Fingerprint Identification Using Noise in the Horizontal-to-Vertical Spectral Ratio: Retrieving the Impedance Contrast Structure for the Almaty Basin (Kazakhstan)

Detailed knowledge of the 3D basin structure underlying urban areas is of major importance for improving the assessment of seismic hazard and risk. However, mapping the major features of the shallow geological layers becomes expensive where large areas need to be covered. In this study, we propose an innovative tool, based mainly on single station noise recordings and the horizontal-to-vertical spectral ratio (H/V), to identify and locate the depth of major impedance contrasts. The method is based on an identification of so-called fingerprints of the major impedance discontinuities and their migration to depth by means of an analytical procedure. The method is applied to seismic noise recordings collected in the city of Almaty (Kazakhstan). The estimated impedance contrasts vs. depth profiles are interpolated in order to derive a three-dimensional (3D) model, which after calibration with some available boreholes data allows the major tectonic features in the subsurface to be identified

Maternal and Neonatal Vitamin D Binding Protein Polymorphisms and 25-Hydroxyvitamin D Cutoffs as Determinants of Neonatal Birth Anthropometry

BACKGROUND: Vitamin D-binding protein (VDBP) is a vital regulator of optimal vitamin D homeostasis and bioavailability. Apart from its well-documented role as a key component in vitamin D dynamic transfer and circulation, it has a myriad of immunoregulatory functions related to innate immunity, which becomes particularly critical in states of increased immunological tolerance including pregnancy. In this regard, VDBP dyshomeostasis is considered to contribute to the development of several fetal, maternal, and neonatal adverse outcomes. However, precise physiological pathways, including the contribution of specific VDBP polymorphisms behind such phenomena, are yet to be fully deciphered. Our aim was to assess the combined effect of maternal and neonatal VDBP polymorphism heterogeneity in conjunction with different maternal and neonatal 25(OH)D cutoffs on the neonatal anthropometric profile at birth. METHODS: The study included data and samples from a cohort of 66 mother-child pairs at birth. The inclusion criterion was full-term pregnancy (gestational weeks 37-42). Neonatal and maternal 25(OH)D cutoffs were included according to vitamin D status at birth and delivery. Concentrations of 25(OH)D2 and 25(OH)D3 were measured using liquid chromatography-tandem mass spectrometry. RESULTS: The upper arm length of neonates with 25(OH)D ≤ 25 nmol/L was higher in neonate CC carriers for rs2298850. The upper thigh neonatal circumference was also higher in the ones with either 25(OH)D ≤ 50 or ≤75 nmol/L in rs2298850 CG + GG or rs4588 GT + TT carriers. We did not observe any significant effect for maternal VDBP polymorphisms nor for birth maternal 25(OH)D concentrations, on birth neonatal anthropometry. CONCLUSIONS: Our findings emphasize a potential role for neonatal VDBP genotypes rs2298850 and rs4588, in conjunction with specific neonatal 25(OH)D cutoffs, in the range of sufficiency on neonatal growth and development

Robust automatic mapping algorithms in a network monitoring scenario

Automatically generating maps of a measured variable of interest can be problematic. In this work we focus on the monitoring network context where observations are collected and reported by a network of sensors, and are then transformed into interpolated maps for use in decision making. Using traditional geostatistical methods, estimating the covariance structure of data collected in an emergency situation can be difficult. Variogram determination, whether by method-of-moment estimators or by maximum likelihood, is very sensitive to extreme values. Even when a monitoring network is in a routine mode of operation, sensors can sporadically malfunction and report extreme values. If this extreme data destabilises the model, causing the covariance structure of the observed data to be incorrectly estimated, the generated maps will be of little value, and the uncertainty estimates in particular will be misleading. Marchant and Lark [2007] propose a REML estimator for the covariance, which is shown to work on small data sets with a manual selection of the damping parameter in the robust likelihood. We show how this can be extended to allow treatment of large data sets together with an automated approach to all parameter estimation. The projected process kriging framework of Ingram et al. [2007] is extended to allow the use of robust likelihood functions, including the two component Gaussian and the Huber function. We show how our algorithm is further refined to reduce the computational complexity while at the same time minimising any loss of information. To show the benefits of this method, we use data collected from radiation monitoring networks across Europe. We compare our results to those obtained from traditional kriging methodologies and include comparisons with Box-Cox transformations of the data. We discuss the issue of whether to treat or ignore extreme values, making the distinction between the robust methods which ignore outliers and transformation methods which treat them as part of the (transformed) process. Using a case study, based on an extreme radiological events over a large area, we show how radiation data collected from monitoring networks can be analysed automatically and then used to generate reliable maps to inform decision making. We show the limitations of the methods and discuss potential extensions to remedy these

Matrix-associated autologous chondrocyte transplantation in a compartmentalized early stage of osteoarthritis

SummaryObjectiveCartilage restoration in joints with an early stage of osteoarthritis (OA) is an important clinical challenge. In this study, a compartmentalized, early-stage OA was generated surgically in sheep stifle joints, and this model was used to evaluate a matrix-associated cell transplantation approach for cartilage repair.MethodEighteen sheep were operated twice. During the first operation, a unicompartmental OA in a stable joint was induced by creating a critical-size defect. The second operation served as a regeneration procedure. The eighteen sheep were divided into three groups. One group was treated with spongialization (SPONGIO), while the two others had spongialization followed by implantation of a hyaluronan matrix with (MACT) or without chondrocytes (MATRIX). The follow-up took place 4 months after the second operation. Gross Assessment of Joint Changes score and Brittberg score were used for the macroscopic evaluation, Mankin score, O'Driscoll score, and immunohistochemistry for collagen type I and type II for histological evaluation.ResultsThe MACT group achieved significantly better results in both macroscopic and histological examinations. In the regeneration area, a Mankin score of 7.88 (6.20; 9.55) [mean (upper 95% confidence interval; lower 95% confidence interval)] was reached in the MACT group, 10.38 (8.03; 12.72) in the MATRIX group, and 10.33 (8.80; 11.87) in the SPONGIO group. The O'Driscoll score revealed a highly significant difference in the degree of defect repair: 15.92 (14.58; 17.25) for the MACT group compared to the two other groups [5.04 (1.21; 8.87) MATRIX and 6.58 (5.17; 8.00) SPONGIO; P < 0.0001].ConclusionThis study demonstrates promising results toward the development of a biological regeneration technique for early-stage OA

Inspection of Computed Tomography (CT) Data and Finite Element (FE) Simulation of Additive Manufactured (AM) Components

This is the author accepted manuscript. The final version is available from the publisherOne of the challenges of working with Additive Manufactured (AM) metal parts involves checking accuracy and reliability before production. Techniques used Computed Tomography (CT) scans, 3D image processing, and Finite Element (FE) simulation help detect problems prior to costly faults. A workflow has been developed by Synopsys, ANSYS, North Star Imaging, and the University of Pittsburgh to streamline this often-complex process, with applications to analyzing metal AM-produced lightweight brackets and a component from Moog, Inc. Software like Synopsys Simpleware™ is used to generate robust models from 3D scans of AM parts to compare original CAD models with ‘as-built’ geometries, and to export a FE mesh for simulation in ANSYS. This method enables identification of design deviations early in the design process, and how their impact might be tackled prior to production. For the Moog application, unexpected defects were identified for aerospace parts to inform future design iteration