Mechanisms of the interaction between Pr(DNR)3 and Herring-Sperm DNA

Research on the interaction mechanism of drugs with DNA is essential to understand their pharmacokinetics. The interaction between rare earth complexes Pr(DNR)3 and Herring-Sperm DNA was studied in Tris-HCl buffer solution (pH 7.4) by absorption and fluorescence spectroscopy and viscosity measurements. The results showed that the modes of interaction between Pr(DNR)3 and Herring-Sperm DNA were electrostatic and intercalation. The binding ratio was nPr(DNA)3 ׃ nDNA = 5׃1 and the binding constant was KΘ292K = 4.34×10exp3 L mol-1. Furthermore, according to the double reciprocal method and the thermodynamic equation, the intercalative interaction was cooperatively driven by an enthalpy effect and an entropy effect

Detecting single molecules inside a carbon nanotube to control molecular sequences using inertia trapping phenomenon

Here we show the detection of single gas molecules inside a carbon nanotube based on the change in resonance frequency and amplitude associated with the inertia trapping phenomenon. As its direct implication, a method for controlling the sequence of small molecule is then proposed to realize the concept of manoeuvring of matter atom by atom in one dimension. The detection as well as the implication is demonstrated numerically with the molecular dynamics method. It is theoretically assessed that it is possible for a physical model to be fabricated in the very near future

Real-time Local Feature with Global Visual Information Enhancement

Local feature provides compact and invariant image representation for various visual tasks. Current deep learning-based local feature algorithms always utilize convolution neural network (CNN) architecture with limited receptive field. Besides, even with high-performance GPU devices, the computational efficiency of local features cannot be satisfactory. In this paper, we tackle such problems by proposing a CNN-based local feature algorithm. The proposed method introduces a global enhancement module to fuse global visual clues in a light-weight network, and then optimizes the network by novel deep reinforcement learning scheme from the perspective of local feature matching task. Experiments on the public benchmarks demonstrate that the proposal can achieve considerable robustness against visual interference and meanwhile run in real time.Comment: 6 pages, 5 figures, 2 tables. Accepted by ICIEA 202

Bis(2,2-dinitro­prop­yl)formal

The complete mol­ecule of the title compound [systematic name: bis(2,2-dinitro­prop­oxy)methane], C7H12N4O10, which was synthesized by the condensation reaction between 2,2-dinitro­propanol and paraformaldehyde in methyl­ene chloride, is generated by crystallographic twofold symmetry with one C atom lying on the rotation axis. In the crystal structure, mol­ecules are linked into chains running parallel to the b axis by inter­molecular C—H⋯O hydrogen-bond inter­actions, generating rings of graph-set motif R 2 2(14)

IEBins: Iterative Elastic Bins for Monocular Depth Estimation

Monocular depth estimation (MDE) is a fundamental topic of geometric computer vision and a core technique for many downstream applications. Recently, several methods reframe the MDE as a classification-regression problem where a linear combination of probabilistic distribution and bin centers is used to predict depth. In this paper, we propose a novel concept of iterative elastic bins (IEBins) for the classification-regression-based MDE. The proposed IEBins aims to search for high-quality depth by progressively optimizing the search range, which involves multiple stages and each stage performs a finer-grained depth search in the target bin on top of its previous stage. To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty. Furthermore, we develop a dedicated framework composed of a feature extractor and an iterative optimizer that has powerful temporal context modeling capabilities benefiting from the GRU-based architecture. Extensive experiments on the KITTI, NYU-Depth-v2 and SUN RGB-D datasets demonstrate that the proposed method surpasses prior state-of-the-art competitors. The source code is publicly available at https://github.com/ShuweiShao/IEBins.Comment: Accepted by NeurIPS 202

Graph-based Facial Affect Analysis: A Review of Methods, Applications and Challenges

Facial affect analysis (FAA) using visual signals is important in human-computer interaction. Early methods focus on extracting appearance and geometry features associated with human affects, while ignoring the latent semantic information among individual facial changes, leading to limited performance and generalization. Recent work attempts to establish a graph-based representation to model these semantic relationships and develop frameworks to leverage them for various FAA tasks. In this paper, we provide a comprehensive review of graph-based FAA, including the evolution of algorithms and their applications. First, the FAA background knowledge is introduced, especially on the role of the graph. We then discuss approaches that are widely used for graph-based affective representation in literature and show a trend towards graph construction. For the relational reasoning in graph-based FAA, existing studies are categorized according to their usage of traditional methods or deep models, with a special emphasis on the latest graph neural networks. Performance comparisons of the state-of-the-art graph-based FAA methods are also summarized. Finally, we discuss the challenges and potential directions. As far as we know, this is the first survey of graph-based FAA methods. Our findings can serve as a reference for future research in this field.Comment: 20 pages, 12 figures, 5 table

Characterization of a thermostable β-glucosidase from Aspergillus fumigatus Z5, and its functional expression in Pichia pastoris X33

