Temporal-spatial variations, source apportionment, and ecological risk of trace elements in sediments of water-level-fluctuation zone in the Three Gorges Reservoir, China

The Three Gorges Reservoir (TGR) plays a crucial role in providing electricity for mega-cities across China. However, since the impoundment was completed in 2006, attention to environmental concerns has also been intensive. In order to determine the distribution, sources, and pollution status of trace elements in the water fluctuation zone of the TGR following ten years of repeated “submergence” and “exposure”, we systematically collected 16 paired surface sediment samples (n = 32) covering the entire main body of the TGR in March 2018 (following 6 months of submergence) and September 2018 (after 6 months of exposure), and quantitatively analyzed 13 elements (e.g., Mn, Fe, V, Cr, Ni, Cu, Zn, As, Sr, Y, Zr, Ba, and Pb) using X-ray fluorescence spectrophotometry (XRF). The results showed that, except for Sr, concentrations of trace metals following submergence were generally higher than those after exposure due to the less settling of suspended solids at the faster flow velocity during the drawdown period. Assessment using enrichment factors (EFs) and a geo-accumulation index (Igeo) both characterized a relatively serious anthropogenic pollution status of metals in the upper reaches of the TGR with respect to the middle-lower reaches. Source apportionment by positive matrix factorization (PMF) analysis indicated that agricultural activities (24.8 and 24.3%, respectively) and industrial emissions (24.5 and 22.9%, respectively) were the two major sources in these two periods, followed by natural sources, domestic sewage, and ore mining. Ecological risk assessment showed that metalloid arsenic (As) could be the main potential issue of risk to aquatic organisms and human health. A new source-specific risk assessment method (pRI) combined with PMF revealed that agricultural activities could be the major source of potential ecological risk and should be prioritized as the focus of metal/metalloid risk management in the TGR

Genetically-predicted life-long lowering of low-density lipoprotein cholesterol is associated with decreased frailty: A Mendelian randomization study in UK biobank

BACKGROUND: High circulating low-density lipoprotein cholesterol (LDL-C) is a major risk factor for atherosclerosis and age-associated cardiovascular events. Long-term dyslipidaemia could contribute to the development of frailty in older individuals through its role in determining cardiovascular health and potentially other physiological pathways. METHODS: We conducted Mendelian randomization (MR) analyses using genetic variants to estimate the effects of long-term LDL-C modification on frailty in UK Biobank (n = 378,161). Frailty was derived from health questionnaire and interview responses at baseline when participants were aged 40 to 69 years, and calculated using an accumulation-of-deficits approach, i.e. the frailty index (FI). Several aggregated instrumental variables (IVs) using 50 and 274 genetic variants were constructed from independent single-nucleotide polymorphisms (SNPs) to instrument circulating LDL-C concentrations. Specific sets of variants in or near genes that encode six lipid-lowering drug targets (HMGCR, PCSK9, NPC1L1, APOB, APOC3, and LDLR) were used to index effects of exposure to related drug classes on frailty. SNP-LDL-C effects were available from previously published studies. SNP-FI effects were obtained using adjusted linear regression models. Two-sample MR analyses were performed with the IVs as instruments using inverse-variance weighted, MR-Egger, weighted median, and weighted mode methods. To address the stability of the findings, MR analyses were also performed using i) a modified FI excluding the cardiometabolic deficit items and ii) data from comparatively older individuals (aged ≥60 years) only. Several sensitivity analyses were also conducted. FINDINGS: On average 0.14% to 0.23% and 0.16% to 0.31% decrements in frailty were observed per standard deviation reduction in LDL-C exposure, instrumented by the general IVs consisting of 50 and 274 variants, respectively. Consistent, though less precise, associations were observed in the HMGCR-, APOC3-, NPC1L1-, and LDLR-specific IV analyses. In contrast, results for PCSK9 were in the same direction but more modest, and null for APOB. All sensitivity analyses produced similar findings. INTERPRETATION: A genetically-predicted life-long lowering of LDL-C is associated with decreased frailty in midlife and older age, representing supportive evidence for LDL-C's role in multiple health- and age-related pathways. The use of lipid-lowering therapeutics with varying mechanisms of action may differ by the extent to which they provide overall health benefits

A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

© The Author(s) 2018. A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities

Conformative Filtering for Implicit Feedback Data

Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and is domain independent. However, there is a lack of negative examples. Previous work tackles this problem by assuming that users are not interested or not as much interested in the unconsumed items. Those assumptions are often severely violated since non-consumption can be due to factors like unawareness or lack of resources. Therefore, non-consumption by a user does not always mean disinterest or irrelevance. In this paper, we propose a novel method called Conformative Filtering (CoF) to address the issue. The motivating observation is that if there is a large group of users who share the same taste and none of them have consumed an item before, then it is likely that the item is not of interest to the group. We perform multidimensional clustering on implicit feedback data using hierarchical latent tree analysis (HLTA) to identify user `tastes' groups and make recommendations for a user based on her memberships in the groups and on the past behavior of the groups. Experiments on two real-world datasets from different domains show that CoF has superior performance compared to several common baselines

How to perform better intervention to prevent and control diabetic retinopathy among patients with type 2 diabetes

This meta-analysis of randomized controlled trials (RCTs) aims to investigate how to perform better interventions targeting modifiable risk factors of diabetic retinopathy (DR) to prevent and control DR in patients with type 2 diabetes by comparing different intervention types and follow-up intervals. Literature published before June 1st, 2019 were searched on Pubmed, Embase and ScienceDirect. RCTs targeting modifiable risk factors of DR (including blood glucose, blood pressure, lipid, dietary, physical activity and smoking) were selected by two reviewers and double checked for accuracy. Random effects models were estimated to calculate pooled Odds Ratios (OR). Twenty-two RCTs (n = 22,511) were included. In general, interventions targeting modifiable risk factor of DR reduced the risk of developing DR (I 2 = 26.7%; OR = 0.60; 95% CI 0.45 to 0.79) and DR worsening (I 2 = 0.0%; OR = 0.62; 95% CI 0.47 to 0.80; P < 0.001). Multifactorial interventions had better effect on reducing the risk of development and progression of DR in comparison with other interventions, while only blood-pressure-control interventions showed significant effect on slowing down DR worsening. Additionally, interventions with follow-up >5 years had better effect on reduction of DR development, and interventions with follow-up >2 years had better effect on reducing the risk of DR worsenin

Positive matrix factorization on source apportionment for typical pollutants in different environmental media: a review

A bibliometric analysis of published papers with the key words "positive matrix factorization" and "source apportionment" in 'Web of Science', reveals that more than 1000 papers are associated with this research and that approximately 50% of these were produced in Asia. As a receptor-based model, positive matrix factorization (PMF) has been widely used for source apportionment of various environmental pollutants, such as persistent organic pollutants (POPs), heavy metals, volatile organic compounds (VOCs) as well as inorganic cations and anions in the last decade. In this review, based on the papers mainly from 2008 to 2018 that focused on source apportionment of pollutants in different environmental media, we provide a comparison and summary of the source categories of typical environmental pollutants, with a special focus on polycyclic aromatic hydrocarbons (PAHs), apportioned using PMF. Based on the statistical average, coal combustion and vehicular emission, are shown to be the two most common sources of PAHs, and contribute much more to emissions than other sources, such as biomass burning, biogenic sources and waste incineration. Heavy metals were mainly from agricultural activities, industrial and vehicular emissions and mining activities. Quantitative source apportionment on pollutants such as VOCs and particulate matter were also apportioned, showing a prominent contribution from fossil-fuel combustion. We conclude that, aside from natural sources, abatement strategies should be focused on changes in energy structure and industrial activities, especially in China. Source apportionment of typical POPs including polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs), polychlorinated biphenyls (PCBs), halogenated flame retardants (HFRs) and perfluorinated compounds (PFCs) is less comprehensive and further study is required

Quantum fluctuations in high field magnetization of 2D square lattice J1-J2 antiferromagnets

The J1-J2 square lattice Heisenberg model with spin S=1/2 has three phases with long-range magnetic order and two unconventionally ordered phases depending on the ratio of exchange constants. It describes a number of recently found layered vanadium oxide compounds. A simple means of investigating the ground state is the study of the magnetization curve and high-field susceptibility. We discuss these quantities by using the spin-wave theory and the exact diagonalization in the whole J1-J2 plane. We compare both results and find good overall agreement in the sectors of the phase diagram with magnetic order. Close to the disordered regions the magnetization curve shows strong deviations from the classical linear behaviour caused by large quantum fluctuations and spin-wave approximation breaks down. On the FM side (J1<0) where one approaches the quantum gapless spin nematic ground state this region is surprisingly large. We find that inclusion of second order spin-wave corrections does not lead to fundamental improvement. Quantum corrections to the tilting angle of the ordered moments are also calculated. They may have both signs, contrary to the always negative first order quantum corrections to the magnetization. Finally we investigate the effect of the interlayer coupling and find that the quasi-2D picture remains valid up to |J_\perp/J1| ~ 0.3.Comment: 13 pages, 6figure

Intergrain Effects in the AC Susceptibility of Polycrystalline LaFeAsO_{0.94}F_{0.06}

The AC susceptibility, chi, at zero DC magnetic field of a polycrystalline sample of LaFeAsO_{0.94}F_{0.06} (Tc ≈ 24 K) has been investigated as a function of the temperature, the amplitude of the AC magnetic field (in the range Hac = 0.003 Oe - 4 Oe) and the frequency (in the range f = 10 kHz - 100 kHz). The chi(T) curve exhibits the typical two-step transition arising from the combined response of superconducting grains and intergranular weak-coupled medium. The intergranular part of chi strongly depends on both the amplitude and the frequency of the AC driving field, from few Kelvin below Tc down to T = 4.2 K. Our results show that, in the investigated sample, the intergrain critical current is not determined by pinning of Josephson vortices but by Josephson critical current across neighboring grains

Implications of Shallower Memory Controller Transaction Queues in Scalable Memory Systems

Scalable memory systems provide scalable bandwidth to the core growth demands in multicores and embedded systems processors. In these systems, as memory controllers (MCs) are scaled, memory traffic per MC is reduced, so transaction queues become shallower. As a consequence, there is an opportunity to explore transaction queue utilization and its impact on energy utilization. In this paper, we propose to evaluate the performance and energy-per-bit impact when reducing transaction queue sizes along with the MCs of these systems. Experimental results show that reducing 50 % on the number of entries, bandwidth and energy-per-bit levels are not affected, whilst reducing aggressively of about 90 %, bandwidth is similarly reduced while causing significantly higher energy-per-bit utilization