6,054 research outputs found

    Magnetic Moments of JP=3/2+J^P={3/2}^+ Pentaquarks

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    If the JPJ^P of Θ5+\Theta_5^+ and Ξ5−−\Xi_5^{--} pentaquarks is really found to be 12+{1\over 2}^+ by future experiments, they will be accompanied by JP=32+J^P={3\over 2}^+ partners in some models. It is reasonable to expect that these JP=32+J^P={3\over 2}^+ states will also be discovered in the near future with the current intensive experimental and theoretical efforts. We estimate JP=3/2+J^P={3/2}^+ pentaquark magnetic moments using different models.Comment: 13 page

    Zipper-interacting protein kinase promotes epithelial-mesenchymal transition, invasion and metastasis through AKT and NF-kB signaling and is associated with metastasis and poor prognosis in gastric cancer patients

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    Zipper-interacting Protein Kinase (ZIPK) belongs to the death-associated protein kinase family. ZIPK has been characterized as a tumor suppressor in various tumors, including gastric cancer. On the other hand, ZIPK also promotes cell survival. In this study, both in vitro and in vivo assays indicated that ZIPK promoted cell growth, proliferation, migration, invasion, tumor formation and metastasis in nude mice. ZIPK induced epithelial-mesenchymal transition (EMT) with increasing expression of ÎČ-catenin, mesenchymal markers, Snail and Slug, and with decreasing expression of E-cadherin. Furthermore, ZIPK activated the AKT/IÎșB/NF-ÎșB pathway, which can promote EMT and metastasis. Additionally, ZIPK expression was detected in human primary gastric cancer and their matched metastatic lymph node samples by immunohistochemistry. Increased expression of ZIPK in lymph node metastases was significantly associated with stage VI and abdominal organ invasion. Survival analysis revealed that patients with increased ZIPK expression in metastatic lymph nodes had poor disease-specific survival. Taken together, our study reveals that ZIPK is a pro-oncogenic factor, which promotes cancer metastasis.published_or_final_versio

    Search for D to phi l nu and measurement of the branching fraction for D to phi pi

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    Using a data sample of integrated luminosity of about 33 pb−1^{-1} collected around 3.773 GeV with the BESII detector at the BEPC collider, the semileptonic decays D+→ϕe+ÎœeD^+ \to \phi e ^+\nu_e, D+→ϕΌ+ΜΌD^+ \to \phi \mu^+\nu_\mu and the hadronic decay D+→ϕπ+D^+ \to \phi \pi^+ are studied. The upper limits of the branching fractions are set to be BF(D+→ϕe+Îœe)<BF(D^+ \to \phi e ^+\nu_e) < 2.01% and BF(D+→ϕΌ+ΜΌ)<BF(D^+ \to \phi \mu^+ \nu_\mu) < 2.04% at the 90% confidence level. The ratio of the branching fractions for D+→ϕπ+D^+ \to \phi \pi^+ relative to D+→K−π+π+D^+ \to K^-\pi^+\pi^+ is measured to be 0.057±0.011±0.0030.057 \pm 0.011 \pm 0.003. In addition, the branching fraction for D+→ϕπ+D^+ \to \phi \pi^+ is obtained to be (5.2±1.0±0.4)×10−3(5.2 \pm 1.0 \pm 0.4) \times 10^{-3}.Comment: 6 pages, 5 figures, to be published in Eur.Phys.J.

    Heavy and Light Pentaquark Chiral Lagrangian

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    Using the SU(3) flavor symmetry, we construct the chiral Lagrangians for the light and heavy pentaquarks. The correction from the nonzero quark is taken into account perturbatively. We derive the Gell-Mann−-Okubo type relations for various pentaquark multiplet masses and Coleman-Glashow relations for anti-sextet heavy pentaquark magnetic moments. We study possible decays of pentaquarks into conventional hadrons. We also study the interactions between and within various pentaquark multiplets and derive their coupling constants in the symmetry limit. Possible kinematically allowed pionic decay modes are pointed out

    Prevalence of phase variable epigenetic invertons among host-associated bacteria.

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    Type I restriction-modification (R-M) systems consist of a DNA endonuclease (HsdR, HsdM and HsdS subunits) and methyltransferase (HsdM and HsdS subunits). The hsdS sequences flanked by inverted repeats (referred to as epigenetic invertons) in certain Type I R-M systems undergo invertase-catalyzed inversions. Previous studies in Streptococcus pneumoniae have shown that hsdS inversions within clonal populations produce subpopulations with profound differences in the methylome, cellular physiology and virulence. In this study, we bioinformatically identified six major clades of the tyrosine and serine family invertases homologs from 16 bacterial phyla, which potentially catalyze hsdS inversions in the epigenetic invertons. In particular, the epigenetic invertons are highly enriched in host-associated bacteria. We further verified hsdS inversions in the Type I R-M systems of four representative host-associated bacteria and found that each of the resultant hsdS allelic variants specifies methylation of a unique DNA sequence. In addition, transcriptome analysis revealed that hsdS allelic variations in Enterococcus faecalis exert significant impact on gene expression. These findings indicate that epigenetic switches driven by invertases in the epigenetic invertons broadly operate in the host-associated bacteria, which may broadly contribute to bacterial host adaptation and virulence beyond the role of the Type I R-M systems against phage infection

    Direct Measurements of the Branching Fractions for Inclusive K±K^\pm and Inclusive Semileptonic Decays of D+D^+ and D0D^0 Mesons

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    With singly-tagged Dˉ\bar D samples selected from the data collected at and around 3.773 GeV with the BESII detector at the BEPC collider, we have measured the branching fractions for the inclusive K±K^\pm decays of D+D^+ and D0D^0 mesons, which are BF(D+→K−X)=(24.7±1.3±1.2)BF(D^+\to K^-X) = (24.7 \pm 1.3 \pm 1.2)%, BF(D+→K+X)=(6.1±0.9±0.4)BF(D^+\to K^+X) = (6.1 \pm 0.9 \pm 0.4) %, BF(D0→K−X)=(57.8±1.6±3.2)BF(D^0\to K^-X) = (57.8 \pm 1.6 \pm 3.2) % and BF(D0→K+X)=(3.5±0.7±0.3)BF(D^0\to K^+X) = (3.5 \pm 0.7 \pm 0.3) %, respectively. We have also measured the branching fractions for the inclusive semileptonic decays of D+D^+ and D0D^0 mesons to be BF(D+→e+X)=(15.2±0.9±0.8)BF(D^+ \to e^+ X)=(15.2 \pm 0.9 \pm 0.8)% and BF(D0→e+X)=(6.3±0.7±0.4)BF(D^0 \to e^+ X) =(6.3 \pm 0.7 \pm 0.4) %. These yield the ratio of their partial widths to be Γ(D+→e+X)/Γ(D0→e+X)=0.95±0.12±0.07\Gamma(D^+ \to e^+X)/\Gamma(D^0 \to e^+X)=0.95 \pm 0.12 \pm 0.07.Comment: 6 pages, 5 figure

    Accurate Diagnosis of Colorectal Cancer Based On Histopathology Images Using Artificial Intelligence

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    Background: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, \u3e 14,680 WSIs, from \u3e 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results: Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions: This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition
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