Modeling induction machine winding faults for diagnosis

International audienceMonitoring and diagnosis of electrical machine faults is a scientific and economic issue which is motivated by objectives for reliability and serviceability in electrical drives. This book provides a survey of the techniques used to detect the faults occurring in electrical drives: electrical, thermal and mechanical faults of the electrical machine, faults of the static converter and faults of the energy storage unit. Diagnosis of faults occurring in electrical drives is an essential part of a global monitoring system used to improve reliability and serviceability. This diagnosis is performed with a large variety of techniques: parameter estimation, state observation, Kalman filtering, spectral analysis, neural networks, fuzzy logic, artificial intelligence, etc. Particular emphasis in this book is put on the modeling of the electrical machine in faulty situations. Electrical Machines Diagnosis presents original results obtained mainly by French researchers in different domains. It will be useful as a guideline for the conception of more robust electrical machines and indeed for engineers who have to monitor and maintain electrical drives. As the monitoring and diagnosis of electrical machines is still an open domain, this book will also be very useful to researchers

Diagnostic des entraînements électriques : Détection de courts-circuits statoriques dans la machine asynchrone par identification paramétrique

Nous nous intéressons dans cet article à la détection de courts-circuits d'un nombre très faible de spires dans les machines asynchrones utilisées pour les applications à vitesse variable. Nous proposons pour cela une modification du modèle usuel de Park en introduisant un paramètre électrique supplémentaire dont le suivi permet de caractériser l'importance du défaut. Les paramètres du modèle sont estimés par la méthode d'erreur de prédiction sur le modèle d'erreur de sortie. Nous testons cette approche sur des enregistrements expérimentaux réalisés avec une machine spécialement rebobinée afin de permettre des courts-circuits réalistes et réversibles. Les résultats obtenus montrent l'efficacité de la méthode proposée

ICDS database: interrupted CoDing sequences in prokaryotic genomes

Unrecognized frameshifts, in-frame stop codons and sequencing errors lead to Interrupted CoDing Sequence (ICDS) that can seriously affect all subsequent steps of functional characterization, from in silico analysis to high-throughput proteomic projects. Here, we describe the Interrupted CoDing Sequence database containing ICDS detected by a similarity-based approach in 80 complete prokaryotic genomes. ICDS can be retrieved by species browsing or similarity searches via a web interface (). The definition of each interrupted gene is provided as well as the ICDS genomic localization with the surrounding sequence. Furthermore, to facilitate the experimental characterization of ICDS, we propose optimized primers for re-sequencing purposes. The database will be regularly updated with additional data from ongoing sequenced genomes. Our strategy has been validated by three independent tests: (i) ICDS prediction on a benchmark of artificially created frameshifts, (ii) comparison of predicted ICDS and results obtained from the comparison of the two genomic sequences of Bacillus licheniformis strain ATCC 14580 and (iii) re-sequencing of 25 predicted ICDS of the recently sequenced genome of Mycobacterium smegmatis. This allows us to estimate the specificity and sensitivity (95 and 82%, respectively) of our program and the efficiency of primer determination

Genetic analysis of egg production traits in turkeys (Meleagris gallopavo) using a single-step genomic random regression model.

BACKGROUND Egg production traits are economically important in poultry breeding programs. Previous studies have shown that incorporating genomic data can increase the accuracy of genetic prediction of egg production. Our objective was to estimate the genetic and phenotypic parameters of such traits and compare the prediction accuracy of pedigree-based random regression best linear unbiased prediction (RR-PBLUP) and genomic single-step random regression BLUP (RR-ssGBLUP). Egg production was recorded on 7422 birds during 24 consecutive weeks from first egg laid. Hatch-week of birth by week of lay and week of lay by age at first egg were fitted as fixed effects and body weight as a covariate, while additive genetic and permanent environment effects were fitted as random effects, along with heterogeneous residual variances over 24 weeks of egg production. Predictions accuracies were compared based on two statistics: (1) the correlation between estimated breeding values and phenotypes divided by the square root of the trait heritability, and (2) the ratio of the variance of BLUP predictions of individual Mendelian sampling effects divided by one half of the estimate of the additive genetic variance. RESULTS Heritability estimates along the production trajectory obtained with RR-PBLUP ranged from 0.09 to 0.22, with higher estimates for intermediate weeks. Estimates of phenotypic correlations between weekly egg production were lower than the corresponding genetic correlation estimates. Our results indicate that genetic correlations decreased over the laying period, with the highest estimate being between traits in later weeks and the lowest between early weeks and later ages. Prediction accuracies based on the correlation-based statistic ranged from 0.11 to 0.44 for RR-PBLUP and from 0.22 to 0.57 for RR-ssGBLUP using the correlation-based statistic. The ratios of the variances of BLUP predictions of Mendelian sampling effects and one half of the additive genetic variance ranged from 0.17 to 0.26 for RR-PBLUP and from 0.17 to 0.34 for RR-ssGBLUP. Although the improvement in accuracies from RR-ssGBLUP over those from RR-PBLUP was not uniform over time for either statistic, accuracies obtained with RR-ssGBLUP were generally equal to or higher than those with RR-PBLUP. CONCLUSIONS Our findings show the potential advantage of incorporating genomic data in genetic evaluation of egg production traits using random regression models, which can contribute to the genetic improvement of egg production in turkey populations

