astrophysical noise into radial velocity measurements by shifting the centroid of the observed spectral lines. Shifting the centroid does not necessarily introduce line asymmetries. This is fundamentally different from other types of stellar jitter so far identified, which do result from line asymmetries. Furthermore, only a very small change in stellar radius, ~0.01%, is necessary to generate a gravitational redshift variation large enough to mask or mimic an Earth-twin. We explore possible mechanisms for stellar radius fluctuations in low-mass stars. Convective inhibition due to varying magnetic field strengths and the Wilson depression of starspots are both found to induce substantial gravitational redshift variations. Finally, we investigate a possible method for monitoring/correcting this newly identified potential source of jitter and comment on its impact for future exoplanet searches.Comment: 6 pages, 1 figure, 1 tabl

Affect and mental health across the lifespan during a year of the COVID-19 pandemic: The role of emotion regulation strategies and mental flexibility

Online First Publication, May 18, 2023. OnlinePublDuring the COVID-19 pandemic, there has been a rise in common mental health problems compared to prepandemic levels, especially in young people. Understanding the factors that place young people at risk is critical to guide the response to increased mental health problems. Here we examine whether age-related differences in mental flexibility and frequency of use of emotion regulation strategies partially account for the poorer affect and increased mental health problems reported by younger people during the pandemic. Participants (N= 2,367; 11–100 years) from Australia, the UK, and US were surveyed thrice at 3-month intervals between May 2020 and April 2021. Participants completed measures of emotion regulation, mental flexibility, affect, and mental health. Younger age was associated with less positive (b=0.008, p,.001) and more negative (b=−0.015, p,.001) affect across the first year of the pandemic. Maladaptive emotion regulation partially accounted for age-related variance in negative affect (β=−0.013, p=.020), whereby younger age was associated with more frequent use of maladaptive emotion regulation strategies, which, in turn, was associated with more negative affect at our third assessment point. More frequent use of adaptive emotion regulation strategies, and in turn, changes in negative affect from our first to our third assessment, partially accounted for age-related variance in mental health problems (β= 0.007, p=.023). Our findings add to the growing literature demonstrating the vulnerability of younger people during the COVID-19 pandemic and suggest that emotion regulation may be a promising target for intervention.Savannah Minihan, Annabel Songco, Elaine Fox, Cecile D. Ladouceur, Louise Mewton, Michelle Moulds, Jennifer H. Pfeifer, Anne-Laura Van Harmelen, and Susanne Schweize