Molecularly imprinted macroporous polymer monolithic layers for L-phenylalanine recognition in complex biological fluids

In present work, the development of macroporous monolithic layers bearing the artificial recognition sites toward L-phenylalanine has been carried out. The set of macroporous poly(2-aminoethyl methacrylate-co-2-hydroxyethyl methacrylate-co-ethylene glycol dimethacrylate) materials with average pore size ranged in 340–1200 nm was synthesized. The applicability of Hildebrand's and Hansen's theories for the prediction of polymer compatibility with porogenic solvents was evaluated. The dependences of average pore size on theoretically calculated parameters were plotted. The linear trend detected for Hansen's theory has indicated the high suitability of this approach to select appropriate porogens. The synthesized monolithic MIP layers were tested toward the ability to rebind phenylalanine-derivative in microarray format. The influence of such factors as average pore size of the material, the concentration of template molecule in polymerization mixture, interaction time of analyte with its imprinted sites on binding efficiency were studied. The developed materials demonstrated good analyte rebinding from buffer solution with recognition factors 2.5–3.4 depending on the MIP sample. The comparable rebinding efficiency was also detected when the analysis was carried using complex biological media. The selectivity of phenylalanine binding from the equimolar mixture of structural analogues was 81.9% for free amino acid and 91.2% for labeled one.     

Identification of the Primary Factors Determining the Specificity of Human VKORC1 Recognition by Thioredoxin-Fold Proteins

Redox (reduction–oxidation) reactions control many important biological processes in all organisms, both prokaryotes and eukaryotes. This reaction is usually accomplished by canonical disulphide-based pathways involving a donor enzyme that reduces the oxidised cysteine residues of a target protein, resulting in the cleavage of its disulphide bonds. Focusing on human vitamin K epoxide reductase (hVKORC1) as a target and on four redoxins (protein disulphide isomerase (PDI), endoplasmic reticulum oxidoreductase (ERp18), thioredoxin-related transmembrane protein 1 (Tmx1) and thioredoxin-related transmembrane protein 4 (Tmx4)) as the most probable reducers of VKORC1, a comparative in-silico analysis that concentrates on the similarity and divergence of redoxins in their sequence, secondary and tertiary structure, dynamics, intraprotein interactions and composition of the surface exposed to the target is provided. Similarly, hVKORC1 is analysed in its native state, where two pairs of cysteine residues are covalently linked, forming two disulphide bridges, as a target for Trx-fold proteins. Such analysis is used to derive the putative recognition/binding sites on each isolated protein, and PDI is suggested as the most probable hVKORC1 partner. By probing the alternative orientation of PDI with respect to hVKORC1, the functionally related noncovalent complex formed by hVKORC1 and PDI was found, which is proposed to be a first precursor to probe thiol–disulphide exchange reactions between PDI and hVKORC1.     

Quality assessment for color correction-based stitched images via bi-directional matching

With the deepening of social information, the panoramic image has drawn a significant interest of viewers and researchers as it can provide a very wide field of view (FoV). Since panoramic images are usually obtained by capturing images with the overlapping regions and then stitching them together, image stitching plays an important role in generating panoramic images. In order to effectively evaluate the quality of stitched images, a novel quality assessment method based on bi-directional matching is proposed for stitched images. Specifically, dense correspondences between the testing and benchmark stitched images are first established by bi-directional SIFT-flow matching. Then, color-aware, geometric-aware and structure-aware features are respectively extracted and fused via support vector regression (SVR) to obtain the final quality score. Experiments on our newly constructed database and ISIQA database demonstrate that the proposed method can achieve comparable performance compared with the conventional blind quality metrics and the quality metrics specially designed for stitched images.     

Split Enzyme-Based Biosensors for Structural Characterization of Soluble and Insoluble β-Glucans

β-Glucan is widely distributed in various plants and microorganisms and is composed of β-1,3-linked d-glucose units. It may have a branched short or long side chain of glucose units with β-1,6- or β-1,4-linkage. Numerous studies have investigated different β-glucans and revealed their bioactivities. To understand the structure-function relationship of β-glucan, we constructed a split-luciferase complementation assay for the structural analysis of long-chain β-1,6-branched β-1,3-glucan. The N- and C-terminal fragments of luciferase from deep-sea shrimp were fused to insect-derived β-1,3-glucan recognition protein and fungal endo-β-1,6-glucanase (Neg1)-derived β-1,6-glucan recognition protein, respectively. In this approach, two β-glucan recognition proteins bound to β-glucan molecules come into close proximity, resulting in the assembly of the full-length reporter enzyme and induction of transient luciferase activity, indicative of the structure of β-glucan. To test the applicability of this assay, β-glucan and two β-glucan recognition proteins were mixed, resulting in an increase in the luminescence intensity in a β-1,3-glucan with a long polymer of β-1,6-glucan in a dose-dependent manner. This simple test also allows the monitoring of real-time changes in the side chain structure and serves as a convenient method to distinguish between β-1,3-glucan and long-chain β-1,6-branched β-1,3-glucan in various soluble and insoluble β-glucans.     

