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Abstract Background Air pollution is a leading cause of global disease burden. Lack of suitable methods for long term measuring exposure level at individual level is crippling environmental epidemiology research of air pollution. Methods We report an integrative system, Bio3Air, for long term measurement of individual level air pollution exposure, currently focusing on ambient particulate matter (PM). The novel system in real-time quantifies individual's outdoor/indoor status, geological location, lung ventilation rate and PM concentration of individual's surrounding environment, and these metrics are subsequently incorporated in calculating PM exposure. Results The system is fully developed and tested in China, USA and Canada, and has been successfully applied in epidemiology study. Bio3Air offers high reliability, sensitivity, reproducibility (>99%) and accuracy. It has high time- and spatial- resolution (≤ 2 min and ≤ 20 m, respectively). Bio3Air achieved 91.89% consistency with "gold-standard" method (membrane collection and off-line analysis). Conclusions Bio3Air represents a substantial methodological advance in environmental health research of air pollution. It captures information relevant in measuring individual's PM exposure (e.g. real-time outdoor/indoor status, location and lung ventilation rate). Such information is typically missed by conventional approaches. Additional features of Bio3Air include easy-to-use, cost-effectiveness and automated data collection, making it a powerful tool facilitating studies of air pollution exposure and health consequences. Highlights • We developed a novel system, Bio3Air, for long-term measure of particulate matter (PM) exposure at individual level. • The system demonstrated high time- and spatial-resolution, data reproducibility and accuracy in quantifying PM exposure. • Bio3Air is potentially useful tool facilitating studies on air pollutant exposure and health consequences. [ABSTRACT FROM AUTHOR]

Abstract Background Ambient particulate matter (PM) exposure has been associated with respiratory function decline in epidemiological studies. We hypothesize that a possible underlying mechanism is the perturbation of airway microbiome by PM exposure. Methods During October 2016–October 2017, on two human cohorts (n = 115 in total) in Shanghai China, we systematically collected three categories of data: (1) respiratory functions, (2) airway microbiome from sputum, and (3) PM 2.5 (PM of ≤ 2.5 µm in diameter) level in ambient air. We investigated the impact of PM 2.5 on airway microbiome as well as the link between airway microbiome and respiratory functions using linear mixed regression models. Results The respiratory function of our primary interest includes forced vital capacity (FVC) and forced expiratory volume in 1st second (FEV 1). FEV 1 /FVC, an important respiratory function trait and key diagnosis criterion of COPD, was significantly associated with airway bacteria load (p = 0.0038); and FEV 1 was associated with airway microbiome profile (p = 0.013). Further, airway microbiome was significantly influenced by PM 2.5 exposure (p = 4.48E-11). Conclusions To our knowledge, for the first time, we demonstrated the impact of PM 2.5 on airway microbiome, and reported the link between airway microbiome and respiratory functions. The results expand our understanding on the scope of PM 2.5 exposure's influence on human respiratory system, and point to novel etiological mechanism of PM 2.5 exposure induced diseases. Highlights • On human cohorts, we demonstrate the impact of PM 2.5 exposure on respiratory tract microbiome. • The strongest impact is attributed to exposure occurred ≥6 days prior to microbiome collection. • This study reports the link between airway microbiome and human respiratory functions. [ABSTRACT FROM AUTHOR]

Highlights: [•] First, we have shown that upregulated of the Loc554202 in breast cancer tissues. [•] Second, we demonstrated the function of Loc554202 in breast cancer cell. [•] Finally, we demonstrated that LOC554202 knockdown could inhibit tumor growth in vivo. [ABSTRACT FROM AUTHOR]

Many videos capture and follow salient objects in a scene. Detecting such salient objects is thus of great interests to video analytics and search. However, the discovery of salient objects in an unsupervised way is a challenging problem as there is no prior knowledge of the salient objects provided. Different from existing salient object detection methods, we propose to detect and track salient object by finding a spatio-temporal path which has the largest accumulated saliency density in the video. Inspired by the observation that salient video objects usually appear in consecutive frames, we leverage the motion coherence of videos into the path discovery and make the salient object detection more robust. Without any prior knowledge of the salient objects, our method can detect salient objects of various shapes and sizes, and is able to handle noisy saliency maps and moving cameras. Experimental results on two public datasets validate the effectiveness of the proposed method in both qualitative and quantitative terms. Comparisons with the state-of-the-art methods further demonstrate the superiority of our method on salient object detection in videos. [ABSTRACT FROM AUTHOR]

