CmNAC25 targets CmMYB6 to positively regulate anthocyanin biosynthesis during the post-flowering stage in chrysanthemum.

BACKGROUND: Anthocyanin is a class of important secondary metabolites that determines colorful petals in chrysanthemum, a famous cut flower. 'Arctic Queen' is a white chrysanthemum cultivar that does not accumulate anthocyanin during the flowering stage. During the post-flowering stage, the petals of 'Arctic Queen' accumulate anthocyanin and turn red. However, the molecular mechanism underlying this flower color change remains unclear. RESULTS: In this study, by using transcriptome analysis, we identified CmNAC25 as a candidate gene promoting anthocyanin accumulation in the post-flowering stage of 'Arctic Queen'. CmNAC25 is directly bound to the promoter of CmMYB6, a core member of the MBW protein complex that promotes anthocyanin biosynthesis in chrysanthemum, to activate its expression. CmNAC25 also directly activates the promoter of CmDFR, which encodes the key enzyme in anthocyanin biosynthesis. CmNAC25 was highly expressed during the post-flowering stage, while the expression level of CmMYB#7, a known R3 MYB transcription factor interfering with the formation of the CmMYB6-CmbHLH2 complex, significantly decreased. Genetic transformation of both chrysanthemum and Nicotiana tabacum verified that CmNAC25 was a positive regulator of anthocyanin biosynthesis. Another two cultivars that turned red during the post-flowering stages also demonstrated a similar mechanism. CONCLUSIONS: Altogether, our data revealed that CmNAC25 positively regulates anthocyanin biosynthesis in chrysanthemum petals during the post-flowering stages by directly activating CmMYB6 and CmDFR. Our results thus revealed a crucial role of CmNAC25 in regulating flower color change during petal senescence and provided a target gene for molecular design breeding of flower color in chrysanthemum.

Structured sparsity regularized multiple kernel learning for Alzheimer's disease diagnosis.

Multimodal data fusion has shown great advantages in uncovering information that could be overlooked by using single modality. In this paper, we consider the integration of high-dimensional multi-modality imaging and genetic data for Alzheimer's disease (AD) diagnosis. With a focus on taking advantage of both phenotype and genotype information, a novel structured sparsity, defined by ℓ 1, p-norm (p > 1), regularized multiple kernel learning method is designed. Specifically, to facilitate structured feature selection and fusion from heterogeneous modalities and also capture feature-wise importance, we represent each feature with a distinct kernel as a basis, followed by grouping the kernels according to modalities. Then, an optimally combined kernel presentation of multimodal features is learned in a data-driven approach. Contrary to the Group Lasso (i.e., ℓ 2, 1-norm penalty) which performs sparse group selection, the proposed regularizer enforced on kernel weights is to sparsely select concise feature set within each homogenous group and fuse the heterogeneous feature groups by taking advantage of dense norms. We have evaluated our method using data of subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The effectiveness of the method is demonstrated by the clearly improved prediction diagnosis and also the discovered brain regions and SNPs relevant to AD.

Multi-task diagnosis for autism spectrum disorders using multi-modality features: A multi-center study.

Autism spectrum disorder (ASD) is a neurodevelopment disease characterized by impairment of social interaction, language, behavior, and cognitive functions. Up to now, many imaging-based methods for ASD diagnosis have been developed. For example, one may extract abundant features from multi-modality images and then derive a discriminant function to map the selected features toward the disease label. A lot of recent works, however, are limited to single imaging centers. To this end, we propose a novel multi-modality multi-center classification (M3CC) method for ASD diagnosis. We treat the classification of each imaging center as one task. By introducing the task-task and modality-modality regularizations, we solve the classification for all imaging centers simultaneously. Meanwhile, the optimal feature selection and the modeling of the discriminant functions can be jointly conducted for highly accurate diagnosis. Besides, we also present an efficient iterative optimization solution to our formulated problem and further investigate its convergence. Our comprehensive experiments on the ABIDE database show that our proposed method can significantly improve the performance of ASD diagnosis, compared to the existing methods. Hum Brain Mapp 38:3081-3097, 2017. © 2017 Wiley Periodicals, Inc.