Various density light field image coding based on distortion minimization interpolation

In recent years, the light field (LF) as a new imaging modality has attracted wide interest. The large data volume of LF images poses great challenge to LF image coding, and the LF images captured by different devices show significant differences in angular domain. In this paper we propose a view prediction framework to handle LF image coding with various sampling density. All LF images are represented as view arrays. We first partition the views into reference view (RV) set and intermediate view (IV) set. The RVs are rearranged into a pseudo sequence and directly compressed by a video encoder. Other views are then predicted by the RVs. To exploit the four dimensional signal structure, we propose the linear approximation prior (LAP) to reveal the correlation among LF views and efficiently remove the LF data redundancy. Based on the LAP, a distortion minimization interpolation (DMI) method is used to predict IVs. To robustly handle the LF images with different sampling density, we propose an Iteratively Updating depth image based rendering (IU-DIBR) method to extend our DMI. Some auxiliary views are generated to cover the target region and then the DMI calculates reconstruction coefficients for the IVs. Different view partition patterns are also explored. Extensive experiments on different types LF images also valid the efficiency of the proposed method.     

Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.     

Supramolecular Zn(II)-Dipicolylamine-Azobenzene-Aminocyclodextrin-ATP Complex: Design and ATP Recognition in Water

Cyclodextrins (CyDs) are water-soluble host molecules possessing a nanosized hydrophobic cavity. In the realm of molecular recognition, this cavity is used not only as a recognition site but also as a reaction medium, where a hydrophobic sensor recognizes a guest molecule. Based on the latter concept, we have designed a novel supramolecular sensing system composed of Zn(II)-dipicolylamine metal complex-based azobenzene (1-Zn) and 3A-amino-3A-deoxy-(2AS,3AS)-γ-cyclodextrin (3-NH₂-γ-CyD) for sensing adenosine-5′-triphosphate (ATP). 1-Zn showed redshifts in the UV-Vis spectra and induced circular dichroism (ICD) only when both ATP and 3-NH₂-γ-CyD were present. Calculations of equilibrium constants indicated that the amino group of 3-NH₂-γ-CyD was involved in the formation of supramolecular 1-Zn/3-NH₂-γ-CyD/ATP. The Job plot of the ICD spectral response revealed that the stoichiometry of 1-Zn/3-NH₂-γ-CyD/ATP was 2:1:1. The pH effect was examined and 1-Zn/3-NH₂-γ-CyD/ATP was most stable in the neutral condition. The NOESY spectrum suggested the localization of 1-Zn in the 3-NH₂-γ-CyD cavity. Based on the obtained results, the metal coordination interaction of 1-Zn and the electrostatic interaction of 3-NH₂-γ-CyD were found to take place for ATP recognition. The “reaction medium approach” enabled us to develop a supramolecular sensing system that undergoes multi-point interactions in water. This study is the first step in the design of a selective sensing system based on a good understanding of supramolecular structures.     

一种改进的特征匹配算法在碎片云测量建模中的应用

为满足超高速撞击典型Whipple防护构型的损伤评估需求,利用图像处理技术对碎片云序列阴影图像进行深入研究。使用超高速序列激光阴影成像仪得到三组不同实验条件下碎片云发展过程的高清阴影图像,分别对每组最具代表性的2帧进行图像处理分析;根据碎片云图像特点以及碎片运动特性,提出了一种改进的碎片二次特征匹配算法,该方法包含碎片粗定位、特征定义及初匹配和精确匹配三步策略;通过运用改进的匹配算法,对选取的相邻两帧图片完成碎片高效匹配,并提取匹配碎片的运动参数,进而分析碎片的速度分布和飞行角度分布,获取二次碎片云相关运动特性;得到三组实验各自的轨迹模拟图。根据得到的轨迹分析结果分别对三组实验的后板损伤进行估计,并通过与防护构型的实际损伤结果进行比较,验证了该方法的有效性。