T-Cell Infiltration and Adaptive Treg Resistance in Response to Androgen Deprivation With or Without Vaccination in Localized Prostate Cancer

Purpose: Previous studies suggest that androgen deprivation therapy (ADT) promotes antitumor immunity in prostate cancer. Whether a vaccine-based approach can augment this effect remains unknown. Experimental Design: Therefore, we conducted a neoadjuvant, randomized study to quantify the immunologic effects of a granulocyte-macrophage colony-stimulating factor (GM-CSF)-secreting allogeneic cellular vaccine in combination with low-dose cyclophosphamide (Cy/GVAX) followed by degarelix versus degarelix alone in patients with high-risk localized prostate adenocarcinoma who were planned for radical prostatectomy. Results: Both Cy/GVAX plus degarelix and degarelix alone led to significant increases in intratumoral CD8+ T cell infiltration and PD-L1 expression as compared to a cohort of untreated, matched controls. However, the CD8+ T cell infiltrate was accompanied by a proportional increase in regulatory T cells (Treg), suggesting that adaptive Treg resistance may dampen the immunogenicity of ADT. Although Cy/GVAX followed by degarelix was associated with a modest improvement in time-to-PSA progression and time-to-next treatment as well as an increase in PD-L1, there was no difference in the CD8 T-cell infiltrate as compared to degarelix alone. Gene expression profiling demonstrated that CHIT1, a macrophage marker, was differentially upregulated with Cy/GVAX plus degarelix compared to degarelix alone. Conclusions: Our results highlight that ADT with or without Cy/GVAX induces a complex immune response within the prostate tumor microenvironment. These data have important implications for combining ADT with immunotherapy. In particular, our finding that ADT increases both CD8+ T cells and Tregs, supports the development of regimens combining ADT with Treg-depleting agents in the treatment of prostate cancer

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration \u3c2%, relative calibration of 0.2%, polarization sensitivity \u3c1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications

Both XPD alleles contribute to the phenotype of compound heterozygote xeroderma pigmentosum patients

Mutations in the XPD subunit of the DNA repair/transcription factor TFIIH result in the rare recessive genetic disorder xeroderma pigmentosum (XP). Many XP patients are compound heterozygotes with a “causative” XPD point mutation R683W and different second mutant alleles, considered “null alleles.” However, there is marked clinical heterogeneity (including presence or absence of skin cancers or neurological degeneration) in these XPD/R683W patients, thus suggesting a contribution of the second allele. Here, we report XP patients carrying XPD/R683W and a second XPD allele either XPD/Q452X, /I455del, or /199insPP. We performed a systematic study of the effect of these XPD mutations on several enzymatic functions of TFIIH and found that each mutation exhibited unique biochemical properties. Although all the mutations inhibited the nucleotide excision repair (NER) by disturbing the XPD helicase function, each of them disrupted specific molecular steps during transcription: XPD/Q452X hindered the transactivation process, XPD/I455del disturbed RNA polymerase II phosphorylation, and XPD/199insPP inhibited kinase activity of the cdk7 subunit of TFIIH. The broad range and severity of clinical features in XP patients arise from a broad set of deficiencies in NER and transcription that result from the combination of mutations found on both XPD alleles

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Applications 28 (2018): 749-760, doi: 10.1002/eap.1682.The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.National Center for Ecological Analysis and Synthesis (NCEAS); National Aeronautics and Space Administration (NASA) Grant Numbers: NNX16AQ34G, NNX14AR62A; National Ocean Partnership Program; NOAA US Integrated Ocean Observing System/IOOS Program Office; Bureau of Ocean and Energy Management Ecosystem Studies program (BOEM) Grant Number: MC15AC0000

Satellite Sensor Requirements for Monitoring Essential Biodiversity Variables of Coastal Ecosystems

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibratio