Photoelectrochemical immunoassay of cardiac troponin I based on ZnTCPP/CdIn2S4 type-II heterojunction and co-catalyzed precipitation biolabeling.

CdIn2S4 and zinc tetrakis(4-carboxyphenyl)porphyrin (ZnTCPP) were synthesized by hydrothermal method, and an organic dye-sensitized inorganic semiconductor ZnTCPP/CdIn2S4 type II heterojunction was constructed on a fluorine-doped tin oxide (FTO) substrate electrode. A sandwich immunostructure for signal-attenuation photoelectrochemical (PEC) detection of cardiac troponin I (cTnI) was constructed using the ZnTCPP/CdIn2S4/FTO photoanode and a horseradish peroxidase (HRP)-ZnFe2O4-Ab2-bovine serum albumin (BSA) immunolabeling complex. The bioenzyme HRP and the HRP-like nanozyme ZnFe2O4 can co-catalyze the oxidation of 4-chloro-1-naphthol (4-CN) by H2O2 to produce an insoluble precipitate on the photoanode, thus notably reducing the anodic photocurrent for quantitative determination of cTnI. Under the optimal conditions, the photocurrent at 0 V vs. SCE in 0.1 M phosphate buffer solution (pH 7.40) containing 0.1 M ascorbic acid was linear with the logarithm of cTnI concentration from 500 fg mL-1 to 50.0 ng mL-1, and the limit of detection (LOD, S/N = 3) is 0.15 pg mL-1. Spiked recoveries were 95.1% ~ 104% for assay of cTnI in human serum samples.

Photoelectrochemical aptasensing of lincomycin based on a AgI-carboxylated multiwalled carbon nanotubes-BiOI Z-scheme heterojunction.

Lincomycin (LIN) is a common antibiotic that is widely used in animal husbandry and other fields, and the residual problem caused by its abuse has attracted widespread attention. Herein, a novel AgI-carboxylated multiwalled carbon nanotubes (cMWCNT)-BiOI Z-scheme heterojunction material was synthesized via a one-pot hydrothermal method, modified on a fluorine-doped tin oxide (FTO) electrode surface, and used for detecting LIN. The photocurrent on the AgI-cMWCNT-BiOI/FTO photoelectrode is 4.6 times that on the control AgI-BiOI/FTO photoelectrode. An amino-functionalized LIN aptamer was fixed on the AgI-cMWCNT-BiOI/FTO photoelectrode by the cross-linking reaction between chitosan and glutaraldehyde, and then Ru(NH3)63+ was electrostatically attached to the LIN aptamer to increase the photocurrent response to the LIN binding. When LIN binds competitively with Ru(NH3)63+ to the aptamer, the photocurrent signal can be quantitatively decreased. Under optimized conditions, the anodic photocurrent at 0 V vs KCl-saturated calomel electrode in 0.1 M phosphate buffer (pH 7.0) containing 0.100 M ascorbic acid was linear with the common logarithm of LIN concentration from 10.0 pM to 500 nM, with a limit of detection of 2.8 pM (S/N = 3). Satisfactory recovery results were obtained in the analysis of cow milk samples.

Identifying restoration priorities for wetlands based on historical distributions of biodiversity features and restoration suitability.

Wetland restoration is a major objective of environmental management worldwide. We present a frameworkat the regional level that prioritizes historical biodiversity and restoration suitability. The goal of the framework is to maximize biodiversity gains from restoration while minimizing the cost. We used C-Plan, a prioritization tool for systematic conservation planning (SCP), to balance the biodiversity gains withthe costs of restoration, or restoration suitability. We overlaid historical spatial data from 1995 to estimate historical distributions of 91 biodiversity features. These features were used to conduct an irreplaceability analysis to assess the restoration value of historical biodiversity. We then modelled restoration suitability based on environmental data of six criteria. Finally, we applied a complementarity analysis to achieve the quantitative targets of all biodiversity features while minimizing the cost of restoration. We tested this framework in the highly degraded wetlands ofSanjiang Plain, China. By applying our framework to Sanjiang Plain, we successfully identified areas with both high restoration value and high restoration suitability. The area of this cost-effective plan was an extension of 4620 km2, covering 80% of the disappearing wetlands and 4% of the total Sanjiang Plain. Compared to the restoration value-only plan, which had an extension of 4486 km2, the cost-effective plan covered a little more area to achievethe targets forall biodiversity features but with lower implementation costs where the proportion of high restoration suitability increases from 43% to 50%.Our prioritization framework can be used to analyse regional restoration efforts in other regions and ecosystems, and inform planners on how to maximize biodiversity gains while minimizing costs.

Crafting GBD-Net for Object Detection.

The visual cues from multiple support regions of different sizes and resolutions are complementary in classifying a candidate box in object detection. Effective integration of local and contextual visual cues from these regions has become a fundamental problem in object detection. In this paper, we propose a gated bi-directional CNN (GBD-Net) to pass messages among features from different support regions during both feature learning and feature extraction. Such message passing can be implemented through convolution between neighboring support regions in two directions and can be conducted in various layers. Therefore, local and contextual visual patterns can validate the existence of each other by learning their nonlinear relationships and their close interactions are modeled in a more complex way. It is also shown that message passing is not always helpful but dependent on individual samples. Gated functions are therefore needed to control message transmission, whose on-or-offs are controlled by extra visual evidence from the input sample. The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO. Besides the GBD-Net, this paper also shows the details of our approach in winning the ImageNet object detection challenge of 2016, with source code provided on https://github.com/craftGBD/craftGBD. In this winning system, the modified GBD-Net, new pretraining scheme and better region proposal designs are provided. We also show the effectiveness of different network structures and existing techniques for object detection, such as multi-scale testing, left-right flip, bounding box voting, NMS, and context.

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection.

In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN [16], which was the state-of-the-art, from 31% to 50.3% on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1%. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provides a global view for people to understand the deep learning object detection pipeline.

Single-Pedestrian Detection Aided by Two-Pedestrian Detection.

In this paper, we address the challenging problem of detecting pedestrians who appear in groups. A new approach is proposed for single-pedestrian detection aided by two-pedestrian detection. A mixture model of two-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single- and two-pedestrian detectors, and to refine the single-pedestrian detection result using two-pedestrian detection. The two-pedestrian detector can integrate with any single-pedestrian detector. Twenty-five state-of-the-art single-pedestrian detection approaches are combined with the two-pedestrian detector on three widely used public datasets: Caltech, TUD-Brussels, and ETH. Experimental results show that our framework improves all these approaches. The average improvement is 9 percent on the Caltech-Test dataset, 11 percent on the TUD-Brussels dataset and 17 percent on the ETH dataset in terms of average miss rate. The lowest average miss rate is reduced from 37 to percent on the Caltech-Test dataset, from 55 to 50 percent on the TUD-Brussels dataset and from 43 to 38 percent on the ETH dataset.