Hierarchical Encoder-Decoder With Soft Label-Decomposition for Mitochondria Segmentation in EM Images.

Semantic segmentation of mitochondria from electron microscopy (EM) images is an essential step to obtain reliable morphological statistics about mitochondria. However, automatically delineating plenty of mitochondria of varied shapes from complex backgrounds with sufficient accuracy is challenging. To address these challenges, we develop a hierarchical encoder-decoder network (HED-Net), which has a three-level nested U-shape architecture to capture rich contextual information. Given the irregular shape of mitochondria, we introduce a novel soft label-decomposition strategy to exploit shape knowledge in manual labels. Rather than simply using the ground truth label maps as the unique supervision in the model training, we introduce additional subcategory-aware supervision by softly decomposing each manual label map into two complementary label maps according to mitochondria's ovality. The three label maps are integrated with our HED-Net to supervise the model training. While the original label map guides the network to segment all the mitochondria of varied shapes, the auxiliary label maps guide the network to segment subcategories of mitochondria of circular shape and elliptic shape, respectively, which are much more manageable tasks. Extensive experiments on two public benchmarks show that our HED-Net performs favorably against state-of-the-art methods.

LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation.

Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, which are often unavailable in clinical practices. Besides, top-performing methods usually have a vast number of parameters, which result in high computation complexity for model training and testing. This study addresses cardiac image segmentation in scenarios where few labeled data are available with a lightweight cross-consistency network named LCC-Net. Specifically, to reduce the risk of overfitting on small labeled datasets, we substitute computationally intensive standard convolutions with a lightweight module. To leverage plenty of unlabeled data, we introduce extreme consistency learning, which enforces equivariant constraints on the predictions of different perturbed versions of the input image. Cutting and mixing different training images, as an extreme perturbation on both the labeled and unlabeled data, are utilized to enhance the robust representation learning. Extensive comparisons demonstrate that the proposed model shows promising performance with high annotation- and computation-efficiency. With only two annotated subjects for model training, the LCC-Net obtains a performance gain of 14.4% in the mean Dice over the baseline U-Net trained from scratch.

HDC-Net: Hierarchical Decoupled Convolution Network for Brain Tumor Segmentation.

Accurate segmentation of brain tumor from magnetic resonance images (MRIs) is crucial for clinical treatment decision and surgical planning. Due to the large diversity of the tumors and complex boundary interactions between sub-regions, it is of a great challenge. Besides accuracy, resource constraint is another important consideration. Recently, impressive improvement has been achieved for this task by using deep convolutional networks. However, most of state-of-the-art models rely on expensive 3D convolutions as well as model cascade/ensemble strategies, which result in high computational overheads and undesired system complexity. For clinical usage, the challenge is how to pursue the best accuracy within very limited computational budgets. In this study, we segment 3D volumetric image in one-pass with a hierarchical decoupled convolution network (HDC-Net), which is a light-weight but efficient pseudo-3D model. Specifically, we replace 3D convolutions with a novel hierarchical decoupled convolution (HDC) module, which can explore multi-scale multi-view spatial contexts with high efficiency. Extensive experiments on the BraTS 2018 and 2017 challenge datasets show that our method performs favorably against state of the art in accuracy yet with greatly reduced computational complexity.

HIVE-Net: Centerline-aware hierarchical view-ensemble convolutional network for mitochondria segmentation in EM images.

BACKGROUND AND OBJECTIVE: With the advancement of electron microscopy (EM) imaging technology, neuroscientists can investigate the function of various intracellular organelles, e.g, mitochondria, at nano-scale. Semantic segmentation of electron microscopy (EM) is an essential step to efficiently obtain reliable morphological statistics. Despite the great success achieved using deep convolutional neural networks (CNNs), they still produce coarse segmentations with lots of discontinuities and false positives for mitochondria segmentation. METHODS: In this study, we introduce a centerline-aware multitask network by utilizing centerline as an intrinsic shape cue of mitochondria to regularize the segmentation. Since the application of 3D CNNs on large medical volumes is usually hindered by their substantial computational cost and storage overhead, we introduce a novel hierarchical view-ensemble convolution (HVEC), a simple alternative of 3D convolution to learn 3D spatial contexts using more efficient 2D convolutions. The HVEC enables both decomposing and sharing multi-view information, leading to increased learning capacity. RESULTS: Extensive validation results on two challenging benchmarks show that, the proposed method performs favorably against the state-of-the-art methods in accuracy and visual quality but with a greatly reduced model size. Moreover, the proposed model also shows significantly improved generalization ability, especially when training with quite limited amount of training data. Detailed sensitivity analysis and ablation study have also been conducted, which show the robustness of the proposed model and effectiveness of the proposed modules. CONCLUSIONS: The experiments highlighted that the proposed architecture enables both simplicity and efficiency leading to increased capacity of learning spatial contexts. Moreover, incorporating shape cues such as centerline information is a promising approach to improve the performance of mitochondria segmentation.

The E3 ligase MREL57 modulates microtubule stability and stomatal closure in response to ABA.

Regulation of stomatal movement is critical for plant adaptation to environmental stresses. The microtubule cytoskeleton undergoes disassembly, which is critical for stomatal closure in response to abscisic acid (ABA). However, the mechanism underlying this regulation largely remains unclear. Here we show that a ubiquitin-26S proteasome (UPS)-dependent pathway mediates microtubule disassembly and is required for ABA-induced stomatal closure. Moreover, we identify and characterize the ubiquitin E3 ligase MREL57 (MICROTUBULE-RELATED E3 LIGASE57) and the microtubule-stabilizing protein WDL7 (WAVE-DAMPENED2-LIKE7) in Arabidopsis and show that the MREL57-WDL7 module regulates microtubule disassembly to mediate stomatal closure in response to drought stress and ABA treatment. MREL57 interacts with, ubiquitinates and degrades WDL7, and this effect is clearly enhanced by ABA. ABA-induced stomatal closure and microtubule disassembly are significantly suppressed in mrel57 mutants, and these phenotypes can be restored when WDL7 expression is decreased. Our results unravel UPS-dependent mechanisms and the role of an MREL57-WDL7 module in microtubule disassembly and stomatal closure in response to drought stress and ABA.

Mitochondria Segmentation From EM Images via Hierarchical Structured Contextual Forest.

Delineation of mitochondria from electron microscopy (EM) images is crucial to investigate its morphology and distribution, which are directly linked to neural dysfunction. However, it is a challenging task due to the varied appearances, sizes and shapes of mitochondria, and complicated surrounding structures. Exploiting sufficient contextual information about interactions in extended neighborhood is crucial to address the challenges. To this end, we introduce a novel class of contextual features, namely local patch pattern (LPP), to eliminate the ambiguity of local appearance and texture features. To achieve accurate segmentation, we propose an automatic method by iterative learning of hierarchical structured contextual forest. With a novel median fusion strategy, the probability predictions from long history iterations are augmented to encode spatial and temporal contexts and suppress false detections. Moreover, the LPP features are extracted on both images and history predictions, resulting in a hierarchy of contextual features with increasing receptive fields. Other than using computationally demanding graph based methods, we perform joint label prediction using structured random forest. In addition to direct 3D segmentation of EM volumes, we introduce a 2D variant without sacrificing accuracy using a novel hierarchical multi-view fusion strategy. We evaluated our proposed methods on public EPFL Hippocampus benchmark, achieving state-of-the-art performance of 90.9% in Dice. Quantitative comparison showed the effectiveness of the proposed features and strategies.

Case Report: Opportunities for Treatment of Severe COVID-19 Patients-Lessons From a Death Case.

With the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the incidence of coronavirus disease (COVID-19) increases each day. To date, there is no specific anti-SARS-CoV-2 drug. The usual approach to treating COVID-19 is treating its symptoms. However, this approach is limited by the different conditions of each area. We treated a 57-year-old man who was initially diagnosed with a severe type of the infection, but he progressed to a critical condition and eventually died. We learned valuable lessons from this case. The first lesson is the need to use immediate invasive mechanical ventilation if there is no obvious improvement after using non-invasive ventilation for several hours, which directly affects the prognosis. Another lesson is the risk involved in transferring severe COVID-19 patients. In the process of transfer, various threats may be encountered at any time. Thus, accurate assessment of the patient's condition and strict medical conditions are highly required. During the patient's 25-day treatment, we performed cardiopulmonary resuscitation twice. Currently, many patients require invasive mechanical ventilation and transfer to a superior hospital. We hope our findings will provide some advice and help for treating severe and critical COVID-19 cases.

Nourseothricin N-acetyl transferase (NAT), a new selectable marker for nuclear gene expression in Chlamydomonas.

BACKGROUND: Chlamydomonas reinhardtii is a unicellular green alga, which is a most commonly used model organism for basic research and biotechnological applications. Generation of transgenic strains, which usually requires selectable markers, is instrumental in such studies/applications. Compared to other organisms, the number of selectable markers is limited in this organism. Nourseothricin (NTC) N-acetyl transferase (NAT) has been reported as a selectable marker in a variety of organisms but not including C. reinhardtii. Thus, we investigated whether NAT was useful and effective for selection of transgenic strains in C. reinhardtii. The successful use of NAT would provide alterative choice for selectable markers in this organism and likely in other microalgae. RESULTS: C. reinhardtii was sensitive to NTC at concentrations as low as 5 µg/ml. There was no cross-resistance to nourseothricin in strains that had been transformed with hygromycin B and/or paromomycin resistance genes. A codon-optimized NAT from Streptomyces noursei was synthesized and assembled into different expression vectors followed by transformation into Chlamydomonas. Around 500 transformants could be obtained by using 50 ng DNA on selection with 10 µg/ml NTC. The transformants exhibited normal growth rate and were stable at least for 10 months on conditions even without selection. We successfully tested that NAT could be used as a selectable marker for ectopic expression of IFT54-HA in strains with paromomycin and hygromycin B resistance markers. We further showed that the selection rate for IFT54-HA positive clones was greatly increased by fusing IFT54-HA to NAT and processing with the FMDV 2A peptide. CONCLUSIONS: This work represents the first demonstration of stable expression of NAT in the nuclear genome of C. reinhardtii and provides evidence that NAT can be used as an effective selectable marker for transgenic strains. It provides alterative choice for selectable markers in C. reinhardtii. NAT is compatible with paromomycin and hygromycin B resistance genes, which allows for multiple selections.

[Nano-silver colloids prepared by electrolysis and research on its SERS activity].

Three kinds of nano-silver colloids have been prepared by electrolysis of silver rod using sodium citrate solution and AgNO3 mixed with polyvinyl alcohol solution as electrolyte and applying 7 V direct current for one hour. Nano-silver colloids have been investigated by means of TEM, absorption spectrum, electrophoresis experiment and SERS. The particle size ranges roughly from 20 nm to 25 nm (spheroid) for sample 1, from 20 nm to 35 nm (spheroid) for sample 2, and from 30 to 80 (many-sided) for sample 3, featuring absorption maximum at 404, 421 and 434 nm, respectively. The surface charge of these three kinds of colloidal silver particles is positive. In order to test if these nano-silver colloids can be used for SERS research, the cationic molecular fuchsine basic, methylene blue, anionic molecular benzoic acid, methyl orange, neutral molecular alcidine orange, and Sudan red were used. It was found that these nano-silver colloids have strong SERS activity. Furthermore, the nano-silver colloids that used AgNO3 mixed with polyvinyl alcohol solution as electrolyte has the strongest SERS activity among all the tested molecules. The SERS of methyl orange has been obtained on the nano-silver colloids, which has not been obtaind on the colloids prepared by electrolysis of silver rod using sodium citrate solution and on the gray and yellow silver colloids prepared by traditional means. The possible reason has been explained. One major advantage of this method (using AgNO3 mixed with polyvinyl alcohol solution as electrolyte) is the absence of the spectral interference.

Effects of terrestrial and marine organic matters on deposition of dechlorane plus (DP) in marine sediments from the Southern Yellow Sea, China: Evidence from multiple biomarkers.

