Action Prediction in Humans and Robots

Efficient action prediction is of central importance for the fluent workflow between humans and equally so for human-robot interaction. To achieve prediction, actions can be encoded by a series of events, where every event corresponds to a change in a (static or dynamic) relation between some of the objects in a scene. Manipulation actions and others can be uniquely encoded this way and only, on average, less than 60% of the time series has to pass until an action can be predicted. Using a virtual reality setup and testing ten different manipulation actions, here we show that in most cases humans predict actions at the same event as the algorithm. In addition, we perform an in-depth analysis about the temporal gain resulting from such predictions when chaining actions and show in some robotic experiments that the percentage gain for humans and robots is approximately equal. Thus, if robots use this algorithm then their prediction-moments will be compatible to those of their human interaction partners, which should much benefit natural human-robot collaboration

Towards a cross-domain interoperable framework for natural hazards and disaster risk reduction information

According to the United Nations’ International Strategy for Disaster Reduction, “natural hazards are processes or phenomena that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage”. They are at the interface between human and natural systems. From this perspective, natural hazards are a multidimensional domain including environmental issues, the private and public sector and citizens and governance ranging from local to supranational. The vast amount of information and data necessary for comprehensive hazard and risk assessment present many challenges regarding the lack of accessibility, comparability, quality, organisation and dissemination of natural hazards spatial data. In order to mitigate these limitations, an interoperability framework has been developed and published in the INSPIRE Data Specification on Natural Risk Zones—technical guidelines (DS) document. This framework provides means for facilitating access, integration, harmonisation and dissemination of natural hazard data from different domains and sources. The objective of this paper is twofold. Firstly, the paper highlights the key aspects of the interoperability to the various natural hazard communities and illustrates the applicability of the interoperability framework developed in the DS. And secondly, the paper “translates” into common language the main features and potentiality of the interoperability framework of the DS for a wider audience of scientists and practitioners in the natural hazard domain. In this paper, the four pillars of the interoperability framework will be presented. First, the adoption of a common terminology for the natural hazard domain will be addressed. A common data model to facilitate cross-domain data integration will then follow. Thirdly, the common methodology developed to express qualitative or quantitative assessments of natural hazards is presented. Fourthly, the extensible classification schema for natural hazards developed from a literature review and key reference documents from the contributing community of practice is discussed. Furthermore, the applicability of the interoperability framework for the various stakeholder groups is illustrated. This paper closes discussing main advantages, limitations and next steps regarding the sustainability and evolution of the interoperability framework

Advance EFFIS report on forest fires in Europe, Middle East and North Africa 2019

This report contains an anticipated annual summary of the fire season of 2019 with an analysis of fire danger and areas mapped in the European Forest Fire Information System (EFFIS). This report precedes that to be published in August/September 2020, which will include detailed reports prepared by countries in the Expert Group on Forest Fires.JRC.E.1-Disaster Risk Managemen

Ongoing Laboratory Performance Study on Chemical Analysis of Hydrophobic and Hydrophilic Compounds in Three Aquatic Passive Samplers

The quality of chemical analysis is an important aspect of passive sampling-based environmental assessments. The present study reports on a proficiency testing program for the chemical analysis of hydrophobic organic compounds in silicone and low-density polyethylene (LDPE) passive samplers and hydrophilic compounds in polar organic chemical integrative samplers. The median between-laboratory coefficients of variation (CVs) of hydrophobic compound concentrations in the polymer phase were 33% (silicone) and 38% (LDPE), similar to the CVs obtained in four earlier rounds of this program. The median CV over all rounds was 32%. Much higher variabilities were observed for hydrophilic compound concentrations in the sorbent: 50% for the untransformed data and a factor of 1.6 after log transformation. Limiting the data to the best performing laboratories did not result in less variability. Data quality for hydrophilic compounds was only weakly related to the use of structurally identical internal standards and was unrelated to the choice of extraction solvent and extraction time. Standard deviations of the aqueous concentration estimates for hydrophobic compound sampling by the best performing laboratories were 0.21 log units for silicone and 0.27 log units for LDPE (factors of 1.6 to 1.9). The implications are that proficiency testing programs may give more realistic estimates of uncertainties in chemical analysis than within-laboratory quality control programs and that these high uncertainties should be taken into account in environmental assessments

Forest Fires in Europe, Middle East and North Africa 2018

This is the 19th issue of the EFFIS annual report on forest fires for the year 2018. This report is consolidated as highly appreciated documentation of the previous year's forest fires in Europe, Middle East and North Africa. In its different sections, the report includes information on the evolution of fire danger in the European and Mediterranean regions, the damage caused by fires and detailed description of the fire conditions during the 2017 fire campaign in the majority of countries in the EFFIS network. The chapter on national reporting gives an overview of the efforts undertaken at national and regional levels, and provides inspiration for countries exposed to forest fire risk.JRC.E.1-Disaster Risk Managemen

