Polycythemia vera (PV) is a human clonal hematological disorder. The molecular etiology of the disease has not been identified. PV hematopoietic progenitor cells exhibit hypersensitivity to growth factors and cytokines, suggesting possible abnormalities in protein-tyrosine kinases and phosphatases. By sequencing the entire coding regions of cDNAs of candidate enzymes, we identified a G:C3 T:A point mutation of the JAK2 tyrosine kinase in 20 of 24 PV blood samples but none in 12 normal samples. The mutation has varying degrees of heterozygosity and is apparently acquired. It changes conserved Val 617 to Phe in the pseudokinase domain of JAK2 that is known to have an inhibitory role. The mutant JAK2 has enhanced kinase activity, and when overexpressed together with the erythropoietin receptor in cells, it caused hyperactivation of erythropoietin-induced cell signaling. This gain-of-function mutation of JAK may explain the hypersensitivity of PV progenitor cells to growth factors and cytokines. Our study thus defines a molecular defect of PV. Polycythemia vera (PV)1 is a clonal myeloproliferative disorder characterized by increased production of red cells, granulocytes, and platelets (1-3). It mainly affects people between 40 and 60 years of age with an annual incidence of about 14 per million in the population. Thus far there is no effective cure for the disease. Phlebotomy is the mainstay of treatment for the disease, and hydroxyurea, interferon-␣, and anagrelide drug therapies and 32 P radiation therapy have commonly been used (3). The mortality rate is high if the disease is untreated or is associated with leukemia. Despite extensive studies in recent years, the molecular etiology of PV remains unknown.A major feature of PV is that hematopoietic progenitors in patients display hypersensitive responses to many growth factors and cytokines (1-3). Despite these abnormal responses, the numbers of receptors for the growth factors and cytokines on the surface of these cells are normal, suggesting a primary defect in a shared signaling pathway in these cells. Tyrosine phosphorylation is a fundamental regulatory mechanism for cell growth and development, and this process is controlled by coordinate actions of protein-tyrosine kinases (PTKs) and phosphatases (PTPs) (4). Both families of enzymes have highly diverse structures and functions. Their activities are tightly regulated under normal conditions, and deregulation of the enzymes produces human diseases. According to the human genomic data base, there are about 90 PTKs and 38 phosphotyrosine-specific PTPs (5-7). Both mutation of PTKs and PTPs have been implicated in human cancers. Recent studies using a large scale sequencing-based approach revealed that PTK and PTP genes are mutated more often in human cancers than previously anticipated. A minimum of 30% of colorectal cancers contains at least one mutation in PTKs, and 26% of them has a mutation in PTPs (8, 9). It is highly expected that mutations of PTKs and PTPs may also be a major manifestation of other types o...

Abstract. In this paper, we present a simple yet fast and robust algorithm which exploits the dense spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its locally dense contexts in a Bayesian framework, which models the statistical correlation between the simple low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is then posed by computing a confidence map which takes into account the prior information of the target location and thereby alleviates target location ambiguity effectively. We further propose a novel explicit scale adaptation scheme, which is able to deal with target scale variations efficiently and effectively. The Fast Fourier Transform (FFT) is adopted for fast learning and detection in this work, which only needs 4 FFT operations. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.

Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by (1) the large number of nuclei and the size of high resolution digitized pathology images, and (2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of “Deep Learning” strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 × 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed 9 other state of the art nuclear detection strategies.

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets.

The neuroprotective effects of 3,6′-disinapoyl sucrose (DISS) from Radix Polygala against glutamate-induced SH-SY5Y neuronal cells injury were evaluated in the present study. SH-SY5Y neuronal cells were pretreated with glutamate (8 mM) for 30 min followed by cotreatment with DISS for 12 h. Cell viability was determined by (3,4,5-dimethylthiazol-2-yl)-2,5-diphenylte-trazolium bromide (MTT) assay, and apoptosis was confirmed by cell morphology and flow cytometry assay, evaluated with propidium iodide dye. Treatment with DISS (0.6, 6, and 60 μmol/L) increased cell viability dose dependently, inhibited LDH release, and attenuated apoptosis. The mechanisms by which DISS protected neuron cells from glutamate-induced excitotoxicity included the downregulation of proapoptotic gene Bax and the upregulation of antiapoptotic gene Bcl-2. The present findings indicated that DISS exerts neuroprotective effects against glutamate toxicity, which might be of importance and contribute to its clinical efficacy for the treatment of neurodegenerative diseases.

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In this paper, we present a new idea to analyze facial expression by exploring some common and specific information among different expressions. Inspired by the observation that only a few facial parts are active in expression disclosure (e.g. around mouth, eye), we try to discover the common and specific patches which are important to discriminate all the expressions and only a particular expression, respectively. A two-stage multi-task sparse learning (MTSL) framework is proposed to efficiently locate those discriminative patches. In the first stage MTSL, expression recognition tasks, each of which aims to find dominant patches for each expression, are combined to located common patches. Second, two related tasks, facial expression recognition and face verification tasks, are coupled to learn specific facial patches for individual expression. Extensive experiments validate the existence and significance of common and specific patches. Utilizing these learned patches, we achieve superior performances on expression recognition compared to the state-of-the-arts.

Deep networks have been successfully applied to visual tracking by learning a generic representation offline from numerous training images. However, the offline training is time-consuming and the learned generic representation may be less discriminative for tracking specific objects. In this paper, we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to learn robust representations for visual tracking. In the first frame, we extract a set of normalized patches from the target region as fixed filters, which integrate a series of adaptive contextual filters surrounding the target to define a set of feature maps in the subsequent frames. These maps measure similarities between each filter and useful local intensity patterns across the target, thereby encoding its local structural information. Furthermore, all the maps together form a global representation, via which the inner geometric layout of the target is also preserved. A simple soft shrinkage method that suppresses noisy values below an adaptive threshold is employed to de-noise the global representation. Our convolutional networks have a lightweight structure and perform favorably against several state-of-the-art methods on the recent tracking benchmark data set with 50 challenging videos.