Contrast Optimization for Size Invariant Visual Cryptography Scheme.

Visual cryptography scheme (VCS) serves as an effective tool in image security. Size-invariant VCS (SI-VCS) can solve the pixel expansion problem in traditional VCS. On the other hand, it is anticipated that the contrast of the recovered image in SI-VCS should be as high as possible. The investigation of contrast optimization for SI-VCS is carried out in this article. We develop an approach to optimize the contrast by stacking t ( k ≤ t ≤ n ) shadows in (k, n) -SI-VCS. Generally, a contrast-maximizing problem is linked with a (k, n) -SI-VCS, where the contrast by t shadows is considered as an objective function. An ideal contrast by t shadows can be produced by addressing this problem using linear programming. However, there exist (n-k+1) different contrasts in a (k, n) scheme. An optimization-based design is further introduced to provide multiple optimal contrasts. These (n-k+1) different contrasts are regarded as objective functions and it is transformed into a multi-contrast-maximizing problem. The ideal point method and lexicographic method are adopted to address this problem. Additionally, if the Boolean XOR operation is used for secret recovery, a technique is also provided to offer multiple maximum contrasts. The effectiveness of the proposed schemes is verified by extensive experiments. Comparisons illustrate significant advancement on contrast is provided.

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning.

Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients' responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients' EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients' outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%-100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home.

Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging.

Traditional methods used to monitor the aboveground biomass (AGB) and belowground biomass (BGB) of slash pine (Pinus elliottii) rely on on-ground measurements, which are time- and cost-consuming and suited only for small spatial scales. In this paper, we successfully applied unmanned aerial vehicle (UAV) integrated with structure from motion (UAV-SfM) data to estimate the tree height, crown area (CA), AGB, and BGB of slash pine for in slash pine breeding plantations sites. The CA of each tree was segmented by using marker-controlled watershed segmentation with a treetop and a set of minimum three meters heights. Moreover, the genetic variation of these traits has been analyzed and employed to estimate heritability (h 2). The results showed a promising correlation between UAV and ground truth data with a range of R 2 from 0.58 to 0.85 at 70 m flying heights and a moderate estimate of h 2 for all traits ranges from 0.13 to 0.47, where site influenced the h 2 value of slash pine trees, where h 2 in site 1 ranged from 0.13~0.25 lower than that in site 2 (range: 0.38~0.47). Similar genetic gains were obtained with both UAV and ground truth data; thus, breeding selection is still possible. The method described in this paper provides faster, more high-throughput, and more cost-effective UAV-SfM surveys to monitor a larger area of breeding plantations than traditional ground surveys while maintaining data accuracy.

Colorizing Grayscale CT images of human lungs using deep learning methods.

Image colorization refers to computer-aided rendering technology which transfers colors from a reference color image to grayscale images or video frames. Deep learning elevated notably in the field of image colorization in the past years. In this paper, we formulate image colorization methods relying on exemplar colorization and automatic colorization, respectively. For hybrid colorization, we select appropriate reference images to colorize the grayscale CT images. The colours of meat resemble those of human lungs, so the images of fresh pork, lamb, beef, and even rotten meat are collected as our dataset for model training. Three sets of training data consisting of meat images are analysed to extract the pixelar features for colorizing lung CT images by using an automatic approach. Pertaining to the results, we consider numerous methods (i.e., loss functions, visual analysis, PSNR, and SSIM) to evaluate the proposed deep learning models. Moreover, compared with other methods of colorizing lung CT images, the results of rendering the images by using deep learning methods are significantly genuine and promising. The metrics for measuring image similarity such as SSIM and PSNR have satisfactory performance, up to 0.55 and 28.0, respectively. Additionally, the methods may provide novel ideas for rendering grayscale X-ray images in airports, ferries, and railway stations.

Salient Object Detection Based on Visual Perceptual Saturation and Two-Stream Hybrid Networks.

Inspired by the perceived saturation of human visual system, this paper proposes a two-stream hybrid networks to simulate binocular vision for salient object detection (SOD). Each stream in our system consists of unsupervised and supervised methods to form a two-branch module, so as to model the interaction between human intuition and memory. The two-branch module parallel processes visual information with bottom-up and top-down SODs, and output two initial saliency maps. Then a polyharmonic neural network with random-weight (PNNRW) is utilized to fuse two-branch's perception and refine the salient objects by learning online via multi-source cues. Depend on visual perceptual saturation, we can select optimal parameter of superpixel for unsupervised branch, locate sampling regions for PNNRW, and construct a positive feedback loop to facilitate perception saturated after the perception fusion. By comparing the binary outputs of the two-stream, the pixel annotation of predicted object with high saturation degree could be taken as new training samples. The presented method constitutes a semi-supervised learning framework actually. Supervised branches only need to be pre-trained initial, the system can collect the training samples with high confidence level and then train new models by itself. Extensive experiments show that the new framework can improve performance of the existing SOD methods, that exceeds the state-of-the-art methods in six popular benchmarks.

