Model-driven quality and resource management for CPSs

A Cyber-Physical System (CPS) integrates cyber systems, human users, networks and physical systems. Thus, a CPS needs visual context and awareness to make autonomous and correct decisions. Advanced image and video processing is computationally intensive and challenging. Moreover, a CPS comprises increasingly complex and distributed configurations, which is reflectedin the growing number of sensors, actuators and other smart devices. This leadsto an exponential number of dynamic system configurations. To make mattersworse, a CPS needs to simultaneously satisfy many rigorous constraints, e.g.,hard deadlines, safety, quality, and performance. Hence, the system designeris confronted with an immense number of potential configurations of which anumber meet the constraints and only a fraction are optimal regarding certainqualities. This makes finding the optimal configurations hard, especially duringrun-time. A domain-specific language (DSL) for quality and resource managment (QRM) is presented to specify these configurations conveniently and reasonabout them in an automated manner

Bivalirudin started during emergency transport for primary PCI.

BACKGROUND: Bivalirudin, as compared with heparin and glycoprotein IIb/IIIa inhibitors, has been shown to reduce rates of bleeding and death in patients undergoing primary percutaneous coronary intervention (PCI). Whether these benefits persist in contemporary practice characterized by prehospital initiation of treatment, optional use of glycoprotein IIb/IIIa inhibitors and novel P2Y12 inhibitors, and radial-artery PCI access use is unknown. METHODS: We randomly assigned 2218 patients with ST-segment elevation myocardial infarction (STEMI) who were being transported for primary PCI to receive either bivalirudin or unfractionated or low-molecular-weight heparin with optional glycoprotein IIb/IIIa inhibitors (control group). The primary outcome at 30 days was a composite of death or major bleeding not associated with coronary-artery bypass grafting (CABG), and the principal secondary outcome was a composite of death, reinfarction, or non-CABG major bleeding. RESULTS: Bivalirudin, as compared with the control intervention, reduced the risk of the primary outcome (5.1% vs. 8.5%; relative risk, 0.60; 95% confidence interval [CI], 0.43 to 0.82; P=0.001) and the principal secondary outcome (6.6% vs. 9.2%; relative risk, 0.72; 95% CI, 0.54 to 0.96; P=0.02). Bivalirudin also reduced the risk of major bleeding (2.6% vs. 6.0%; relative risk, 0.43; 95% CI, 0.28 to 0.66; P<0.001). The risk of acute stent thrombosis was higher with bivalirudin (1.1% vs. 0.2%; relative risk, 6.11; 95% CI, 1.37 to 27.24; P=0.007). There was no significant difference in rates of death (2.9% vs. 3.1%) or reinfarction (1.7% vs. 0.9%). Results were consistent across subgroups of patients. CONCLUSIONS: Bivalirudin, started during transport for primary PCI, improved 30-day clinical outcomes with a reduction in major bleeding but with an increase in acute stent thrombosis. (Funded by the Medicines Company; EUROMAX ClinicalTrials.gov number, NCT01087723.)

Easy Wireless: broadband ad-hoc networking for emergency services

Wireless ad-hoc networks will enable emergency services to continuously overview and act upon the actual status of the situation by retrieving and exchanging detailed up-to-date information between the rescue workers. Deployment of high-bandwidth, robust, self-organising ad-hoc networks will enable quicker response to typical what/where/when questions, than the more vulnerable low-bandwidth communication networks currently in use. This paper addresses a number of results of the Easy Wireless project that enable high bandwidth robust ad-hoc networking. Most of the concepts presented here have been experimentally verified and/or prototyped

An exploratory study of perinatal hair cortisol concentrations in mother–infant dyads with severe psychiatric disorders versus healthy controls

Background Maternal psychopathology during pregnancy is associated with negative outcomes in offspring. Increased placental transfer of maternal cortisol may contribute to mediate this association. Hair cortisol concentrations (HCCs) appear to be a good biomarker of long-term prenatal stress exposure. Little is known about the associations between severe maternal psychopathology and perinatal infant HCCs. Aims We assessed HCCs in the perinatal period in mother–infant dyads with and without severe psychiatric disorders. Method We examined group differences in HCCs of mother–infant dyads (n = 18) subjected to severe maternal psychiatric disorders versus healthy control dyads (n = 27). We assessed the correlation of HCCs between mother and infant within both groups, and the association between current maternal symptoms and HCCs in patient dyads. Results Median (interquartile range) and distribution of HCC differed in patients compared with control mothers (U = 468.5, P = 0.03). HCCs in infants of patients did not differ from control infants (U = 250.0, P = 0.67). Subsequently, we found that HCCs within healthy control dyads were correlated (n = 27, r 0.55 (0.14), P = 0.003), but were not within patient dyads (n = 18, r 0.082 (0.13), P = 0.746). HCCs in infants of patients showed a positive correlation with maternal symptoms (n = 16, r = 0.63 (0.06), P = 0.008). Conclusions These preliminary findings suggest that infant HCC reflect perinatal stress exposure. In infants, these early differences could influence lifetime hypothalamic–pituitary–adrenal axis functioning, which might be associated with increased susceptibility to later disease

