Dominant BIN1-related centronuclear myopathy (CNM) revealed by lower limb myalgia and moderate CK elevation

We report a BIN1-related CNM family with unusual clinical phenotype. The proband, a 56-year-old man suffered of lower limbs myalgia since the age of 52. Clinical examination showed short stature, mild symmetric eyelid ptosis without ophthalmoplegia, scapular winging and Achilles tendon retraction. A muscle weakness was not noted. CK levels were up to 350 UI/L. Deltoid muscle biopsy showed nuclear centralization and clustering, deep sarcolemmal invaginations and type 1 fiber hypotrophy. Whole body MRI revealed fatty infiltration of posterior legs compartments, lumbar paraspinal and serratus muscles. Myotonic dystrophy type1 and 2, Pompe disease and MTM1 and DNM2-related CNM were ruled out. By sequencing BIN1, we identified a heterozygous pathogenic mutation [c.107C > A (p.A36E)], and we demonstrate that the mutation strongly impairs the membrane tubulation property of the protein. One affected sister carried the same mutation. Her clinical examination and muscle MRI revealed a similar phenotype. Our findings expand the clinical and genetic spectrum of the autosomal dominant CNM associated with BIN1 mutations

An epistatic mini-circuitry between the transcription factors Snail and HNF4a controls liver stem cell and hepatocyte features exhorting opposite regulation on stemness-inhibiting microRNAs

Preservation of the epithelial state involves the stable repression of EMT program while maintenance of the stem compartment requires the inhibition of differentiation processes. A simple and direct molecular mini-circuitry between master elements of these biological processes, may provide the best device to keep balanced such complex phenomena. In this work, we show that in hepatic stem cell Snail, a transcriptional repressor of the hepatocyte differentiation master gene HNF4, directly represses the expression of the epithelial microRNAs-200c and -34a, which in turn target several stem cell genes. Notably, in differentiated hepatocytes HNF4, previously identified as a transcriptional repressor of Snail, induces the microRNAs-34a and -200a, b, c that, when silenced, causes epithelial dedifferentiation and reacquisition of stem traits. Altogether these data unveiled Snail, HNF4 and microRNAs -200a, b, c and -34a as epistatic elements controlling hepatic stem cell maintenance/differentiation

Update of High Resolution (e,e'K^+) Hypernuclear Spectroscopy at Jefferson Lab's Hall A

Updated results of the experiment E94-107 hypernuclear spectroscopy in Hall A of the Thomas Jefferson National Accelerator Facility (Jefferson Lab), are presented. The experiment provides high resolution spectra of excitation energy for 12B_\Lambda, 16N_\Lambda, and 9Li_\Lambda hypernuclei obtained by electroproduction of strangeness. A new theoretical calculation for 12B_\Lambda, final results for 16N_\Lambda, and discussion of the preliminary results of 9Li_\Lambda are reported.Comment: 8 pages, 5 figures, submitted to the proceedings of Hyp-X Conferenc

Diagnosis of faulty wind turbine bearings using tower vibration measurements †

Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring

Wind turbine drive-train condition monitoring through tower vibrations measurement and processing

A new method for wind turbine drive-train condition monitoring is proposed: the innovative idea is that vibrations are measured at the tower. The critical point is extracting knowledge about the drive-train from tower measurements: this is achieved by measuring simultaneously at the highest possible number of nearby wind turbines. One wind turbine is selected as target and the others are used as reference. The data are analyzed in the time domain basing on statistical features (root mean square, peak, crest factor, skewness, kurtosis). The data set in the feature space reduces to a matrix, from which the observations at the target wind turbine should be distinguishable. The application of this algorithm is supported by univariate statistical tests and by Principal Component Analysis. A novelty index based on the Mahalanobis distance is finally used to detect the statistical novelty of the damaged wind turbine. This work is based on field measurement campaigns, performed by the authors in 2018 and 2019 at wind farms owned by the Renvico company