Recharge processes within the Cacapon Mountain Aquifer, Ridge and Valley Province, West Virginia

During the 2006-2007 water year, apparent recharge rates for three streams draining the Cacapon Mountain Aquifer, in the Valley and Ridge Province of northern West Virginia, were estimated using hydrograph separation techniques. The techniques use stream baseflow, dominated by groundwater discharge, as a surrogate for groundwater recharge. Two of the streams draining the aquifer were strike-normal (Rock Gap Run and Breakneck Run) and one was strike-parallel (Sir Johns Run). The strike-normal streams had significantly lower apparent recharge rates (Rock Gap Run: 2.52 in/yr; Breakneck Run: 6.57 in/yr) than the strike-parallel stream (Sir Johns Run: 13.31 in/yr). The large variations in recharge rate are interpreted to be due to water lost to the Helderberg Limestone, a local conduit-forming unit, in the two former drainages. In this particular geologic setting, apparent recharge rates of strike-parallel and strike-normal streams draining the same aquifer may differ substantially. Estimating recharge rates from stream flow data may give inaccurate numbers if the stream flows over highly transmissive conduit forming limestone or extensive fractures

Enhancing the significance of gravitational wave bursts through signal classification

The quest to observe gravitational waves challenges our ability to discriminate signals from detector noise. This issue is especially relevant for transient gravitational waves searches with a robust eyes wide open approach, the so called all- sky burst searches. Here we show how signal classification methods inspired by broad astrophysical characteristics can be implemented in all-sky burst searches preserving their generality. In our case study, we apply a multivariate analyses based on artificial neural networks to classify waves emitted in compact binary coalescences. We enhance by orders of magnitude the significance of signals belonging to this broad astrophysical class against the noise background. Alternatively, at a given level of mis-classification of noise events, we can detect about 1/4 more of the total signal population. We also show that a more general strategy of signal classification can actually be performed, by testing the ability of artificial neural networks in discriminating different signal classes. The possible impact on future observations by the LIGO-Virgo network of detectors is discussed by analysing recoloured noise from previous LIGO-Virgo data with coherent WaveBurst, one of the flagship pipelines dedicated to all-sky searches for transient gravitational waves

Senescence in hepatic stellate cells as a mechanism of liver fibrosis reversal: a putative synergy between retinoic acid and PPAR-gamma signalings

Hepatic stellate cells (HSCs), also known as perisinusoidal cells, are pericytes found in the perisinusoidal space of the liver. HSCs are the major cell type involved in liver fibrosis, which is the formation of scar tissue in response to liver damage. When the liver is damaged, stellate cells can shift into an activated state, characterized by proliferation, contractility and chemotaxis. The activated HSCs secrete collagen scar tissue, which can lead to cirrhosis. Recent studies have shown that in vivo activation of HSCs by fibrogenic agents can eventually lead to senescence of these cells, which would contribute to reversal of fibrosis although it may also favor the insurgence of liver cancer. HSCs in their non-active form store huge amounts of retinoic acid derivatives in lipid droplets, which are progressively depleted upon cell activation in injured liver. Retinoic acid is a metabolite of vitamin A (retinol) that mediates the functions of vitamin A, generally required for growth and development. The precise function of retinoic acid and its alterations in HSCs has yet to be elucidated, and nonetheless in various cell types retinoic acid and its receptors (RAR and RXR) are known to act synergistically with peroxisome proliferator-activated receptor gamma (PPAR-gamma) signaling through the activity of transcriptional heterodimers. Here, we review the recent advancements in the understanding of how retinoic acid signaling modulates the fibrogenic potential of HSCs and proposes a synergistic combined action with PPAR-gamma in the reversal of liver fibrosis

Probabilistic inversions of electrical resistivity tomography data with a machine learning-based forward operator

Casting a geophysical inverse problem into a Bayesian setting is often discouraged by the computational workload needed to run many forward modeling evaluations. Here we present probabilistic inversions of electrical resistivity tomography data in which the forward operator is replaced by a trained residual neural network that learns the non-linear mapping between the resistivity model and the apparent resistivity values. The use of this specific architecture can provide some advantages over standard convolutional networks as it mitigates the vanishing gradient problem that might affect deep networks. The modeling error introduced by the network approximation is properly taken into account and propagated onto the estimated model uncertainties. One crucial aspect of any machine learning application is the definition of an appropriate training set. We draw the models forming the training and validation sets from previously defined prior distributions, while a finite element code provides the associated datasets. We apply the approach to two probabilistic inversion frameworks: a Markov Chain Monte Carlo algorithm is applied to synthetic data, while an ensemble-based algorithm is employed for the field measurements. For both the synthetic and field tests, the outcomes of the proposed method are benchmarked against the predictions obtained when the finite element code constitutes the forward operator. Our experiments illustrate that the network can effectively approximate the forward mapping even when a relatively small training set is created. The proposed strategy provides a forward operator three that is orders of magnitude faster than the accurate but computationally expensive finite element code. Our approach also yields most likely solutions and uncertainty quantifications comparable to those estimated when the finite element modeling is employed. The presented method allows solving the Bayesian electrical resistivity tomography with a reasonable computational cost and limited hardware resources

A New Stiffness Parameter in Air Puff Induced Corneal Deformation Analysis

OCULUS Optikgerate GmbHOhio State Univ, Ophthalmol & Visual Sci & Biomed Engn, Columbus, OH 43210 USAOhio State Univ, Mech & Aerosp Engn, Columbus, OH 43210 USAUniv Liverpool, Sch Engn, Liverpool, Merseyside, EnglandUniv Insubria, Div Ophthalmol, Varese, ItalyHumanitas Clin & Res Ctr, Ctr Eye, Rozzano, ItalyVincieye Clin, Milan, ItalyRio de Janeiro Corneal Tomog & Biomech Study Grp, Rio De Janeiro, BrazilUniv Fed Sao Paulo, Ophthalmol, Rio De Janeiro, BrazilUniv Fed Sao Paulo, Ophthalmol, Rio De Janeiro, BrazilWeb of Scienc

DAPK1 Promoter Methylation and Cervical Cancer Risk: A Systematic Review and a Meta-Analysis.

Objective: The Death-Associated Protein Kinase 1 (DAPK1) gene has been frequently investigated in cervical cancer (CC). The aim of the present study was to carry out a systematic review and a meta-analysis in order to evaluate DAPK1 promoter methylation as an epigenetic marker for CC risk. Methods A systematic literature search was carried out. The Cochrane software package Review Manager 5.2 was used. The fixed-effects or random-effects models, according to heterogeneity across studies, were used to calculate odds ratios (ORs) and 95% Confidence Intervals (CIs). Furthermore, subgroup analyses were conducted by histological type, assays used to evaluate DAPK1 promoter methylation, and control sample source. Results: A total of 20 papers, published between 2001 and 2014, on 1929 samples, were included in the meta-analysis. DAPK1 promoter methylation was associated with an increased CC risk based on the random effects model (OR: 21.20; 95%CI = 11.14–40.35). Omitting the most heterogeneous study, the between study heterogeneity decreased and the association increased (OR: 24.13; 95% CI = 15.83–36.78). The association was also confirmed in all the subgroups analyses. Conclusions: A significant strong association between DAPK1 promoter methylation and CC was shown and confirmed independently by histological tumor type, method used to evaluate methylation and source of control samples. Methylation markers may have value in early detection of CC precursor lesions, provide added reassurances of safety for women who are candidates for less frequent screens, and predict outcomes of women infected with human papilloma virus