BcB_c Exclusive Decays to Charmonium and a Light Meson at Next-to-Leading Order Accuracy

In this paper the next-to-leading order (NLO) corrections to $B_c$ meson exclusive decays to S-wave charmonia and light pseudoscalar or vector mesons, i.e. $\pi$, $K$, $\rho$, and $K^*$, are performed within non-relativistic (NR) QCD approach. The non-factorizable contribution is included, which is absent in traditional naive factorization (NF). And the theoretical uncertainties for their branching ratios are reduced compared with that of direct tree level calculation. Numerical results show that NLO QCD corrections markedly enhance the branching ratio with a K factor of 1.75 for $B_{c}^{\pm}\to \eta_{c} \pi^{\pm}$ and 1.31 for $B_{c}^{\pm}\to J/\psi \pi^{\pm}$. In order to investigate the asymptotic behavior, the analytic form is obtained in the heavy quark limit, i.e. $m_b \to \infty$. We note that annihilation topologies contribute trivia in this limit, and the corrections at leading order in $z= m_c/m_b$ expansion come from form factors and hard spectator interactions. At last, some related phenomenologies are also discussed.Comment: 20 pages, 7 figures and 5 table

The optimal connection model for blood vessels segmentation and the MEA-Net

Vascular diseases have long been regarded as a significant health concern. Accurately detecting the location, shape, and afflicted regions of blood vessels from a diverse range of medical images has proven to be a major challenge. Obtaining blood vessels that retain their correct topological structures is currently a crucial research issue. Numerous efforts have sought to reinforce neural networks' learning of vascular geometric features, including measures to ensure the correct topological structure of the segmentation result's vessel centerline. Typically, these methods extract topological features from the network's segmentation result and then apply regular constraints to reinforce the accuracy of critical components and the overall topological structure. However, as blood vessels are three-dimensional structures, it is essential to achieve complete local vessel segmentation, which necessitates enhancing the segmentation of vessel boundaries. Furthermore, current methods are limited to handling 2D blood vessel fragmentation cases. Our proposed boundary attention module directly extracts boundary voxels from the network's segmentation result. Additionally, we have established an optimal connection model based on minimal surfaces to determine the connection order between blood vessels. Our method achieves state-of-the-art performance in 3D multi-class vascular segmentation tasks, as evidenced by the high values of Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) metrics. Furthermore, our approach improves the Betti error, LR error, and BR error indicators of vessel richness and structural integrity by more than 10% compared to other methods, and effectively addresses vessel fragmentation and yields blood vessels with a more precise topological structure.Comment: 19 page

PVP: Pre-trained Visual Parameter-Efficient Tuning

Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. However, it is still highly challenging to fully fine-tune these models for downstream tasks due to their high computational and storage costs. Recently, Parameter-Efficient Tuning (PETuning) techniques, e.g., Visual Prompt Tuning (VPT) and Low-Rank Adaptation (LoRA), have significantly reduced the computation and storage cost by inserting lightweight prompt modules into the pre-trained models and tuning these prompt modules with a small number of trainable parameters, while keeping the transformer backbone frozen. Although only a few parameters need to be adjusted, most PETuning methods still require a significant amount of downstream task training data to achieve good results. The performance is inadequate on low-data regimes, especially when there are only one or two examples per class. To this end, we first empirically identify the poor performance is mainly due to the inappropriate way of initializing prompt modules, which has also been verified in the pre-trained language models. Next, we propose a Pre-trained Visual Parameter-efficient (PVP) Tuning framework, which pre-trains the parameter-efficient tuning modules first and then leverages the pre-trained modules along with the pre-trained transformer backbone to perform parameter-efficient tuning on downstream tasks. Experiment results on five Fine-Grained Visual Classification (FGVC) and VTAB-1k datasets demonstrate that our proposed method significantly outperforms state-of-the-art PETuning methods

Homo-binding character of LMO2 isoforms and their both synergic and antagonistic functions in regulating hematopoietic-related target genes

<p>Abstract</p> <p>Background</p> <p>The human <it>lmo2 </it>gene plays important roles in hematopoiesis and is associated with acute T lymphocyte leukemia. The gene encodes two protein isoforms, a longer form LMO2-L and a shorter form LMO2-S. Both isoforms function as bridge molecules to assemble their partners together to regulate their target genes. A typical LMO2 binding site consists of two elements, a GATA site and an E-box, with an interval of 9~12 bp.</p> <p>Methods</p> <p>In this study, the combination of MBP pulldown assay and mammalian two hybrid assay were used to confirm the homo-binding character of LMO2-L/-S isoforms. Luciferase reporter assay and Real-time PCR assay were used to detect expression levels and relative promoter activities of LMO2-L/-S isoforms. Co-transfection and Luciferase reporter assay were used to reveal the detailed regulatory pattern of LMO2-L/-S isoforms on their targets.</p> <p>Results</p> <p>Herein we report the homo-interaction character of LMO2-L and LMO2-S and their major difference in manner of regulating their target genes. Our results showed that LMO2-L and LMO2-S could only bind to themselves but not each other. It was also demonstrated that LMO2-L could either positively or negatively regulate the transcription of its different target genes, depending on the arrangement and strand location of the two elements GATA site and E-box, LMO2-S, however, performed constitutively transcriptional inhibiting function on all target genes.</p> <p>Conclusion</p> <p>These results suggest that LMO2 isoforms have independent functions while there is no interaction between each other and they could play synergetic or antagonistic roles precisely in regulating their different genes involved in normal and aberrant hematopoiesis.</p

Conserved chemosensory proteins in the proboscis and eyes of Lepidoptera

Odorant-binding proteins (OBPs) and chemosensory proteins (CSPs) are endowed with several different functions besides being carriers for pheromones and odorants. Based on a previous report of a CSP acting as surfactant in the proboscis of the moth Helicoverpa armigera, we revealed the presence of orthologue proteins in two other moths Plutella xylostella and Chilo suppressalis, as well as two butterflies Papilio machaon and Pieris rapae, using immunodetection and proteomic analysis. The unusual conservation of these proteins across large phylogenetic distances indicated a common specific function for these CSPs. This fact prompted us to search for other functions of these proteins and discovered that CSPs are abundantly expressed in the eyes of H. armigera and possibly involved as carriers for carotenoids and visual pigments. This hypothesis is supported by ligand-binding experiments and docking simulations with retinol and β-carotene. This last orange pigment, occurring in many fruits and vegetables, is an antioxidant and the precursor of visual pigments. We propose that structurally related CSPs solubilise nutritionally important carotenoids in the proboscis, while they act as carriers of both β-carotene and its derived products 3-hydroxyretinol and 3-hydroxyretinal in the eye. The use of soluble olfactory proteins, such as CSPs, as carriers for visual pigments in insects, here reported for the first time, parallels the function of retinol-binding protein in vertebrates, a lipocalin structurally related to vertebrate odorant-binding proteins