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Aimed at sufficiently utilizing available data and prior distribution information, we introduce a data-driven Bayesian-type approach to solve multi-stage convex stochastic optimization, which can easily cope with the uncertainty about data process's distributions and their inter-stage dependence. To unravel the properties of the proposed multi-stage Bayesian expectation optimization (BEO) problem, we establish the consistency of optimal value functions and solutions. Two kinds of algorithms are designed for the numerical solution of single-stage and multi-stage BEO problems, respectively. A queuing system and a multi-stage inventory problem are adopted to numerically demonstrate the advantages and practicality of the new framework and corresponding solution methods, compared with the usual formulations and solution methods for stochastic optimization problems. [ABSTRACT FROM AUTHOR]

Regulatory T cells (Tregs) are a subset of T cells participating in a variety of diseases including mycoplasmal pneumonia, contagious ecthyma, and so on. The role of Tregs in goat contagious ecthyma is not completely understood due to the lack of species-specific antibodies. Here, we developed a combination of CD4 and CD25 fluorescence monoclonal antibodies (mAb) to recognize goat Tregs and assessed its utility in flow cytometry, immunofluorescence staining. Using immunofluorescence staining, we found that the frequency of Treg cells was positively correlated with the viral load during orf virus infection. These antibodies could serve as important tools to monitor Tregs during orf virus infection in goats. Key points: • A combination of fluorescent mAbs (C11 and D12) was prepared for the detection of goat Tregs. • C11 and D12 are effective in flow cytometry, immunofluorescence staining, and C11 has excellent species specificity. • The frequency of Treg cells was positively correlated with the viral load during orf virus infection. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA), a chronic degenerative disease characterized mainly by damage to the articular cartilage, is increasingly relevant to the pathological processes of senescence, apoptosis, autophagy, proliferation, and differentiation of chondrocytes. Clinical strategies for osteoarthritis can only improve symptoms and even along with side effects due to age, sex, disease, and other factors. Therefore, there is an urgent need to identify new ideas and targets for current clinical treatment. The tumor suppressor gene p53, which has been identified as a potential target for tumor therapeutic intervention, is responsible for the direct induction of the pathological processes involved in OA modulation. Consequently, deciphering the characteristics of p53 in chondrocytes is essential for investigating OA pathogenesis due to p53 regulation in an array of signaling pathways. This review highlights the effects of p53 on senescence, apoptosis, and autophagy of chondrocytes and its role in the development of OA. It also elucidates the underlying mechanism of p53 regulation in OA, which may help provide a novel strategies for the clinical treatment of OA. [ABSTRACT FROM AUTHOR]

A sensitive immunochromatographic assay (ICA) using time-resolved fluorescence microspheres (TRFMs) coupled with an indirect-labeling mode was developed for simultaneously determining 22 kinds of β-lactams in milk samples. The TRFMs labeled anti-receptor monoclonal antibodies (mAbs) conjugated to penicillin-binding proteins (PBPs) as ternary TRFMs-mAb-PBPs (TMP) nanoscaffolds provide excellent solubility, brightness, and stability. Thanks to the fact that they not only fully expose the binding sites of PBPs, thereby enhancing the biological affinity of PBPs towards the target, but also generated superb fluorescence signals, the versatile TMP manifested unique possibilities as efficient probes for ICA with remarkable enhancement in sensitivity in β-lactams screening. The results showed that the standard curves of the 22 varying β-lactams displayed linearity in their respective concentration ranges (R2 > 0.98), with the cutoff values of 1–100 ng/mL. The constructed TMP-ICA was successfully applied to the analysis of real milk, with consistent results compared with liquid chromatography-tandem mass spectrometry (LC-MS), providing an effective method for sensing β-lactams in food matrices. [ABSTRACT FROM AUTHOR]

Despite remarkable success has been achieved by convolutional neural networks (CNNs) in no-reference image quality assessment (NR-IQA), there still exist many challenges in improving the performance of IQA for authentically distorted images. An important factor is that the insufficient annotated data limits the training of high-capacity CNNs to accommodate diverse distortions, complicated semantic structures and high-variance quality scores of these images. To address this problem, this paper proposes a hierarchical curriculum learning (HCL) framework for NR-IQA. The main idea of the proposed framework is to leverage the external data to learn the prior knowledge about IQA widely and progressively. Specifically, as a closely-related task with NR-IQA, image restoration is used as the first curriculum to learn the image quality related knowledge (i.e., semantic and distortion information) on massive distorted-reference image pairs. Then multiple lightweight subnetworks are designed to learn human scoring rules on multiple available synthetic IQA datasets independently, and a cross-dataset quality assessment correlation (CQAC) module is proposed to fully explore the similarities and diversities of different scoring rules. Finally, the whole model is fine-tuned on the target authentic IQA dataset to fuse the learned knowledge and adapt to the target data distribution. Experimental results show that our model achieves state-of-the-art performance on multiple standard authentic IQA datasets. Moreover, the generalization of our model is fully validated by the cross-dataset evaluation and the gMAD competition. In addition, extensive analyses prove that the proposed HCL framework is effective in improving the performance of our model. [ABSTRACT FROM AUTHOR]

To effectively control CaCO3 scaling in oilfield-produced water, terpolymer scale inhibitor P(IA-AA-SHP) was synthesized by aqueous solution polymerization using itaconic acid (IA), acrylic acid (AA) and sodium hypophosphite (SHP) as raw materials and ammonium persulfate as an initiator. The optimal monomer ratio, initiator dosage, reaction temperature, and time for copolymer synthesis were determined by the controlled variable method. According to the static scale inhibition performance test method in the oilfield, the copolymer dose reached 95.8% inhibition efficiency of CaCO3 at 30 mg L−1. A study of the inhibitory efficiency of copolymer on the CaCO3 scale at different Ca2+ concentrations, CO32− concentrations, and temperatures was also carried out, and the excellent scale inhibition efficiency was maintained despite a change in the water quality conditions. The structure of copolymer P(IA-AA-SHP) was characterized by FTIR, UV, 1H-NMR, and 13C-NMR. Since the molecular weight (Mw) has a great influence on the scale inhibition performance, the relationship between the molecular weight of the polymer and the scale inhibition performance was discussed using gel permeation chromatography (GPC) to detect the molecular weight of the product. The copolymers were shown to have excellent thermal stability using thermogravimetric (TGA) analysis. The effect of copolymers on the morphological structure of CaCO3 crystals was analyzed by SEM and XRD, and the mechanism of scale inhibition was discussed. [ABSTRACT FROM AUTHOR]

A interaction model between discrete dislocation emission and the grain boundary at the crack tip of microscale grain is described. When the dislocation emission condition is satisfied, dislocations will be emitted from the crack tip and will move along a slip plane to the grain boundary. Dislocations will pile at the grain boundary and create stress concentration. At this time dislocation penetration and crack nucleation at the grain boundary may occur. The distribution and penetration of dislocations at the crack tip will have a new effect on the crack. In this paper, different grain boundary angles and grain sizes are studied to find the relationship between dislocations and cracks. The results show that the main crack tends to propagate in large grain boundary angle and in small grain size crystalline materials. And wedge crack in the grain boundary tends to initiate with large grain boundary angle and in large grain size crystalline materials. [ABSTRACT FROM AUTHOR]

An unprecedented warm sea surface temperature (SST) anomaly event, namely, the Blob, occurred in the northeast Pacific during the winter (October–January) of 2013/2014, causing substantial economic and ecological impacts. Here, we explore the driving forces of the Blob from both atmospheric and oceanic perspectives and show that the Blob primarily resulted from weak wintertime cooling due to the reduced air-sea heat flux transfer from the ocean to the atmosphere and the reduced horizontal advection of cold water in the upper ocean. Both mechanisms were attributed to an anomalous high-pressure system over the study region. Specifically, the anomalous air-sea heat flux, which was dominated by turbulent heat flux anomalies, was mainly induced by the increased air temperature (i.e., with a contribution of approximately 70%) and the weakened wind speed associated with the high-pressure system. The reduced horizontal heat advection was mainly due to the weakened winds acting on the ocean temperature meridional gradient. Using a regional ocean numerical model with different experimental runs, we evaluated the contributions of air temperature and wind drivers to the Blob at both the surface and subsurface of the ocean. The Blob was absent when the model was forced by climatology-air-temperature. Both the SST and integrated ocean heat content (OHC, 0–150 m) decreased, and the mixed layer depth (MLD) was deeper than that in the control run forced by real atmospheric conditions. In the climatology-winds experiment, obvious warm anomalies still existed, which were similar to but weaker than the control run. The SST (OHC) and MLD values in the climatology-winds run were between those of the climatology-air-temperature run and the control run. Compared to former studies that attribute the formation of the Blob to an anomalous air-sea heat flux and horizontal advection mainly induced by reduced winds, our study demonstrates that anomalous warm air temperatures played a more important role in its formation. [ABSTRACT FROM AUTHOR]

