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in this paper, the solubility of 7-azaindole was measured in nine pure solvents (ethanol, isopropanol, n-propanol, methanol, EA, acetone, acetonitrile, n-hexane, tetrahydrofuran, THF) as well as in...

Sulfur dioxide, known as an environmental pollutant, produced during industrial productions is also a common food additive that is permitted worldwide. In living organisms, sulfur dioxide forms hydrates of sulfite (SO2·H2O), bisulfite (HSO3−) and sulfite (SO32−) under physiological pH conditions; these three exist in a dynamic balance and play a role in maintaining redox balance, further participating in a wide range of physiological and pathological processes. On the basis of the differences in nucleophilicity between SO32− and HSO3−, for the first time, we built a mitochondrion-targeted dual-site fluorescent probe (Mito-CDTH-CHO) based on benzopyran for the highly specific detection of SO32− and HSO3− with two diverse emission channels. Mito-CDTH-CHO can discriminatively respond to the levels of HSO3− and SO32−. Besides, its advantages of low cytotoxicity, superior biocompatibility and excellent mitochondrial enrichment ability contribute to the detection and observation of the distribution of sulfur dioxide derivatives in living organisms as well as allowing further studies on the physiological functions of sulfur dioxide.

The solubility of 3-hydroxy-2-nitropyridine in ten pure solvents and two binary mixture solvents (tetrahydrofuran + n-hexane and tetrahydrofuran + isopropanol) was measured from 278.15 to 318.15 K. The solubility order in pure solvents was tetrahydrofuran > acetone > acetonitrile > ethyl acetate > methanol > ethanol ≈ n-propanol > isopropanol > water > n-hexane. We used two models to fit the monocomponent data and three binary mixed models to fit the binary mixture solvent. For the modified Apelblat model and λh model, the maximum average relative deviation (ARD) values were 1.16 and 1.52%, respectively, and the maximum root-mean-square deviation (rmsd) values were 1.61, and 1.53%, respectively. For the Apelblat–Jouyban–Acree model, Jouyban–Acree model, and van’t Hoff–Jouyban–Acree model, the maximum value of ARD is 3.69%, and the maximum value of rmsd is 0.48%. These models can fit the measured data well. We also discussed the effect of the interaction between solvent and solute on solubility. These expe...

Wound segmentation provides assistance for the diagnosis and treatment of wounds. We find that the wound image has a distinct feature, e.g., the pixel color changes gradually according to its position. Location information is essential to describe this feature. However, the current methods of wound segmentation based on deep learning have not significantly added location information into model training. In order to enhance this information, we propose a deep neural network model based on a location map and location-enhanced convolution kernel. The model effectively encodes the location information to one feature map, which is then concatenated with the inputs of the network and added to the hidden layer of the network after downsampling. Moreover, the model uses a fixed-value initialized convolution kernel to further enhance the location information in the training of the network. At the end of the model, a fixed-value depth-wise convolution layer is added to eliminate minor errors.

Sulfur dioxide, known as an environmental pollutant, produced during industrial productions is also a common food additive that is permitted worldwide. In living organisms, sulfur dioxide forms hydrates of sulfite (SO

Based on the addition reaction of sulfur dioxide derivatives and C C , a series of D-π-A and A-π-A' fluorescent probes were designed by combining different electronic groups with benzoindole to study the influence of different end groups on the performance of such probes. When sulfite is detected, the fluorescence of ratio-type probe changes from red to blue, the fluorescence of turn-on probe changes from colorless to blue. The response time ranges from 90–300 s, the detection limit ranges from 10 nM–70 μM and the Stokes shift ranges from 1800 to 6800 cm−1, all with high specificity. In addition, A2 has been successfully applied to cell imaging of endogenous sulfur dioxide derivatives.

In this research, the solubility properties of flutamide were studied by static equilibrium method under atmospheric pressure in twelve pure solvents (acetone, methanol, tetrahydrofuran, ethyl alcohol, ethyl acetate, isopropanol, n-propanol, n-butylalcohol, isobutanol, acetonitrile, methyl acetate, 1,4-dioxane) and three mixed solvents (acetone + isopropanol, ethyl acetate + isopropanol, tetrahydrofuran + isopropanol) with the experiment temperature was set from 278.15 K to 323.15 K. The purpose of this article was studying the solubility properties of flutamide in diverse solvent systems, which can provide references for selecting good solvent and antisolvent in process of crystallize. Modified Apelblat and λh model fit the solubility data which were get from pure solvents. Their RADmax were 1.73% and 2.05%, respectively, their RMSDmax were 0.29% and 0.44%, respectively. At the same time, CNIBS/R–K and Jouyban–Acree model match the solubility data which were get from binary solvent mixtures. Their RADmax were 0.47% and 4.21%, respectively, their RMSDmax were 0.22% and 0.32%, respectively.

