This correction provides updated acknowledgements: This work was supported by Istituto Nazionale di Fisica Nucleare; the Swiss National Science Foundation Ambizione Grant (No. 154833); a Deutsche Forschungsgemeinschaft research grant; an excellence initiative of Heidelberg University; Marie Sklodowska-Curie Innovative Training Network Fellowship of the European Commission's Horizon 2020 Programme (No. 721559 AVA); European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement ANGRAM No 748826; European Research Council under the European Union's Seventh Framework Program FP7/2007-2013 (Grants Nos. 291242 and 277762); Austrian Ministry for Science, Research, and Economy; Research Council of Norway; Bergen Research Foundation; John Templeton Foundation; Ministry of Education and Science of the Russian Federation and Russian Academy of Sciences; and the European Social Fund within the framework of realizing the project, in support of intersectoral mobility and quality enhancement of research teams at Czech Technical University in Prague (Grant No. CZ.1.07/2.3.00/30.0034).
The European Red List is a review of the conservation status of European species according to IUCN regional Red Listing guidelines. It identifies those species that are threatened with extinction at the regional level, so that appropriate conservation action can be taken to improve their status. This Red List publication summarises results for all hitherto described native European Orthoptera species (grasshoppers, crickets and bush-crickets). All Orthoptera species (grasshoppers, crickets and bushcrickets) native to or naturalised in Europe before AD 1500 (a total of 1,082 species), have been assessed in this Red List. The geographical scope is continent-wide, extending from Iceland in the west to the Urals in the east, and from Franz Josef Land in the north to the Canary Islands in the south. The Caucasus region is not included. Red List assessments were made at two regional levels: for geographical Europe, and for the 28 Member States of the European Union in 2016. The status of all species was assessed using the IUCN Red List Categories and Criteria (IUCN 2012a), which is the world's most widely accepted system for measuring extinction risk. All assessments followed the Guidelines for Application of IUCN Red List Criteria at Regional and National Levels (IUCN 2012b). The assessments were compiled based on the data and knowledge from a network of leading European experts on Orthoptera. The assessments were then completed and reviewed at six workshops held in Italy, Greece, France, Bulgaria, Spain and Germany as well as through email correspondence with relevant experts. More than 145 experts participated in the assessment and review process for European Orthoptera species. Assessments are available on the European Red List website and data portal: http://ec.europa.eu/environment/nature/ conservation/species/redlist and http://www.iucnredlist. org/initiatives/europe. Overall, 25.7% and 28% of Orthoptera species are assessed as threatened at the European and EU 28 levels, respectively. However, the exact proportion of threatened species is uncertain, as there are 107 (10%) Data Deficient (DD) species in Europe and 84 DD species (8.5%) in the EU 28. Estimating that a similar relative proportion of the DD assessments are likely to be threatened (IUCN 2011), the best estimate of the threatened share of Orthoptera species is thus 28.5% in Europe and 30.6% in the EU 28. Further research on DD species to clarify their status is therefore critical. A further 13.9% (149 species) and 13% (128 species) are considered Near Threatened in Europe and in the EU 28, respectively. By comparison, the best estimate of threatened species of those other groups that have been assessed comprehensively in Europe is 58% of freshwater molluscs, 40% of freshwater fishes, 23% of amphibians, 20% of reptiles, 17% of mammals, 16% of dragonflies, 13% of birds, 9% of butterflies and bees, 8% of aquatic plants and marine fishes and 2% of medicinal plants (IUCN 2015). Additional European Red Lists assessing a selection of species showed that 22% of terrestrial molluscs, 16% of crop wild relatives and 15% of saproxylic beetles are also threatened (IUCN 2015). No other groups have yet been assessed at the European level. Looking at the population trends of European Orthoptera species, 30.2% (325 species) have declining populations, 7.6% (82 species) are believed to be more or less stable and 3.2% (34 species) are increasing. However, the population trends for the majority of species (59%, 634 species) remain unknown. Out of the 739 species that are endemic to Europe (i.e., they are found nowhere else in the world), 231 (31.3%) are threatened, highlighting the responsibility that European countries have to protect the global populations of these species. Overall, the European areas with the highest diversity of species are found in southern Europe, especially in