PM2.5, Household Income, and Health Hazard: The Role of Economic Integration in the Process of Decarbonization in the Developing Economies

The goal of this study is to examine the impact of household income and air pollution on the health of developing-country residents. The panel dataset of twelve developing nations used for this purpose. High levels of fine particulate matter in the air are linked to increased health problems, and lower incomes for households in the economies studied. However, effective environmental management and renewable energy resources have a significant role in controlling the harmful impact of fine particulate matter in the air. It highlights that developing economies could lower the fine particulate matter in the air by strengthening the regional environmental policies and adopting renewable resources. In emerging countries, environmentally friendly strategies and the shift from carbon base to non-carbon-based energy would minimize pollution in the atmosphere and improve the quality of life for inhabitants and other organisms. Improved quality of life and lower levels of fine particulate matter pollution are expected to increase people’s per capita income in the region. Finally, air pollution is a transboundary phenomenon; therefore, strict compliance with environmental protection policies at the regional level is a prerequisite for improved quality of the natural environment

Tomato TFT1 Is Required for PAMP-Triggered Immunity and Mutations that Prevent T3S Effector XopN from Binding to TFT1 Attenuate Xanthomonas Virulence

XopN is a type III effector protein from Xanthomonas campestris pathovar vesicatoria that suppresses PAMP-triggered immunity (PTI) in tomato. Previous work reported that XopN interacts with the tomato 14-3-3 isoform TFT1; however, TFT1's role in PTI and/or XopN virulence was not determined. Here we show that TFT1 functions in PTI and is a XopN virulence target. Virus-induced gene silencing of TFT1 mRNA in tomato leaves resulted in increased growth of Xcv ΔxopN and Xcv ΔhrpF demonstrating that TFT1 is required to inhibit Xcv multiplication. TFT1 expression was required for Xcv-induced accumulation of PTI5, GRAS4, WRKY28, and LRR22 mRNAs, four PTI marker genes in tomato. Deletion analysis revealed that the XopN C-terminal domain (amino acids 344–733) is sufficient to bind TFT1. Removal of amino acids 605–733 disrupts XopN binding to TFT1 in plant extracts and inhibits XopN-dependent virulence in tomato, demonstrating that these residues are necessary for the XopN/TFT1 interaction. Phos-tag gel analysis and mass spectrometry showed that XopN is phosphorylated in plant extracts at serine 688 in a putative 14-3-3 recognition motif. Mutation of S688 reduced XopN's phosphorylation state but was not sufficient to inhibit binding to TFT1 or reduce XopN virulence. Mutation of S688 and two leucines (L64,L65) in XopN, however, eliminated XopN binding to TFT1 in plant extracts and XopN virulence. L64 and L65 are required for XopN to bind TARK1, a tomato atypical receptor kinase required for PTI. This suggested that TFT1 binding to XopN's C-terminal domain might be stabilized via TARK1/XopN interaction. Pull-down and BiFC analyses show that XopN promotes TARK1/TFT1 complex formation in vitro and in planta by functioning as a molecular scaffold. This is the first report showing that a type III effector targets a host 14-3-3 involved in PTI to promote bacterial pathogenesis

Preparation of magnetic hierarchically porous microspheres with temperature-controlled wettability for removal of oils

A series of monodispersed microspheres with hierarchically porous structures were prepared by microfluidic devices. Phase separation of the silica sol in microdroplets was adopted to construct these structures. The effects of velocity ratios (for both the continuous and the dispersed phases), collection solvents and calcination temperatures were investigated. The diameters of the microspheres were tuned from 148 mu m to 940 mu m by adjusting the velocity ratio. Tests revealed that the surface areas and pore volumes of the microspheres can reach 495 m(2) g(-1) and 0.6068 ml g(-1), respectively. The macroporous structure can be controlled by the collection solvents, and the wettability of the microspheres is determined by the calcination temperature. A calcination temperature of 450 degrees C leads to a hydrophilic surface property. Fe3O4 nanoparticles were added to the silica sol to form magnetic microspheres, and the porous structure was not affected. This kind of hybrid microsphere adsorbs 3.29 times its own weight in toluene. These spheres can adsorb oil on water surfaces, and then be removed from the water with an external magnetic field. The microspheres can be recovered and reused more than 10 times. (C) 2016 Elsevier Inc. All rights reserved

Fast Adaptive Coding Unit Depth Range Selection Algorithm for High Efficiency Video Coding

The emerging high-efficiency video coding standard employs a new coding structure characterized by coding unit, prediction unit and transform unit. It improves the coding efficiency significantly, but also introduces great computational complexity on the decision of optimal coding unit, prediction unit and transforms unit sizes. To reduce the encoding complexity, a fast adaptive coding unit depth range selection algorithm is proposed. In the proposed scheme, first of all, the average depth error between adjacent and their co-located largest coding unit are utilized to determine depth range of current largest coding unit. And then, depth scaling factor in the previous and back frame are obtained to shrink the depth range. Furthermore, we also propose a depth range correction algorithm for reducing misjudgment of changes in the larger sequences. Experimental results show that the former algorithm can save encoding time of about 10% more than Shen’s algorithm with a BD-bitrates loss of 0.81 % and a BD-PSNR loss of 0.026 dB. Correction algorithm can save same encoding time of Shen’s algorithm with a BD-bitrates lowering 0.76 % and a BD-PSNR improvement of 0.028 dB

HIV-Related High-Risk Behaviors among Chinese Migrant Construction Laborers in Nantong, Jiangsu

Background: HIV transmission in rural areas of China is being fueled in part by migrant workers who acquire HIV outside of their hometowns. Recent surveillance statistics indicate that HIV prevalence among returning migrants has increased significantly. Methods: We conducted a community-based cross-sectional study to assess HIV-related knowledge, attitudes and behaviors among migrant returnees in Nantong, Jiangsu Province, one of the largest exporters of migrant laborers. Results: A total of 1625 subjects were enrolled with a response rate of 89%. All participants were male and of the majority Han ethnicity. The mean age was 39.0 years (SD = 6.7; range: 18 to 63), and most had a stable partner (N = 1533, 94.3%). Most correctly identified the major modes of HIV transmission (68.9%–82.0%), but fewer were able to identify ways that HIV cannot be transmitted. Nearly one-third of participants held positive attitudes toward having multiple sex partners, and nearly half believed that sex work should be legalized. Multiple logistic regression analysis indicated that risky sexual behavior (defined as sex with a casual or commercial sex partner) was associated with no stable partner; working abroad; correct condom use; age,22 at first sex; higher coital frequency; and having a positive attitude towards multiple sex partners. Conclusions: We found high levels of reported sex with a casual or commercial sex partner and low levels of consisten

Unsupervised cross-domain damage detection and localization for vibration isolators in metro floating-slab track

The steel-spring vibration isolators (SVIs) are critical components of the floating-slab track (FST) in metro system, aiming at ensuring a good performance regarding vibration attenuation. Supervised learning-based models have been recently developed for the SVI damage detection. As they are mostly trained on simulation datasets, the damage detection performance of these models is greatly spoiled on real-life datasets due to the so-called domain shift issue. In this study, a multi-strategy-based domain adaptation methodology is proposed for cross-domain SVI damage detection and localization, where domain adversarial training and feature distribution discrepancy regularization are adopted. The core of the proposed procedure is to extract damagesensitive and domain-invariant features from the dynamic responses of the track system. The main advantage is that only the source domain dataset needs to be well-labeled while label information of recorded acceleration response related to the real structure is not required for model training. Vehicle-slab track coupled dynamic simulations are conducted to build the labeled source dataset, allowing for different operational scenarios and SVI health conditions. Then, modeling uncertainties are introduced concerning model parameters, and background noise is added to the computed responses to mimic real engineering scenarios in the target domain datasets. A noteworthy good detection performance is reported, when the trained network is finally tested on the target dataset. Ablation studies and feature visualization are then reported, to get insights into the reasons why the proposed method proves superior to a traditional CNN and other domain adaption methods. Finally, an experimental dataset collected during field tests is exploited to validate the effectiveness of the proposed domain adaptation method