The ELR-CXC chemokines are important to neutrophil inflammation in many acute and chronic diseases. Among them, CXCL8 (interleukin-8, IL-8), the expression levels of which are elevated in many inflammatory diseases, binds to both the CXCR1 and CXCR2 receptors with high affinity. Recently, an analogue of human CXCL8, CXCL8_(3–72)K11R/G31P (hG31P) has been developed. It has been demonstrated that hG31P is a high affinity antagonist for both the CXCR1 and CXCR2. Herein, we have determined the solution structure and the CXCR1 N-terminal peptide binding sites of hG31P by NMR spectroscopy. We have found that the displacement within the tertiary structure of the 30 s loop and the N-terminal region and more specifically change of the loop conformation (especially H33), of hG31P may affect its binding to the CXCR1 receptor and thereby inhibit human neutrophil chemotactic responses induced by ELR-CXC chemokines. Our results provide a structural basis for future clinical investigations of this CXCR1/CXCR2 receptor antagonist and for the further development of CXCL8 based antagonists. Full article

Hyperspectral image (HSI) classification aims at assigning each pixel a pre-defined class label, which underpins lots of vision related applications, such as remote sensing, mineral exploration and ground object identification, etc. Lots of classification methods thus have been proposed for better hyperspectral imagery interpretation. Witnessing the success of convolutional neural networks (CNNs) in the traditional images based classification tasks, plenty of efforts have been made to leverage CNNs to improve HSI classification. An advanced CNNs architecture uses the kernels generated from the clustering method, such as a K-means network uses K-means to generate the kernels. However, the above methods are often obtained heuristically (e.g., the number of kernels should be assigned manually), and how to data-adaptively determine the number of convolutional kernels (i.e., filters), and thus generate the kernels that better represent the data, are seldom studied in existing CNNs based HSI classification methods. In this study, we propose a new CNNs based HSI classification method where the convolutional kernels can be automatically learned from the data through clustering without knowing the cluster number. With those data-adaptive kernels, the proposed CNNs method achieves better classification results. Experimental results from the datasets demonstrate the effectiveness of the proposed method. Full article

Background: This study investigated whether glycemic index (GI) or glycemic load (GL) of morning or evening intake and morning or evening carbohydrate intake from low- or higher-GI food sources (low-GI-CHO, higher-GI-CHO) during adolescence are relevant for risk markers of type 2 diabetes in young adulthood. Methods: Analyses included DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study participants who had provided at least two 3-day weighed dietary records (median: 7 records) during adolescence and one blood sample in young adulthood. Using multivariable linear regression analyses, estimated morning and evening GI, GL, low-GI-CHO (GI < 55) and higher-GI-CHO (GI ≥ 55) were related to insulin sensitivity (N = 252), hepatic steatosis index (HSI), fatty liver index (FLI) (both N = 253), and a pro-inflammatory-score (N = 249). Results: Morning intakes during adolescence were not associated with any of the adult risk markers. A higher evening GI during adolescence was related to an increased HSI in young adulthood (p = 0.003). A higher consumption of higher-GI-CHO in the evening was associated with lower insulin sensitivity (p = 0.046) and an increased HSI (p = 0.006), while a higher evening intake of low-GI-CHO was related to a lower HSI (p = 0.009). Evening intakes were not related to FLI or the pro-inflammatory-score (all p > 0.1). Conclusion: Avoidance of large amounts of carbohydrates from higher-GI sources in the evening should be considered in preventive strategies to reduce the risk of type 2 diabetes in adulthood. Full article

In this paper, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse representation. The proposed method combines the high spectral resolution from the HSI with the high spatial resolution from a multispectral image (MSI) of the same scene and high resolution images from unrelated scenes. The fusion method is based on a spectral unmixing procedure for which the endmember matrix and the abundance fractions are estimated from the HSI and MSI, respectively. A Bayesian formulation of this method leads to an ill-posed fusion problem. A sparse representation regularization term is added to convert it into a well-posed inverse problem. In the sparse representation, dictionaries are constructed from the MSI, high optical resolution images, synthetic aperture radar (SAR) or combinations of them. The proposed algorithm is applied to real datasets and compared with state-of-the-art fusion algorithms based on spectral unmixing and sparse representation, respectively. The proposed method significantly increases the spatial resolution and decreases the spectral distortion efficiently. Full article

The fusion of spatial and spectral information in hyperspectral images (HSIs) is useful for improving the classification accuracy. However, this approach usually results in features of higher dimension and the curse of the dimensionality problem may arise resulting from the small ratio between the number of training samples and the dimensionality of features. To ease this problem, we propose a novel algorithm for spatial-spectral feature extraction based on hypergraph embedding. Firstly, each HSI pixel is regarded as a vertex and the joint of extended morphological profiles (EMP) and spectral features is adopted as the feature associated with the vertex. A hypergraph is then constructed by the K-Nearest-Neighbor method, in which each pixel and its most K relevant pixels are linked as one hyperedge to represent the complex relationships between HSI pixels. Secondly, the hypergraph embedding model is designed to learn a low dimensional feature with the reservation of geometric structure of HSI. An adaptive hyperedge weight estimation scheme is also introduced to preserve the prominent hyperedges by the regularization constraint on the weight. Finally, the learned low-dimensional features are fed to the support vector machine (SVM) for classification. The experimental results on three benchmark hyperspectral databases are presented. They highlight the importance of spatial–spectral joint features embedding for the accurate classification of HSI data. The weight estimation is better for further improving the classification accuracy. These experimental results verify the proposed method. Full article

Acquisition of hyperspectral imagery (HSI) from cameras mounted on terrestrial platforms is a relatively recent development that enables spectral analysis of dominantly vertical structures. Although solar shadowing is prevalent in terrestrial HSI due to the vertical scene geometry, automated shadow detection and restoration algorithms have not yet been applied to this capture modality. We investigate the fusion of terrestrial laser scanning (TLS) spatial information with terrestrial HSI for geometric shadow detection on a rough vertical surface and examine the contribution of radiometrically calibrated TLS intensity, which is resistant to the influence of solar shadowing, to HSI shadow restoration. Qualitative assessment of the shadow detection results indicates pixel level accuracy, which is indirectly validated by shadow restoration improvements when sub-pixel shadow detection is used in lieu of single pixel detection. The inclusion of TLS intensity in existing shadow restoration algorithms that use regions of matching material in sun and shade exposures was found to have a marginal positive influence on restoring shadow spectrum shape, while a proposed combination of TLS intensity with passive HSI spectra boosts restored shadow spectrum magnitude precision by 40% and band correlation with respect to a truth image by 45% compared to existing restoration methods. Full article

Knee instability resulting from anterior cruciate ligament (ACL) rupture is a high-risk factor for posttraumatic osteoarthritis (PTOA) in the patellofemoral joint (PFJ). However, whether non-weight-bearing and weight-bearing treatments have chondroprotective effects remains unclear. Twenty-four adult New Zealand White male rabbits were employed in this study. All animals received ACL transection in the right knee and sham surgery in the left knee. The rabbits were randomly assigned to the following groups: (I) In the sedentary (SED) group, the rabbits (n = 6) were simply kept in their cage; (II) In the continuous passive motion (CPM) group, the rabbits (n = 6) performed CPM exercise for 7 days, starting from the first postoperative day; (III) In the active treadmill exercise (TRE) group, the rabbits (n = 6) performed TRE for 2 weeks; (IV) In the CPM + TRE group, the rabbits (n = 6) executed CPM exercise, followed by TRE. Two joint surfaces (the retropatella and femoral trochlear groove) were assessed at 4 weeks after operation. Although the gross appearance in each group was comparable, histological examination revealed significant differences in the articular cartilage status. The CPM group exhibited a greater thickness of articular cartilage, maintenance of tidemark continuity, abundant glycosaminoglycan (GAG), and significantly lower inflammatory cytokine 9, e.g., tumor necrosis factor-alpha (TNF-α) 0 levels, with modest cell apoptosis (i.e., caspase-3). By contrast, the TRE group displayed the worst pathological features: an irregular cartilage surface and chondrocyte disorganization, reduced cartilage thickness, breakdown of the tidemark, depletion of collagen fibers, loss of GAG, and the highest levels of TNF-α and caspase-3 expression. Furthermore, the CPM + TRE group had more favorable outcomes than the SED group, indicating that suitable exercise is needed. The sham treatment displayed no variance in the changes in the two joint surfaces among groups. These data indicate that the application of early CPM rehabilitation is suggested for subjects in order to decrease the risk of PTOA without ACL reconstruction in the PFJ compartment in rabbits. The early TRE program, however, had harmful outcomes. Additionally, inactivity was discovered to initiate the development of PTOA. Full article

Luminosity and correlated color temperature (CCT) have gradually become two of the most important factors in the evaluation of the performance of light sources. However, although most color performance evaluation metrics are highly correlated with CCT, these metrics often do not account for light sources with different CCTs. This paper proposes the existence of a relationship between luminosity and CCT to remove the effects of CCT and to allow for a fairer judgment of light sources under the current color performance evaluation metrics. This paper utilizes the Hyper-Spectral Imaging (HSI) technique to recreate images of a standard color checker under different luminosities, CCT, and light sources. The images are then analyzed and transformed into interpolation figures and equal color difference curves. This paper utilizes statistic tools and symmetry properties to determine an exponential relationship between luminosity and CCT in red-green-blue (RGB) LED and OLED light sources. Such a relationship presents an option to remove the effects of CCT in color evaluation standards, as well as provide a guide line for adjusting visual experience solely by adjusting luminosity when creating a lighting system. Full article

Assessing the quality of a reconstructed hyperspectral image (HSI) is of significance for restoration and super-resolution. Current image quality assessment methods such as peak signal-noise-ratio require the availability of pristine reference image, which is often not available in reality. In this paper, we propose a no-reference hyperspectral image quality assessment method based on quality-sensitive features extraction. Difference of statistical properties between pristine and distorted HSIs is analyzed in both spectral and spatial domains, then multiple statistics features that are sensitive to image quality are extracted. By combining all these statistics features, we learn a multivariate Gaussian (MVG) model as benchmark from the pristine hyperspectral datasets. In order to assess the quality of a reconstructed HSI, we partition it into different local blocks and fit a MVG model on each block. A modified Bhattacharyya distance between the MVG model of each reconstructed HSI block and the benchmark MVG model is computed to measure the quality. The final quality score is obtained by average pooling over all the blocks. We assess five state-of-the-art super-resolution methods on Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Hyperspec-VNIR-C (HyperspecVC) data using our proposed method. It is verified that the proposed quality score is consistent with current reference-based assessment indices, which demonstrates the effectiveness and potential of the proposed no-reference image quality assessment method. Full article

This paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboringregions. SSRformsadeeparchitectureandisabletolearndiscriminativespectral-spatial features of the input HSI at different scales. It includes several existing spectral-spatial-based methods as special scenarios within a single unified framework. Based on SSR, we further propose the Subspace Learning-based Networks (SLN) as an example of SSR for HSI classification. In SLN, the joint spectral and spatial features are learned using templates simply learned by Marginal Fisher Analysis (MFA) and Principal Component Analysis (PCA). An important contribution to the success of SLN is the exploitation of label information of training samples and the local spatial structure of HSI. Extensive experimental results on four challenging HSI datasets taken from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) airborne sensors show the implementational simplicity of SLN and verify the superiority of SSR for HSI classification. Full article

Recently, superpixel segmentation has been proven to be a powerful tool for hyperspectral image (HSI) classification. Nonetheless, the selection of the optimal superpixel size is a nontrivial task. In addition, compared with single-scale superpixel segmentation, the same image segmented on a different scale can obtain different structure information. To overcome such a drawback also utilizing the structural information, a multiscale superpixel-based sparse representation (MSSR) algorithm for the HSI classification is proposed. Specifically, a modified segmentation strategy of multiscale superpixels is firstly applied on the HSI. Once the superpixels on different scales are obtained, the joint sparse representation classification is used to classify the multiscale superpixels. Furthermore, majority voting is utilized to fuse the labels of different scale superpixels and to obtain the final classification result. Two merits are realized by the MSSR. First, multiscale information fusion can more effectively explore the spatial information of HSI. Second, in the multiscale superpixel segmentation, except for the first scale, the superpixel number on a different scale for different HSI datasets can be adaptively changed based on the spatial complexity of the corresponding HSI. Experiments on four real HSI datasets demonstrate the qualitative and quantitative superiority of the proposed MSSR algorithm over several well-known classifiers. Full article

This paper proposes a novel method of segment-tree filtering to improve the classification accuracy of hyperspectral image (HSI). Segment-tree filtering is a versatile method that incorporates spatial information and has been widely applied in image preprocessing. However, to use this powerful framework in hyperspectral image classification, we must reduce the original feature dimensionality to avoid the Hughes problem; otherwise, the computational costs are high and the classification accuracy by original bands in the HSI is unsatisfactory. Therefore, feature extraction is adopted to produce new salient features. In this paper, the Semi-supervised Local Fisher (SELF) method of discriminant analysis is used to reduce HSI dimensionality. Then, a tree-structure filter that adaptively incorporates contextual information is constructed. Additionally, an initial classification map is generated using multi-class support vector machines (SVMs), and segment-tree filtering is conducted using this map. Finally, a simple Winner-Take-All (WTA) rule is applied to determine the class of each pixel in an HSI based on the maximum probability. The experimental results demonstrate that the proposed method can improve HSI classification accuracy significantly. Furthermore, a comparison between the proposed method and the current state-of-the-art methods, such as Extended Morphological Profiles (EMPs), Guided Filtering (GF), and Markov Random Fields (MRFs), suggests that our method is both competitive and robust. Full article

Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record. Full article

Sulfur-fumigated Chinese medicine is a common issue in the process of Chinese medicines. Detection of sulfur dioxide (SO₂) residual content in Fritillaria thunbergii Bulbus is important to evaluate the degree of sulfur fumigation and its harms. It helps to control the use of sulfur fumigation in Fritillaria thunbergii Bulbus. Near-infrared hyperspectral imaging (NIR-HSI) was explored as a rapid, non-destructive, and accurate technique to detect SO₂ residual contents in Fritillaria thunbergii Bulbus. An HSI system covering the spectral range of 874–1734 nm was used. Partial least squares regression (PLSR) was applied to build calibration models for SO₂ residual content detection. Successive projections algorithm (SPA), weighted regression coefficients (Bw), random frog (RF), and competitive adaptive reweighted sampling (CARS) were used to select optimal wavelengths. PLSR models using the full spectrum and the selected optimal wavelengths obtained good performance. The Bw-PLSR model was applied on a hyperspectral image to form a prediction map, and the results were satisfactory. The overall results in this study indicated that HSI could be used as a promising technique for on-line visualization and monitoring of SO₂ residual content in Fritillaria thunbergii Bulbus. Detection and visualization of Chinese medicine quality by HSI provided a new rapid and visual method for Chinese medicine monitoring, showing great potential for real-world application. Full article

Sparse representation (SR)-driven classifiers have been widely adopted for hyperspectral image (HSI) classification, and many algorithms have been presented recently. However, most of the existing methods exploit the single layer hard assignment based on class-wise reconstruction errors on the subspace assumption; moreover, the single-layer SR is biased and less stable due to the high coherence of the training samples. In this paper, motivated by category sparsity, a novel multi-layer spatial-spectral sparse representation (mlSR) framework for HSI classification is proposed. The mlSR assignment framework effectively classifies the test samples based on the adaptive dictionary assembling in a multi-layer manner and intrinsic class-dependent distribution. In the proposed framework, three algorithms, multi-layer SR classification (mlSRC), multi-layer collaborative representation classification (mlCRC) and multi-layer elastic net representation-based classification (mlENRC) for HSI, are developed. All three algorithms can achieve a better SR for the test samples, which benefits HSI classification. Experiments are conducted on three real HSI image datasets. Compared with several state-of-the-art approaches, the increases of overall accuracy (OA), kappa and average accuracy (AA) on the Indian Pines image range from 3.02% to 17.13%, 0.034 to 0.178 and 1.51% to 11.56%, respectively. The improvements in OA, kappa and AA for the University of Pavia are from 1.4% to 21.93%, 0.016 to 0.251 and 0.12% to 22.49%, respectively. Furthermore, the OA, kappa and AA for the Salinas image can be improved from 2.35% to 6.91%, 0.026 to 0.074 and 0.88% to 5.19%, respectively. This demonstrates that the proposed mlSR framework can achieve comparable or better performance than the state-of-the-art classification methods. Full article

During the acquisition process hyperspectral images (HSI) are inevitably corrupted by various noises, which greatly influence their visual impression and subsequent applications. In this paper, a novel Bayesian approach integrating hierarchical sparse learning and spectral-spatial information is proposed for HSI denoising. Based on the structure correlations, spectral bands with similar and continuous features are segmented into the same band-subset. To exploit local similarity, each subset is then divided into overlapping cubic patches. All patches can be regarded as consisting of clean image component, Gaussian noise component and sparse noise component. The first term is depicted by a linear combination of dictionary elements, where Gaussian process with Gamma distribution is applied to impose spatial consistency on dictionary. The last two terms are utilized to fully depict the noise characteristics. Furthermore, the sparseness of the model is adaptively manifested through Beta-Bernoulli process. Calculated by Gibbs sampler, the proposed model can directly predict the noise and dictionary without priori information of the degraded HSI. The experimental results on both synthetic and real HSI demonstrate that the proposed approach can better suppress the existing various noises and preserve the structure/spectral-spatial information than the compared state-of-art approaches. Full article

Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details. Full article

Recently, sparse representation has yielded successful results in hyperspectral image (HSI) classification. In the sparse representation-based classifiers (SRCs), a more discriminative representation that preserves the spectral-spatial information can be exploited by treating the HSI as a whole entity. Based on this observation, a tensor block-sparsity based representation method is proposed for spectral-spatial classification of HSI in this paper. Unlike traditional vector/matrix-based SRCs, the proposed method consists of tensor block-sparsity based dictionary learning and class-dependent block sparse representation. By naturally regarding the HSI cube as a third-order tensor, small local patches centered at the training samples are extracted from the HSI to maintain the structural information. All the patches are then partitioned into a number of groups, on which a dictionary learning model is constructed with a tensor block-sparsity constraint. A test sample is also expressed as a small local patch and the block sparse representation is then performed in a class-wise manner to take advantage of the class label information. Finally, the category of the test sample is determined by using the minimal residual. Experimental results of two real-world HSIs show that our proposed method greatly improves the classification performance of SRC. Full article

In chronic liver diseases, regardless of their etiology, the development of fibrosis is the first step toward the progression to cirrhosis, portal hypertension, and hepatocellular carcinoma. Hepatic stellate cells (HSCs) are the main profibrogenic cells that promote the pathogenesis of liver fibrosis, and so it is important to identify the molecules that regulate HSCs activation and liver fibrosis. Niemann-Pick type C2 (NPC2) protein plays an important role in the regulation of intracellular cholesterol homeostasis by directly binding with free cholesterol. However, the roles of NPC2 in HSCs activation and liver fibrosis have not been explored in detail. Since a high-cholesterol diet exacerbates liver fibrosis progression in both rodents and humans, we propose that the expression of NPC2 affects free cholesterol metabolism and regulates HSCs activation. In this study, we found that NPC2 is decreased in both thioacetamide- and carbon tetrachloride-induced liver fibrosis tissues. In addition, NPC2 is expressed in quiescent HSCs, but its activation status is down-regulated. Knockdown of NPC2 in HSC-T6 cells resulted in marked increases in transforming growth factor-β1 (TGF-β1)-induced collagen type 1 α1 (Col1a1), α-smooth muscle actin (α-SMA) expression, and Smad2 phosphorylation. In contrast, NPC2 overexpression decreased TGF-β1-induced HSCs activation. We further demonstrated that NPC2 deficiency significantly increased the accumulation of free cholesterol in HSCs, increasing Col1a1 and α-SMA expression and activating Smad2, and leading to sensitization of HSCs to TGF-β1 activation. In contrast, overexpression of NPC2 decreased U18666A-induced free cholesterol accumulation and inhibited the subsequent HSCs activation. In conclusion, our study has demonstrated that NPC2 plays an important role in HSCs activation by regulating the accumulation of free cholesterol. NPC2 overexpression may thus represent a new treatment strategy for liver fibrosis. Full article

Hyperspectral imaging (HSI) technology has been extensively studied in the classification of seed variety. A novel procedure for the classification of maize seed varieties based on HSI was proposed in this study. The optimal wavelengths for the classification of maize seed varieties were selected using the successive projections algorithm (SPA) to improve the acquiring and processing speed of HSI. Subsequently, spectral and imaging features were extracted from regions of interest of the hyperspectral images. Principle component analysis and multidimensional scaling were then introduced to transform/reduce the classification features for overcoming the risk of dimension disaster caused by the use of a large number of features. Finally, the integrating features were used to develop a least squares–support vector machines (LS–SVM) model. The LS–SVM model, using the integration of spectral and image features combined with feature transformation methods, achieved more than 90% of test accuracy, which was better than the 83.68% obtained by model using the original spectral and image features, and much higher than the 76.18% obtained by the model only using the spectral features. This procedure provides a possible way to apply the multispectral imaging system to classify seed varieties with high accuracy. Full article

Myxomatous mitral valve prolapse (MMVP) and fibroelastic deficiency (FED) are two common variants of degenerative mitral valve disease (DMVD), which is a leading cause of mitral regurgitation worldwide. While pathohistological studies have revealed differences in extracellular matrix content in MMVP and FED, the molecular mechanisms underlying these two disease entities remain to be elucidated. By using surgically removed valvular specimens from MMVP and FED patients that were categorized on the basis of echocardiographic, clinical and operative findings, a cluster of microRNAs that expressed differentially were identified. The expressions of has-miR-500, -3174, -17, -1193, -646, -1273e, -4298, -203, -505, and -939 showed significant differences between MMVP and FED after applying Bonferroni correction (p < 0.002174). The possible involvement of microRNAs in the pathogenesis of DMVD were further suggested by the presences of in silico predicted target sites on a number of genes reported to be involved in extracellular matrix homeostasis and marker genes for cellular composition of mitral valves, including decorin (DCN), aggrecan (ACAN), fibromodulin (FMOD), α actin 2 (ACTA2), extracellular matrix protein 2 (ECM2), desmin (DES), endothelial cell specific molecule 1 (ESM1), and platelet/ endothelial cell adhesion molecule 1 (PECAM1), as well as inverse correlations of selected microRNA and mRNA expression in MMVP and FED groups. Our results provide evidence that distinct molecular mechanisms underlie MMVP and FED. Moreover, the microRNAs identified may be targets for the future development of diagnostic biomarkers and therapeutics. Full article

The name “ginseng” is collectively used to describe several plant species, including Panax ginseng (Asian/Oriental ginseng), P. quinquefolius (American ginseng), P. pseudoginseng (Pseudoginseng) and Eleutherococcus senticosus (Siberian ginseng), each with different applications in traditional medicine practices. The use of a generic name may lead to the interchangeable use or substitution of raw materials which poses quality control challenges. Quality control methods such as vibrational spectroscopy-based techniques are here proposed as fast, non-destructive methods for the distinction of four ginseng species and the identification of raw materials in commercial ginseng products. Certified ginseng reference material and commercial products were analysed using hyperspectral imaging (HSI), mid-infrared (MIR) and near-infrared (NIR) spectroscopy. Principal component analysis (PCA) and (orthogonal) partial least squares discriminant analysis models (OPLS-DA) were developed using multivariate analysis software. UHPLC-MS was used to analyse methanol extracts of the reference raw materials and commercial products. The holistic analysis of ginseng raw materials revealed distinct chemical differences using HSI, MIR and NIR. For all methods, Eleutherococcus senticosus displayed the greatest variation from the three Panax species that displayed closer chemical similarity. Good discrimination models with high R²X and Q² cum vales were developed. These models predicted that the majority of products contained either /P. ginseng or P. quinquefolius. Vibrational spectroscopy and HSI techniques in tandem with multivariate data analysis tools provide useful alternative methods in the authentication of ginseng raw materials and commercial products in a fast, easy, cost-effective and non-destructive manner. Full article

Hyperspectral inspection using imaging systems is becoming more and more important for quality control tasks in several industries, replacing well trained operators or established machine vision systems based on RGB-systems. Hyperspectral imaging (HSI) on thin coated substrates is challenging due to the high reflectivity of the substrates. Nevertheless, the thin films contribute to the spectral data and can be evaluated. Therefore, the performance of inspection systems can be increased significantly. However, the large amount of data generated by HSI has to be processed and evaluated for quality information about the product. In this paper, thin aluminum oxide (Al₂O₃) layers on stainless steel foil are investigated by HSI. These substrates can be used for the growth of vertically aligned carbon nanotubes (VA-SWCNT) for battery electrodes. HSI and spectral ellipsometry in combination with Partial Least Squares regression (PLS) was used to estimate the thickness of the Al₂O₃ layers and to calculate quality parameters for a possible monitoring process. The PLS model shows a R²_CV of 0.979 and a RMSECV of 3.6. Full article

A novel Symmetric Sparse Representation (SSR) method has been presented to solve the band selection problem in hyperspectral imagery (HSI) classification. The method assumes that the selected bands and the original HSI bands are sparsely represented by each other, i.e., symmetrically represented. The method formulates band selection into a famous problem of archetypal analysis and selects the representative bands by finding the archetypes in the minimal convex hull containing the HSI band points (i.e., one band corresponds to a band point in the high-dimensional feature space). Without any other parameter tuning work except the size of band subset, the SSR optimizes the band selection program using the block-coordinate descent scheme. Four state-of-the-art methods are utilized to make comparisons with the SSR on the Indian Pines and PaviaU HSI datasets. Experimental results illustrate that SSR outperforms all four methods in classification accuracies (i.e., Average Classification Accuracy (ACA) and Overall Classification Accuracy (OCA)) and three quantitative evaluation results (i.e., Average Information Entropy (AIE), Average Correlation Coefficient (ACC) and Average Relative Entropy (ARE)), whereas it takes the second shortest computational time. Therefore, the proposed SSR is a good alternative method for band selection of HSI classification in realistic applications. Full article