As an emerging halogenated organic contaminant, Dechlorane Plus (DP) was scarcely reported in marine environments, especially in China. In this work, 35 surface sediments and a sediment core were collected across the Southern Yellow Sea (SYS) to comprehensively explore the spatio-temporal distribution and possible migration pathway of DP. DP concentrations ranged from 14.3 to 245.5 pg/g dry weight in the surface sediments, displaying a seaward increasing trend with the high levels in the central mud zone. This spatial distribution pattern was ascribed to that fine particles with the elevated DP levels were preferentially transported to the central mud zone under hydrodynamic forcing and/or via long-range atmospheric transportation and deposition. DP concentrations in sediment core gradually increased from the mid-1950s to present, which corresponded well with the historical production and usage of DP, as well as the economic development in China. Significantly positive correlation between DP and total organic carbon (TOC) in both surface sediments and sediment core indicated TOC-dependent natural deposition of DP in the SYS. We used multiple biomarkers, for the first time, to explore the potential effects of terrestrial and marine organic matters (TOM and MOM) on DP deposition. The results showed that competition may occur between TOM and MOM for DP adsorption, and MOM was the predominant contributor in controlling DP deposition in the marine sediments from the SYS.

Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

PURPOSE: Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. METHODS: The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. RESULTS: Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. CONCLUSION: A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.

Characterizing distributions, composition profiles, sources and potential health risk of polybrominated diphenyl ethers (PBDEs) in the coastal sediments from East China Sea.

Sediment samples (n = 20) were collected from Yangtze River Estuary (YRE) and the adjacent East China Sea (ECS) inner shelf to explore spatial and temporal distributions, environmental fate, sources and potential health risk of polybrominated diphenyl ethers (PBDEs). Concentrations of BDE-209 and total 7 PBDEs (without BDE-209; ∑7PBDEs) ranged from 62.3 to 1758 pg g(-1) and from 36.9 to 233.6 pg g(-1) dry weight, respectively; both of the highest values occurred near the city of Wenzhou. Concentrations of BDE-209 and ∑7PBDEs both indicated a decreasing trend from inshore areas toward outer shelf. Significantly positive linear correlations were only observed between logBDE-183 concentrations and TOC/grain size (r(2) = 0.6734 and 0.5977 for TOC and grain size, respectively) as well as BDE-209 and TOC/grain size (r(2) = 0.4137 and 0.5332 for TOC and grain size, respectively) in the north of 28(°)N, indicating that YR had significant influence on the distribution of higher brominated congeners only in the north part. Depth profiles of PBDEs in a sediment core P01 (n = 1, m = 11) collected from YRE showed that the input of BDE-209 gradually increased from 1930 to 2010, while the levels of ∑7PBDEs peaked in 1986 and obviously decreased in recent years. Partial Least-Squares Regression (PLSR) revealed that PBDEs in the coastal ECS were mainly from direct discharge of local anthropogenic activities (80.7%), followed by surface runoff of contaminated soils (15.1%), microbial degradation after sedimentation (2.6%) and photodegradation during atmospheric transportation (1.6%). The cancer risk of human exposure to BDE-209 at the 95% confidence level was 3.09 × 10(-7), 1.67 × 10(-7) and 8.86 × 10(-7) for children, teens and adults, respectively, significantly lower than the threshold level (10(-6)). Hazard index (HI) calculated for non-cancer risk was also far less than 1 for the three groups, suggesting no non-cancer risk.

Polybrominated diphenyl ethers in sediments from the Southern Yellow Sea: Concentration, composition profile, source identification and mass inventory.

The Southern Yellow Sea (SYS) is believed to be influenced by the contaminants from mainland China and the Korean peninsula. Here we report the first record about concentrations of polybrominated diphenyl ethers (PBDEs) in the sediments of the SYS. The concentrations of ∑(7)PBDEs (BDE-28, 47, 99, 100, 153, 154, 183) and BDE-209 were 0.064-0.807 ng g(-1) (dry weight) and 0.067-1.961 ng g(-1) with a mean value of 0.245 ng g(-1) and 0.652 ng g(-1), respectively. These are distinctively low compared with the PBDE levels previously reported in other regions of the world. PBDE concentrations gradually increased from the coastal areas to the central mud area. BDE-209 was the dominant congener, accounting for 70.2-91.6% of the total PBDEs. Congener profiles of PBDEs were similar to those in sediments from the Bohai Sea (BS), Laizhou Bay and modern Yellow River, which might be a tentative indication that they shared similar sources. Principal component analysis (PCA) revealed that PBDEs in the SYS were mainly from continental runoff (69.0%) and atmospheric deposition (31.0%). Depth profile of PBDEs in a sediment core collected from the edge of the central mud area showed that concentration of BDE-209 rapidly increased in recent years, which is in accordance with the replacement in demand and consumption of Penta- and Octa-BDEs by the Deca-BDE. Compared with BS, East China Sea, Erie and Ontario, the SYS was a relatively weak sink of PBDEs (0.102-1.288 t yr(-1) for ∑(7)PBDEs and 0.107-3.129 t yr(-1) for BDE-209) in the world.

Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution.

The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of [Formula: see text], yielding a mean Dice similarity coefficient of [Formula: see text], and an average symmetric surface distance of [Formula: see text] mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.

Distribution and region-specific sources of Dechlorane Plus in marine sediments from the coastal East China Sea.

Dechlorane Plus (DP) is a highly chlorinated flame retardant and found to be ubiquitously present in the environment. We reported here the first record of DP in sediments from the coastal East China Sea (ECS). DP was detected in most of the surface sediments, and the concentrations ranged from 14.8 to 198pg/g dry weight (dw) with a mean value of 64.4pg/g dw. Overall, DP levels exhibited a seaward decreasing trend from the inshore toward outer sea. The fractional abundance of anti-DP (fanti) showed regional discrepancies, attributing to different environmental behaviors of DP isomers. Depth profiles of DP in a sediment core from estuarine environment showed distinct fluctuation, and the core in open sea had stable deposition environment with two peak values of DP in ~1978 and 2000. The fanti exhibited downward decreasing trend prior to mid-1950s, indicating a preferential degradation of anti-DP and/or a greater adsorption capacity of syn-DP after its burial. Lignin and lipid biomarkers (∑C27+C29+C31n-alkanes) of terrestrial organic matters were introduced to identify region-specific sources of DP, and the results showed that DP in the northern inner shelf, southern inner shelf of 29 °N and mud area southwest of Cheju Island was mainly come from Yangtze River (YR) input, surface runoffs after discharge of local sources close to the Taizhou-Wenzhou Region and the atmospheric deposition from the North China and East Asia, respectively. The coastal ECS was an important reservoir of DP in the world, with mass inventory of approximately 310.7kg in the surface sediments (0-5cm).

Multi-surface analysis for human action recognition in video.

The majority of methods for recognizing human actions are based on single-view video or multi-camera data. In this paper, we propose a novel multi-surface video analysis strategy. The video can be expressed as three-surface motion feature (3SMF) and spatio-temporal interest feature. 3SMF is extracted from the motion history image in three different video surfaces: horizontal-vertical, horizontal- and vertical-time surface. In contrast to several previous studies, the prior probability is estimated by 3SMF rather than using a uniform distribution. Finally, we model the relationship score between each video and action as a probability inference to bridge the feature descriptors and action categories. We demonstrate our methods by comparing them to several state-of-the-arts action recognition benchmarks.

Current levels, composition profiles, source identification and potentially ecological risks of polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) in the surface sediments from Bohai Sea.

Polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) were analyzed to assess the environmental quality in the surface sediments from Bohai Sea (BS), China. Concentrations of ∑37PCBs, ∑7PBDEs and BDE-209 were 0.157-1.699, 0.100-0.479 and 0.464-6.438 ng/g (dry weight), respectively. All of these concentrations decreased generally from the coastal areas towards the outer sea, indicating intensive influences of anthropogenic activities. Principal component analysis (PCA) coupled with multiple linear regression (MLR) revealed that 82.1% of the PCBs in BS came from direct discharge of local anthropogenic activities, 16.3% from surface runoff of contaminated soils and 1.6% from atmospheric deposition. PBDEs were mainly derived from the usage and dismantling of products containing commercial Penta-, Octa- and Deca-BDEs. According to sediment quality guidelines (SQGs), the ecological risks of PCBs could be negligible, and penta- and deca-BDE homologs might be the major contributors of ecological risks in the BS sediments.