the Mediterranean region and the Balkans. Hotspots of endemic species are found in the Iberian, the Italian and the Balkan Peninsulas, and in some large mountain areas (the Alps, Pyrenees, Carpathians and Appenines). The greatest concentration of threatened species is found along some Mediterranean coasts and Mediterranean mountain blocks. Finally, the number of Data Deficient species reflects the general distribution of Orthoptera species, being highest in the Mediterranean and the Lower Volga region in southern European Russia. The main threat to European Orthoptera is the loss, degradation and fragmentation of their habitats as a consequence of agricultural land use intensification. This includes direct destruction by transformation of permanent grassland or shrubland habitats into cropland, degradation of habitat quality caused by overgrazing, abandonment, use of fertilisers or heavy machinery and direct mortality from frequent mowing or the use of pesticides. Other important threats to Orthoptera are the increasing frequency of wildfires, touristic development and urbanisation, climate change, afforestation and intensive forest management, drainage and river regulations, recreational activities, deforestation, limestone quarrying and sand excavations and invasive species.Orthoptera are a diverse group of insects with more than 1,000 species known to occur in Europe and play important roles in the ecosystem such as being part of the food chain and prey to many vertebrate species. They are also good indicators of land use intensity, which makes them one of the most important invertebrate groups for environmental monitoring and assessment. Conservation strategies for the European Orthoptera species with the highest extinction risk should be developed and implemented. The European Red List should be used to inform nature and biodiversity policies to improve the status of threatened species. The Common Agricultural Policy (CAP) should be enhanced by promoting traditional low-intensity agricultural land use systems, particularly pastoralism in Europe, and committing to a long-term reduction in the use of pesticides and fertilisers, encouraging the uptake of alternative pest management. Orthoptera species should be made a standard group for inclusion in Environmental Impact Assessments to avoid negative impacts of new development projects on threatened species.Degraded habitats of threatened Orthoptera species throughout Europe should be restored and guidelines for the optimal management of Orthoptera habitats should be developed. The protection of Orthoptera habitats throughout Europe should be improved, so that each threatened and endemic European species is present in at least one protected area with an adequate adaptive management scheme and monitoring for threatened Orthoptera species. Orthoptera inventories in protected areas should be made mandatory to identify priority species for the respective area and develop strategies for their protection. A pan-European monitoring programme for Orthoptera species should be developed, by merging all existing recording schemes. Specific research on those species that have not been recently recorded in Europe to clarify if they may be Extinct or Regionally Extinct, or have been assessed as Data Deficient should be conducted and funding mechanisms should be put in place to support this research. The effects of the lesser understood threats (e.g., wildfires, pesticides, climate change) on Orthoptera should be studied. The European Red List of Grasshoppers, Crickets and Bush-crickets should be revised at regular intervals of ten years, and whenever new data becomes available.
Evidence-based guidelines on the management of pancreatic cystic neoplasms (PCN) are lacking. This guideline is a joint initiative of the European Study Group on Cystic Tumours of the Pancreas, United European Gastroenterology, European Pancreatic Club, European-African Hepato-Pancreato-Biliary Association, European Digestive Surgery, and the European Society of Gastrointestinal Endoscopy. It replaces the 2013 European consensus statement guidelines on PCN. European and non-European experts performed systematic reviews and used GRADE methodology to answer relevant clinical questions on nine topics (biomarkers, radiology, endoscopy, intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), serous cystic neoplasm, rare cysts, (neo)adjuvant treatment, and pathology). Recommendations include conservative management, relative and absolute indications for surgery. A conservative approach is recommended for asymptomatic MCN and IPMN measuring = 40 mm. Absolute indications for surgery in IPMN, due to the high-risk of malignant transformation, include jaundice, an enhancing mural nodule > 5 mm, and MPD diameter > 10 mm. Lifelong follow-up of IPMN is recommended in patients who are fit for surgery. The European evidence-based guidelines on PCN aim to improve the diagnosis and management of PCN.
