Comparing Agricultural Conservation Planning Framework (ACPF) practice placements for runoff mitigation and controlled drainage among 32 watersheds representing Iowa landscapes

Precision conservation planning tools can use high-resolution data to identify conservation practice-placement options for watershed improvement plans. Use of these tools across multiple watersheds could help to identify regional conservation strategies. This study evaluated practice-placement options determined using the Agricultural Conservation Planning Framework (ACPF) ArcGIS tools for controlled drainage (CD), contour buffer strips (CBS), water and sediment control basins (WASCOBs), and grassed waterways (GWWs) across 32 headwater hydrological unit code (HUC)12 watersheds in Iowa. The watersheds represented three Major Land Resource Areas (MLRAs) and four Agro-Hydrologic Landscape (AHL) classes, with four watersheds randomly chosen from each of eight combined MLRA-AHL landscape groupings. Placement options for the practices identified using the ACPF were quantified by watershed as densities (km km−2 of cropland) for GWWs, counts of proposed practice locations per square kilometer for CBS and WASCOBs, and as fractions of tile-drained land for CD. The influence of the landscape groupings on practice-placement densities among watersheds was tested using analysis of variance and contrast comparisons. Significant differences were found that led to nuanced interpretations. Differences attributed to slope steepness were captured by AHL classes, while differences attributed to slope shape and convergence were best captured by MLRA, which better segregated the watersheds based on landscape age and stream dissection. Grassed waterway placements showed minor differences among MLRAs but provided data to better inform the choices that ACPF users can make when running the GWW tool. The MLRA/AHL landscape classifications could be used together to develop effective regional conservation strategies using precision planning tools

ARA-talojen hoitokulut ja kulurakenne

Tässä selvityksessä tarkastellaan ARA-vuokra-asuntokiinteistöjen hoitokuluja ja kulurakennetta, hoitokulujen osuutta vuokralaisilta perittävästä vuokrasta sekä eri osapuolten vaikutusmahdollisuuksia kulutasoihin. Hoitokulujen hallinta ja optimointi ovat ensiarvoisen tärkeitä kohtuuhintaisen asumisen varmistamisessa. Selvitys on toteutettu pääosin analysoimalla vuoden 2013 toteutuneita hoitokuluja, jotka on koottu 15 yhteisöltä yhteensä 440 ARA-talosta. Selvityksessä analysoitiin myös 52 erityisryhmille suunnatun ARA-talon hoitokuluja. Lisäksi toteutettiin tietoja toimittaneille yhteisöille suunnattu kysely, jolla tarkennettiin toimitettuja tietoja sekä kartoitettiin kiinteistönhoito- ja hallintopalvelun tuotantotapoja ja osapuolten vaikutusmahdollisuuksia kulujen muodostumiseen. Normaalien ARA-vuokra-asuntokiinteistöjen hoitokulut olivat keskimäärin 5,96 €/asm²/kk vuonna 2013. Suurimmat kuluerät olivat korjaukset (22 %), lämmitys (17 %) sekä hallinto (15 %). Pääkaupunkiseudulla kustannukset ovat 0,84 €/asm²/kk korkeammat kuin muissa kaupungeissa. Suurimmat alueelliset erot olivat tontinvuokrissa. Vanhempien rakennusten korjauskustannukset nostivat niiden hoitokulut uusia rakennuksia korkeammiksi. Valtakunnallisten yhteisöjen omistamien kohteiden kokonaishoitokulut olivat kunnallisten yhteisöjen kuluja korkeammat, ja kustannukset jakautuivat eri tavoin eri hoitokuluerien välille. Omistajan rooli hoitokulujen optimoinnissa sekä kiinteistöjen arvon säilymisessä nähdään huomattavasti asukkaiden merkitystä suuremmaksi. Suurin vaikutusmahdollisuus asukkaalla oli veden kulutukseen ja kustannuksiin. Muiden kulujen osalta asukkaan vaikutusmahdollisuus nähtiin kohtalaiseksi tai jopa suhteellisen pieneksi. Erityisryhmille suunnattujen kohteiden hoitokulut olivat huomattavasti normaaleja vuokra-asuntokohteita korkeammat, 7,09 €/htm²/kk. Normaaleissa ARA-taloissa vastaava huoneistoalaan suhteutettu kulu on 5,91 €/htm²/kk. Hoitokulut ovat erityisryhmille suunnatuissa ARA-taloissa korkeammat, koska kuluissa on mukana asukkaiden sähkö- ja siivouskulut. Nämä laskutetaan tyypillisimmin asukkailta erillisinä palvelumaksuina. Lisäksi kohteet ovat kooltaan merkittävästi pienempiä kuin muut ARA-talot. Selvitys liittyy valtioneuvoston asuntopoliittiseen toimenpideohjelmaan vuosille 2012–2015

Vuokrataloyhteisöjen toimintatavat ARA-asuntojen omakustannusvuokrien määrityksessä

Tässä selvityksessä tarkastellaan valtion tukemien ARA-vuokra-asuntojen vuokrien omakustannusperiaatteen toteutumista sekä vuokrien tasausjärjestelmää ja omakustannusvuokrien määräytymisperusteisiin kuuluvaa korjauksiin varautumista käytännössä. Lisäksi selvitetään kuntien suorittaman vuokrien omakustannusperiaatteen valvonnan toimivuutta. Selvityksen perustiedot kerättiin 55 ARA-asuntoja omistavalta yhteisöltä. Tietoja tarkennettiin vielä 15 yhteisöltä lisäkyselyn ja haastattelujen avulla. Selvityksen mukaan kaikki selvityksessä mukana olleet ARA-asuntoja omistavat yhteisöt määrittävät vuokrat omakustannusperiaatteen mukaisesti. Vuokrien sisältö ja rakenne kuitenkin vaihtelevat eri toimijoiden välillä. Suurimpia eroavaisuuksia eri toimijoiden välillä aiheuttavat omille varoille perittävä korko, peruskorjauksiin varautuminen ja korjausten rahoituksen toimintatavat. Vuokrantasausjärjestelmä on käytössä kaikissa selvityksessä mukana olleissa yhteisöissä. Yleisimmin tasataan pääomamenoja sekä peruskorjauksiin varautumisen kustannuksia. Kuntien suorittaman omakustannusvuokrien valvonnan tavoitteet, merkitys ja tehtävät ovat epäselviä niin valvojille kuin valvottaville yhteisöillekin. Kuntien asuntoviranomaiset arvioivat resurssinsa ja osaamisensa riittämättömiksi etenkin valtakunnallisten yhteisöjen vuokrien valvontaan. Selvitys liittyy valtioneuvoston asuntopoliittiseen toimenpideohjelmaan vuosille 2012–2015

Age-dependent impact of the major common genetic risk factor for COVID-19 on severity and mortality

AG has received support by NordForsk Nordic Trial Alliance (NTA) grant, by Academy of Finland Fellow grant N. 323116 and the Academy of Finland for PREDICT consortium N. 340541. The Richards research group is supported by the Canadian Institutes of Health Research (CIHR) (365825 and 409511), the Lady Davis Institute of the Jewish General Hospital, the Canadian Foundation for Innovation (CFI), the NIH Foundation, Cancer Research UK, Genome Québec, the Public Health Agency of Canada, the McGill Interdisciplinary Initiative in Infection and Immunity and the Fonds de Recherche Québec Santé (FRQS). TN is supported by a research fellowship of the Japan Society for the Promotion of Science for Young Scientists. GBL is supported by a CIHR scholarship and a joint FRQS and Québec Ministry of Health and Social Services scholarship. JBR is supported by an FRQS Clinical Research Scholarship. Support from Calcul Québec and Compute Canada is acknowledged. TwinsUK is funded by the Welcome Trust, the Medical Research Council, the European Union, the National Institute for Health Research-funded BioResource and the Clinical Research Facility and Biomedical Research Centre based at Guy’s and St. Thomas’ NHS Foundation Trust in partnership with King’s College London. The Biobanque Québec COVID19 is funded by FRQS, Genome Québec and the Public Health Agency of Canada, the McGill Interdisciplinary Initiative in Infection and Immunity and the Fonds de Recherche Québec Santé. These funding agencies had no role in the design, implementation or interpretation of this study. The COVID19-Host(a)ge study received infrastructure support from the DFG Cluster of Excellence 2167 “Precision Medicine in Chronic Inflammation (PMI)” (DFG Grant: “EXC2167”). The COVID19-Host(a)ge study was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the Computational Life Sciences funding concept (CompLS grant 031L0165). Genotyping in COVID19-Host(a)ge was supported by a philantropic donation from Stein Erik Hagen. The COVID GWAs, Premed COVID-19 study (COVID19-Host(a)ge_3) was supported by "Grupo de Trabajo en Medicina Personalizada contra el COVID-19 de Andalucia"and also by the Instituto de Salud Carlos III (CIBERehd and CIBERER). Funding comes from COVID-19-GWAS, COVID-PREMED initiatives. Both of them are supported by "Consejeria de Salud y Familias" of the Andalusian Government. DMM is currently funded by the the Andalussian government (Proyectos Estratégicos-Fondos Feder PE-0451-2018). The Columbia University Biobank was supported by Columbia University and the National Center for Advancing Translational Sciences, NIH, through Grant Number UL1TR001873. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or Columbia University. The SPGRX study was supported by the Consejería de Economía, Conocimiento, Empresas y Universidad #CV20-10150. The GEN-COVID study was funded by: the MIUR grant “Dipartimenti di Eccellenza 2018-2020” to the Department of Medical Biotechnologies University of Siena, Italy; the “Intesa San Paolo 2020 charity fund” dedicated to the project NB/2020/0119; and philanthropic donations to the Department of Medical Biotechnologies, University of Siena for the COVID-19 host genetics research project (D.L n.18 of March 17, 2020). Part of this research project is also funded by Tuscany Region “Bando Ricerca COVID-19 Toscana” grant to the Azienda Ospedaliero Universitaria Senese (CUP I49C20000280002). Authors are grateful to: the CINECA consortium for providing computational resources; the Network for Italian Genomes (NIG) (http://www.nig.cineca.it) for its support; the COVID-19 Host Genetics Initiative (https://www.covid19hg.org/); the Genetic Biobank of Siena, member of BBMRI-IT, Telethon Network of Genetic Biobanks (project no. GTB18001), EuroBioBank, and RD-Connect, for managing specimens. Genetics against coronavirus (GENIUS), Humanitas University (COVID19-Host(a)ge_4) was supported by Ricerca Corrente (Italian Ministry of Health), intramural funding (Fondazione Humanitas per la Ricerca). The generous contribution of Banca Intesa San Paolo and of the Dolce&Gabbana Fashion Firm is gratefully acknowledged. Data acquisition and sample processing was supported by COVID-19 Biobank, Fondazione IRCCS Cà Granda Milano; LV group was supported by MyFirst Grant AIRC n.16888, Ricerca Finalizzata Ministero della Salute RF-2016-02364358, Ricerca corrente Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, the European Union (EU) Programme Horizon 2020 (under grant agreement No. 777377) for the project LITMUS- “Liver Investigation: Testing Marker Utility in Steatohepatitis”, Programme “Photonics” under grant agreement “101016726” for the project “REVEAL: Neuronal microscopy for cell behavioural examination and manipulation”, Fondazione Patrimonio Ca’ Granda “Liver Bible” PR-0361. DP was supported by Ricerca corrente Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, CV PREVITAL “Strategie di prevenzione primaria nella popolazione Italiana” Ministero della Salute, and Associazione Italiana per la Prevenzione dell’Epatite Virale (COPEV). Genetic modifiers for COVID-19 related illness (BeLCovid_1) was supported by the "Fonds Erasme". The Host genetics and immune response in SARS-Cov-2 infection (BelCovid_2) study was supported by grants from Fondation Léon Fredericq and from Fonds de la Recherche Scientifique (FNRS). The INMUNGEN-CoV2 study was funded by the Consejo Superior de Investigaciones Científicas. KUL is supported by the German Research Foundation (LU 1944/3-1) SweCovid is funded by the SciLifeLab/KAW national COVID-19 research program project grant to Michael Hultström (KAW 2020.0182) and the Swedish Research Council to Robert Frithiof (2014-02569 and 2014-07606). HZ is supported by Jeansson Stiftelser, Magnus