Various kinds of three-dimensional (3D) scaffolds have been designed to mimic the biological spontaneous bone formation characteristics by providing a suitable microenvironment for osteogenesis. In view of this, a natural bone-liked composite scaffold, which was combined with inorganic (hydroxyapatite, Hap) and organic (type I collagen, Col) phases, has been developed through a self-assembly process. This 3D porous scaffold consisting of a c-axis of Hap nanocrystals (nHap) aligning along Col fibrils arrangement is similar to natural bone architecture. A significant increase in mechanical strength and elastic modulus of nHap/Col scaffold is achieved through biomimetic mineralization process when compared with simple mixture of collagen and hydroxyapatite method. It is suggested that the self-organization of Hap and Col produced in vivo could also be achieved in vitro. The oriented nHap/Col composite not only possesses bone-like microstructure and adequate mechanical properties but also enhances the regeneration and reorganization abilities of bone tissue. These results demonstrated that biomimetic nHap/Col can be successfully reconstructed as a bone graft substitute in bone tissue engineering. Full article

Currently, the environment and sustainability are important topics for every industry. The food industry is particularly complicated in this regard because of the dynamic and complex character of food products and their production. This study uses structural equation modeling (SEM) and a fuzzy analytic hierarchy process (FAHP) to investigate which factors are suitable for evaluating the environmental performance of Thailand’s food industry. A first-stage questionnaire survey was conducted with 178 managers in the food industry that obtained a certificate from the Department of Industrial Work of Thailand to synthesize the performance measurement model and the significance of the relationship between the indicators. A second-stage questionnaire measured 18 experts’ priorities regarding the criteria and sub-factors involved in the different aspects and assessment items regarding environmental performance. SEM showed that quality management, market orientation, and innovation capability have a significantly positive effect on environmental performance. The FAHP showed that the experts were most concerned about quality management, followed by market orientation and innovation capability; the assessment items for quality policy, quality assurance, and customer orientation were of the most concern. The findings of this study can be referenced and support managerial decision making when monitoring environmental performance. Full article

In this paper, a general nearest feature line (NFL) embedding (NFLE) transformation called fuzzy-kernel NFLE (FKNFLE) is proposed for hyperspectral image (HSI) classification in which kernelization and fuzzification are simultaneously considered. Though NFLE has successfully demonstrated its discriminative capability, the non-linear manifold structure cannot be structured more efficiently by linear scatters using the linear NFLE method. According to the proposed scheme, samples were projected into a kernel space and assigned larger weights based on that of their neighbors. The within-class and between-class scatters were calculated using the fuzzy weights, and the best transformation was obtained by maximizing the Fisher criterion in the kernel space. In that way, the kernelized manifold learning preserved the local manifold structure in a Hilbert space as well as the locality of the manifold structure in the reduced low-dimensional space. The proposed method was compared with various state-of-the-art methods to evaluate the performance using three benchmark data sets. Based on the experimental results: the proposed FKNFLE outperformed the other, more conventional methods. Full article

Species distribution models (SDMs) are extensively used to project habitat suitability of species in stream ecological studies. Owing to complex sources of uncertainty, such models may yield projections with varying degrees of uncertainty. To better understand projected spatial distributions and the variability between habitat suitability projections, this study uses five SDMs that are based on the outputs of a two-dimensional hydraulic model to project the suitability of habitats and to evaluate the degree of variability originating from both differing model types and the split-sample procedure. The habitat suitability index (HSI) of each species is based on two stream flow variables, including current velocity (V), water depth (D), as well as the heterogeneity of these flow conditions as quantified by the information entropy of V and D. The six SDM approaches used to project fish abundance, as represented by HSI, included two stochastic models: the generalized linear model (GLM) and the generalized additive model (GAM); as well as three machine learning models: the support vector machine (SVM), random forest (RF) and the artificial neural network (ANN), and an ensemble model (where the latter is the average of the preceding five models). The target species Sicyopterus japonicas was found to prefer habitats with high current velocities. The relationship between mesohabitat diversity and fish abundance was indicated by the trends in information entropy and weighted usable area (WUA) over the study area. This study proposes a method for quantifying habitat suitability, and for assessing the uncertainties in HSI and WUA that are introduced by the various SDMs and samples. This study also demonstrated both the merits of the ensemble modeling approach and the necessity of addressing model uncertainty. Full article

Two new 15-hydroxycembranoid diterpenes, 2β-hydroxy-7β,8α-epoxynephthenol (1) and 2β-hydroxy-11α,12β-epoxynephthenol (2), were isolated from extracts of the octocoral Nephthea columnaris along with a new natural cembrane, epoxynephthenol (3) and a known sterol, nephalsterol A (4). The structures of cembranes 1–3 were elucidated by spectroscopic methods and comparison of the spectroscopic data with those of related analogues. The cytotoxicity of metabolites 1–4 against a panel of tumor cells is also described. Full article

Two new cembranes, columnariols A (1) and B (2), were isolated from the cultured soft coral Nephthea columnaris. The structures of cembranes 1 and 2 were elucidated by spectroscopic methods. In the anti-inflammatory effects test, cembranes 1 and 2 were found to significantly inhibit the accumulation of the pro-inflammatory iNOS and COX-2 protein of the lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage cells. Compound 1 exhibited moderate cytotoxicity toward LNCaP cells with an IC₅₀ value of 9.80 μg/mL. Full article

Prolonged treatment with a large dose of propofol may cause diffuse cellular cytotoxicity; however, the detailed underlying mechanism remains unclear, particularly in vascular endothelial cells. Previous studies showed that a propofol overdose induces endothelial injury and vascular barrier dysfunction. Regarding the important role of endothelial glycocalyx on the maintenance of vascular barrier integrity, we therefore hypothesized that a propofol overdose-induced endothelial barrier dysfunction is caused by impaired endothelial glycocalyx. In vivo, we intraperitoneally injected ICR mice with overdosed propofol, and the results showed that a propofol overdose significantly induced systemic vascular hyperpermeability and reduced the expression of endothelial glycocalyx, syndecan-1, syndecan-4, perlecan mRNA and heparan sulfate (HS) in the vessels of multiple organs. In vitro, a propofol overdose reduced the expression of syndecan-1, syndecan-4, perlecan, glypican-1 mRNA and HS and induced significant decreases in the nicotinamide adenine dinucleotide (NAD+)/NADH ratio and ATP concentrations in human microvascular endothelial cells (HMEC-1). Oligomycin treatment also induced significant decreases in the NAD+/NADH ratio, in ATP concentrations and in syndecan-4, perlecan and glypican-1 mRNA expression in HMEC-1 cells. These results demonstrate that a propofol overdose induces a partially ATP-dependent reduction of endothelial glycocalyx expression and consequently leads to vascular hyperpermeability due to the loss of endothelial barrier functions. Full article

Dihydroaustrasulfone alcohol (DA), an active compound firstly isolated from marine corals, has been reported to reveal anti-cancer and anti-inflammation activities. These reported activities of DA raised a possible application in anti-restenosis. Abnormal proliferation and migration of vascular smooth muscle cells (VSMCs) and the stimulation of platelet-derived growth factor (PDGF)-BB play major pathological processes involved in the development of restenosis. Experimental results showed that DA markedly reduced balloon injury-induced neointima formation in the rat carotid artery model and significantly inhibited PDGF-BB-stimulated proliferation and migration of VSMCs. Our data further demonstrated that translational and active levels of several critical signaling cascades involved in VSMC proliferation, such as extracellular signal-regulated kinase/ mitogen-activated protein kinases (ERK/MAPK), phosphatidylinositol 3-kinase (PI3K)/AKT, and signal transducer and activator of transcription (STAT), were obviously inhibited. In addition, DA also decreased the activation and expression levels of gelatinases (matrix metalloproteinase (MMP)-2 and MMP-9) involved in cell migration. In conclusion, our findings indicate that DA can reduce balloon injury-neointimal hyperplasia, the effect of which may be modulated through suppression of VSMC proliferation and migration. These results suggest that DA has potential application as an anti-restenotic agent for the prevention of restenosis. Full article

A new process for modifying a polymeric material for use as a hole injection transport layer in organic light-emitting diodes has been studied, which is through 2π + 2π photodimerization of a DNA-mimetic π-conjugated poly(triphenylamine-carbazole) presenting pendent uracil groups (PTC-U) under 1 h of UV irradiation. Multilayer florescence OLED (Organic light-emitting diodes) device with the PTC-U-1hr as a hole injection/transport layer (ITO (Indium tin oxide)/HITL (hole-injection/transport layer) (15 nm)/N,N'-di(1-naphthyl)- N,N'-diphenyl-(1,1'-biphenyl)-4,4'-diamine (NPB) (15 nm)/Tris-(8-hydroxyquinoline) aluminum (Alq₃) (60 nm)/LiF (1 nm)/Al (100 nm)) is fabricated, a remarkable improvement in performance (Q_max (external quantum efficiency) = 2.65%, B_max (maximum brightness) = 56,704 cd/m², and LE (luminance efficiency)_max = 8.9 cd/A) relative to the control PTC-U (Q_max = 2.40%, B_max = 40,490 cd/m², and LE_max = 8.0 cd/A). Multilayer phosphorescence OLED device with the PTC-U-1hr as a hole injection/transport layer (ITO/HITL (15 nm)/Ir(ppy)₃:PVK (40 nm)/BCP (10nm)/Alq₃ (40 nm)/LiF (1 nm)/Al (100 nm)) is fabricated by successive spin-coating processes, a remarkable improvement in performance (Q_max = 9.68%, B_max = 41,466 cd/m², and LE_max = 36.6 cd/A) relative to the control PTC-U (Q_max = 8.35%, B_max = 34,978 cd/m², and LE_max = 30.8 cd/A) and the commercial product (poly(3,4-ethylenedioxythiophene):polystyrenesulfonate) PEDOT:PSS (Q_max = 4.29%, B_max = 15,678 cd/m², and LE_max = 16.2 cd/A) has been achieved. Full article

The large volume of hyperspectral images (HSI) generated creates huge challenges for transmission and storage, making data compression more and more important. Compressive Sensing (CS) is an effective data compression technology that shows that when a signal is sparse in some basis, only a small number of measurements are needed for exact signal recovery. Distributed CS (DCS) takes advantage of both intra- and inter- signal correlations to reduce the number of measurements needed for multichannel-signal recovery. HSI can be observed by the DCS framework to reduce the volume of data significantly. The traditional method for estimating endmembers (spectral information) first recovers the images from the compressive HSI and then estimates endmembers via the recovered images. The recovery step takes considerable time and introduces errors into the estimation step. In this paper, we propose a novel method, by designing a type of coherent measurement matrix, to estimate endmembers directly from the compressively observed HSI data via convex geometry (CG) approaches without recovering the images. Numerical simulations show that the proposed method outperforms the traditional method with better estimation speed and better (or comparable) accuracy in both noisy and noiseless cases. Full article

Traditional dynamic shading systems are usually driven by electricity for continuously controlling the angle of blind slats to minimize the indoor solar heat gain over times. This paper proposed a novel design of buoyancy driven dynamic shading system, using only minimum amount of electricity. The energy performance and the improved thermal comfort induced by the system were simulated by EnergyPlus for a typical office space under the context of Taiwanese climate. The design processes are composed of three parts: an alterable angle of blind slats that raises the energy performance to be suitable for every orientation, the buoyancy driven transmission mechanism, and a humanized controller that ensures its convenience. The environmental friendly design aspects and control mechanisms to fulfill demands for manufacturing, assembling, maintenance and recycling, etc., were also presented as readily for building application. Besides, the effectiveness of cooling energy saving and thermal comfort enhancing were compared against the cases without exterior blinds and with traditional fixed blinds installed. The results show that the cooling energy is drastically reduced over times and the blind system is effectively enhancing the indoor thermal comfort. Full article

Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a novel hyperspectral imagery super-resolution (HSI-SR) method via dictionary learning and spatial-spectral regularization. The main contributions of this paper are twofold. First, inspired by the compressive sensing (CS) framework, for learning the high resolution dictionary, we encourage stronger sparsity on image patches and promote smaller coherence between the learned dictionary and sensing matrix. Thus, a sparsity and incoherence restricted dictionary learning method is proposed to achieve higher efficiency sparse representation. Second, a variational regularization model combing a spatial sparsity regularization term and a new local spectral similarity preserving term is proposed to integrate the spectral and spatial-contextual information of the HSI. Experimental results show that the proposed method can effectively recover spatial information and better preserve spectral information. The high spatial resolution HSI reconstructed by the proposed method outperforms reconstructed results by other well-known methods in terms of both objective measurements and visual evaluation. Full article

The phosphatidylserine receptor (PSR) recognizes a surface marker on apoptotic cells and initiates engulfment. This receptor is important for effective apoptotic cell clearance and maintains normal tissue homeostasis and regulation of the immune response. However, the regulation of PSR expression remains poorly understood. In this study, we determined that interferon regulatory factor-1 (IRF-1) was dramatically upregulated upon viral infection in the fish cell. We observed apoptosis in virus-infected cells and found that both PSR and IRF-1 increased simultaneously. Based on a bioinformatics promoter assay, IRF-1 binding sites were identified in the PSR promoter. Compared to normal viral infection, we found that PSR expression was delayed, viral replication was increased and virus-induced apoptosis was inhibited following IRF-1 suppression with morpholino oligonucleotides. A luciferase assay to analyze promoter activity revealed a decreasing trend after the deletion of the IRF-1 binding site on PSR promoter. The results of this study indicated that infectious pancreatic necrosis virus (IPNV) infection induced both the apoptotic and interferon (IFN) pathways, and IRF-1 was involved in regulating PSR expression to induce anti-viral effects. Therefore, this work suggests that PSR expression in salmonid cells during IPNV infection is activated when IRF-1 binds the PSR promoter. This is the first report to show the potential role of IRF-1 in triggering the induction of apoptotic cell clearance-related genes during viral infection and demonstrates the extensive crosstalk between the apoptotic and innate immune response pathways. Full article