Evidence-based guidelines on the management of pancreatic cystic neoplasms (PCN) are lacking. This guideline is a joint initiative of the European Study Group on Cystic Tumours of the Pancreas, United European Gastroenterology, European Pancreatic Club, European-African Hepato-Pancreato-Biliary Association, European Digestive Surgery, and the European Society of Gastrointestinal Endoscopy. It replaces the 2013 European consensus statement guidelines on PCN. European and non-European experts performed systematic reviews and used GRADE methodology to answer relevant clinical questions on nine topics (biomarkers, radiology, endoscopy, intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), serous cystic neoplasm, rare cysts, (neo)adjuvant treatment, and pathology). Recommendations include conservative management, relative and absolute indications for surgery. A conservative approach is recommended for asymptomatic MCN and IPMN measuring=40 mm. Absolute indications for surgery in IPMN, due to the high-risk of malignant transformation, include jaundice, an enhancing mural nodule>5 mm, and MPD diameter>10 mm. Lifelong follow-up of IPMN is recommended in patients who are fit for surgery. The European evidence-based guidelines on PCN aim to improve the diagnosis and management of PCN.
Evidence-based guidelines on the management of pancreatic cystic neoplasms (PCN) are lacking. This guideline is a joint initiative of the European Study Group on Cystic Tumours of the Pancreas, United European Gastroenterology, European Pancreatic Club, European-African Hepato-Pancreato-Biliary Association, European Digestive Surgery, and the European Society of Gastrointestinal Endoscopy. It replaces the 2013 European consensus statement guidelines on PCN. European and non-European experts performed systematic reviews and used GRADE methodology to answer relevant clinical questions on nine topics (biomarkers, radiology, endoscopy, intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), serous cystic neoplasm, rare cysts, (neo)adjuvant treatment, and pathology). Recommendations include conservative management, relative and absolute indications for surgery. A conservative approach is recommended for asymptomatic MCN and IPMN measuring 5 mm, and MPD diameter >10 mm. Lifelong follow-up of IPMN is recommended in patients who are fit for surgery. The European evidence-based guidelines on PCN aim to improve the diagnosis and management of PCN.
ERECON (2014) Strengthening the European rare earths supply chain: Challengesand policy options. Kooroshy, J., G. Tiess, A. Tukker, and A. Walton (eds.). ; Policy recommendations:1.Maintaining and strengthening the European Rare Earth Elements (REE) skills and knowledge base through research funding, science and technology education and international cooperation.Without cutting-edge research and technical expertise, a European high-tech REE industry cannot flourish. The EC and Member States should support funding for research grants, scholarships, and training networks, and enhance European and international cooperation through coordinated calls, researcher exchanges, and joint high-level conferences.2.Creating the basis for informed decision-making on REEs through a European Critical Materials Observatory.Mapping and monitoring of REE supply chains is necessary for informed decision-making. Expertise in Europe could be pooled in a virtual Critical Materials Observatory that provides the public with consistent and authoritative knowledge on REEs (e.g., information on advanced exploration projects, prices, key demand and supply trends, and the urban mine potential).3.Support promising technologies through funding industry-led pilot plants for innovative HREE processing.The EC, industry and Member States should accelerate the commercialization and scaling up of key technologies through co-financing industry-led pilot plants. This should include pilots for REE recovery from heavy rare earths-rich minerals, direct-alloy recycling routes, process and sensor equipment for REE recycling, and REE recovery from industrial residues.4.Levelling the playing field for European HREE exploration through co-funding for prefeasibility and bankable feasibility studies.Support from federal and state governments in the U.S., Australia and Canada has played a critical role in advancing project exploration. The EC and Member States should evaluate possibilities for supporting the extensive R&D necessary for pre-feasibility and bankable feasibility studies, to avoid high quality deposits in Europe simply going unexplored.5.Making waste management REE-friendly through eco-design, incentive schemes for collecting priority waste products, and streamlining policy and waste regulation.The EC and Member States should promote recycling-friendly design to help identify and recover REE components in waste more easily. Potential incentives for stimulating REE waste collection should be evaluated and the shipment of REE wastes should be facilitated. More consistency should also be created in implementing and applying existing waste regulations.6.Boost supply security and de-risk strategic REE investment cases through enhanced cooperation among European end-users and other stakeholders.Leading end-users should engage in strategic cooperation across industry and with governments. This could include setting up a voluntary European 'critical raw materials fund', establishing a 'European Resource Alliance' similar to the German Rohstoffallianz, and convening a high-level taskforce to examine ways in which public funding could support resilient REE supply chains for Europe.