Human Gait Recognition Based on Self-Adaptive Hidden Markov Model.

Human gait recognition has numerous challenges due to view angle changing, human dressing, bag carrying, and pedestrian walking speed, etc. In order to increase gait recognition accuracy under these circumstances, in this paper we propose a method for gait recognition based on a self-adaptive hidden Markov model (SAHMM). First, we present a feature extraction algorithm based on local gait energy image (LGEI) and construct an observation vector set. By using this set, we optimize parameters of the SAHMM-based method for gait recognition. Finally, the proposed method is evaluated extensively based on the CASIA Dataset B for gait recognition under various conditions such as cross view, human dressing, or bag carrying, etc. Furthermore, the generalization ability of this method is verified based on the OU-ISIR Large Population Dataset. Both experimental results show that the proposed method exhibits superior performance in comparison with those existing methods.

Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory.

Human gait recognition is one of the most promising biometric technologies, especially for unobtrusive video surveillance and human identification from a distance. Aiming at improving recognition rate, in this paper we study gait recognition using deep learning and propose a novel method based on convolutional Long Short-Term Memory (Conv-LSTM). First, we present a variation of Gait Energy Images, i.e. frame-by-frame GEI (ff-GEI), to expand the volume of available Gait Energy Images (GEI) data and relax the constraints of gait cycle segmentation required by existing gait recognition methods. Second, we demonstrate the effectiveness of ff-GEI by analyzing the cross-covariance of one person's gait data. Then, making use of the temporality of our human gait, we design a novel gait recognition model using Conv-LSTM. Finally, the proposed method is evaluated extensively based on the CASIA Dataset B for cross-view gait recognition, furthermore the OU-ISIR Large Population Dataset is employed to verify its generalization ability. Our experimental results show that the proposed method outperforms other algorithms based on these two datasets. The results indicate that the proposed ff-GEI model using Conv-LSTM, coupled with the new gait representation, can effectively solve the problems related to cross-view gait recognition.

Chemometrics and hyperspectral imaging applied to assessment of chemical, textural and structural characteristics of meat.

Spectroscopy in the visible near-infrared spectral (Vis-NIRS) range combined with imaging techniques (hyperspectral imaging, HSI) allows assessment of chemical composition, texture, and meat structure. The use of HSI in the meat and food industry has observed a significant growth in the last decade, yet its use for assessment of meat it is not optimal yet. The application of HSI for assessment of meat is reviewed with focus on its ability to capture meat unique chemical and structural characteristics. While HSI is widely used for assessment of chemical composition, a limited number of evidences on its ability to handle the effect of different sources of variation on the assessment is found. The use of spatially resolved spectroscopy has been able to detect structural information related to animal background, muscle type, rigor process and ageing. Similarly the use of texture features seem to capture unique characteristics of meat.

Promotion of osteogenic differentiation of stem cells and increase of bone-bonding ability in vivo using urease-treated titanium coated with calcium phosphate and gelatin.

Because of its excellent biocompatibility and low allergenicity, titanium has been widely used for bone replacement and tissue engineering. To produce a desirable composite with enhanced bone response and mechanical strength, in this study bioactive calcium phosphate (CaP) and gelatin composites were coated onto titanium (Ti) via a novel urease technique. The cellular responses to the CaP/gelatin/Ti (CaP/gel/Ti) and bone bonding ability were evaluated with proliferation and osteogenic differentiation of mesenchymal stem cells (MSCs) on CaP/gel/Ti and CaP/Ti in vitro. The results showed that the optical density values, alkaline phosphatase expression and genes expression of MSCs on CaP/gel/Ti were similar to those on CaP/Ti, yet significantly higher than those on pure Ti (p < 0.05). CaP/gel/Ti and CaP/Ti rods (2 mm in diameter, 10 mm in length) were also implanted into femoral shaft of rabbits and pure Ti rods served as control (n = 10). Histological examination, scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) measurements were performed at 4 and 8 weeks after the operation. The histological and SEM observations demonstrated clearly that more new bone formed on the surface of CaP/gel/Ti than in the other two groups at each time point. The CaP/gel/Ti bonded to the surrounding bone directly with no intervening soft tissue layer. An interfacial layer, containing Ti, Ca and P, was found to form at the interface between bone and the implant on all three groups by EDS analysis. However, the content of Ca, P in the surface of CaP/gel/Ti implants was more than in the other two groups at each time point. The CaP/gel/Ti modified by the urease method was not only beneficial for MSCs proliferation and osteogenic differentiation, but also favorable for bone bonding ability on Ti implants in vivo, suggesting that Ti functionalized with CaP and gelatin might have a great potential in clinical joint replacement or dental implants.

