Integrated remediation of sulfate reducing bacteria and nano zero valent iron on cadmium contaminated sediments.

Integrated-remediation technologies on heavy metal polluted sediments have received much attention. In this study, Cd contaminated sediments were treated with various conditions: sulfate reducing bacteria (SRB) only and SRB combined with different dosages of nano zero valent iron (nZVI (0.5-10 mg/g)). The immobilization of Cd was found in all remediation treatments according to the decreases of mobile Cd and the increases of more stable Cd compared with control. Five typical SRBs (Desulfobulbaceae, Desulfobacteraceae, Syntrophobacteraceae, Desulfovibrionaceae and Desulfomicrobiaceae) were identified having significant influences on Cd speciation transformation and they could stabilize Cd into sulfide precipitation through dissimilatory sulfate reduction (DSR). The ANOVA results of mobilization index and Cd concentration in overlying water both demonstrated that integrated-remediation systems with 5 mg/g and 10 mg/g of nZVI (Fe5 and Fe10 systems, respectively) presented better immobilization performance than conventional SRB only system (P < 0.05). It is confirmed that nZVI could stimulate the SRB bio-immobilization possibily through providing electrons and enhancing enzyme activities during DSR. The XPS analyses and Pourbaix diagrams revealed that mackinawite may be produced in the Fe10, resulting in the possible formation of Cd-S-Fe. This study indicates that integrated-remediation of SRB and nZVI have great potential in Cd immobilization of sediments, especially with higher addition of nZVI.

Two-generational effects and recovery of arsenic and arsenate on Daphnia magna in the presence of nano-TiO2.

The toxicity of arsenic (As) can be influenced by many environmental factors. Among them, nanomaterials can adsorb arsenic and alter its bioavailability in organisms. However, the studies on long-term effects of arsenic in the presence of nanoparticles are limited. Thus, the 21-d effect of titanium dioxide nanoparticles (nano-TiO2) on chronic toxicity of arsenic (arsenate and arsenite) was investigated in two generations of Daphnia magna. The exposed concentration of nano-TiO2 was 1 mg/L and the concentration of As(Ⅲ) or As(Ⅴ) was 0.2 mg/L which was lower than the 48 h-NOEC (no observed effect concentration). The survival, body length, average number of offspring and time of first brood were determined. Our results indicated that the exposure to nano-TiO2 and As during the parental generation can affect the health of offspring. Nano-TiO2 was found to significantly alleviate the mortality and reproduction inhibition of As on D. magna, and the alleviation of As(Ⅴ) was more prominent than that of As(Ⅲ). It is likely that nano-TiO2 alters the metabolism and adsorption condition of arsenic in the gastrointestinal tract of D. magna. Overall, these results indicate that the increase of arsenic adsorption onto nano-TiO2 in the gut of D. magna could alleviate the toxicity of arsenic. Nonetheless, further research should be conducted to study the influence of arsenic on the multi-generations of aquatic organisms, especially when it is coexisted with other substances.

Recurrent Shape Regression.

An end-to-end network architecture, the Recurrent Shape Regression (RSR), is presented to deal with the task of facial shape detection, a crucial step in many computer vision problems. The RSR generalizes the conventional cascaded regression into a recurrent dynamic network through abstracting common latent models with stage-to-stage operations. Instead of invariant regression transformation, we construct shape-dependent dynamic regressors to attain the recurrence of regression action itself. The regressors can be stacked into a high-order regression network to represent more complex shape regression. By further integrating feature learning as well as global shape constraint, the RSR becomes more controllable in entire optimization of shape regression, where the gradient computation can be efficiently back-propagated through time. To handle the possible partial occlusions of shapes, we propose a mimic virtual occlusion strategy by randomly disturbing certain point cliques without the requirement of any annotations of occlusion information or even occluded training data. Extensive experiments on five face datasets demonstrate that the proposed RSR outperforms the recent state-of-the-art cascaded approaches.

Hierarchical Contextual Refinement Networks for Human Pose Estimation.

Predicting human pose in the wild is a challenging problem due to high flexibility of joints and possible occlusion. Existing approaches generally tackle the difficulties either by holistic prediction or multi-stage processing, which suffer from poor performance for locating challenging joints or high computational cost. In this paper, we propose a new Hierarchical Contextual Refinement Network (HCRN) to robustly predict human poses in an efficient manner, where human body joints of different complexities are processed at different layers in a context hierarchy. Different from existing approaches, our proposed model predicts positions of joints from easy to difficult in a single stage through effectively exploiting informative contexts provided in the previous layer. Such approach offers two appealing advantages over state-of-the-arts: (1) more accurate than predicting all the joints together and (2) more efficient than multi-stage processing methods. We design a Contextual Refinement Unit (CRU) to implement the proposed model, which enables auto-diffusion of joint detection results to effectively transfer informative context from easy joints to difficult ones. In this way, difficult joints can be reliably detected even in presence of occlusion or severe distracting factors. Multiple CRUs are organized into a tree-structured hierarchy which is end-to-end trainable and does not require processing joints for multiple iterations. Comprehensive experiments evaluate the efficacy and efficiency of the proposed HCRN model to improve well-established baselines and achieve new state-of-the-art on multiple human pose estimation benchmarks.