BACKGROUND: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. RESULTS: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. CONCLUSION: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. ; The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnología Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto käyttöön grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC – IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124. The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660 /2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI / ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Università degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443 ; Sí
BACKGROUND: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. RESULTS: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. CONCLUSION: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. ; The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto kayttoon grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC -IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124. The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660/2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Universita degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443. ; Sí
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; FINEP (Brazil) ; NSFC (China) ; CNRS/IN2P3 (France) ; BMBF (Germany) ; DFG (Germany) ; HGF (Germany) ; SFI (Ireland) ; INFN (Italy) ; NASU (Ukraine) ; STFC (UK) ; NSF (USA) ; BMWFW (Austria) ; FWF (Austria) ; FNRS (Belgium) ; FWO (Belgium) ; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ; MES (Bulgaria) ; CAS (China) ; MoST (China) ; COLCIENCIAS (Colombia) ; MSES (Croatia) ; CSF (Croatia) ; RPF (Cyprus) ; MoER (Estonia) ; ERC IUT (Estonia) ; ERDF (Estonia) ; Academy of Finland (Finland) ; MEC (Finland) ; HIP (Finland) ; CEA (France) ; GSRT (Greece) ; OTKA (Hungary) ; NIH (Hungary) ; DAE (India) ; DST (India) ; IPM (Iran) ; NRF (Republic of Korea) ; WCU (Republic of Korea) ; LAS (Lithuania) ; MOE (Malaysia) ; UM (Malaysia) ; CINVESTAV (Mexico) ; CONACYT (Mexico) ; SEP (Mexico) ; UASLP-FAI (Mexico) ; MBIE (New Zealand) ; PAEC (Pakistan) ; MSHE (Poland) ; NSC (Poland) ; FCT (Portugal) ; JINR (Dubna) ; MON (Russia) ; RosAtom (Russia) ; RAS (Russia) ; RFBR (Russia) ; MESTD (Serbia) ; SEIDI (Spain) ; CPAN (Spain) ; MST (Taipei) ; ThEPCenter (Thailand) ; IPST (Thailand) ; STAR (Thailand) ; NSTDA (Thailand) ; TUBITAK (Turkey) ; TAEK (Turkey) ; SFFR (Ukraine) ; DOE (USA) ; MPG (Germany) ; FOM (The Netherlands) ; NWO (The Netherlands) ; MNiSW (Poland) ; NCN (Poland) ; MEN/IFA (Romania) ; MinES (Russia) ; FANO (Russia) ; MinECo (Spain) ; SNSF (Switzerland) ; SER (Switzerland) ; Marie-Curie programme ; European Research Council ; EPLANET (European Union) ; Leventis Foundation ; A. P. Sloan Foundation ; Alexander von Humboldt Foundation ; Belgian Federal Science Policy Office ; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIABelgium) ; Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium) ; Ministry of Education, Youth and Sports (MEYS) of the Czech Republic ; Council of Science and Industrial Research, India ; Foundation for Polish Science ; European Union, Regional Development Fund ; Compagnia di San Paolo (Torino) ; Consorzio per la Fisica (Trieste) ; MIUR (Italy) ; Thalis programme ; Aristeia programme ; EU-ESF ; Greek NSRF ; National Priorities Research Program by Qatar National Research Fund ; EPLANET ; Marie Sklodowska-Curie Actions ; ERC (European Union) ; Conseil general de Haute-Savoie ; Labex ENIGMASS ; OCEVU ; Region Auvergne (France) ; XuntaGal (Spain) ; GENCAT (Spain) ; Royal Society (UK) ; Royal Commission for the Exhibition of 1851 (UK) ; MIUR (Italy): 20108T4XTM ; The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. The probabilities, or branching fractions, of the strange B meson (B-s(0)) and the B-0 meson decaying into two oppositely charged muons (mu(+) and mu(-)) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the B-s(0)->mu(+)mu(-) and B-0 ->mu(+)mu(-) decays are very rare, with about four of the former occurring for every billion B-s(0) mesons produced, and one of the latter occurring for every ten billion B-0 mesons(1). A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN2 started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb(Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton-proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the B-s(0)->mu(+)mu(-) decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. Furthermore, we obtained evidence for the B-0 ->mu(+)mu(-) decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton-proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of B-s(0) and B-0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.