Diethylstilbestrol affects LGR8 expression in mouse gubernaculum testis / 中华男科学杂志

<p><b>OBJECTIVE</b>To investigate the impact of prenatal exposure to diethylstilbestrol (DES) on the specific receptor LGR8 of insulin-like factor 3 (INSL3) in the mouse gubernaculum testis, and that of exoestrogens on descensus testis in mice.</p><p><b>METHODS</b>A total of 120 pregnant KM mice aged 8 to 10 weeks were assigned to a normal, a blank control and 4 DES groups of equal number, the blank controls injected subcutaneously with dimethyl sulfoxide plus normal saline, and the DES groups with DES at 0.1, 1, 10 and 100 microg/kg body weight, respectively, from embryonic day 9 (ED9) through ED17. Immunohistochemistry and RT-PCR were used to detect the expressions of LGR8 protein and mRNA in the gubernaculum testis of the ED18 fetuses and PND20 (postnatal day 20) offspring of the mice.</p><p><b>RESULTS</b>Histological analysis showed that the gubernaculum testis of the ED18 fetuses were well developed in both the normal and control groups, with an inner mesenchymal core and muscular outer layer. In contrast, the gubernaculum testis were poorly developed in the experimental groups, morphologically abnormal and without visible dividing line between the mesenchymal tissue and the muscular outer layer. No obvious differences were found in the gubernaculum testis development of the neonates between the normal and experimental groups. Positive immunostaining was seen in the mesenchymal core and muscular outer layer, but mainly in the latter. The expression of LGR8 was weaker in the experimental groups than in the normal group (P < 0.05), but that of LGR8 mRNA was increased in the high-dose (10 and 100 microg/kg) DES groups (P < 0.05). No obvious mutations were observed in the PCR products in any of the experimental groups.</p><p><b>CONCLUSION</b>Prenatal exposure to diethylstilbestrol affected the expression of LGR8 mRNA in the mouse gubernaculum testis, which suggests that diethylstilbestrol may induce cryptorchidism by interfering with the INSL3-LGR8 signaling system and consequently the development of the gubernaculum testis.</p>

Th1 polarization and low-T3 syndrome are correlated with hyperleptinemia in hemodialysis patients.

BACKGROUND: Patients undergoing chronic hemodialysis (HD) have an impaired immune response with a dysregulated Th1/Th2 cytokine network and altered the levels of thyroid hormone (TH) in euthyroid sick syndrome. Leptin, an adipocyte-secreted hormone, is considered to be a proinflammatory adipocytokine, with multiple effects on several tissues acting on the intermediate and energy metabolism. The aims of the present study were to assess the changes in serum levels of leptin and their correlation with Th1/Th2 cytokine and TH production in HD patients. METHODS: Fifty-three uremic patients with hemodialysis were evaluated; 30 healthy volunteers served as controls. Baseline serum concentrations of interleukin-2 (IL-2), sIL-2R, interferon-gamma (IFN-gamma), IL-4 and IL-10 were analyzed using ELISA. Serum levels of leptin, total triiodothyronine (TT3), free triiodothyronine (FT3), total thyroxine (TT4), free thyroxine (FT4) and thyroid-stimulating hormone (TSH) were determined by radioimmunoassay (RIA). Other metabolic variables were measured in all patients and control subjects. Multiple correlation analysis was performed among variables. RESULTS: Mean serum leptin concentration was significantly higher in HD patients than that in controls (p<0.01), especially in women (p<0.001). While the fasting serum levels of sIL-2R and Th1-type cytokines including IL-2 and IFN-gamma were significantly higher in HD patients compared with controls, Th2-type cytokine, including IL-4 and IL-10, levels did not differ between patients and controls. The serum TT3 and FT3 levels were lower in patients than controls, but TT4, FT4 and TSH were no different. Serum leptin levels in HD patients were significantly positively correlated with IL-2, IFN-gamma, sIL-2R and TSH; and negatively correlated with IL-4, IL-10, TT3 and FT3. Serum IL-2 levels correlated positively with serum IL-4, sIL-2R, TT3 and FT3. A negative correlation was observed between serum IFN-gamma and IL-4 levels in the patients. CONCLUSIONS: These data suggest that hyperleptinemia in HD patients correlated with cytokine dysregulation with a high level of Th1-type cytokines, and euthyroid sick syndrome with low T3 levels which might be involved in Th1 polarization and low-T3 syndrome in dialysis patients.

Effects of naringin on the proliferation and osteogenic differentiation of human bone mesenchymal stem cell.

Rhizoma drynariae is used commonly in the treatment of osteoporosis and bone nonunion in traditional Chinese medicine. Modern pharmacological research indicates that naringin is the main effective component of rhizoma drynariae, which can induce the expression of the osteogenic marker in the osteoblast cell line. However, no former study has described its effect on bone mesenchymal stem cells (BMSCs). In our experiment, we co-cultured human BMSCs with different concentrations of naringin solution, then the osteogenic differentiation markers and proliferation ability were analyzed. The results indicated that a certain concentration (1-100 microg/ml) of the naringin solution may enhance the proliferation and osteogenic differentiation of human BMSCs. Also, our research explains excellently the anti-osteoporotic and bone nonunion treatment mechanism of rhizoma drynariae, thus contributing to the exploration of osteogenic differentiation agents from Chinese herbs.