TOWARDS A DATA MODEL FOR PLM APPLICATION IN BIO-MEDICAL IMAGING

International audienceBio-Medical Imaging (BMI) is currently confronted to data issues similar to those of the manufacturing industry twenty years ago. In particular, the need for data sharing and reuse has never been so strong to foster major discoveries in neuroimaging. Some data management systems have been developed to meet the requirements of BMI large-scale research studies. However, many efforts to integrate the data provenance along a research study, from the specifications to the published results, are to be done. Product Lifecycle Management (PLM) systems are designed to comply with manufacturing industry expectations of providing the right information at the right time and in the right context. Consequently PLM systems are proposed to be relevant for the management of BMI data. From a need analysis led with the GIN research group, the BMI-LM data model is designed: it is PLM-oriented, generic (enabling the management of many types of data such as imaging, clinical, psychology or genetics), flexible (enabling users’ customisation) and it covers the whole stages of a BMI study from specifications to publication. The test implementation of the BMI-LM model into a PLM system is detailed. The preliminary feed-back of the GIN researchers is discussed in this paper: the BMI-LM data model and the PLM concepts are relevant to manage BMI data, but PLM systems interfaces are unsuitable for BMI researchers

In Vivo Inhibition Followed by Exogenous Supplementation Demonstrates Galactopoietic Effects of Prolactin on Mammary Tissue and Milk Production in Dairy Cows

ABSTRACT: It has been previously shown that the long-term inhibition of milking-induced prolactin (PRL) release by quinagolide (QN), a dopamine agonist, reduces milk yield in dairy cows. To further demonstrate that PRL is galactopoietic in cows, we performed a short-term experiment that used PRL injections to restore the release of PRL at milking in QN-treated cows. Nine Holstein cows were assigned to treatments during three 5-d periods in a 3 × 3 Latin square design: 1) QN: twice-daily i.m. injections of 1 mg of QN; 2) QN-PRL: twice-daily i.m. injections of 1 mg of QN and twice-daily (at milking time) i.v. injections of PRL (2 μg/kg body weight); and 3) control: twice-daily injections of the vehicles. Mammary epithelial cells (MEC) were purified from milk so that their viability could be assessed, and mammary biopsies were harvested for immunohistological analyses of cell proliferation using PCNA and STAT5 staining. In both milk-purified MEC and mammary tissue, the mRNA levels of milk proteins and BAX were determined using real-time reverse-transcription PCR. Daily QN injections reduced milking-induced PRL release. The area under the PRL curve was similar in the control and PRL injection treatments, but the shape was different. The QN treatment decreased milk, lactose, protein, and casein production. Injections of PRL did not restore milk yield but tended to increase milk protein yield. In mammary tissue, the percentage of STAT5-positive cells was reduced during QN but not during QN-PRL in comparison with the control treatment. The percentage of PCNA-positive cells was greater during QN-PRL injections than during the control or QN treatment and tended to be lower during QN than during the control treatment. In milk-purified MEC, κ-casein and α-lactalbumin mRNA levels were lower during QN than during the control treatment, but during QN-PRL, they were not different from the control treatment. In mammary tissue, the BAX mRNA level was lower during QN-PRL than during QN. The number of MEC exfoliated into milk was increased by QN injections but tended to be decreased by PRL injections. Injections of PRL also increased the viability of MEC harvested from milk. Although PRL injections at milking could not reverse the effect of QN treatment on milk production, their effects on cell survival and exfoliation and on gene expression suggest that the effect of QN treatment on the mammary gland is due to QN’s inhibition of PRL secretion

Hum Brain Mapp

White matter hyperintensities (WMHs) are well-established markers of cerebral small vessel disease, and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for detecting small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e., with confluency) in evaluation datasets from three distinct databases: magnetic resonance imaging-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LPA: Schmidt), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al.), and HyperMapper tool (Mojiri Forooshani et al.), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH.Etude de cohorte sur la santé des étudiantsStopping cognitive decline and dementia by fighting covert cerebral small vessel diseaseLaboratoire pour les applications en imagerie biomédicaleTranslational Research and Advanced Imaging LaboratoryInitiative d'excellence de l'Université de Bordeau

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all "visible" PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm(3) and 0.95 for PVSs larger than 15 mm(3). We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.Stopping cognitive decline and dementia by fighting covert cerebral small vessel diseas

Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease

Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.Etude de cohorte sur la santé des étudiantsStopping cognitive decline and dementia by fighting covert cerebral small vessel diseaseStudy on Environmental and GenomeWide predictors of early structural brain Alterations in Young student

Luminescence du cuivre monovalent dans les solides isolants oxygenes

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : T 79556 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

On the luminescence of Bi 3+ pairs in oxidic compounds

International audienceAn empirical equation is introduced to calculate the absorption/excitation energy of Bi3+ pairs in oxidic compounds from the knowledge of their crystal structure. The method is based on recent reports that describe the optical processes within the pair as resulting from an intervalence charge transfer of the type Bi3+, Bi3+ → Bi2+, Bi4+. The data are used to locate the vacuum referred binding energy of the electron in the 2P1/2 ground state of Bi2+ ions in few oxidic compounds