Background Surgery is the main modality of cure for solid cancers and was prioritised to continue during COVID-19 outbreaks. This study aimed to identify immediate areas for system strengthening by comparing the delivery of elective cancer surgery during the COVID-19 pandemic in periods of lockdown versus light restriction. Methods This international, prospective, cohort study enrolled 20 006 adult (≥18 years) patients from 466 hospitals in 61 countries with 15 cancer types, who had a decision for curative surgery during the COVID-19 pandemic and were followed up until the point of surgery or cessation of follow-up (Aug 31, 2020). Average national Oxford COVID-19 Stringency Index scores were calculated to define the government response to COVID-19 for each patient for the period they awaited surgery, and classified into light restrictions (index 60). The primary outcome was the non-operation rate (defined as the proportion of patients who did not undergo planned surgery). Cox proportional-hazards regression models were used to explore the associations between lockdowns and non-operation. Intervals from diagnosis to surgery were compared across COVID-19 government response index groups. This study was registered at ClinicalTrials.gov, NCT04384926. Findings Of eligible patients awaiting surgery, 2003 (10·0%) of 20 006 did not receive surgery after a median follow-up of 23 weeks (IQR 16–30), all of whom had a COVID-19-related reason given for non-operation. Light restrictions were associated with a 0·6% non-operation rate (26 of 4521), moderate lockdowns with a 5·5% rate (201 of 3646; adjusted hazard ratio [HR] 0·81, 95% CI 0·77–0·84; p<0·0001), and full lockdowns with a 15·0% rate (1775 of 11 827; HR 0·51, 0·50–0·53; p<0·0001). In sensitivity analyses, including adjustment for SARS-CoV-2 case notification rates, moderate lockdowns (HR 0·84, 95% CI 0·80–0·88; p<0·001), and full lockdowns (0·57, 0·54–0·60; p<0·001), remained independently associated with non-operation. Surgery beyond 12 weeks from diagnosis in patients without neoadjuvant therapy increased during lockdowns (374 [9·1%] of 4521 in light restrictions, 317 [10·4%] of 3646 in moderate lockdowns, 2001 [23·8%] of 11 827 in full lockdowns), although there were no differences in resectability rates observed with longer delays. Interpretation Cancer surgery systems worldwide were fragile to lockdowns, with one in seven patients who were in regions with full lockdowns not undergoing planned surgery and experiencing longer preoperative delays. Although short-term oncological outcomes were not compromised in those selected for surgery, delays and non-operations might lead to long-term reductions in survival. During current and future periods of societal restriction, the resilience of elective surgery systems requires strengthening, which might include protected elective surgical pathways and long-term investment in surge capacity for acute care during public health emergencies to protect elective staff and services. Funding National Institute for Health Research Global Health Research Unit, Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, Medtronic, Sarcoma UK, The Urology Foundation, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research.
Background Surgery is the main modality of cure for solid cancers and was prioritised to continue during COVID-19 outbreaks. This study aimed to identify immediate areas for system strengthening by comparing the delivery of elective cancer surgery during the COVID-19 pandemic in periods of lockdown versus light restriction. Methods This international, prospective, cohort study enrolled 20 006 adult (≥18 years) patients from 466 hospitals in 61 countries with 15 cancer types, who had a decision for curative surgery during the COVID-19 pandemic and were followed up until the point of surgery or cessation of follow-up (Aug 31, 2020). Average national Oxford COVID-19 Stringency Index scores were calculated to define the government response to COVID-19 for each patient for the period they awaited surgery, and classified into light restrictions (index 60). The primary outcome was the non-operation rate (defined as the proportion of patients who did not undergo planned surgery). Cox proportional-hazards regression models were used to explore the associations between lockdowns and non-operation. Intervals from diagnosis to surgery were compared across COVID-19 government response index groups. This study was registered at ClinicalTrials.gov, NCT04384926. Findings Of eligible patients awaiting surgery, 2003 (10·0%) of 20 006 did not receive surgery after a median follow-up of 23 weeks (IQR 16–30), all of whom had a COVID-19-related reason given for non-operation. Light restrictions were associated