Advanced radiometric and interferometric milimeter-wave scene simulations

Smart munitions and weapons utilize various imaging sensors (including passive IR, active and passive millimeter-wave, and visible wavebands) to detect/identify targets at short standoff ranges and in varied terrain backgrounds. In order to design and evaluate these sensors under a variety of conditions, a high-fidelity scene simulation capability is necessary. Such a capability for passive millimeter-wave scene simulation exists at TRW. TRW's Advanced Radiometric Millimeter-Wave Scene Simulation (ARMSS) code is a rigorous, benchmarked, end-to-end passive millimeter-wave scene simulation code for interpreting millimeter-wave data, establishing scene signatures and evaluating sensor performance. In passive millimeter-wave imaging, resolution is limited due to wavelength and aperture size. Where high resolution is required, the utility of passive millimeter-wave imaging is confined to short ranges. Recent developments in interferometry have made possible high resolution applications on military platforms. Interferometry or synthetic aperture radiometry allows the creation of a high resolution image with a sparsely filled aperture. Borrowing from research work in radio astronomy, we have developed and tested at TRW scene reconstruction algorithms that allow the recovery of the scene from a relatively small number of spatial frequency components. In this paper, the TRW modeling capability is described and numerical results are presented

ASSESSING PROBLEMS AND PROSPECTS OF URBAN AGRICULTURE IN CEBU CITY, PHILIPPINES: TOWARDS DEVELOPING ACTION PLANS

Background and Purpose: Urban agriculture (UA) has become an even more attractive option for food security and safety brought by the spread of COVID-19 which causes global health crisis. However, studies examining the perceived values, challenges, and needs towards urban agriculture are very limited and this does not exempt even the context of Cebu City in the Philippines. In this regard, this study aimed to assess the problems and prospects concerning urban agriculture in the aforementioned City. The purposes are to propose action plans and offer insights in designing and implementing food and agricultural programs and policies in the planned participatory action research (PAR) for sustainable urban agriculture.   Methodology: This study used Kemmis and McTaggart’s (1988) Model of Action Research. The use of this research method is an essential approach towards constructing sustainable developments of urban agricultural systems. However, only the planning stage was accomplished in this phase of action research of which it employed sequential explanatory research design. There were 509 household representatives in this stage of the study who participated in an online survey, 217 and 292 were from the north district and south district, respectively. Subsequently, each district had five representatives who were subjected to interviews to explain the quantitative results.   Findings: Results indicate that Cebuanos positively perceives the social, economic, health, environmental, and aesthetic values of urban agriculture. However, the presence of urban agriculture in the metropolitan is only from moderate to nonexistent due to lack of space or designated area. In addition, the participants disclosed a lack of training and capital or funding, thus further disengaging them from adopting urban agriculture. Nonetheless, the majority (n=463) expressed willingness to be trained in urban agriculture if given the opportunity.   Contributions: This study provides key points as to how urban agriculture can be promoted. These include designating some portions of public and privately unused lands as “urban agriculture areas”. In addition, financing institutions may also allocate small subsidies for marginally low-income families as their starting capital for urban agriculture activities. Finally, universities, non-government, and government agencies in the agriculture sector may train Cebuanos in backyard and rooftop gardening being the widely accepted urban agriculture form.   Keywords: Action research, perceived challenges, perceived needs, perceived values, urban agriculture.   Cite as: Cortes, S. T., Bugtai, V. H., Lampawog, E. Q., Sadili, C. B., Agero, A. D., Ramas, C. B., … Lorca, A. S. (2022). Understanding the issues of citizen participation.  Journal of Nusantara Studies, 7(1), 264-291. http://dx.doi.org/10.24200/jonus.vol7iss1pp264-29

Pollutant exposure in Manila Bay: Effects on the allometry and histological structures of Perna viridis (Linn.)

Objective: To determine the effects of the water quality of Manila Bay on allometric parameters and histological biomarkers of selected organs of P. viridis. Methods: Green mussels were collected from two coastal sites of Manila Bay, Las Piñas – Parañaque (LPP) and Bacoor, Cavite (BC). Twenty-four green mussels from each site were used for the assessment of allometric parameters, and six green mussels from LPP and eight from BC were used for the assessment of histological structures of gonads, gut, and digestive glands. Gonad development was categorized into five stages, whereas gut and digestive glands were scored into four categories. Results: Allometric parameters that include shell height, weight, and total wet and dry soft tissue weight were significantly different between LPP and BC. It was also observed that exposure to the pollutants in Manila Bay resulted to delays in gonadal development, and detrimental changes and lesions in the histostructure of digestive gland and gut. Conclusions: Pollutants in Manila Bay have detrimental effects to the growth, reproductive development, and histological structure of digestive organs of P. viridis

Visual Servoing

International audienceVisual servoing refers to the use of visual data as input of real-time closed-loop control schemes for controlling the motion of a dynamic system, a robot typically. It can be defined as sensor-based control from a vision sensor and relies on techniques from image processing, computer vision, and control theory

Robot Visual Control

International audienc

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)