Organic photovoltaics (OPVs) and dye-sensitized solar cells (DSSCs) have drawn great interest from both academics and industry, due to the possibility of low-cost conversion of photovoltaic energy at reasonable efficiencies. This review focuses on recent progress in molecular engineering and technological aspects of fused-thiophene-based organic dye molecules for applications in solar cells. Particular attention has been paid to the design principles and stability of these dye molecules, as well as on the effects of various electrolyte systems for DSSCs. Importantly, it has been found that incorporation of a fused-thiophene unit into the sensitizer has several advantages, such as red-shift of the intramolecular charge transfer band, tuning of the frontier molecular energy level, and improvements in both photovoltaic performance and stability. This work also examines the correlation between the physical properties and placement of fused-thiophene in the molecular structure with regard to their performance in OPVs and DSSCs. Full article

Remotely sensed nighttime lights (NTL) datasets derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) have been identified as a good indicator of the urbanization process and have been widely used to study such demographic and economic variables as population distribution and density, electricity consumption, and carbon emission. However, one issue must be considered in the application of NTL data, i.e., saturation in the bright cores of urban centers. In this study, we evaluate four correction methods in China’s cities: the linear regression model and the cubic regression model at the regional level, and the Human Settlement Index (HSI) and the Vegetation Adjusted NTL Urban Index (VANUI) at a pixel level. The results suggest that both correction methods at the regional level improve the correlation between NTL data and socioeconomic variables. However, since the methods can only be used on saturated pixels, the correction effects are limited, as the saturated area in Chinese cities is rather small. HSI and VANUI increase the inter-urban variability within certain cities, especially when their vegetation health and abundance is negatively correlated with NTL. However, the indices may induce bias when applied in a large region with a diverse natural environment and vegetation, and the application of HSI with a relatively high sensitivity of HSI to NDVI may be limited as NTL approaches maximum. Proper methods for reducing saturation effects should thus vary with different study areas and research purposes. Full article

Due to the special hydrographic and physiographic conditions in Taiwan, flooding is likely to occur in the middle and lower reaches of a plain whenever serious rainstorm events occurred. Note worthily, the loss of lives and property caused by flooding are always most considerable in a metropolitan area, and the densely distributed buildings would, not only increase the impervious area, but also decrease the water storage area. Furthermore, a large number of intensive buildings have changed the original land flow conditions, resulting in a beam shrinking flow and the additional form drag phenomenon, which makes the flooding phenomenon more serious. The main purpose of this research is to find the correlation between building coverage and the Manning’s coefficient n through a water flume model experiment. To probe into this issue, the Manning’s roughness adjustment is further divided into a part caused by the surface impedance and a part caused by the building impedance. Thus, building coverage can be added to the general computing grid to reflect the flooding situation with buildings. The two-dimensional inundation model, based on this research, was applied to Taichung City for an actual case simulation. The simulation result of Typhoon Kalmaegi showed that the presented model can obtain a more accurate flooding situation in urban area by considering the blockage effects of buildings and adjusting the surface roughness. Full article

After decades of strict pollution control and municipal sewage treatment, the water quality of the Tanshui River increased significantly after pollution mitigation as indicated by the River Pollution Index (RPI). The pollution level of the estuarine region decreased from severe pollution to mostly moderately impaired. The most polluted waters are presently restricted to a flow track length between 15–35 km relative to the river mouth. From July 2011 to September 2012, four surveys of fish and benthic macroinvertebrates were conducted at 45 sampling sites around the Tanshui River basin. The pollution level of all the study area indicated by the RPI could also be explained by the Family Biotic Index (FBI) and Biotic Index (BI) from the benthic macroinvertebrate community, and the Index of Biotic Integrity (IBI) of the fish community. The result of canonical correlation analysis between aquatic environmental factors and community structure indicated that the community structure was closely related to the level of water pollution. Fish species richness in the estuarine area has increased significantly in recent years. Some catadromous fish and crustaceans could cross the moderate polluted water into the upstream freshwater, and have re-colonized their populations. The benthic macroinvertebrate community relying on the benthic substrate of the estuarine region is still very poor, and the water layer was still moderately polluted. Full article

Vaccination against AH1N1pdm09 infection (human swine infection, HSI) is an effective measure of preventing pandemic infection, especially for high-risk groups like children between the ages of 6 months and 6 years. This study used a cross-sectional correlation design and aimed to identify predicting factors of parental acceptance of the HSI vaccine (HSIV) and uptake of the vaccination by their preschool-aged children in Hong Kong. A total of 250 parents were recruited from four randomly selected kindergartens. A self-administered questionnaire based on the health belief framework was used for data collection. The results showed that a number of factors significantly affected the tendency toward new vaccination uptake; these factors included parental age, HSI vaccination history of the children in their family, preferable price of the vaccine, perceived severity, perceived benefits, perceived barriers, and motivating factors for taking new vaccines. Using these factors, a logistic regression model with a high Nagelkerke R² of 0.63 was generated to explain vaccination acceptance. A strong correlation between parental acceptance of new vaccinations and the motivating factors of vaccination uptake was found, which indicates the importance of involving parents in policy implementation for any new vaccination schemes. Overall, in order to fight against pandemics and enhance vaccination acceptance, it is essential for the government to understand the above factors determining parental acceptance of new vaccinations for their preschool-aged children. Full article

There are many major causes of cancer death, including metastasis of cancer. Dihydroaustrasulfone alcohol, which is isolated from marine coral, has shown antioxidant activity, but has not been reported to have an anti-cancer effect. We first discovered that dihydroaustrasulfone alcohol provided a concentration-dependent inhibitory effect on the migration and motility of human non-small cell lung carcinoma (NSCLC) A549 cells by trans-well and wound healing assays. The results of a zymography assay and Western blot showed that dihydroaustrasulfone alcohol suppressed the activities and protein expression of matrix metalloproteinase (MMP)-2 and MMP-9. Further investigation revealed that dihydroaustrasulfone alcohol suppressed the phosphorylation of ERK1/2, p38, and JNK1/2. Dihydroaustrasulfone alcohol also suppressed the expression of PI3K and the phosphorylation of Akt. Furthermore, dihydroaustrasulfone alcohol markedly inhibited tumor growth in Lewis lung cancer (LLC)-bearing mice. We concluded that dihydroaustrasulfone alcohol is a new pure compound with anti-migration and anti-tumor growth activity in lung cancer and might be applied to clinical treatment in the future. Full article

