ACOUSTIC RESONNANCE IN CAVITATION FREE AND CAVITATING FLOWS

At part load operation of Francis turbine, the swirl in the draft tube leads to flow instability known as vortex breakdown. This flow instability can interact with the rest of the hydraulic circuit through axially propagating plane-waves. Moreover, at low cavitation index , the gaseous rope is suspected to modify locally the propagation velocity. Acoustic models have been commonly used to tackle this problematic. In order to validate the parameters of those models, an experiment with equivalent phenomenology has been setup. The experiment is designed so that the flow characteristic are similar to draft tube surge, but with strong simplification in order to facilitate the study of the flow instability and its interaction with the acoustic field. The hydraulic circuit consists in a square pipe connecting two constant pressure reservoirs. The excitation mechanism is obtained by the shedding of vortices in the wake of a bluff body placed at ¾ of the pipe length. The excitation frequency can be adjusted through the flow velocity. To examine the influence of vapor cavity formed in the wake of the obstacle, the mean pressure inside the pipe is also adjustable. Without cavitation, the analyses of the pressure field along the circuit highlights the acoustic modes shapes and Eigen frequencies of the system. It is also demonstrated that the amplitudes along the circuit are strongly increased as the excitation frequency matches the Eigen frequency of the system. In a second step, the relation of the cavitation index with the Eigen mode shapes and frequencies has been systematically analyzed. A direct influence of the vapor cavity on the local propagation velocity is shown. Finally, at particular cavitation index, important amplification of fluctuations has been noticed. From the author’s point of view, it is a consequence of the vapor volume unsteadiness

Simulation of pump-turbine prototype fast mode transition for grid stability support

The paper explores the additional services that Full Size Frequency Converter, FSFC, solution can provide for the case of an existing pumped storage power plant of 2x210 MW, for which conversion from fixed speed to variable speed is investigated with a focus on fast mode transition. First, reduced scale model tests experiments of fast transition of Francis pump-turbine which have been performed at the ANDRITZ HYDRO Hydraulic Laboratory in Linz Austria are presented. The tests consist of linear speed transition from pump to turbine and vice versa performed with constant guide vane opening. Then existing pumped storage power plant with pump-turbine quasi homologous to the reduced scale model is modelled using the simulation software SIMSEN considering the reservoirs, penstocks, the two Francis pump-turbines, the two downstream surge tanks, and the tailrace tunnel. For the electrical part, an FSFC configuration is considered with a detailed electrical model. The transitions from turbine to pump and vice versa are simulated, and similarities between prototype simulation results and reduced scale model experiments are highlighted

mTORC1 Directly Phosphorylates and Regulates Human MAF1▿

mTORC1 is a central regulator of growth in response to nutrient availability, but few direct targets have been identified. RNA polymerase (pol) III produces a number of essential RNA molecules involved in protein synthesis, RNA maturation, and other processes. Its activity is highly regulated, and deregulation can lead to cell transformation. The human phosphoprotein MAF1 becomes dephosphorylated and represses pol III transcription after various stresses, but neither the significance of the phosphorylations nor the kinase involved is known. We find that human MAF1 is absolutely required for pol III repression in response to serum starvation or TORC1 inhibition by rapamycin or Torin1. The protein is phosphorylated mainly on residues S60, S68, and S75, and this inhibits its pol III repression function. The responsible kinase is mTORC1, which phosphorylates MAF1 directly. Our results describe molecular mechanisms by which mTORC1 controls human MAF1, a key repressor of RNA polymerase III transcription, and add a new branch to the signal transduction cascade immediately downstream of TORC1

A Swiss database and biobank to better understand and manage congenital lung anomalies.

Congenital lung anomalies are a group of rare malformations, often diagnosed during the prenatal period. Guidelines on how to manage these patients are currently under debate, especially with regard to prophylactic surgery in asymptomatic patients, or how to proceed with conservative follow-up. Currently, there is no clear consensus on management strategies. A Swiss congenital lung anomaly national database and biobank was created in 2016 to enable data recording and collection of surgical lung samples in order to help define the most appropriate management strategies. This national observational cohort study represents an important step towards a better understanding of the pathophysiology and clinical course of the diseases included under congenital lung anomalies, especially in the context of a small country like Switzerland

Neonatal Outcomes of Prenatally Diagnosed Congenital Pulmonary Malformations

BACKGROUND AND OBJECTIVE: Congenital pulmonary malformations (CPM) are mostly recognized on prenatal ultrasound scans. In a minority of cases, they may impair breathing at birth. The factors predictive of neonatal respiratory distress are not well defined, but an understanding of these factors is essential for decisions concerning the need for the delivery to take place in a tertiary care center. The aim of this study was to identify potential predictors of respiratory distress in neonates with CPM. METHODS: We selected cases of prenatal diagnosis of hyperechoic and/or cystic lung lesions from RespiRare, the French prospective multicenter registry for liveborn children with rare respiratory diseases (2008–2013). Prenatal parameters were correlated with neonatal respiratory outcome. RESULTS: Data were analyzed for 89 children, 22 (25%) of whom had abnormal breathing at birth. Severe respiratory distress, requiring oxygen supplementation or ventilatory support, was observed in 12 neonates (13%). Respiratory distress at birth was significantly associated with the following prenatal parameters: mediastinal shift (P = .0003), polyhydramnios (P = .05), ascites (P = .0005), maximum prenatal malformation area (P = .001), and maximum congenital pulmonary malformation volume ratio (CVR) (P = .001). Severe respiratory distress, requiring oxygen at birth, was best predicted by polyhydramnios, ascites, or a CVR &amp;gt;0.84. CONCLUSIONS: CVR &amp;gt;0.84, polyhydramnios, and ascites increased the risk of respiratory complications at birth in fetuses with CPM, and especially of severe respiratory distress, requiring oxygen supplementation or more intensive intervention. In such situations, the delivery should take place in a tertiary care center. </jats:sec

DeepBreath—automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries

Abstract The interpretation of lung auscultation is highly subjective and relies on non-specific nomenclature. Computer-aided analysis has the potential to better standardize and automate evaluation. We used 35.9 hours of auscultation audio from 572 pediatric outpatients to develop DeepBreath : a deep learning model identifying the audible signatures of acute respiratory illness in children. It comprises a convolutional neural network followed by a logistic regression classifier, aggregating estimates on recordings from eight thoracic sites into a single prediction at the patient-level. Patients were either healthy controls (29%) or had one of three acute respiratory illnesses (71%) including pneumonia, wheezing disorders (bronchitis/asthma), and bronchiolitis). To ensure objective estimates on model generalisability, DeepBreath is trained on patients from two countries (Switzerland, Brazil), and results are reported on an internal 5-fold cross-validation as well as externally validated (extval) on three other countries (Senegal, Cameroon, Morocco). DeepBreath differentiated healthy and pathological breathing with an Area Under the Receiver-Operator Characteristic (AUROC) of 0.93 (standard deviation [SD] ± 0.01 on internal validation). Similarly promising results were obtained for pneumonia (AUROC 0.75 ± 0.10), wheezing disorders (AUROC 0.91 ± 0.03), and bronchiolitis (AUROC 0.94 ± 0.02). Extval AUROCs were 0.89, 0.74, 0.74 and 0.87 respectively. All either matched or were significant improvements on a clinical baseline model using age and respiratory rate. Temporal attention showed clear alignment between model prediction and independently annotated respiratory cycles, providing evidence that DeepBreath extracts physiologically meaningful representations. DeepBreath provides a framework for interpretable deep learning to identify the objective audio signatures of respiratory pathology