Bergvalls Stiftelse. The COMRI cohort is funded by Technical University of Munich, Munich, Germany. Genotyping for the COMRI cohort was performed and funded by the Genotyping Laboratory of Institute for Molecular Medicine Finland FIMM Technology Centre, University of Helsinki, Helsinki, Finland. These funding agencies had no role in the design, implementation or interpretation of this study.Background: There is considerable variability in COVID-19 outcomes amongst younger adults—and some of this variation may be due to genetic predisposition. We characterized the clinical implications of the major genetic risk factor for COVID-19 severity, and its age-dependent effect, using individual-level data in a large international multi-centre consortium. Method: The major common COVID-19 genetic risk factor is a chromosome 3 locus, tagged by the marker rs10490770. We combined individual level data for 13,424 COVID-19 positive patients (N=6,689 hospitalized) from 17 cohorts in nine countries to assess the association of this genetic marker with mortality, COVID-19-related complications and laboratory values. We next examined if the magnitude of these associations varied by age and were independent from known clinical COVID-19 risk factors. Findings: We found that rs10490770 risk allele carriers experienced an increased risk of all-cause mortality (hazard ratio [HR] 1·4, 95% confidence interval [CI] 1·2–1·6) and COVID-19 related mortality (HR 1·5, 95%CI 1·3–1·8). Risk allele carriers had increased odds of several COVID-19 complications: severe respiratory failure (odds ratio [OR] 2·0, 95%CI 1·6-2·6), venous thromboembolism (OR 1·7, 95%CI 1·2-2·4), and hepatic injury (OR 1·6, 95%CI 1·2-2·0). Risk allele carriers ≤ 60 years had higher odds of death or severe respiratory failure (OR 2·6, 95%CI 1·8-3·9) compared to those > 60 years OR 1·5 (95%CI 1·3-1·9, interaction p-value=0·04). Amongst individuals ≤ 60 years who died or experienced severe respiratory COVID-19 outcome, we found that 31·8% (95%CI 27·6-36·2) were risk variant carriers, compared to 13·9% (95%CI 12·6-15·2%) of those not experiencing these outcomes. Prediction of death or severe respiratory failure among those ≤ 60 years improved when including the risk allele (AUC 0·82 vs 0·84, p=0·016) and the prediction ability of rs10490770 risk allele was similar to, or better than, most established clinical risk factors. Interpretation: The major common COVID-19 risk locus on chromosome 3 is associated with increased risks of morbidity and mortality—and these are more pronounced amongst individuals ≤ 60 years. The effect on COVID-19 severity was similar to, or larger than most established risk factors, suggesting potential implications for clinical risk management.Academy of Finland Fellow grant N. 323116Academy of Finland for PREDICT consortium N. 340541.Canadian Institutes of Health Research (CIHR) (365825 and 409511)Lady Davis Institute of the Jewish General HospitalCanadian Foundation for Innovation (CFI)NIH FoundationCancer Research UKGenome QuébecPublic Health Agency of CanadaMcGill Interdisciplinary Initiative in Infection and Immunity and the Fonds de Recherche Québec Santé (FRQS)Japan Society for the Promotion of Science for Young ScientistsCIHR scholarship and a joint FRQS and Québec Ministry of Health and Social Services scholarshipFRQS Clinical Research ScholarshipCalcul QuébecCompute CanadaWelcome TrustMedical Research CouncEuropean UnionNational Institute for Health Research-funded BioResourceClinical Research Facility and Biomedical Research Centre based at Guy’s and St. Thomas’ NHS Foundation TrustKing’s College LondonGenome QuébecPublic Health Agency of CanadaMcGill Interdisciplinary Initiative in Infection and ImmunityFonds de Recherche Québec Santé(DFG Grant: “EXC2167”)(CompLS grant 031L0165)Stein Erik Hagen"Grupo de Trabajo en Medicina Personalizada contra el COVID-19 de Andalucia"Instituto de Salud Carlos III (CIBERehd and CIBERER)COVID-19-GWASCOVID-PREMED initiatives"Consejeria de Salud y Familias" of the Andalusian GovernmentAndalusian government (Proyectos Estratégicos-Fondos Feder PE-0451-2018)Columbia UniversityNational Center for Advancing Translational SciencesNIH Grant Number UL1TR001873Consejería de Economía, Conocimiento, Empresas y Universidad #CV20-10150MIUR grant “Dipartimenti di Eccellenza 2018-2020”“Intesa San Paolo 2020 charity fund” dedicated to the project NB/2020/0119Tuscany Region “Bando Ricerca COVID-19 Toscana”CINECA consortiumNetwork for Italian Genomes (NIG)COVID-19 Host Genetics InitiativeGenetic Biobank of SienaEuroBioBankRD-ConnectRicerca Corrente (Italian Ministry of Health)Fondazione Humanitas per la RicercaBanca Intesa San PaoloDolce&Gabbana Fashion FirmCOVID-19 BiobankFondazione IRCCS Cà Granda MilanoMyFirst Grant AIRC n.16888Ricerca Finalizzata Ministero della Salute RF-2016-02364358Ricerca corrente Fondazione IRCCS Ca’ Granda Ospedale Maggiore PoliclinicoEuropean Union (EU) Programme Horizon 2020 (under grant agreement No. 777377)“Photonics” “101016726”Fondazione Patrimonio Ca’ Granda “Liver Bible” PR-0361CV PREVITAL “Strategie di prevenzione primaria nella popolazione Italiana” Ministero della Salute, and Associazione Italiana per la Prevenzione dell’Epatite Virale (COPEV)"Fonds Erasme"Fondation Léon FredericqFonds de la Recherche Scientifique (FNRS)Consejo Superior de Investigaciones CientíficasGerman Research Foundation (LU 1944/3-1)SciLifeLab/KAW national COVID-19 research program project (KAW 2020.0182)Swedish Research Council (2014-02569 and 2014-07606)Jeansson Stiftelser, Magnus Bergvalls StiftelseTechnical University of Munich, Munich, GermanyGenotyping Laboratory of Institute for Molecular Medicine Finland FIMM Technology Centre, University of Helsinki, Helsinki, Finlan

CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey - The Hubble Space Telescope Observations, Imaging Data Products and Mosaics

This paper describes the Hubble Space Telescope imaging data products and data reduction procedures for the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS). This survey is designed to document the evolution of galaxies and black holes at $z\sim1.5-8$, and to study Type Ia SNe beyond $z>1.5$. Five premier multi-wavelength sky regions are selected, each with extensive multiwavelength observations. The primary CANDELS data consist of imaging obtained in the Wide Field Camera 3 / infrared channel (WFC3/IR) and UVIS channel, along with the Advanced Camera for Surveys (ACS). The CANDELS/Deep survey covers \sim125 square arcminutes within GOODS-N and GOODS-S, while the remainder consists of the CANDELS/Wide survey, achieving a total of \sim800 square arcminutes across GOODS and three additional fields (EGS, COSMOS, and UDS). We summarize the observational aspects of the survey as motivated by the scientific goals and present a detailed description of the data reduction procedures and products from the survey. Our data reduction methods utilize the most up to date calibration files and image combination procedures. We have paid special attention to correcting a range of instrumental effects, including CTE degradation for ACS, removal of electronic bias-striping present in ACS data after SM4, and persistence effects and other artifacts in WFC3/IR. For each field, we release mosaics for individual epochs and eventual mosaics containing data from all epochs combined, to facilitate photometric variability studies and the deepest possible photometry. A more detailed overview of the science goals and observational design of the survey are presented in a companion paper.Comment: 39 pages, 25 figure

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Addressing climate change with behavioral science: a global intervention tournament in 63 countries

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks