A joint Monte Carlo analysis of seafloor compliance, Rayleigh wave dispersion and receiver functions at ocean bottom seismic stations offshore New Zealand

Author Posting. © American Geophysical Union, 2014. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geochemistry, Geophysics, Geosystems 15 (2014): 5051–5068, doi:10.1002/2014GC005412.Teleseismic body-wave imaging techniques such as receiver function analysis can be notoriously difficult to employ on ocean-bottom seismic data due largely to multiple reverberations within the water and low-velocity sediments. In lieu of suppressing this coherently scattered noise in ocean-bottom receiver functions, these site effects can be modeled in conjunction with shear velocity information from seafloor compliance and surface wave dispersion measurements to discern crustal structure. A novel technique to estimate 1-D crustal shear-velocity profiles from these data using Monte Carlo sampling is presented here. We find that seafloor compliance inversions and P-S conversions observed in the receiver functions provide complimentary constraints on sediment velocity and thickness. Incoherent noise in receiver functions from the MOANA ocean bottom seismic experiment limit the accuracy of the practical analysis at crustal scales, but synthetic recovery tests and comparison with independent unconstrained nonlinear optimization results affirm the utility of this technique in principle.This research was supported by the National Science Foundation under grants EAR-0409835 and EAR-0409609. J.C.S. was supported by a CIRES visiting fellowship at the University of Colorado and A.F.S. was supported by a visiting professorship at the Earthquake Research Institute, University of Tokyo, for a portion of this work.2015-06-1

Lithospheric shear velocity structure of South Island, New Zealand, from amphibious Rayleigh wave tomography

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Solid Earth 121 (2016): 3686–3702, doi:10.1002/2015JB012726.We present a crust and mantle 3-D shear velocity model extending well offshore of New Zealand's South Island, imaging the lithosphere beneath the South Island as well as the Campbell and Challenger Plateaus. Our model is constructed via linearized inversion of both teleseismic (18–70 s period) and ambient noise-based (8–25 s period) Rayleigh wave dispersion measurements. We augment an array of 4 land-based and 29 ocean bottom instruments deployed off the South Island's east and west coasts in 2009–2010 by the Marine Observations of Anisotropy Near Aotearoa experiment with 28 land-based seismometers from New Zealand's permanent GeoNet array. Major features of our shear wave velocity (Vs) model include a low-velocity (Vs 50 km) beneath the central South Island exhibits strong spatial correlation with upper mantle earthquake hypocenters beneath the Alpine Fault. The ~400 km long low-velocity zone we image beneath eastern South Island and the inner Bounty Trough underlies Cenozoic volcanics and the locations of mantle-derived helium measurements, consistent with asthenospheric upwelling in the region.National Science Foundation Grant Number: EAR-0409564, EAR-0409609, and EAR-04098352016-11-2

Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production:AirSurf-Lettuce

Aerial imagery is regularly used by crop researchers, growers and farmers to monitor crops during the growing season. To extract meaningful information from large-scale aerial images collected from the field, high-throughput phenotypic analysis solutions are required, which not only produce high-quality measures of key crop traits, but also support professionals to make prompt and reliable crop management decisions. Here, we report AirSurf, an automated and open-source analytic platform that combines modern computer vision, up-to-date machine learning, and modular software engineering in order to measure yield-related phenotypes from ultra-large aerial imagery. To quantify millions of in-field lettuces acquired by fixed-wing light aircrafts equipped with normalised difference vegetation index (NDVI) sensors, we customised AirSurf by combining computer vision algorithms and a deep-learning classifier trained with over 100,000 labelled lettuce signals. The tailored platform, AirSurf-Lettuce, is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution across the field, based on which associated global positioning system (GPS) tagged harvest regions have been identified to enable growers and farmers to conduct precision agricultural practises in order to improve the actual yield as well as crop marketability before the harvest

Towards understanding the clinical significance of QT peak prolongation: a novel marker of myocardial ischemia independently demonstrated in two prospective studies

