Weak Decays of Stable Open-bottom Tetraquark by SU(3) Symmetry Analysis

The exotic state $X(5568)$ which was observed by D0 Collaboration is very likely to be a tetraquark state with four different valence quark flavors, though the existence was not confirmed by other collaborations. The possibility of such state still generate lots of interests in theory. In the paper, we will study the properties of the state under the SU(3) flavor symmetry. This four quark state with a heavy bottom quark and three light quarks(anti-quark) can form a $6$ or $\overline {15}$ representation. The weak decays can be dominant and should be discussed carefully while such state is stable against the strong interaction. Therefor we will study the multi-body semileptonic and nonleptonic weak decays systematically. With the help of SU(3) flavor symmetry, we can give the Hamiltonian in the hadronic level, then obtain the parameterized irreducible amplitudes and the relations of different channels. At the end of the article, we collect some Cabibbo allowed two-body and three-body weak decay channels which can be used to reconstruct $X_{b6}$ states at the branching fraction up to be $10^{-5}$.Comment: 53 pages, 2 figure

Planar Tur\'an number of the 7-cycle

The $\textit{planar Tur\'an number}$ $\textrm{ex}_{\mathcal P}(n,H)$ of a graph $H$ is the maximum number of edges in an $n$-vertex planar graph without $H$ as a subgraph. Let $C_{\ell}$ denote the cycle of length $\ell$. The planar Tur\'an number $\textrm{ex}_{\mathcal P}(n,C_{\ell})$ behaves differently for $\ell\le 10$ and for $\ell\ge 11$, and it is known when $\ell \in \{3,4,5,6\}$. We prove that $\textrm{ex}_{\mathcal P}(n,C_7) \le \frac{18n}{7} - \frac{48}{7}$ for all $n > 38$, and show that equality holds for infinitely many integers $n$

Dense circuit graphs and the planar Tur\'an number of a cycle

The $\textit{planar Tur\'an number}$ $\textrm{ex}_{\mathcal P}(n,H)$ of a graph $H$ is the maximum number of edges in an $n$-vertex planar graph without $H$ as a subgraph. Let $C_k$ denote the cycle of length $k$. The planar Tur\'an number $\textrm{ex}_{\mathcal P}(n,C_k)$ is known for $k\le 7$. We show that dense planar graphs with a certain connectivity property (known as circuit graphs) contain large near triangulations, and we use this result to obtain consequences for planar Tur\'an numbers. In particular, we prove that there is a constant $D$ so that $\textrm{ex}_{\mathcal P}(n,C_k) \le 3n - 6 - Dn/k^{\log_2^3}$ for all $k, n\ge 4$. When $k \ge 11$ this bound is tight up to the constant $D$ and proves a conjecture of Cranston, Lidick\'y, Liu, and Shantanam

The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey

Driver models play a vital role in developing and verifying autonomous vehicles (AVs). Previously, they are mainly applied in traffic flow simulation to model realistic driver behavior. With the development of AVs, driver models attract much attention again due to their potential contributions to AV certification. The simulation-based testing method is considered an effective measure to accelerate AV testing due to its safe and efficient characteristics. Nonetheless, realistic driver models are prerequisites for valid simulation results. Additionally, an AV is assumed to be at least as safe as a careful and competent driver. Therefore, driver models are inevitable for AV safety assessment. However, no comparison or discussion of driver models is available regarding their utility to AVs in the last five years despite their necessities in the release of AVs. This motivates us to present a comprehensive survey of driver models in the paper and compare their applicability. Requirements for driver models in terms of their application to AV safety assessment are discussed. A summary of driver models for simulation-based testing and AV certification is provided. Evaluation metrics are defined to compare their strength and weakness. Finally, an architecture for a careful and competent driver model is proposed. Challenges and future work are elaborated. This study gives related researchers especially regulators an overview and helps them to define appropriate driver models for AVs

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed

Functional imaging of interleukin 1 beta expression in inflammatory process using bioluminescence imaging in transgenic mice

<p>Abstract</p> <p>Background</p> <p>Interleukin 1 beta (IL-1β) plays an important role in a number of chronic and acute inflammatory diseases. To understand the role of IL-1β in disease processes and develop an <it>in vivo </it>screening system for anti-inflammatory drugs, a transgenic mouse line was generated which incorporated the transgene firefly luciferase gene driven by a 4.5-kb fragment of the human IL-1β gene promoter. Luciferase gene expression was monitored in live mice under anesthesia using bioluminescence imaging in a number of inflammatory disease models.</p> <p>Results</p> <p>In a LPS-induced sepsis model, dramatic increase in luciferase activity was observed in the mice. This transgene induction was time dependent and correlated with an increase of endogenous IL-1β mRNA and pro-IL-1β protein levels in the mice. In a zymosan-induced arthritis model and an oxazolone-induced skin hypersensitivity reaction model, luciferase expression was locally induced in the zymosan injected knee joint and in the ear with oxazolone application, respectively. Dexamethasone suppressed the expression of luciferase gene both in the acute sepsis model and in the acute arthritis model.</p> <p>Conclusion</p> <p>Our data suggest that the transgenic mice model could be used to study transcriptional regulation of the IL-1β gene expression in the inflammatory process and evaluation the effect of anti-inflammatory drug <it>in vivo</it>.</p

Receptor compaction and GTPase rearrangement drive SRP-mediated cotranslational protein translocation into the ER

The conserved signal recognition particle (SRP) cotranslationally delivers ~30% of the proteome to the eukaryotic endoplasmic reticulum (ER). The molecular mechanism by which eukaryotic SRP transitions from cargo recognition in the cytosol to protein translocation at the ER is not understood. Here, structural, biochemical, and single-molecule studies show that this transition requires multiple sequential conformational rearrangements in the targeting complex initiated by guanosine triphosphatase (GTPase)–driven compaction of the SRP receptor (SR). Disruption of these rearrangements, particularly in mutant SRP54G226E linked to severe congenital neutropenia, uncouples the SRP/SR GTPase cycle from protein translocation. Structures of targeting intermediates reveal the molecular basis of early SRP-SR recognition and emphasize the role of eukaryote-specific elements in regulating targeting. Our results provide a molecular model for the structural and functional transitions of SRP throughout the targeting cycle and show that these transitions provide important points for biological regulation that can be perturbed in genetic diseases

Mir-382 Promotes Differentiation of Rat Liver Progenitor Cell WB-F344 by Targeting Ezh2

Background/Aims: Liver progenitor cells (LPCs) were considered as a promising hepatocyte source of cell therapy for liver disease due to their self-renewal and differentiation capacities, while little is known about the mechanism of LPC differentiate into hepatocytes. This study aims to explore the effect of miR-382, a member of Dlk1-Dio3 microRNA cluster, during hepatic differentiation from LPCs. Methods: In this study, we used rat liver progenitor cell WB-F344 as LPC cell model and HGF as inducer to simulate the process of LPCs hepatic differentiation, then microRNAs were quantified by qPCR. Next, WB-F344 cell was transfected with miR-382 mimics, then hepatocyte cell trait was characterized by multiple experiments, including that periodic acid schiff staining and cellular uptake and excretion of indocyanine green to evaluate the hepatocellular function, qPCR and Western Blotting analysis to detect the hepatocyte-specific markers (ALB, Ttr, Apo E and AFP) and transmission electron microscopy to observe the hepatocellular morphology. Moreover, Luciferase reporter assay was used to determine whether Ezh2 is the direct target of miR-382. Results: We found that miR-382 increased gradually and was inversely correlated with the potential target, Ezh2, during WB-F344 hepatic differentiation. In addition, functional studies indicated that miR-382 increased the level of hepatocyte-specific genes. Conclusions: This study demonstrates that miR-382 may be a novel regulator of LPCs differentiation by targeting Ezh2