Development of monitoring systems for anomaly detection using ASTD specifications

Anomaly-based intrusion detection systems are essential defenses against cybersecurity threats because they can identify anomalies in current activities. However, these systems have difficulties providing entity processing independence through a programming language. In addition, a degradation of the detection process is caused by the complexity of scheduling the training and detection processes, which are required to keep the anomaly detection system continuously updated. This paper shows how to use the algebraic state-transition diagram (ASTD) language to develop flexible anomaly detection systems. This paper provides a model for detecting point anomalies using the unsupervised non-parametric technique Kernel Density Estimation to estimate the probability density of event occurrence. The proposed model caters for both the training and the detection phase continuously. The ASTD language streamlines the modeling of detection systems thanks to its process algebraic operators that provide a solution to overcome these challenges. By delegating the combination of anomaly-based detection processes to the ASTD language, the effort and complexity are reduced during detection models development. Finally, using a qualitative evaluation, this study demonstrates that the algebraic operators in the ASTD specification language overcome these challenges

Two-stage automatic diagnosis of Flavescence Dorée based on proximal imaging and artificial intelligence: a multi-year and multi-variety experimental study

“Flavescence dorée” (FD) is a grape vine disease caused by the bacterial agent “Candidatus Phytoplasma vitis” and spread by the leafhopper Scaphoideus titanus Ball (Hemiptera: Cicadellidae). The disease is very closely monitored in Europe, as it reduces vine productivity and causes vine death and is also highly transmissible. Currently, the control method used against this disease is a two-pronged approach: i) the spraying of insecticide on a regular basis to kill the vector, and ii) a survey of each row in a vineyard by experts in this disease. Unfortunately, these experts are not able to carry out such a task every year on every vineyard and need an aid for planning their survey.In this study, we propose and evaluate an original automatic method for the detection of FD based on computer vision and artificial intelligence algorithms applied to images acquired by proximal sensing. A two-step approach was used, mimicking an expert’s scouting in the vine rows: (i) the three known isolated symptoms (red or yellow leaves depending on variety, together with a lack of shoot lignification and the presence of desiccated bunches) were detected, (ii) isolated detections were combined to make a diagnosis at image scale; i.e., vine scale. A detection network was used to detect and classify non-healthy leaves into three classes: ‘FD symptomatic leaf', 'Esca leaf' and 'Confounding leaf'; while a segmentation network was used for the retrieval of FD symptomatic shoots and bunches. Finally, the association of detected symptoms was performed by a RandomForest classifier for diagnosis at the image scale. The experimental evaluation was conducted on more than 1000 images collected from 14 blocks planted with five different grape varieties. The detection of the isolated symptoms achieved a precision of between 0.67 and 0.82 and a recall of between 0.39 and 0.59. The classification at the image scale obtained very good results when applied to images acquired under the same conditions, with the same grape varieties as the training images (precision and recall of more than 0.89). The results of the tests on the other grape varieties show the importance of having some of them in the training base in these AI-based approaches.Prospect FD : développement d'un outil d'aide à la décision pour la prospection de la flavescence dorée en vign

Coding Variation in ANGPTL4, LPL, and SVEP1 and the Risk of Coronary Disease.

