VecTest as Diagnostic and Surveillance Tool for West Nile Virus in Dead Birds

The VecTest WNV assay is adequate for diagnostic and surveillance purposes in American Crows, Blue Jays, and House Sparrows

Kepler Presearch Data Conditioning I - Architecture and Algorithms for Error Correction in Kepler Light Curves

Kepler provides light curves of 156,000 stars with unprecedented precision. However, the raw data as they come from the spacecraft contain significant systematic and stochastic errors. These errors, which include discontinuities, systematic trends, and outliers, obscure the astrophysical signals in the light curves. To correct these errors is the task of the Presearch Data Conditioning (PDC) module of the Kepler data analysis pipeline. The original version of PDC in Kepler did not meet the extremely high performance requirements for the detection of miniscule planet transits or highly accurate analysis of stellar activity and rotation. One particular deficiency was that astrophysical features were often removed as a side-effect to removal of errors. In this paper we introduce the completely new and significantly improved version of PDC which was implemented in Kepler SOC 8.0. This new PDC version, which utilizes a Bayesian approach for removal of systematics, reliably corrects errors in the light curves while at the same time preserving planet transits and other astrophysically interesting signals. We describe the architecture and the algorithms of this new PDC module, show typical errors encountered in Kepler data, and illustrate the corrections using real light curve examples.Comment: Submitted to PASP. Also see companion paper "Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction" by Jeff C. Smith et a

Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: a.) Detectors; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT makes increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.Comment: 51 pages, 31 figures; Overview of ultrafast radiographic imaging and tracking as a part of ULITIMA 2023 conference, Mar. 13-16,2023, Menlo Park, CA, US

Assays to Detect West Nile Virus in Dead Birds

Using oral swab samples to detect West Nile virus in dead birds, we compared the Rapid Analyte Measurement Platform (RAMP) assay with VecTest and real-time reverse-transcriptase–polymerase chain reaction. The sensitivities of RAMP and VecTest for testing corvid species were 91.0% and 82.1%, respectively

Synthesis, characterisation and in vitro anticancer activity of hexanuclear thiolato-bridged arene ruthenium metallaprisms

Hexanuclear thiolato-bridged arene ruthenium metalla-prisms of the general formula [(p-cymene)6Ru6(SR)6(tpt)2]6+ (R=CH2Ph, CH2C6H4-p-tBu, CH2CH2Ph; tpt=2,4,6-tris(4-pyridyl)-1,3,5-triazine), obtained from the dinuclear precursors [(p-cymene)2Ru2(SR)2Cl2], AgCF3SO3 and tpt, have been isolated and fully characterised as triflate salts. The metalla-prisms are highly cytotoxic against human ovarian cancer cells, especially towards the cisplatin-resistant cell line A2780cisR (IC50 <0.25M)

Uterine Inflammatory Myofibroblastic Tumors: Proposed Risk Stratification Model Using Integrated Clinicopathologic and Molecular Analysis.

Inflammatory myofibroblastic tumor (IMT) of the uterus is a rare mesenchymal tumor with largely benign behavior; however, a small subset demonstrate aggressive behavior. While clinicopathologic features have been previously associated with aggressive behavior, these reports are based on small series, and these features are imperfect predictors of clinical behavior. IMTs are most commonly driven by ALK fusions, with additional pathogenic molecular alterations being reported only in rare examples of extrauterine IMTs. In this study, a series of 11 uterine IMTs, 5 of which demonstrated aggressive behavior, were evaluated for clinicopathologic variables and additionally subjected to capture-based next-generation sequencing with or without whole-transcriptome RNA sequencing. In the 6 IMTs without aggressive behavior, ALK fusions were the sole pathogenic alteration. In contrast, all 5 aggressive IMTs harbored pathogenic molecular alterations and numerous copy number changes in addition to ALK fusions, with the majority of the additional alterations present in the primary tumors. We combined our series with cases previously reported in the literature and performed statistical analyses to propose a novel clinicopathologic risk stratification score assigning 1 point each for: age above 45 years, size≥5 cm,≥4 mitotic figures per 10 high-power field, and infiltrative borders. No tumors with 0 points had an aggressive outcome, while 21% of tumors with 1 to 2 points and all tumors with ≥3 points had aggressive outcomes. We propose a 2-step classification model that first uses the clinicopathologic risk stratification score to identify low-risk and high-risk tumors, and recommend molecular testing to further classify intermediate-risk tumors

Antecedent longitudinal changes in body mass index are associated with diurnal cortisol curve features: The multi-ethnic study of atherosclerosis

CONTEXT: Prior studies have shown a cross-sectional association between body mass index (BMI) and salivary diurnal cortisol profile features (cortisol features); however, to our knowledge prior population-based studies have not examined the longitudinal association of body-mass index (BMI) with cortisol features. OBJECTIVE: To examine the association of (1) prior annual BMI percent change over 7 years with cortisol features, (2) baseline cortisol features with subsequent change in BMI over 6 years and (3) the association of change in cortisol features with change in BMI over 6 years. DESIGN: Longitudinal study SETTING: Multi-Ethnic Study of Atherosclerosis (MESA) Stress I & II Studies (2004-2006 & 2010-2012) PARTICIPANTS: 1,685 ethnically diverse men and women attended either MESA Stress exam (mean age 65 ± 10 years at MESA Stress I; mean age 69 ± 9 years at MESA Stress II). OUTCOME MEASURES: Log-transformed cortisol features including wake-up cortisol, cortisol awakening response, early decline slope (30 minutes to 2 hours post-awakening), late decline slope (2 hours post-awakening to bedtime), bedtime, and total area under the curve (AUC) cortisol. RESULTS: Over 7 years, following multivariable adjustment, (1) a 1% higher prior annual BMI % increase was associated with a 2.9% (95% CI: −5.0%, −0.8%) and 3.0% (95% CI: −4.7%, −1.4%) lower current wake-up and total AUC cortisol, respectively; (2) there was no significant association between baseline cortisol features and subsequent change in BMI and (3) among participants with BMI ≥ 30 kg/m(2), flattening of the late decline slope was associated with increases in BMI (every 1-unit increase late decline slope were associated with a 12.9% increase (95%CI: −1%, 26.8%) in BMI, respectively). CONCLUSIONS: We found a significant association between prior annual BMI % change and cortisol features, but no significant association between baseline cortisol features and subsequent change in BMI. In participants with obesity increases in BMI were associated with less pronounced declined. Collectively, our results suggest that greater adiposity may lead to a blunted diurnal cortisol profile