The solubility of Progesterone at T = 278.15–323.15 K was measured by static equilibrium method combined with HPLC in twelve solvents (methanol, ethanol, isopropanol, n-propanol, n-butanol, acetone, tetrahydrofuran, 1,4-dioxane, n-heptane, ethyl acetate, acetonitrile, and toluene) and three binary solvents (tetrahydrofuran + n-heptane, 1,4-dioxane + n-heptane, and toluene + n-heptane). The results showed that progesterone has the highest solubility in toluene (0.2249 in mole fraction) and other low-dielectric constant solvents, and the smallest in n-heptane (1.120 × 10−4 in mole fraction). Moreover, the solubility increases as the temperature rise both in pure and mixed solvents. As expected, the DSC and XRDP results showed that there was no crystal form difference between the raw material and all the precipitate. Herein, the Modified Apelblat model, Buchowski-Ksiazaczak λh model, Jouyban-Acree model, CNIBS/R-K model, and SUN model were employed to fit the experimental data. As a result, the maximum RAD doesn't exceed 5.0%, and the maximum RMSD was less than 0.1%. Furthermore, we chose KAT-LSER model to explore the solubility of progesterone and the solute-solvent interaction relationships. The results showed that when hydrogen bond acidity and polarizability/dipolarity of solvents increases, the solubility of progesterone increases. Meanwhile, comparing the experimental data and the fitting results, it can be concluded that the experimental data was reliable and almost consistent with the fitting results. Furthermore, it also proved that the above mentioned five thermodynamic equations were advantageous and trustworthy.

Spoken language understanding (SLU) in multi-turn task-oriented dialogue systems usually needs to take historical contextual information into account. To achieve this, current approaches encode dialogue utterances with contextual information to improve the accuracy of slot filling and intent detection. However, this kind of approach ignores the different influences of contextual information on dialogue utterances. Some utterances with independent context will be misunderstood due to the addition of noisy contextual information. In this paper, we propose a Dual Semantics model(CSD) that is based on dual semantics. Each dialogue utterance is encoded based on independent semantics and contextual semantics, where the contributions of these two semantic information are dynamically decided. A large number of experiments on two datasets verify the effectiveness of our approach. Specifically, the accuracy of slot filling at the KVRET dataset increases by 3.3% compared to the baseline model. A dataset of psychological service appointments(PSA), is further collected for the verification of our approach. Compared with existing methods, the accuracy is improved by 4.9% on this dataset.

As a biothiol, cysteine plays an important role in physiological and pathological processes. Hence, it is very significant to detect cysteine in vivo selectively and sensitively. In this paper, we designed a large Stokes shift (104 nm) fluorescent probe (TPC-Cys) with high selectivity, high sensitivity for the detection of cysteine. Upon the addition of cysteine, TPC-Cys shows a dramatic fluorescence enhancement around 504 nm. TPC-Cys can achieve good linear detection in the range of 0–80 μM, and the detection limit is 80 nM. Moreover, TPC-Cys has been successfully applied for real-time imaging of cysteine in living cells.

In this research, we employed a static equilibrium method to measure the solubility of 5-Fluoro-2-oxindole in nine pure solvents (n-hexane, n-propanol, tetrahydrofuran, isopropanol, methanol, ethyl acetate, N,N-dimethylformamide, acetonitrile, acetone) and three binary solvents (n-hexane+ tetrahydrofuran, ethyl acetate + tetrahydrofuran, acetonitrile + tetrahydrofuran) from 278.15 K to 323.15 K. The results demonstrate that as the temperature increases, the solubility of 5-Fluoro-2-oxindole increases in nine solvents selected. The modified Apelblat model and the λh model were adopted to correlate the solubility in pure solvents; meanwhile, we employed the CNIBS/R–K model and the Jouyban–Acree model to correlate the solubility in binary solvent mixtures. The maximum RAD values of modified Apelblat model and λh model are 0.64% and 0.71%; respectively; the maximum RMSD values are 0.076% and 0.058%. The corresponding maximum RAD values of the CNIBS/R–K model and the Jouyban–Acree model are 1.27% and 2.22%. In general, these equations emerge an excellent fitting effect.

Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.

