274 research outputs found

    Image quality in double- and triple-intensity ghost imaging with classical partially polarized light

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    Classical ghost imaging is a correlation-imaging technique in which the image of the object is found through intensity correlations of light. We analyze three different quality parameters, namely the visibility, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR), to assess the performance of double- and triple-intensity correlation-imaging setups. The source is a random partially polarized beam of light obeying Gaussian statistics and the image quality is evaluated as a function of the degree of polarization (DoP). We show that the visibility improves when the DoP and the order of imaging increase, while the SNR behaves oppositely. The CNR is for the most part independent of DoP and the imaging order. The results are important for the development of new imaging devices using partially polarized light.Comment: Added 2 references, corrected a few typos and revised text slightly. Results unchange

    Hedyotis diffusa Willd Inhibits Colorectal Cancer Growth in Vivo via Inhibition of STAT3 Signaling Pathway

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    Signal Transducer and Activator of Transcription 3 (STAT3), a common oncogenic mediator, is constitutively activated in many types of human cancers; therefore it is a major focus in the development of novel anti-cancer agents. Hedyotis diffusa Willd has been used as a major component in several Chinese medicine formulas for the clinical treatment of colorectal cancer (CRC). However, the precise mechanism of its anti-tumor activity remains largely unclear. Using a CRC mouse xenograft model, in the present study we evaluated the effect of the ethanol extract of Hedyotis diffusa Willd (EEHDW) on tumor growth in vivo and investigated the underlying molecular mechanisms. We found that EEHDW reduced tumor volume and tumor weight, but had no effect on body weight gain in CRC mice, demonstrating that EEHDW can inhibit CRC growth in vivo without apparent adverse effect. In addition, EEHDW treatment suppressed STAT3 phosphorylation in tumor tissues, which in turn resulted in the promotion of cancer cell apoptosis and inhibition of proliferation. Moreover, EEHDW treatment altered the expression pattern of several important target genes of the STAT3 signaling pathway, i.e., decreased expression of Cyclin D1, CDK4 and Bcl-2 as well as up-regulated p21 and Bax. These results suggest that suppression of the STAT3 pathway might be one of the mechanisms by which EEHDW treats colorectal cancer

    A survey on independence-based Markov networks learning

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    This work reports the most relevant technical aspects in the problem of learning the \emph{Markov network structure} from data. Such problem has become increasingly important in machine learning, and many other application fields of machine learning. Markov networks, together with Bayesian networks, are probabilistic graphical models, a widely used formalism for handling probability distributions in intelligent systems. Learning graphical models from data have been extensively applied for the case of Bayesian networks, but for Markov networks learning it is not tractable in practice. However, this situation is changing with time, given the exponential growth of computers capacity, the plethora of available digital data, and the researching on new learning technologies. This work stresses on a technology called independence-based learning, which allows the learning of the independence structure of those networks from data in an efficient and sound manner, whenever the dataset is sufficiently large, and data is a representative sampling of the target distribution. In the analysis of such technology, this work surveys the current state-of-the-art algorithms for learning Markov networks structure, discussing its current limitations, and proposing a series of open problems where future works may produce some advances in the area in terms of quality and efficiency. The paper concludes by opening a discussion about how to develop a general formalism for improving the quality of the structures learned, when data is scarce.Comment: 35 pages, 1 figur

    Up-regulation of bone marrow stromal protein 2 (BST2) in breast cancer with bone metastasis

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    <p>Abstract</p> <p>Background</p> <p>Bone metastases are frequent complications of breast cancer. Recent literature implicates multiple chemokines in the formation of bone metastases in breast cancer. However, the molecular mechanism of metastatic bone disease in breast cancer remains unknown. We have recently made the novel observation of the BST2 protein expression in human breast cancer cell lines. The purpose of our present study is to investigate the expression and the role of BST2 in bone metastatic breast cancer.</p> <p>Methods</p> <p>cDNA microarray analysis was used to compare the BST2 gene expression between a metastatic to bone human breast cancer cell line (MDA-231BO) and a primary human breast cancer cell line (MDA-231). The BST2 expression in one bone metastatic breast cancer and seven non-bone metastatic breast cancer cell lines were also determined using real-time RT-PCR and Western blot assays. We then employed tissue array to further study the BST2 expression in human breast cancer using array slides containing 20 independent breast cancer tumors that formed metastatic bone lesions, 30 non-metastasis-forming breast cancer tumors, and 8 normal breast tissues. In order to test the feasibility of utilizing BST2 as a serum marker for the presence of bone metastasis in breast cancer, we had measured the BST2 expression levels in human serums by using ELISA on 43 breast cancer patients with bone metastasis, 43 breast cancer patients without bone metastasis, and 14 normal healthy controls. The relationship between cell migration and proliferation and BST2 expression was also studied in a human breast recombinant model system using migration and FACS analysis.</p> <p>Results</p> <p>The microarray demonstrated over expression of the BST2 gene in the bone metastatic breast cancer cell line (MDA-231BO) compared to the primary human breast cancer cell line (MDA-231). The expression of the BST2 gene was significantly increased in the bone metastatic breast cancer cell lines and tumor tissues compared to non-bone metastatic breast cancer cell lines and tumor tissues by real time RT-PCR, Western blot and TMA. Furthermore, serum levels of BST2 measured by ELISA were also significantly higher among patients with breast cancer metastatic to bone compared to breast cancer patients without metastatic to bone (P < .0001). Most importantly, the breast cancer cell line that transfected with BST2 demonstrated increased BST2 expressions, which was associated with increased cancer cell migration and cell proliferation.</p> <p>Conclusion</p> <p>These results provide novel data indicating the BST2 protein expression is associated with the formation of bone metastases in human breast cancer. We believe that BST2 may be a potential biomarker in breast cancer with bone metastasis.</p