<p>Abstract</p> <p>Background</p> <p>Recently, the increased demand of energy has strongly stimulated the research on the conversion of lignocellulosic biomass into reducing sugars for the subsequent production, and β-glucosidases have been the focus because of their important roles in a variety fundamental biological processes and the synthesis of useful β-glucosides. Although the β-glucosidases of different sources have been investigated, the amount of β-glucosidases are insufficient for effective conversion of cellulose. The goal of this work was to search for new resources of β-glucosidases, which was thermostable and with high catalytic efficiency.</p> <p>Results</p> <p>In this study, a thermostable native β-glucosidase (nBgl3), which is secreted by the lignocellulose-decomposing fungus <it>Aspergillus fumigatus </it>Z5, was purified to electrophoretic homogeneity. Internal sequences of nBgl3 were obtained by LC-MS/MS, and its encoding gene, <it>bgl3</it>, was cloned based on the peptide sequences obtained from the LC-MS/MS results. <it>bgl</it>3 contains an open reading frame (ORF) of 2622 bp and encodes a protein with a predicted molecular weight of 91.47 kDa; amino acid sequence analysis of the deduced protein indicated that nBgl3 is a member of the glycoside hydrolase family 3. A recombinant β-glucosidase (rBgl3) was obtained by the functional expression of <it>bgl</it>3 in <it>Pichia pastoris </it>X33. Several biochemical properties of purified nBgl3 and rBgl3 were determined - both enzymes showed optimal activity at pH 6.0 and 60°C, and they were stable for a pH range of 4-7 and a temperature range of 50 to 70°C. Of the substrates tested, nBgl3 and rBgl3 displayed the highest activity toward 4-Nitrophenyl-β-D-glucopyranoside (pNPG), with specific activities of 103.5 ± 7.1 and 101.7 ± 5.2 U mg^-1, respectively. However, these enzymes were inactive toward carboxymethyl cellulose, lactose and xylan.</p> <p>Conclusions</p> <p>An native β-glucosidase nBgl3 was purified to electrophoretic homogeneity from the crude extract of <it>A. fumigatus </it>Z5. The gene <it>bgl</it>3 was cloned based on the internal sequences of nBgl3 obtained from the LC-MS/MS results, and the gene <it>bgl3 </it>was expressed in <it>Pichia pastoris </it>X33. The results of various biochemical properties of two enzymes including specific activity, pH stability, thermostability, and kinetic properties (Km and Vmax) indicated that they had no significant differences.</p

NENet: Monocular Depth Estimation via Neural Ensembles

Depth estimation is getting a widespread popularity in the computer vision community, and it is still quite difficult to recover an accurate depth map using only one single RGB image. In this work, we observe a phenomenon that existing methods tend to exhibit asymmetric errors, which might open up a new direction for accurate and robust depth estimation. We carefully investigate into the phenomenon, and construct a two-level ensemble scheme, NENet, to integrate multiple predictions from diverse base predictors. The NENet forms a more reliable depth estimator, which substantially boosts the performance over base predictors. Notably, this is the first attempt to introduce ensemble learning and evaluate its utility for monocular depth estimation to the best of our knowledge. Extensive experiments demonstrate that the proposed NENet achieves better results than previous state-of-the-art approaches on the NYU-Depth-v2 and KITTI datasets. In particular, our method improves previous state-of-the-art methods from 0.365 to 0.349 on the metric RMSE on the NYU dataset. To validate the generalizability across cameras, we directly apply the models trained on the NYU dataset to the SUN RGB-D dataset without any fine-tuning, and achieve the superior results, which indicate its strong generalizability. The source code and trained models will be publicly available upon the acceptance

Analysis of inclined failure characteristics of floor along working face in Ordovician limestone confined water stope

Accurate prediction of the maximum failure depth of the stope floor on confined water is an important part of preventing water inrush from the coal mine floor. In order to study the failure characteristics of the inclined floor along the working face, the author based on the mine pressure and rock strata control theory, considered the combined action of the inclined bearing pressure of the stope floor, established a mechanical calculation model for the inclined floor of the stope above the confined water, and used the Mohr Coulomb yield criterion with tensile failure to judge the failure of the stope floor. The results show that: under periodic pressure, the failure pattern of the stope floor along the dip of working face tends to be similar to an “inverted saddle shape”, and the maximum failure depth is 12 m; the floor failure depth on both sides of the working face is greater, and the failure depth of the gob floor is small. Numerical simulation calculation results show that the maximum failure depth of the floor near the elastoplastic boundary of the working face is 13 m, and the failure mode is mainly shear failure. Located in the pressure relief section of the gob, the failure depth of the stope floor is small, and the main failure forms are shear failure and tensile failure. This is almost consistent with the failure mode of the stope floor on confined water obtained through theoretical analysis. The maximum failure depth of the floor of 22516 working face in Dongjiahe Coal Mine is 13.52 m, which is relatively close to the 12 m calculated by the author through theoretical analysis and 13 m calculated by numerical simulation. The rationality of the author's theoretical model establishment and the correctness of the numerical simulation analysis are verified. The research method provides a new reference for analyzing the failure characteristics of the confined water stope floor