In the brain MR image segmentation applied the watershed algorithm, there are some problems like the segmented precision is affected by the noise easily and edges are blurred. In order to solving these problems, we improve the watershed algorithm based on the bi-dimensional ensemble empirical mode decomposition (BEEMD), and apply the improved algorithm to the brain MR image segmentation. In this paper, the BEEMD is used to decompose the MR images into multiple BIMFs with different frequency. For different segmented objects, these BIMFs are given different weights to enhance the details of the various regions. Then, the enhanced image is segmented by applying improved watershed algorithm. Finally, we correct the segmentation results and fix their blurring details. Our method improves the over-segmentation problem of watershed and the details blurred problem of the improved watershed. In addition, this method has good robustness to different MR images.

Realistic 3D human model reconstruction is an important component in computer graphics and computer vision. In particular, texturing on the surface of models is a key stage of reconstruction. In this paper, we dispose the texture mapping on the model’s surface as an optimization of image stitching, and present an effective method to generate a seamless, integrated and smooth texture on the surface of 3D human model. First, we build a corresponding Markov Random Field model with respect to color images and triangular meshes of the surface. On the basis of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α-expansion optimization for this Markov Random Field model, a 2D translation coordinate of color image, as an adaptive iterative factor, is introduced into the optimization to match the color content at the boundary of adjacent meshes. That compensates for the misalignment of adjacent color images, which caused by the inaccuracy of depth data and multi-view misregistration. Then we apply Poisson blending to a composite vector field in gradient domain, to resolve the small but noticeable illumination variations between different color images. To repair the blank regions, we parameterize the model’s surface and project it onto a 2D plane. Then the K-Nearest Neighbor algorithm is applied to fill up the blank regions with texture contents. Finally, we evaluate our method by comparison with another three advanced methods on some human models, and the results demonstrate that our method of images stitching creates a best texture on the surface of 3D human model both in visual effect and quantitative analysis.

This paper presents a method for correcting the posture of 3D face with unknown position and posture based on the standard face pose. We define a standard posture of face which is not affected by the head gesture. Then the algorithms of principal component analysis and iterative closest point are used to achieve the preliminary adjustment of the target model. For the precise calibration, we use the method of depth values iterative searching to find the point of nose precisely, and propose an algorithm of searching along a straight line to find the ridge line of nose. Ultimately we determine the difference between the posture of the target model and standard posture to obtain accurate calculation of the target model posture.

Single image super-resolution reconstruction is a challenging ill-posed inverse problem currently, which aims to recover a high-resolution image from a low-resolution input. Natural images contain multiple types of textures or multiple object categories, and the features, especially the high-frequency feature, of each category are remarkably different from others. In this paper, we propose a method based on image segmentation and classification with sparse representation for single image super-resolution reconstruction. We divide the input low-resolution image into several regions, and find out the most correlative category for each segment. Then the high-resolution image for each segment is reconstructed by the sparse coefficients with respect to the dictionaries jointly learned from high-resolution and low-resolution image pairs of corresponding category respectively. The experimental results demonstrate the superior of our method over the several state-of-the-art methods both quantitatively and perceptually.

Single image super-resolution reconstruction is a challenging ill-posed inverse problem currently. In this paper, we propose a method based on image classification and sparse representation for single image super-resolution reconstruction. Various images belong to different image category, and each category contains different contents and structures respectively, especially the high-frequency feature. Therefore, we extract the features of the input low-resolution image, and classify it into the corresponding category. Then the high-resolution image is reconstructed by sparse representation with the dictionary trained from the corresponding database, which consists of high-resolution and low-resolution image patch pairs. The experimental results demonstrate that our method achieves better performance in visual effect and qualitative analysis, by comparison with some well-known methods.

Texture for 3D models is one important component of computer graphics. In this paper, we propose a method for generating a coherent, integrated and seamless texture on 3D head models. In the process, the color images and depth data captured by Kinect are established a correspondence to build initial texture on the head surface. Then the visible mismatching seams in the adjacent texture regions are effectively removed by an iterative coarse-to-fine strategy, resulting in an accurate aligned texture. In the following, the illumination variations among different color images are eliminated by color fusion in gradient domains. Finally, one texture is generated for the whole 3D head model by non-texture repair. The experiments demonstrate that the coherent and seamless texture method for 3D head is effective and robust.

Recently, we have seen a growing trend in the reconstruction of accurate geometric models, acquired by an active method such as laser scanning or structured light. In this paper, we address the problem of creation of 3D head models from multiple RGB-Depth cameras. We propose a quick capture procedure that aims at achieving high accuracy and low-cost 3D head models with high quality. This is processed with registration of multiple RGB-Depth cameras, consolidation and matching of raw data, surface reconstruction and mesh coloring. Experiments demonstrate that it could capture the subject in one second and reconstruct the 3D head models with photo-realistic quality in 3 minutes.