The dielectric elastomer (DE) has attracted significant attention due to its desired features, including large deformation, fast response, and high energy density. However, for a DE actuator (DEA) utilizing a snap-through deformation mode, most existing theoretical models fail to predict its deformation path. This paper develops a new finite element method (FEM) based on the three-parameter Gent-Gent model suitable for capturing strain-stiffening behaviors. The simulation results are verified by experiments, indicating that the FEM can accurately characterize the snap-through path of a DE. The method proposed in this paper provides theoretical guidance and inspiration for designing and applying DEs and bistable electroactive actuators. [ABSTRACT FROM AUTHOR]

We aimed to reuse the propylene oxide sludge (POS). Propylene oxide sludge shell-aggregate (POSS-A) and propylene oxide sludge gradient shell-aggregate (POSGS-A) whose main hydrated phase is tobermorite were successfully manufactured by the hydrothermal synthesis of POS and silica materials under the condition of autoclaved (180 °C, 1.0 MPa) curing. Influences of pre-wetting time of coarse aggregate and pressure application mode on the different concretes were investigated. The experimental results show that the concrete with POSS-A as coarse aggregate (POSS-A concrete), the concrete with POS gradient shell-aggregate as coarse aggregate (POSGS-A concrete), sintered aggregate concrete and common concrete, all have excellent impermeability performance whatever the pre-wetting time of coarse aggregate is 0.5 h or 24 h, and the pre-wetting time of coarse aggregate has a negligible influence on the concrete. The influence degree of pressure application mode on the impermeability performance of the sintered aggregate concrete is the greatest among three kinds of concrete, which has a negligible influence on impermeability performance of the other concretes. POSGS-A can be used as a green building light aggregate in hydraulic concrete. [ABSTRACT FROM AUTHOR]

The South China Sea (SCS) is the largest semi-enclosed marginal sea in the western Pacific. The alternation of East Asian monsoon causes a significant seasonal pattern of chlorophyll, primary productivity, and export flux of sinking particles. However, the source and sink of dissolved organic carbon (DOC) pools with different bioavailability are less studied. Here we evaluated the seasonal production of DOC in labile, semi-labile and refractory forms using a coupled physical-biogeochemical model. This study aims to understand the dynamics and budgets of organic matters in the SCS. Model results show that the production of labile, semi-labile and refractory DOC is highly correlated with the net primary productivity (NPP) which is higher in winter and lower in summer, reflecting a dependence of DOC on the NPP. The seasonal variation in Pearl River discharge dominates the DOC production in the northern coastal region. In the northeast, the Kuroshio intrusion associated frontal system is attributed to cause high winter production. The DOC production in the southwest is controlled by both winter mixing and summer upwelling. The production of refractory DOC with the least bioavailability favors carbon sequestration. Its annual mean production is 1.8±0.5 mg C m−2 d−1, equivalent to 26% of the export flux of particulate organic carbon at 1000 m. [ABSTRACT FROM AUTHOR]

Optical flow is widely used in human action recognition. However, the influence of complex background on optical flow often leads to low recognition efficiency. To deal with this issue, an optical flow-based physical feature-driven action recognition framework is proposed in this paper. We first calculate the original dense optical flow field. Then, for reducing computational burden, joint action relevance that can eliminate the pseudo-optical flow in complex background is developed. After that, a more state flow field is obtained by local spatial–temporal thermal diffusion processing. On this basis, we design a feature descriptor that takes the divergence, curl and gradient features of flow field into consideration. Finally, we adopt Fisher vector to encode descriptors for classification. Experimental on HMDB51, KTH and UCF101 datasets proves that actions in complex background can be recognized accurately by the proposed framework, which outperforms the already developed methods. [ABSTRACT FROM AUTHOR]

Human action recognition (HAR) is one of the most challenging tasks in the field of computer vision due to complex backgrounds and ambiguity action, etc. To tackle these issues, we propose a novel action recognition framework called Semantic Feature and High-order Physical Feature Fusion (SF-HPFF). Concretely, we first calculate attention pooling module with a low-rank approximation to remove the information of irrelevant complex backgrounds and thus capture the interested target motion region. On this basis, motion features based on the physical characteristics of flow field and semantic features based on word embedding are developed to distinguish ambiguity behaviors. These features are of low dimension and high discrimination, which help to reduce computation burden significantly while maintaining an excellent recognition performance. Finally, cascaded convolutional fusion network is adopted to fuse features and accomplish classification. Multiple experiment results validate that the proposed SF-HPFF outperforms the state-of-art action recognition methods. [ABSTRACT FROM AUTHOR]

Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing. In previous studies, the correction of roughness effect mainly depended on wind speeds retrieved from scatterometers or those provided by other means, which necessitates a high requirement for accuracy and synchronicity of wind-speed measurements. The aim of this study is to develop a novel roughness correction model of ocean emissivity for the salinity retrieval application. The combined active/passive observations of normalized radar cross-sections (NRCSs) and emissivities from ocean surfaces given by the L-band Aquarius/SAC-D mission, and the auxiliary wind directions collocated from the National Centers for Environmental Prediction (NCEP) dataset are used for model development. The model is validated against the observations and the Aquarius standard algorithms of roughness-induced emissivity correction. Comparisons between model computations and measurements indicate that the model has better accuracy in computing wind-induced brightness temperature in the upwind/downwind directions or for the surfaces with smaller NRCSs, which can be better than 0.3 K. However, for crosswind directions and larger NRCSs, the model accuracy is relatively low. A model using HH-polarized NRCSs yields better accuracy than that using VV-polarized ones. For a fair comparison to the Aquarius standard algorithms using wind speeds retrieved from multi-source data, the maximum likelihood estimation is employed to produce results combining our model calculations and those using other sources. Numerical simulations show that combined results basically have higher accuracy than the standard algorithms. [ABSTRACT FROM AUTHOR]

The late spring rainfall may account for 15% of the annual total rainfall, which is crucial to early planting in southeastern China. A better understanding of the precipitation variations in the late spring and its predictability not only greatly increase our knowledge of related mechanisms, but it also benefits society and the economy. Four models participating in the North American Multi-Model Ensemble (NMME) were selected to study their abilities to forecast the late spring rainfall over southeastern China and the major sources of heavy rainfall from the perspective of the sea surface temperature (SST) field. We found that the models have better abilities to forecast the heavy rainfall over the middle and lower reaches of the Yangtze River region (MLYZR) with only a 1-month lead time, but they failed for a 3-month lead time since the occurrence of the heavy rainfall was inconsistent with the observations. The observations indicate that the warm SST anomalies in the tropical eastern Indian Ocean are vital to the simultaneously heavy rainfall in the MLYZR in May, but an El Niño event is not a necessary condition for determining the heavy rainfall over the MLYZR. The heavy rainfall over the MLYZR in May is always accompanied by warming of the northeastern Indian Ocean and of the northeastern South China Sea (NSCS) from April to May in the models and observations, respectively. In the models, El Niño events may promote the warming processes over the northeastern Indian Ocean, which leads to heavy rainfall in the MLYZR. However, in the real world, El Niño events are not the main reason for the warming of the NSCS, and further research on the causes of this warming is still needed. [ABSTRACT FROM AUTHOR]

The fruit of the perennial rosebush Rosa roxburghii were valued for their high levels of ascorbic acid, superoxide dismutase activity, and cancer preventing effects. The high cellulose and low pectin content of Rosa roxburghii fruit results in an undesirable fibrous texture and hence needs to be addressed. However, little is known about the molecular mechanisms underlying dietary fiber metabolism in this fruit. Here, we report that the contents of cellulose, pectin, and lignin were increased by shading treatments at the maturation stage of fruit development. Under 50% shading, the soluble pectin content increased by 16.39%, which may improve the fruit palatability. However, deeper shading of 100% caused the lignin content to increase by 28.86%, which conversely may lower fruit quality. Based on transcriptome analysis, we identified candidate genes involved in dietary fiber metabolism, including cellulose synthase (CesA) 1, 2, 3, and 5, â-1,4-xylosyltransferase (IRX), arabinosyltransferase (ARAD) 1 and 2, galacturonosyltransferase (GAUT), cellulolytic enzyme (Cx), and pectin methylesterase, in which CesA1, CesA2, CesA3, IRX, ARAD2, and GAUT3 significantly responded to shading and positively correlated with the content of their corresponding component. Furthermore, cinnamyl alcohol dehydrogenase was significantly regulated by shading treatment and positively correlated with increasing lignin concentration. These results may facilitate a better understanding of the molecular mechanisms of dietary fiber metabolism in R. roxburghii fruit under low light conditions and provide a framework for future crop improvement. [ABSTRACT FROM AUTHOR]

Highly accurate active contour models are widely used in various image segmentation methods. In this paper, we propose an image segmentation model based on Hellinger distance for local region intensity fitting (HD-LRIF). The method defines two different metrics based on the Hellinger distance and constructs a new data fitting term to segment the image efficiently by minimizing the energy function. In addition, our method is independent of the initial contour and the segmentation results consistently obtain high accuracy. The experimental results show that the HD-LRIF model is far superior to state-of-the-art segmentation methods in terms of accuracy and efficiency. Specifically, it can effectively filter the noise interference, thus enhancing robustness and improving the accuracy of image segmentation in general. [ABSTRACT FROM AUTHOR]