Transethnic Genome-Wide Association Study Provides Insights in the Genetic Architecture and Heritability of Long QT Syndrome

BACKGROUND: Long QT syndrome (LQTS) is a rare genetic disorder and a major preventable cause of sudden cardiac death in the young. A causal rare genetic variant with large effect size is identified in up to 80% of probands (genotype positive) and cascade family screening shows incomplete penetrance of genetic variants. Furthermore, a proportion of cases meeting diagnostic criteria for LQTS remain genetically elusive despite genetic testing of established genes (genotype negative). These observations raise the possibility that common genetic variants with small effect size contribute to the clinical picture of LQTS. This study aimed to characterize and quantify the contribution of common genetic variation to LQTS disease susceptibility. METHODS: We conducted genome-wide association studies followed by transethnic meta-analysis in 1656 unrelated patients with LQTS of European or Japanese ancestry and 9890 controls to identify susceptibility single nucleotide polymorphisms. We estimated the common variant heritability of LQTS and tested the genetic correlation between LQTS susceptibility and other cardiac traits. Furthermore, we tested the aggregate effect of the 68 single nucleotide polymorphisms previously associated with the QT-interval in the general population using a polygenic risk score. RESULTS: Genome-wide association analysis identified 3 loci associated with LQTS at genome-wide statistical significance (P&lt;5×10-8) near NOS1AP, KCNQ1, and KLF12, and 1 missense variant in KCNE1(p.Asp85Asn) at the suggestive threshold (P&lt;10-6). Heritability analyses showed that ≈15% of variance in overall LQTS susceptibility was attributable to common genetic variation (h2SNP 0.148; standard error 0.019). LQTS susceptibility showed a strong genome-wide genetic correlation with the QT-interval in the general population (rg=0.40; P=3.2×10-3). The polygenic risk score comprising common variants previously associated with the QT-interval in the general population was greater in LQTS cases compared with controls (P&lt;10-13), and it is notable that, among patients with LQTS, this polygenic risk score was greater in patients who were genotype negative compared with those who were genotype positive (P&lt;0.005). CONCLUSIONS: This work establishes an important role for common genetic variation in susceptibility to LQTS. We demonstrate overlap between genetic control of the QT-interval in the general population and genetic factors contributing to LQTS susceptibility. Using polygenic risk score analyses aggregating common genetic variants that modulate the QT-interval in the general population, we provide evidence for a polygenic architecture in genotype negative LQTS.</p

Global Analysis of the Evolution and Mechanism of Echinocandin Resistance in Candida glabrata

The evolution of drug resistance has a profound impact on human health. Candida glabrata is a leading human fungal pathogen that can rapidly evolve resistance to echinocandins, which target cell wall biosynthesis and are front-line therapeutics for Candida infections. Here, we provide the first global analysis of mutations accompanying the evolution of fungal drug resistance in a human host utilizing a series of C. glabrata isolates that evolved echinocandin resistance in a patient treated with the echinocandin caspofungin for recurring bloodstream candidemia. Whole genome sequencing identified a mutation in the drug target, FKS2, accompanying a major resistance increase, and 8 additional non-synonymous mutations. The FKS2-T1987C mutation was sufficient for echinocandin resistance, and associated with a fitness cost that was mitigated with further evolution, observed in vitro and in a murine model of systemic candidemia. A CDC6-A511G(K171E) mutation acquired before FKS2-T1987C(S663P), conferred a small resistance increase. Elevated dosage of CDC55, which acquired a C463T(P155S) mutation after FKS2-T1987C(S663P), ameliorated fitness. To discover strategies to abrogate echinocandin resistance, we focused on the molecular chaperone Hsp90 and downstream effector calcineurin. Genetic or pharmacological compromise of Hsp90 or calcineurin function reduced basal tolerance and resistance. Hsp90 and calcineurin were required for caspofungin-dependent FKS2 induction, providing a mechanism governing echinocandin resistance. A mitochondrial respiration-defective petite mutant in the series revealed that the petite phenotype does not confer echinocandin resistance, but renders strains refractory to synergy between echinocandins and Hsp90 or calcineurin inhibitors. The kidneys of mice infected with the petite mutant were sterile, while those infected with the HSP90-repressible strain had reduced fungal burden. We provide the first global view of mutations accompanying the evolution of fungal drug resistance in a human host, implicate the premier compensatory mutation mitigating the cost of echinocandin resistance, and suggest a new mechanism of echinocandin resistance with broad therapeutic potential

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

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio