Entering the digital world (Pedometrics 2009)

Development in pedometrics has not only shaped the research agenda in soil science but also attracted the attention of practitioners from other communities such as environmental modelling and land management who require digital information on soils. At the same time, demands from these communities and developments in information technology help to fuel and drive the research agenda of pedometrics. These factors have combined to draw scientists with diverse backgrounds and interests into the field of pedometrics over its short history as a distinctive subdiscipline of soil science

Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism

Recently, Human Attribute Recognition (HAR) has become a hot topic due to its scientific challenges and application potentials, where localizing attributes is a crucial stage but not well handled. In this paper, we propose a novel deep learning approach to HAR, namely Distraction-aware HAR (Da-HAR). It enhances deep CNN feature learning by improving attribute localization through a coarse-to-fine attention mechanism. At the coarse step, a self-mask block is built to roughly discriminate and reduce distractions, while at the fine step, a masked attention branch is applied to further eliminate irrelevant regions. Thanks to this mechanism, feature learning is more accurate, especially when heavy occlusions and complex backgrounds exist. Extensive experiments are conducted on the WIDER-Attribute and RAP databases, and state-of-the-art results are achieved, demonstrating the effectiveness of the proposed approach.Comment: 8 pages, 5 figures, accepted by AAAI-20 as an oral presentatio

Two-particle azimuthal angle correlations and azimuthal charge balance function in relativistic heavy ion collisions

The two-particle azimuthal angle correlation (TPAC) and azimuthal charge balance function (ACBF) are used to study the anisotropic expansion in relativistic heavy ion collisions. It is demonstrated by the relativistic quantum molecular dynamics (RQMD) model and a multi-phase transport (AMPT) model that the small-angle correlation in TPAC indeed presents anisotropic expansion, and the large-angle (or back-to-back) correlation is mainly due to global momentum conservations. The AMPT model reproduces the observed TPAC, but the RQMD model fails to reproduce the strong correlations in both small and large azimuthal angles. The width of ACBF from RQMD and AMPT models decreases from peripheral to central collisions, consistent with experimental data, but in contrast to the expectation from thermal model calculations. The ACBF is insensitive to anisotropic expansion. It is a probe for the mechanism of hadronization, similar to the charge balance function in rapidity

Characteristics of DNA-AuNP networks on cell membranes and real-time movies for viral infection

AbstractThis data article provides complementary data for the article entitled “DNA-AuNP networks on cell membranes as a protective barrier to inhibit viral attachment, entry and budding” Li et al. (2016) [1]. The experimental methods for the preparation and characterization of DNA-conjugated nanoparticle networks on cell membranes were described. Confocal fluorescence images, agarose gel electrophoresis images and hydrodynamic diameter of DNA-conjugated gold nanoparticle (DNA-AuNP) networks were presented. In addition, we have prepared QDs-labeled RSV (QDs-RSV) to real-time monitor the RSV infection on HEp-2 cells in the absence and presence of DNA-AuNP networks. Finally, the cell viability of HEp-2 cells coated by six types of DNA-nanoparticle networks was determined after RSV infection

Impact of fouling on flow-induced vibration characteristics in fluid-conveying pipelines

This paper addresses monitoring problems commonly encountered in petrochemical enterprises caused by fouling and clogging in the circulating water heat exchangers by monitoring the heat exchanger’s wall vibration signal for early failure detection. Due to the difficulties encountered in simulation caused by the large number of tubes inside the heat exchanger, such methods were discussed by studying in the fluid-conveying pipeline fouling. ANSYS was used to establish the normal model and fouling model of a fluid-conveying pipeline so as to analyze the changing rule of various parameters that are influenced by different inlet velocities. As the inlet velocity and fouling severity continuously increased, the wall load and the vibration acceleration increased as well, leading variations in wall vibration signals. This paper conduct extensive experiments by using straight pipes to compare the results from simulation and from normal fluid-conveying pipelines, under the same working conditions. By such comparison, we estimate the accuracy of the simulation model

Prostaglandin E1 Alleviates Cognitive Dysfunction in Chronic Cerebral Hypoperfusion Rats by Improving Hemodynamics

Compensatory vascular mechanisms can restore cerebral blood flow (CBF) but fail to protect against chronic cerebral hypoperfusion (CCH)-mediated neuronal damage and cognitive impairment. Prostaglandin E1 (PGE1) is known as a vasodilator to protect against ischemic injury in animal models, but its protective role in CCH remains unclear. To determine the effect of PGE1 on cerebral hemodynamics and cognitive functions in CCH, bilateral common carotid artery occlusion (BCCAO) was used to mimic CCH in rats, which were subsequently intravenously injected with PGE1 daily for 2 weeks. Magnetic resonance imaging, immunofluorescence staining and Morris water maze (MWM) were used to evaluate CBF, angiogenesis, and cognitive functions, respectively. We found that PGE1 treatment significantly restored CBF by enhancing vertebral artery dilation. In addition, PGE1 treatment increased the number of microvascular endothelial cells and neuronal cells in the hippocampus, and decreased the numbers of astrocyte and apoptotic cells. In the MWM test, we further showed that the escape latency of CCH rats was significantly reduced after PGE1 treatment. Our results suggest that PGE1 ameliorates cognitive dysfunction in CCH rats by enhancing CBF recovery, sustaining angiogenesis, and reducing astrocyte activation and neuronal loss

Rapidity, azimuthal, and multiplicity dependence of mean transverse momentum and transverse momentum correlations in π+p\pi^{+}p and K+pK^{+}p collisions in s\sqrt{s}=22 GeV

Rapidity, azimuthal and multiplicity dependence of mean transverse momentum and transverse momentum correlations of charged particles is studied in pi/sup positive and K/sup positive collisions at 250 GeV/c incident beam momentum. For the first time, it is found that the rapidity dependence of the two-particle transverse momentum correlation is different from that of the mean transverse momentum, but both have similar multiplicity dependence. In particular, the transverse momentum correlations are boost invariant. This is similar to the recently found boost invariance of the charge balance function. A strong azimuthal dependence of the transverse momentum correlations originates from the constraint of energy-momentum conservation. The results are compared with those from the PYTHIA Monte Carlo generator. The similarities to and differences with the results from current heavy ion experiments are discussed

Combined Visualization of Nigrosome-1 and Neuromelanin in the Substantia Nigra Using 3T MRI for the Differential Diagnosis of Essential Tremor and de novo Parkinson's Disease

Differentiating early-stage Parkinson's disease (PD) from essential tremor (ET) remains challenging. In the current study, we aimed to evaluate whether visual analyses of neuromelanin-sensitive magnetic resonance imaging (NM-MRI) combined with nigrosome-1 (N1) imaging using quantitative susceptibility mapping (QSM) in the substantia nigra (SN) are of diagnostic value in the differentiation of de novo PD from untreated ET. Sixty-eight patients with de novo PD, 25 patients with untreated ET, and 34 control participants underwent NM-MRI and QSM. NM and N1 signals in the SN on MR images were visually evaluated using a 3-point ordinal scale. Receiver operating characteristic (ROC) analyses were performed to determine the diagnostic values of the visual ratings of NM and N1. The diagnostic values of the predicted probabilities were calculated via logistic regression analysis using the combination of NM and N1 visual ratings, as well as their quadratic items. The proportions of invisible NM and invisible N1 were significantly higher in the PD group than those in the ET and control groups (p < 0.001). The sensitivity/specificity for differentiating PD from ET was 0.882/0.800 for NM and 0.794/0.920 for N1, respectively. Combining the two biomarkers, the area under the curve (AUC) of the predicted probabilities was 0.935, and the sensitivity/specificity was 0.853/0.920 when the cutoff value was set to 0.704. Our findings demonstrate that visual analyses combing NM and N1 imaging in the SN may aid in differential diagnosis of PD and ET. Furthermore, our results suggest that patients with PD exhibit larger iron deposits in the SN than those with ET

Functional Genomics Complements Quantitative Genetics in Identifying Disease-Gene Associations

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype