For real patients' data, the values of the mentioned indices are accomplished as gFIT = 0.97 ± 0.01, gESOD = 1.18 ± 0.38, and gR = 0.88 ± 0.07. https://www.selleckchem.com/products/tacrine-hcl.html HDFWNN, HDWNN and jump NN method showed the prediction error (root mean square error [RMSE]) of 11.23 ± 2.77 mg/dl, 10.79 ± 3.86 mg/dl and 16.45 ± 4.33 mg/dl, respectively. Furthermore, the generalized estimating equation and tests show that proposed models perform better compared with other proposed methods. Furthermore, the generalized estimating equation and post hoc tests show that proposed models perform better compared with other proposed methods. Deep learning methods have become popular for their high-performance rate in the classification and detection of events in computer vision tasks. Transfer learning paradigm is widely adopted to apply pretrained convolutional neural network (CNN) on medical domains overcoming the problem of the scarcity of public datasets. Some investigations to assess transfer learning knowledge inference abilities in the context of mammogram screening and possible combinations with unsupervised techniques are in progress. We propose a novel technique for the detection of suspicious regions in mammograms that consist of the combination of two approaches based on scale invariant feature transform (SIFT) keypoints and transfer learning with pretrained CNNs such as PyramidNet and AlexNet fine-tuned on digital mammograms generated by different mammography devices. Preprocessing, feature extraction, and selection steps characterize the SIFT-based method, while the deep learning network validates the candidate suspicious regionmages come out from mammography devices with different properties. With the increasing advancement of technology, it is necessary to develop more accurate, convenient, and cost-effective security systems. Handwriting signature, as one of the most popular and applicable biometrics, is widely used to register ownership in banking systems, including checks, as well as in administrative and financial applications in everyday life, all over the world. Automatic signature verification and recognition systems, especially in the case of online signatures, are potentially the most powerful and publicly accepted means for personal authentication. In this article, a novel procedure for online signature verification and recognition has been presented based on Dual-Tree Complex Wavelet Packet Transform (DT-CWPT). In the presented method, three-level decomposition of DT-CWPT has been computed for three time signals of dynamic information including horizontal and vertical positions in addition to the pressure signal. Then, in order to make feature vector corresponding to each signature, log energy entropy measures have been computed for each subband of DT-CWPT decomposition. Finally, to classify the query signature, three classifiers including k-nearest neighbor, support vector machine, and Kolmogorov- Smirnov test have been examined. Experiments have been conducted using three benchmark datasets SVC2004, MCYT-100, as two Latin online signature datasets, and NDSD as a Persian signature dataset. Obtained favorable experimental results, in comparison with literature, confirm the effectiveness of the presented method in both online signature verification and recognition objects. Obtained favorable experimental results, in comparison with literature, confirm the effectiveness of the presented method in both online signature verification and recognition objects. Human gait as an effective behavioral biometric identifier has received much attention in recent years. However, there are challenges which reduce its performance. In this work we aim at improving performance of gait systems under variations in view angles, which present one of the major challenges to gait algorithms. We propose employment of a view transformation model based on sparse and redundant (SR) representation. More specifically, our proposed method trains a set of corresponding dictionaries for each viewing angle, which are then used in identification of a probe. In particular, the view transformation is performed by first obtaining the SR representation of the input image using the appropriate dictionary, then multiplying this representation by the dictionary of destination angle to obtain a corresponding image in the intended angle. Experiments performed using CASIA Gait Database, Dataset B, support the satisfactory performance of our method. It is observed that in most tests, the proposed method outperforms the other methods in comparison. This is especially the case for large changes in the view angle, as well as the average recognition rate. A comparison with state-of-the-art methods in the literature showcases the superior performance of the proposed method, especially in the case of large variations in view angle. A comparison with state-of-the-art methods in the literature showcases the superior performance of the proposed method, especially in the case of large variations in view angle.Purpose This study was intended to find out the impact of alpha mangostin administration on the epithelial-mesenchymal transition (EMT) markers and TGF-β/Smad pathways in hepatocellular carcinoma Hep-G2 cells surviving sorafenib. Methods Hepatocellular carcinoma HepG2 cells were treated with sorafenib 10 μM. Cells surviving sorafenib treatment (HepG2surv) were then treated vehicle, sorafenib, alpha mangostin, or combination of sorafenib and alpha mangostin. Afterward, cells were observed for their morphology with an inverted microscope and counted for cell viability. The concentrations of transforming growth factor (TGF)-β1 in a culture medium were examined using ELISA. The mRNA expressions of TGF-β1, TGF-β1-receptor, Smad3, Smad7, E-cadherin, and vimentin were evaluated using quantitative reverse transcriptase-polymerase chain reaction. The protein level of E-cadherin was also determined using western blot analysis. Results Treatment of alpha mangostin and sorafenib caused a significant decrease in the viability of sorafenib-surviving HepG2 cells versus control (both groups with P 0.05). In line with our findings, the expressions of TGF-β1 and Smad3 are significantly upregulated after alpha mangostin administration (both with P less then 0.05) versus control. Conclusion Alpha mangostin reduced cell viability of sorafenib-surviving HepG2 cells; however, it also enhanced epithelial-mesenchymal transition markers by activating TGF-β/Smad pathways.