[Display omitted] • Explore the dynamic nature of metallocene catalysts. • Comparison of [Zr]/[C*] active sites and k p. • The addition of P and 1H, reactivating the prior inactive sites of the catalyst that were dormant. • Reducing the chain transfer reactions. The copolymerization of ethylene (E) with cyclic diene has been reported often in the literature. However, to evaluate isoprene (IP) and butadiene (BD) as co- and ter-monomers catalyzed by metallocene are comparatively rare. In this work, we performed a set of co- and terpolymerization of IP and BD by using symmetrical metallocene catalysts Cat-I and Cat-II. The E/IP and E/BD copolymers Cat-I catalyzed activity is lower than PE and E/P and significantly increased, reaching 3.1 × 107 gpolymer /mol Cat *h, but unlikely the insertion of IP and BD were not detected. The active sites of Cat-I are not factional for IP and BD, and they are not even activated with the addition of 1-hexene (1H) and P. The 1,4-insertion of these into catalyst active sites produced a stable π-allyl type species that actively produced dormant sites in the consequent propagation chain, and many other reactions that convert to dormant sites occur. In contrast, the Cat-II bearing silicon bridged moiety shows additional activity with IP and BD and an increasing trend with P and IH, demonstrating an activating feature. To conduct an extensive investigation, we performed a systematic comparison of the proportion of catalytically [Zr]/[C*] active sites and polymerization rate constant (k p) for both catalysts to shed light on the Zn-based chain termination and transfer reactions. The [C*]/[Zr] was significantly decreased with IP and BD, but compared to BD, IP shows a higher level of active sites and molecular weight, demonstrating that chain transfer reactions are faster with BD. Adding P and 1H increased the [C*]/[Zr], reactivating the prior inactive sites of the catalyst that were dormant or not functioning during the PE, E/IP, and E/BD polymers and reducing the chain transfer reactions. Compared to Cat-I, Cat-II demonstrates a higher level of [C*]/[Zr]; furthermore, the addition of 1H, a slight change in the [C*]/[Zr], while P with IP overall higher level (92.92 %) of the [C*]/[Zr] showed that P with IP shows a double activation for Cat-II and activates all those active sites that were dormant. BD significantly reduced the level of active sites to 49 % compared to IP and showed poisoning for the active site that was active for ethylene. The k p E of E/BD and E/IP polymers are lower than the k p E of E/BD/1H, E/P/IP, E/BD/1H, and E/IP/P, indicating P and 1H significantly activated the dormant sites of the E, IP, and BD that were dormant during the copolymerization. Cat-I catalyzed E/IP/1Hpolymers, the k p 1H of 1H is 15.8 L/mol·s, and the k p P of P is 83.3 L/mol·s, demonstrating that the P and more involved in the activation of active sites, and k p P of P is more increased with Cat-II, indicating that substituents of the Cat-II make the catalyst more active for P. [ABSTRACT FROM AUTHOR]

This study investigated the effects of grain sink activity regulated by potassium (K) nutrition during sheath rot disease (ShR) infection on rice milling and appearance quality. We quantified the instantaneous grain sink activity and showed that K application had no significant effects on rice milling and appearance quality in the absence of ShR infection. However, K deficiency substantially aggravated the effects of ShR infection on rice quality, such as the rate of broken rice (RBR), and ShR infection notably increased the difference in milling and appearance quality between K-deficient and K-sufficient rice. Specifically, ShR infection and K deficiency contributed 42.2% and 34% to the loss in the quality of Shenliangyou No. 5814 and 52.8% and 27.4% to the loss in the quality of Minhui No. 63, respectively. These findings suggest that K supply is crucial for improving the initial and maximal grain sink activity and thereby alleviating the loss in milling and appearance quality caused by ShR infection. • Potassium (K) deficiency and sheath rot disease (ShR) infection both decreased rice quality by inhibition of sink activity. • ShR infection dramatically increased the differences in quality of rice between K-deficient and K-sufficient grains. • A sufficient K supply improved the maximum grain sink activity to alleviate the loss in rice quality. [ABSTRACT FROM AUTHOR]

Salient object detection is a fundamental problem in both pattern recognition and image processing tasks. Previous salient object detection algorithms usually involve various features based on priors/assumptions about the properties of the objects. Inspired by the effectiveness of recently developed deep feature learning, we propose a novel Salient Object Detection via a Local and Global method based on Deep Residual Network model (SOD-LGDRN) for saliency computation. In particular, we train a deep residual network (ResNet-G) to measure the prominence of the salient object globally and extract multiple level local features via another deep residual network (ResNet-L) to capture the local property of the salient object. The final saliency map is obtained by combining the local-level and global-level saliency via Bayesian fusion. Quantitative and qualitative experiments on six benchmark datasets demonstrate that our SOD-LGDRN method outperforms eight state-of-the-art methods in the salient object detection. [ABSTRACT FROM AUTHOR]