with a 0·6% non-operation rate (26 of 4521), moderate lockdowns with a 5·5% rate (201 of 3646; adjusted hazard ratio [HR] 0·81, 95% CI 0·77–0·84; p<0·0001), and full lockdowns with a 15·0% rate (1775 of 11 827; HR 0·51, 0·50–0·53; p<0·0001). In sensitivity analyses, including adjustment for SARS-CoV-2 case notification rates, moderate lockdowns (HR 0·84, 95% CI 0·80–0·88; p<0·001), and full lockdowns (0·57, 0·54–0·60; p<0·001), remained independently associated with non-operation. Surgery beyond 12 weeks from diagnosis in patients without neoadjuvant therapy increased during lockdowns (374 [9·1%] of 4521 in light restrictions, 317 [10·4%] of 3646 in moderate lockdowns, 2001 [23·8%] of 11827 in full lockdowns), although there were no differences in resectability rates observed with longer delays. Interpretation Cancer surgery systems worldwide were fragile to lockdowns, with one in seven patients who were in regions with full lockdowns not undergoing planned surgery and experiencing longer preoperative delays. Although short-term oncological outcomes were not compromised in those selected for surgery, delays and non-operations might lead to long-term reductions in survival. During current and future periods of societal restriction, the resilience of elective surgery systems requires strengthening, which might include protected elective surgical pathways and long- term investment in surge capacity for acute care during public health emergencies to protect elective staff and services. Funding National Institute for Health Research Global Health Research Unit, Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, Medtronic, Sarcoma UK, The Urology Foundation, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research.
Background: Surgery is the main modality of cure for solid cancers and was prioritised to continue during COVID-19 outbreaks. This study aimed to identify immediate areas for system strengthening by comparing the delivery of elective cancer surgery during the COVID-19 pandemic in periods of lockdown versus light restriction. Methods: This international, prospective, cohort study enrolled 20 006 adult (≥18 years) patients from 466 hospitals in 61 countries with 15 cancer types, who had a decision for curative surgery during the COVID-19 pandemic and were followed up until the point of surgery or cessation of follow-up (Aug 31, 2020). Average national Oxford COVID-19 Stringency Index scores were calculated to define the government response to COVID-19 for each patient for the period they awaited surgery, and classified into light restrictions (index 60). The primary outcome was the non-operation rate (defined as the proportion of patients who did not undergo planned surgery). Cox proportional-hazards regression models were used to explore the associations between lockdowns and non-operation. Intervals from diagnosis to surgery were compared across COVID-19 government response index groups. This study was registered at ClinicalTrials.gov, NCT04384926. Findings: Of eligible patients awaiting surgery, 2003 (10·0%) of 20 006 did not receive surgery after a median follow-up of 23 weeks (IQR 16-30), all of whom had a COVID-19-related reason given for non-operation. Light restrictions were associated with a 0·6% non-operation rate (26 of 4521), moderate lockdowns with a 5·5% rate (201 of 3646; adjusted hazard ratio [HR] 0·81, 95% CI 0·77-0·84; p<0·0001), and full lockdowns with a 15·0% rate (1775 of 11 827; HR 0·51, 0·50-0·53; p<0·0001). In sensitivity analyses, including adjustment for SARS-CoV-2 case notification rates, moderate lockdowns (HR 0·84, 95% CI 0·80-0·88; p<0·001), and full lockdowns (0·57, 0·54-0·60; p<0·001), remained independently associated with non-operation. Surgery beyond 12 weeks from diagnosis in patients without neoadjuvant therapy increased during lockdowns (374 [9·1%] of 4521 in light restrictions, 317 [10·4%] of 3646 in moderate lockdowns, 2001 [23·8%] of 11 827 in full lockdowns), although there were no differences in resectability rates observed with longer delays. Interpretation: Cancer surgery systems worldwide were fragile to lockdowns, with one in seven patients who were in regions with full lockdowns not undergoing planned surgery and experiencing longer preoperative delays. Although short-term oncological outcomes were not compromised in those selected for surgery, delays and non-operations might lead to long-term reductions in survival. During current and future periods of societal restriction, the resilience of elective surgery systems requires strengthening, which might include protected elective surgical pathways and long-term investment in surge capacity for acute care during public health emergencies to protect elective staff and services.