BACKGROUND: The discovery of low-frequency coding variants affecting the risk of coronary artery disease has facilitated the identification of therapeutic targets. METHODS: Through DNA genotyping, we tested 54,003 coding-sequence variants covering 13,715 human genes in up to 72,868 patients with coronary artery disease and 120,770 controls who did not have coronary artery disease. Through DNA sequencing, we studied the effects of loss-of-function mutations in selected genes. RESULTS: We confirmed previously observed significant associations between coronary artery disease and low-frequency missense variants in the genes LPA and PCSK9. We also found significant associations between coronary artery disease and low-frequency missense variants in the genes SVEP1 (p.D2702G; minor-allele frequency, 3.60%; odds ratio for disease, 1.14; P=4.2×10(-10)) and ANGPTL4 (p.E40K; minor-allele frequency, 2.01%; odds ratio, 0.86; P=4.0×10(-8)), which encodes angiopoietin-like 4. Through sequencing of ANGPTL4, we identified 9 carriers of loss-of-function mutations among 6924 patients with myocardial infarction, as compared with 19 carriers among 6834 controls (odds ratio, 0.47; P=0.04); carriers of ANGPTL4 loss-of-function alleles had triglyceride levels that were 35% lower than the levels among persons who did not carry a loss-of-function allele (P=0.003). ANGPTL4 inhibits lipoprotein lipase; we therefore searched for mutations in LPL and identified a loss-of-function variant that was associated with an increased risk of coronary artery disease (p.D36N; minor-allele frequency, 1.9%; odds ratio, 1.13; P=2.0×10(-4)) and a gain-of-function variant that was associated with protection from coronary artery disease (p.S447*; minor-allele frequency, 9.9%; odds ratio, 0.94; P=2.5×10(-7)). CONCLUSIONS: We found that carriers of loss-of-function mutations in ANGPTL4 had triglyceride levels that were lower than those among noncarriers; these mutations were also associated with protection from coronary artery disease. (Funded by the National Institutes of Health and others.).Supported by a career development award from the National Heart, Lung, and Blood Institute, National Institutes of Health (NIH) (K08HL114642 to Dr. Stitziel) and by the Foundation for Barnes–Jewish Hospital. Dr. Peloso is supported by the National Heart, Lung, and Blood Institute of the NIH (award number K01HL125751). Dr. Kathiresan is supported by a Research Scholar award from the Massachusetts General Hospital, the Donovan Family Foundation, grants from the NIH (R01HL107816 and R01HL127564), a grant from Fondation Leducq, and an investigator-initiated grant from Merck. Dr. Merlini was supported by a grant from the Italian Ministry of Health (RFPS-2007-3-644382). Drs. Ardissino and Marziliano were supported by Regione Emilia Romagna Area 1 Grants. Drs. Farrall and Watkins acknowledge the support of the Wellcome Trust core award (090532/Z/09/Z), the British Heart Foundation (BHF) Centre of Research Excellence. Dr. Schick is supported in part by a grant from the National Cancer Institute (R25CA094880). Dr. Goel acknowledges EU FP7 & Wellcome Trust Institutional strategic support fund. Dr. Deloukas’s work forms part of the research themes contributing to the translational research portfolio of Barts Cardiovascular Biomedical Research Unit, which is supported and funded by the National Institute for Health Research (NIHR). Drs. Webb and Samani are funded by the British Heart Foundation, and Dr. Samani is an NIHR Senior Investigator. Dr. Masca was supported by the NIHR Leicester Cardiovascular Biomedical Research Unit (BRU), and this work forms part of the portfolio of research supported by the BRU. Dr. Won was supported by a postdoctoral award from the American Heart Association (15POST23280019). Dr. McCarthy is a Wellcome Trust Senior Investigator (098381) and an NIHR Senior Investigator. Dr. Danesh is a British Heart Foundation Professor, European Research Council Senior Investigator, and NIHR Senior Investigator. Drs. Erdmann, Webb, Samani, and Schunkert are supported by the FP7 European Union project CVgenes@ target (261123) and the Fondation Leducq (CADgenomics, 12CVD02). Drs. Erdmann and Schunkert are also supported by the German Federal Ministry of Education and Research e:Med program (e:AtheroSysMed and sysINFLAME), and Deutsche Forschungsgemeinschaft cluster of excellence “Inflammation at Interfaces” and SFB 1123. Dr. Kessler received a DZHK Rotation Grant. The analysis was funded, in part, by a Programme Grant from the BHF (RG/14/5/30893 to Dr. Deloukas). Additional funding is listed in the Supplementary Appendix.This is the author accepted manuscript. The final version is available from the Massachusetts Medical Society via http://dx.doi.org/10.1056/NEJMoa150765

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

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

A first update on mapping the human genetic architecture of COVID-19

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