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Breast cancer is associated with the highest morbidity rates for cancer diagnoses in the world and has become a major public health issue. Early diagnosis can increase the chance of successful treatment and survival. However, it is a very challenging and time-consuming task that relies on the experience of pathologists. The automatic diagnosis of breast cancer by analyzing histopathological images plays a significant role for patients and their prognosis. However, traditional feature extraction methods can only extract some low-level features of images, and prior knowledge is necessary to select useful features, which can be greatly affected by humans. Deep learning techniques can extract high-level abstract features from images automatically. Therefore, we introduce it to analyze histopathological images of breast cancer via supervised and unsupervised deep convolutional neural networks. First, we adapted Inception_V3 and Inception_ResNet_V2 architectures to the binary and multi-class issues of breast cancer histopathological image classification by utilizing transfer learning techniques. Then, to overcome the influence from the imbalanced histopathological images in subclasses, we balanced the subclasses with Ductal Carcinoma as the baseline by turning images up and down, right and left, and rotating them counterclockwise by 90 and 180 degrees. Our experimental results of the supervised histopathological image classification of breast cancer and the comparison to the results from other studies demonstrate that Inception_V3 and Inception_ResNet_V2 based histopathological image classification of breast cancer is superior to the existing methods. Furthermore, these findings show that Inception_ResNet_V2 network is the best deep learning architecture so far for diagnosing breast cancers by analyzing histopathological images. Therefore, we used Inception_ResNet_V2 to extract features from breast cancer histopathological images to perform unsupervised analysis of the images. We also constructed a new autoencoder network to transform the features extracted by Inception_ResNet_V2 to a low dimensional space to do clustering analysis of the images. The experimental results demonstrate that using our proposed autoencoder network results in better clustering results than those based on features extracted only by Inception_ResNet_V2 network. All of our experimental results demonstrate that Inception_ResNet_V2 network based deep transfer learning provides a new means of performing analysis of histopathological images of breast cancer.

Bioactive sphingosine 1-phosphate (S1P) and S1P receptors (S1PRs) have been implicated in many critical cellular events, including inflammation, cancer, and angiogenesis. However, the role of S1P/S1PR signaling in the pathogenesis of liver fibrosis has not been well documented. In this study, we found that S1P levels and S1P 3 receptor expression in liver tissue were markedly up-regulated in a mouse model of cholestasis-induced liver fibrosis. In addition, the S1P 3 receptor was also expressed in green fluorescent protein transgenic bone marrow (BM)-derived cells found in the damaged liver of transplanted chimeric mice that underwent bile duct ligation. Silencing of S1P 3 expression significantly inhibited S1P-induced BM cell migration in vitro. Furthermore, a selective S1P 3 receptor antagonist, suramin, markedly reduced the number of BM-derived cells during cholestasis. Interestingly, suramin administration clearly ameliorated bile duct ligation-induced hepatic fibrosis, as demonstrated by attenuated deposition of collagen type I and III, reduced smooth muscle ␣-actin expression, and decreased total hydroxyproline content. In conclusion, our data suggest that S1P/S1P 3 signaling plays an important role in cholestasis-induced liver fibrosis through mediating the homing of BM cells. Modulation of S1PR activity may therefore represent a new antifibrotic

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Background: Conventional post-mastectomy radiation therapy is delivered with tangential fields for chest wall and separate fields for regional nodes. Although chest wall and regional nodes delineation has been discussed with RTOG contouring atlas, CT-based planning to treat chest wall and regional nodes as a whole target has not been widely accepted. We herein discuss the dosimetric characteristics of a linac IMRT technique for treating chest wall and regional nodes as a whole PTV after modified radical mastectomy, and observe acute toxicities following irradiation. Methods: Patients indicated for PMRT were eligible. Chest wall and supra/infraclavicular region +/−internal mammary nodes were contoured as a whole PTV on planning CT. A simplified linac IMRT plan was designed using either integrated full beams or two segments of half beams split at caudal edge of clavicle head. DVHs were used to evaluate plans. The acute toxicities were followed up regularly.

K-means clustering is a popular clustering algorithm based on the partition of data. However, K-means clustering algorithm suffers from some shortcomings, such as its requiring a user to give out the number of clusters at first, and its sensitiveness to initial conditions, and its being easily trapped into a local solution et cetera. The global K-means algorithm proposed by Likas <em>et al</em> is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) runs of the K-means algorithm from suitable initial positions. It avoids the depending on any initial conditions or parameters, and considerably outperforms the K-means algorithms, but it has a heavy computational load. In this paper, we propose a new version of the global K-means algorithm. That is an efficient global K-means clustering algorithm. The outstanding feature of our algorithm is its superiority in execution time. It takes less run time than that of the available global K-means algorithms do. In this algorithm we modified the way of finding the optimal initial center of the next new cluster by defining a new function as the criterion to select the optimal candidate center for the next new cluster. Our idea grew under enlightened by Park and Jun's idea of K-medoids clustering algorithm. We chose the best candidate initial center for the next cluster by calculating the value of our new function which uses the information of the natural distribution of data, so that the optimal initial center we chose is the point which is not only with the highest density, but also apart from the available cluster centers. Experiments on fourteen well-known data sets from UCI machine learning repository show that our new algorithm can significantly reduce the computational time without affecting the performance of the global K-means algorithms. Further experiments demonstrate that our improved global K-means algorithm outperforms the global K-means algorithm greatly and is suitable for clustering large data sets. Experiments on colon cancer tissue data set revealed that our new global K-means algorithm can efficiently deal with gene expression data with high dimensions. And experiment results on synthetic data sets with different proportions noisy data points prove that our global k-means can avoid the influence of noisy data on clustering results efficiently