Detection of long repeat expansions from PCR-free whole-genome sequence data

Identifying large expansions of short tandem repeats (STRs) such as those that cause amyotrophic lateral sclerosis (ALS) and fragile X syndrome is challenging for short-read whole-genome sequencing (WGS) data. A solution to this problem is an important step towards integrating WGS into precision medicine. We have developed a software tool called ExpansionHunter that, using PCR-free WGS short-read data, can genotype repeats at the locus of interest, even if the expanded repeat is larger than the read length. We applied our algorithm to WGS data from 3,001 ALS patients who have been tested for the presence of the C9orf72 repeat expansion with repeat-primed PCR (RP-PCR). Compared against this truth data, ExpansionHunter correctly classified all (212/212, 95% CI [0.98, 1.00]) of the expanded samples as either expansions (208) or potential expansions (4). Additionally, 99.9% (2,786/2,789, 95% CI [0.997, 1.00]) of the wild type samples were correctly classified as wild type by this method with the remaining three samples identified as possible expansions. We further applied our algorithm to a set of 152 samples where every sample had one of eight different pathogenic repeat expansions including those associated with fragile X syndrome, Friedreich's ataxia and Huntington's disease and correctly flagged all but one of the known repeat expansions. Thus, ExpansionHunter can be used to accurately detect known pathogenic repeat expansions and provides researchers with a tool that can be used to identify new pathogenic repeat expansions. The software is licensed under GPL v3.0 and the source code is freely available on GitHub

Social rearing environment influences dog behavioral development

Early life experiences are known to influence behavior later in life. In dogs, environmental influences of early home rearing could be exploited to improve the chances of developing adult behavior most suited to the adult environment. For working dog organizations, such as Guide Dogs, suitable adult behavior is important to ensure that dogs can fulfill their role as guides for people with visual impairment. Here, we test the hypothesis that dogs' home rearing environment will influence behavioral development. To investigate this hypothesis, carers of potential guide dogs (puppy walkers) completed a questionnaire, termed the Puppy Walker Questionnaire (PWQ), about the dog's behavior at 5, 8, and 12 months of age. An additional 11 questions were answered about the home environment at the last assessment. Because no questionnaire existed which measured behavior most relevant to Guide Dogs, questions from an existing questionnaire (Canine Behaviour and Research Questionnaire) were combined with additional questions. Thus, a subsidiary aim of the study was to test the reliability of the PWQ for measuring behavioral development of potential guide dogs. The PWQ, scored on a 100-mm visual analogue scale, grouped into 5 new scales: trainability, distractibility, general anxiety, body sensitivity, and stair anxiety, with 4 Canine Behaviour and Research Questionnaire scales: excitability, separation-related behavior, attachment and attention seeking, and energy level. For each reliable scale, multivariate linear regression identified significant predictors from the home environmental questions. The results suggest that home rearing environment is indeed important for behavioral development: 9 of 11 environmental variables were significant predictors of behavioral scores. Those environmental variables that appeared most important were social in nature. Dogs were scored as higher in energy level, excitability, and distractibility if they had been raised in a home with children, lower on energy level and distractibility the more experience of puppy walking their carer had, and lower on separation-related behavior the more they had been able to play with other dogs. These findings have implications for matching between dogs' early and later home environments. Follow-up of dogs in this study could help to elucidate effects on guiding suitability and matching between dog and guide dog owner

A hierarchy of heuristic-based models of crowd dynamics

International audienceWe derive a hierarchy of kinetic and macroscopic models from a noisy variant of the heuristic behavioral Individual-Based Model of Moussaid et al, PNAS 2011, where the pedestrians are supposed to have constant speeds. This IBM supposes that the pedestrians seek the best compromise between navigation towards their target and collisions avoidance. We first propose a kinetic model for the probability distribution function of the pedestrians. Then, we derive fluid models and propose three different closure relations. The first two closures assume that the velocity distribution functions are either a Dirac delta or a von Mises-Fisher distribution respectively. The third closure results from a hydrodynamic limit associated to a Local Thermodynamical Equilibrium. We develop an analogy between this equilibrium and Nash equilibia in a game theoretic framework. In each case, we discuss the features of the models and their suitability for practical use

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education, Scientific Research and Professional Training, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.French National Research Agency (ANR) ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund)European Union (EU)Institut Universitaire de France (IUF)LabEx UnivEarthS ANR-10-LABX-0023 ANR-18-IDEX-0001Shota Rustaveli National Science Foundation of Georgia FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR) Research Projects of National Relevance (PRIN)Ministry of Higher Education, Scientific Research and Professional Training, MoroccoNetherlands Organization for Scientific Research (NWO)National Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades PGC2018-096663-B-C41 A-C42 B-C43 B-C44Severo Ochoa Centre of ExcellenceJunta de Andalucia SOMM17/6104/UGRGeneralitat Valenciana: Grisolia GRISOLIA/2018/119 CIDEGENT/2018/034La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program 71367