The alternation of dry and wet is an important environmental factor affecting the emission of nitrous oxide from soil. However, the consistent or opposite effects on NH 3 and N 2 O emissions caused by adding exogenous urea in this process have not been fully considered. Here, we controlled the initial (slow drying) and final (adding water) water-filled pore space (WFPS) at 70%, 60%, or 50% through microculture experiment to simulate a process of slow drying-fertilization and rapid wetting of the soil from rice harvest to dryland crop fertilization. Through measuring soil chemical properties and the abundance and composition of related microbial communities during drying process, we studied the pathways of influence of drying and rewetting on the emission of N 2 O and NH 3 after urea application. During the progressive drying process (WFPS decreasing from 70% to 60% and 50%), soil N 2 O and NH 3 emissions decreased by 49.77%–72.13% and 17.89%–42.19%, respectively. After rapid rewetting (WFPS increasing from 60% to 70%, 50%–60% and 70%), N 2 O emissions showed a slight increase, while NH 3 volatilization continued to decrease. Soil NH 4 + - N and DOC contents both decreased during progressive drying, while the soil NO 3 − - N content was enhanced. The drying process changed the community structure of ureC and amoA-b and reduced their abundance but had no effect on amoA-a, nirK or nirS. Correlation analysis indicated that the reductions in NH 4 + - N content and the abundances of ureC and amoA-b were the main factors suppressing N 2 O and NH 3 emissions. We believe that drying process limits the related microbial activity and substrate supply during ammonia oxidation process in terms of N 2 O emissions, while in terms of NH 3 volatilization, it reduces the related microbial activity of urea hydrolysis process and increases the ammonium adsorption to the soil. [Display omitted] • Progressive drying decreased soil NH3 and N2O emissions after urea addition. • Soil rewetting cause a limited increase of N2O emissions. • NH3 volatilization decreased throughout soil rewetting. • The decrease in soil NH+4 and the abundances of amoA-b and ureC caused the reduction in NH3 and N2O emissions. • Soil drying reduced the amoA-b (N2O emitted) and ureC (NH3 volatilization) abundances. [ABSTRACT FROM AUTHOR]

Straw incorporation has been broadly demonstrated to be effective for the maintenance of soil potassium (K) fertility in farmlands, which increases K and carbon (C) inputs and improves soil stability due to aggregate formation and physiochemical bonding. However, the response of K retention in aggregate fractions (AFs) to soil organic carbon (SOC) changes is poorly understood. Field trials under a completely random experimental design considering two factors, straw return and K fertilization, were conducted to study the comprehensive effects of SOC and various AFs on soil K adsorption. The results indicated that the soil exchangeable and nonexchangeable K pools (EKP and NKP) increased upon straw incorporation due to an increase in macroaggregates (>2 mm fraction). The synergistic increase in SOC and humic acid (HA) contents, which resulted in a complex molecular structure and improved soil aggregation, promoted K adsorption. Good linear relationships existed between the apparent K balance and the EKP and NKP values in the >2 mm fraction. Structural equation modeling (SEM) indicated that SOC and various AFs exerted positive and significant effects on soil EKP and NKP, and thus verified 96% of the total variation in K adsorption. Thus, combination of straw and K fertilization increased the aggregate-associated C and K, which were primarily correlated with the >2 mm fraction. These direct measurements and estimates provide insights into the aggregates associated with K, which enhances the understanding of the chemical behavior of soil K upon straw incorporation. • The >2 mm aggregate-associated K was mostly correlated with soil K pools. • The increase in SOC resulted in a complex molecular structure and improved soil aggregation. • Straw incorporation improved soil HA and thus promoted K adsorption. [ABSTRACT FROM AUTHOR]

Single image deblurring aims to restore the single blurry image to its sharp counterpart and remains an active topic of enduring interest. Recently, deep Convolutional Neural Network (CNN) based methods have achieved promising performance. However, two primary limitations mainly exist on those CNNs-based image deblurring methods: most of them simply focus on increasing the complexity of the network, and rarely make full use of features extracted by encoder. Meanwhile, most of the methods perform the deblurred image reconstruction immediately after the decoder, and the roles of the decoded features are always underestimated. To address these issues, we propose a single image deblurring method, in which two modules to fuse multiple features learned in encoder (the Cross-layer Feature Fusion (CFF) module) and manipulate the features after decoder (the Consecutive Attention Module (CAM)) are specially designed, respectively. The CFF module is to concatenate different layers of features from encoder to enhance rich structural information to decoder, and the CAM module is able to generate more important and correlated textures to the reconstructed sharp image. Besides, the ranking content loss is employed to further restore more realistic details in the deblurred images. Comprehensive experiments demonstrate that our proposed method can generate less blur and more textures in deblurred image on both synthetic datasets and real-world image examples. • The cross-layer feature fusion module concatenates multi-layer features in encoder to reconstruct image. • The consecutive attention module after decoder enables to restore more correlated textures in deblurred image. • The ranking content loss is introduced to guide the generator to deblur. [ABSTRACT FROM AUTHOR]