A surface plasmon resonance sensor for Mycobacterium tuberculosis (MTB) deoxyribonucleic acid (DNA) is developed using repeatable telecommunication wavelength modulation based on optical fiber communications laser wavelength and stability. MTB DNA concentrations of 1 μg/mL and 10 μg/mL were successfully demonstrated to have the same spectral half-width in the dip for optimum coupling. The sensitivity was shown to be −0.087 dB/(μg/mL) at all applied telecommunication wavelengths and the highest sensitivity achieved was 115 ng/mL without thiolated DNA immobilization onto a gold plate, which is better than the sensor limit of 400 ng/mL possible with commercial biosensor equipment. Full article

The discovery of potent therapeutic compounds against dengue virus is urgently needed. The NS2B-NS3 protease (NS2B-NS3^pro) of dengue fever virus carries out all enzymatic activities needed for polyprotein processing and is considered to be amenable to antiviral inhibition by analogy. Virtual screening of 300,000 compounds using Autodock 3 on the GVSS platform was conducted to identify novel inhibitors against the NS2B-NS3^pro. Thirty-six compounds were selected for in vitro assay against NS2B-NS3^pro expressed in Pichia pastoris. Seven novel compounds were identified as inhibitors with IC₅₀ values of 3.9 ± 0.6–86.7 ± 3.6 μM. Three strong NS2B-NS3^pro inhibitors were further confirmed as competitive inhibitors with K_i values of 4.0 ± 0.4, 4.9 ± 0.3, and 3.4 ± 0.1 μM, respectively. Hydrophobic and hydrogen bond interactions between amino acid residues in the NS3^pro active site with inhibition compounds were also identified. Full article

Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, hyperspectral imaging (HSI) techniques are used to determine the moisture content in cooked chicken breast over the VIS/NIR (400–1,000 nm) spectral range. Moisture measurements were performed using an oven drying method. A partial least squares regression (PLSR) model was developed to extract a relationship between the HSI spectra and the moisture content. In the full wavelength range, the PLSR model possessed a maximum of 0.90 and an SEP of 0.74%. For the NIR range, the PLSR model yielded an of 0.94 and an SEP of 0.71%. The majority of the absorption peaks occurred around 760 and 970 nm, representing the water content in the samples. Finally, PLSR images were constructed to visualize the dehydration and water distribution within different sample regions. The high correlation coefficient and low prediction error from the PLSR analysis validates that HSI is an effective tool for visualizing the chemical properties of meat. Full article

The aim of this study was to evaluate the in cellulo inhibition of hydrogen-peroxide-induced oxidative stress in skin fibroblasts using different low-molecular-weight polysaccharides (LMPS) prepared from agar (LMAG), chitosan (LMCH) and starch (LMST), which contain various different functional groups (i.e., sulfate, amine, and hydroxyl groups). The following parameters were evaluated: cell viability, intracellular oxidant production, lipid peroxidation, and DNA damage. Trolox was used as a positive control in order to allow comparison of the antioxidant efficacies of the various LMPS. The experimentally determined attenuation of oxidative stress by LMPS in skin fibroblasts was: LMCH > LMAG > LMST. The different protection levels of these LMPS may be due to the physic-chemical properties of the LMPS’ functional groups, including electron transfer ability, metal ion chelating capacities, radical stabilizing capacity, and the hydrophobicity of the constituent sugars. The results suggest that LMCH might constitute a novel and potential dermal therapeutic and sun-protective agent. Full article

The Xiemisitai area, West Junggar, Xinjiang, China, is situated at a potential copper mineralization zone in association with small granitic intrusions. In order to identify the alteration zones and mineralization characteristics of the intrusions, Landsat Enhanced Thematic Mapper (ETM+) and Quickbird data of the study area were evaluated in mapping lithological units, small intrusions, and alteration zones. False color composites of the first principal component analyses (PCA1), PCA2, and PCA4 in red (R), green (G), and blue (B) of the ETM+ image, and relevant hue-saturation-intensity (HSI) color model transformations, were performed. This led to the identification of lithologic units and discrimination of granitic intrusions from wall-rocks. A new geological map was generated by integrating the remote sensing results with two internally published local geologic maps and field inspection data. For the selected region, false color composites from PCA and relevant HSI-transformed images of the Quickbird data delineated the details of small intrusions and identified other unknown similar intrusions nearby. Fifteen separate potash-feldspar granites and three separate hornblende biotite granites were identified using ETM+ and Quickbird data. The principal component analysis-based Crosta technique was employed to discriminate alteration minerals. Some of the mapped alteration zones using the Crosta technique agreed very well with the known copper deposits. Field verification led to the discovery of three copper mineralizations and two gold mineralizations for the first time. The results show that the PCA and HSI transformation techniques proved to be robust in processing remote sensing data with moderate to high spatial resolutions. It is concluded that the utilized methods are useful for mapping lithology and the targeting of small intrusion-type mineral resources within the sparsely vegetated regions of Northwest China. Full article

The purpose of this study is to apply the Human Security Index (HSI) as a tool to detect social and economic cumulative risk burden at a county-level in the state of Texas. The HSI is an index comprising a network of three sub-components or “fabrics”; the Economic, Environmental, and Social Fabrics. We hypothesized that the HSI will be a useful instrument for identifying and analyzing socioeconomic conditions that contribute to cumulative risk burden in vulnerable counties. We expected to identify statistical associations between cumulative risk burden and (a) ethnic concentration and (b) geographic proximity to the Texas-Mexico border. Findings from this study indicate that the Texas-Mexico border region did not have consistently higher total or individual fabric scores as would be suggested by the high disease burden and low income in this region. While the Economic, Environmental, Social Fabrics (including the Health subfabric) were highly associated with Hispanic ethnic concentration, the overall HSI and the Crime subfabric were not. In addition, the Education, Health and Crime subfabrics were associated with African American racial composition, while Environment, Economic and Social Fabrics were not. Application of the HSI to Texas counties provides a fuller and more nuanced understanding of socioeconomic and environmental conditions, and increases awareness of the role played by environmental, economic, and social factors in observed health disparities by race/ethnicity and geographic region. Full article

A new phenolic, bis(6-hydroxy-2,3,4-trimethoxylphen-1-yl)methane (1) and a new butanedioate, butylmethyl succinate (2), along with twenty-nine known compounds including one naphthoquinone derivative, two chromanones, eight benzenoids, one lignan, one tocopherol, and sixteen triterpenoids were isolated from the stems of Diospyros maritima. epi-Isoshinanolone (3) was isolated in pure form for the first time. In addition, 5,7-dihydroxy-2-methylchomanone (4) was isolated from a natural source for the first time. Their structures were established on the basis of spectroscopic data as well as direct comparison with authentic samples. Full article