    ERα-LBD, an isoform of estrogen receptor alpha, promotes breast cancer proliferation and endocrine resistance

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    Estrogen receptor alpha (ER alpha) drives mammary gland development and breast cancer (BC) growth through an evolutionarily conserved linkage of DNA binding and hormone activation functions. Therapeutic targeting of the hormone binding pocket is a widely utilized and successful strategy for breast cancer prevention and treatment. However, resistance to this endocrine therapy is frequently encountered and may occur through bypass or reactivation of ER-regulated transcriptional programs. We now identify the induction of an ER alpha isoform, ER alpha-LBD, that is encoded by an alternative ESR1 transcript and lacks the activation function and DNA binding domains. Despite lacking the transcriptional activity, ER alpha-LBD is found to promote breast cancer growth and resistance to the ER alpha antagonist fulvestrant. ER alpha-LBD is predominantly localized to the cytoplasm and mitochondria of BC cells and leads to enhanced glycolysis, respiration and stem-like features. Intriguingly, ER alpha-LBD expression and function does not appear to be restricted to cancers that express full length ER alpha but also promotes growth of triple-negative breast cancers and ER alpha-LBD transcript (ESR1-LBD) is also present in BC samples from both ER alpha(+) and ER alpha(-) human tumors. These findings point to ER alpha-LBD as a potential mediator of breast cancer progression and therapy resistance

    Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties

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    Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies

    Ghost imaging using optical correlations

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    Ghost imaging uses optical correlations to enable an alternative and intriguing image acquisition technique: even though information from either one of the detectors used for the acquisition does not yield an image, an image can be obtained by harnessing the optical correlations. This Review describes a variety of both quantum and classical ghost imaging techniques, and seeks to point out where these techniques may have practical applications

    STAT3 Activation in Skeletal Muscle Links Muscle Wasting and the Acute Phase Response in Cancer Cachexia

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    Cachexia, or weight loss despite adequate nutrition, significantly impairs quality of life and response to therapy in cancer patients. In cancer patients, skeletal muscle wasting, weight loss and mortality are all positively associated with increased serum cytokines, particularly Interleukin-6 (IL-6), and the presence of the acute phase response. Acute phase proteins, including fibrinogen and serum amyloid A (SAA) are synthesized by hepatocytes in response to IL-6 as part of the innate immune response. To gain insight into the relationships among these observations, we studied mice with moderate and severe Colon-26 (C26)-carcinoma cachexia.Moderate and severe C26 cachexia was associated with high serum IL-6 and IL-6 family cytokines and highly similar patterns of skeletal muscle gene expression. The top canonical pathways up-regulated in both were the complement/coagulation cascade, proteasome, MAPK signaling, and the IL-6 and STAT3 pathways. Cachexia was associated with increased muscle pY705-STAT3 and increased STAT3 localization in myonuclei. STAT3 target genes, including SOCS3 mRNA and acute phase response proteins, were highly induced in cachectic muscle. IL-6 treatment and STAT3 activation both also induced fibrinogen in cultured C2C12 myotubes. Quantitation of muscle versus liver fibrinogen and SAA protein levels indicates that muscle contributes a large fraction of serum acute phase proteins in cancer.These results suggest that the STAT3 transcriptome is a major mechanism for wasting in cancer. Through IL-6/STAT3 activation, skeletal muscle is induced to synthesize acute phase proteins, thus establishing a molecular link between the observations of high IL-6, increased acute phase response proteins and muscle wasting in cancer. These results suggest a mechanism by which STAT3 might causally influence muscle wasting by altering the profile of genes expressed and translated in muscle such that amino acids liberated by increased proteolysis in cachexia are synthesized into acute phase proteins and exported into the blood

    A Gaseous Argon-Based Near Detector to Enhance the Physics Capabilities of DUNE

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    This document presents the concept and physics case for a magnetized gaseous argon-based detector system (ND-GAr) for the Deep Underground Neutrino Experiment (DUNE) Near Detector. This detector system is required in order for DUNE to reach its full physics potential in the measurement of CP violation and in delivering precision measurements of oscillation parameters. In addition to its critical role in the long-baseline oscillation program, ND-GAr will extend the overall physics program of DUNE. The LBNF high-intensity proton beam will provide a large flux of neutrinos that is sampled by ND-GAr, enabling DUNE to discover new particles and search for new interactions and symmetries beyond those predicted in the Standard Model

    The DUNE Far Detector Interim Design Report, Volume 3: Dual-Phase Module

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    The DUNE IDR describes the proposed physics program and technical designs of the DUNE far detector modules in preparation for the full TDR to be published in 2019. It is intended as an intermediate milestone on the path to a full TDR, justifying the technical choices that flow down from the high-level physics goals through requirements at all levels of the Project. These design choices will enable the DUNE experiment to make the ground-breaking discoveries that will help to answer fundamental physics questions. Volume 3 describes the dual-phase module's subsystems, the technical coordination required for its design, construction, installation, and integration, and its organizational structure
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