Background: QT peak prolongation identified patients at risk of death or non-fatal MI. We tested the hypothesis that QT peak prolongation might be associated with significant myocardial ischaemia in two separate cohorts to see how widely applicable the concept was. Methods and Results: In the first study, 134 stroke survivors were prospectively recruited and had 12-lead ECGs and Nuclear myocardial perfusion scanning. QT peak was measured in lead I of a 12-lead ECG and heart rate corrected by Bazett’s formula (QTpc). QTpc prolongation to 360ms or more was 92% specific at diagnosing severe myocardial ischaemia. This hypothesis-generating study led us to perform a second prospective study in a different cohort of patients who were referred for dobutamine stress echocardiography. 13 of 102 patients had significant myocardial ischaemia. Significant myocardial ischaemia was associated with QT peak prolongation at rest (mean 354ms, 95% CI 341-367ms, compared with mean 332ms, 95% CI 327-337ms in those without significant ischaemia; p=0.002). QT peak prolongation to 360ms or more was 88% specific at diagnosing significant myocardial ischaemia in the stress echocardiography study. QT peak prolongation to 360ms or more was associated with over 4-fold increase odds ratio of significant myocardial ischaemia. The Mantel- Haenszel Common Odds Ratio Estimate=4.4, 95% CI=1.2-16.0, p=0.023. Conclusion: QT peak (QTpc) prolongation to 360ms or more should make us suspect the presence of significant myocardial ischaemia. Such patients merit further investigations for potentially treatable ischaemic heart disease to reduce their risk of subsequent death or non-fatal MI

On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics ("feature"), detail methods to robustly estimate periodic light-curve features, introduce tree-ensemble methods for accurate variable star classification, and show how to rigorously evaluate the classification results using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is superior to other machine-learned methods in terms of accuracy, speed, and relative immunity to features with no useful class information; the RF classifier can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which further reduces the catastrophic error rate to 7.8%. Excluding low-amplitude sources, our overall error rate improves to 14%, with a catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure

The Economic Ramifications Of Multinational Corporations: Foreign Direct Investments (FDIs) And The Connection Between Technological Transfer And ProductivityGrowth Within Host Economies

This study conducts an investigation into the impact of technology transfer as a derivative of FDI on productive efficiency within developing Asian economies through the implementation of applied empirical analysis

HIV-1 Clade D Is Associated with Increased Rates of CD4 Decline in a Kenyan Cohort

HIV-1 is grouped phylogenetically into clades, which may impact rates of HIV-1 disease progression. Clade D infection in particular has been shown to be more pathogenic. Here we confirm in a Nairobi-based prospective female sex worker cohort (1985-2004) that Clade D (n = 54) is associated with a more rapid CD4 decline than clade A1 (n = 150, 20.6% vs 13.4% decline per year, 1.53-fold increase, p = 0.015). This was independent of "protective" HLA and country of origin (p = 0.053), which in turn were also independent predictors of the rate of CD4 decline (p = 0.026 and 0.005, respectively). These data confirm that clade D is more pathogenic than clade A1. The precise reason for this difference is currently unclear, and requires further study. This is first study to demonstrate difference in HIV-1 disease progression between clades while controlling for protective HLA alleles

The vortex state in geologic materials: a micromagnetic perspective

A wide variety of Earth and planetary materials are very good recorders of paleomagnetic information. However, most magnetic grains in these materials are not in the stable single domain grain size range but are larger and in nonuniform vortex magnetization states. We provide a detailed account of vortex phenomena in geologic materials by simulating first‐order reversal curves (FORCs) via finite‐element micromagnetic modeling of magnetite nanoparticles with realistic morphologies. The particles have been reconstructed from focused ion beam nanotomography of magnetite‐bearing obsidian and accommodate single and multiple vortex structures. Single vortex (SV) grains have fingerprints with contributions to both the transient and transient‐free zones of FORC diagrams. A fundamental feature of the SV fingerprint is a central ridge, representing a distribution of negative saturation vortex annihilation fields. SV irreversible events at multiple field values along different FORC branches determine the asymmetry in the upper and lower lobes of generic bulk FORC diagrams of natural materials with grains predominantly in the vortex state. Multivortex (MV) FORC signatures are modeled here for the first time. MV grains contribute mostly to the transient‐free zone of a FORC diagram, averaging out to create a broad central peak. The intensity of the central peak is higher than that of the lobes, implying that MV particles are more abundant than SV particles in geologic materials with vortex state fingerprints. The abundance of MV particles, as well as their single domain‐like properties point to MV grains being the main natural remanent magnetization carriers in geologic materials

What is cost-efficient phenotyping? Optimizing costs for different scenarios

Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant handling and manpower; (ii) the total costs per plant/microplot are similar in robotized platform or field experiments with drones, hand-held or robotized ground vehicles; (iii) the cost of vehicles carrying sensors represents only 5–26% of the total costs. These conclusions depend on the context, in particular for labor cost, the quantitative demand of phenotyping and the number of days available for phenotypic measurements due to climatic constraints. Data analysis represents 10–20% of total cost if pipelines have already been developed. A trade-off exists between the initial high cost of pipeline development and labor cost of manual operations. Overall, depending on the context and objsectives, “cost-effective” phenotyping may involve either low investment (“affordable phenotyping”), or initial high investments in sensors, vehicles and pipelines that result in higher quality and lower operational costs