Relation of vertebral deformities to bone density, structure, and strength.

Because they are not reliably discriminated by areal bone mineral density (aBMD) measurements, it is unclear whether minimal vertebral deformities represent early osteoporotic fractures. To address this, we compared 90 postmenopausal women with no deformity (controls) with 142 women with one or more semiquantitative grade 1 (mild) deformities and 51 women with any grade 2-3 (moderate/severe) deformities. aBMD was measured by dual-energy X-ray absorptiometry (DXA), lumbar spine volumetric bone mineral density (vBMD) and geometry by quantitative computed tomography (QCT), bone microstructure by high-resolution peripheral QCT at the radius (HRpQCT), and vertebral compressive strength and load-to-strength ratio by finite-element analysis (FEA) of lumbar spine QCT images. Compared with controls, women with grade 1 deformities had significantly worse values for many bone density, structure, and strength parameters, although deficits all were much worse for the women with grade 2-3 deformities. Likewise, these skeletal parameters were more strongly associated with moderate to severe than with mild deformities by age-adjusted logistic regression. Nonetheless, grade 1 vertebral deformities were significantly associated with four of the five main variable categories assessed: bone density (lumbar spine vBMD), bone geometry (vertebral apparent cortical thickness), bone strength (overall vertebral compressive strength by FEA), and load-to-strength ratio (45-degree forward bending ÷ vertebral compressive strength). Thus significantly impaired bone density, structure, and strength compared with controls indicate that many grade 1 deformities do represent early osteoporotic fractures, with corresponding implications for clinical decision making

Identification of Six Novel SOD1 Gene Mutations in Familial Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the premature death of motor neurons. In approximately 10% of the cases the disease is inherited as autosomal dominant trait (FALS). It has been found that mutations in the Cu/Zn superoxide dismutase gene (SODl) are responsible for approximately 15% of FALS kindreds. We screened affected individuals from 70 unrelated FALS kindreds and identified 10 mutations, 6 of which are novel. Surprisingly, we have found a mutation in exon 3, which includes most of the active site loop and Zn2+ binding sites, a region where no previous SOD1 mutations have been found. Our data increase the number of different SODl mutations causing FALS to 55, a significant fraction of the 154 amino acids of this relatively small protei

The envelope of IRC+10216 reflecting the galactic light: UBV surface brightness photometry and interpretation

We present and analyse new optical images of the dust envelope surrounding the high mass-loss carbon star IRC+10216. This envelope is seen due to external illumination by galactic light. Intensity profiles and colors of the nebula were obtained in the UBV bandpasses. The data are compared with the results of a radiative transfer model calculating multiple scattering of interstellar field photons by dust grains with a single radius. The data show that the observed radial shape of the nebula, especially its half maximum radius, does not depend on wavelength (within experimental errors), suggesting that grains scatter in the grey regime, etc, etc (this abstract has been shortened)Comment: accepted by A

Autonomous synthesis of thin film materials with pulsed laser deposition enabled by in situ spectroscopy and automation

Synthesis of thin films has traditionally relied upon slow, sequential processes carried out with substantial human intervention, frequently utilizing a mix of experience and serendipity to optimize material structure and properties. With recent advances in autonomous systems which combine synthesis, characterization, and decision making with artificial intelligence (AI), large parameter spaces can be explored autonomously at rates beyond what is possible by human experimentalists, greatly accelerating discovery, optimization, and understanding in materials synthesis which directly address the grand challenges in synthesis science. Here, we demonstrate autonomous synthesis of a contemporary 2D material by combining the highly versatile pulsed laser deposition (PLD) technique with automation and machine learning (ML). We incorporated in situ and real-time spectroscopy, a high-throughput methodology, and cloud connectivity to enable autonomous synthesis workflows with PLD. Ultrathin WSe2 films were grown using co-ablation of two targets and showed a 10x increase in throughput over traditional PLD workflows. Gaussian process regression and Bayesian optimization were used with in situ Raman spectroscopy to autonomously discover two distinct growth windows and the process-property relationship after sampling only 0.25% of a large 4D parameter space. Any material that can be grown with PLD could be autonomously synthesized with our platform and workflows, enabling accelerated discovery and optimization of a vast number of materials

The extinction law in high redshift galaxies

We estimate the dust extinction laws in two intermediate redshift galaxies. The dust in the lens galaxy of LBQS1009-0252, which has an estimated lens redshift of zl~0.88, appears to be similar to that of the SMC with no significant feature at 2175 A. Only if the lens galaxy is at a redshift of zl~0.3, completely inconsistent with the galaxy colors, luminosity or location on the fundamental plane, can the data be fit with a normal Galactic extinction curve. The dust in the zl=0.68 lens galaxy for B0218+357, whose reddened image lies behind a molecular cloud, requires a very flat ultraviolet extinction curve with (formally) R(V)=12 +- 2. Both lens systems seem to have unusual extinction curves by Galactic standards.Comment: 15 pages, 3 figures. ApJ in pres

Impact Evaluation of Wet-Weather Events on Influent Flow and Loadings of a Water Resource Recovery Facility

Since the introduction of environmental legislation and directives in Europe, the impact of combined sewer overflows (CSO) on receiving water bodies has become a priority concern in water and wastewater treatment industry. Time-consuming and expensive local sampling and monitoring campaigns have been carried out to estimate the characteristic flow and pollutant concentrations of CSO water. This study focused on estimating the frequency and duration of wet-weather events and their impacts on influent flow and wastewater characteristics of the largest Italian water resource recovery facility (WRRF) in Castiglione Torinese. Eight years (viz. 2009–2016) of routinely collected influent data in addition to the arithmetic mean daily precipitation rates (PI) of the plant catchment area, were elaborated. Relationships between PI and volumetric influent flow rate (Qin), chemical oxygen demand (COD), ammonium concentration (N-NH4) and total suspended solids (TSS) are investigated. Time series data mining (TSDM) method is implemented for segmentation of time series by use of sliding window algorithm to partition the available records associated with wet and dry weather events based on the daily variation of PI time series. Appling the methodology in conjunction with results obtained from data reduction techniques, a wet-weather definition is proposed for the plant. The results confirm that applied methodology on routinely collected plant data can be considered as a good substitute for time-consuming and expensive sampling campaigns and plant monitoring programs usually conducted for accurate emergency response and long-term preparedness for extreme climate conditions