Movement Proteins (BC1 and BV1) of Abutilon Mosaic Geminivirus Are Cotransported in and between Cells of Sink but Not of Source Leaves as Detected by Green Fluorescent Protein Tagging

AbstractTwo movement proteins (BV1 and BC1) facilitate the intra- and intercellular transport of begomoviruses in plants. In contrast to other geminiviruses the movement protein BC1 of Abutilon mosaic virus (AbMV) remained in the supernatant after centrifuging plant extracts at 20,000 g. To test whether this unusual behavior results from a distinct intracellular distribution of the protein, the BC1 gene has been fused to the gene of green fluorescent protein (GFP). The resulting plasmids were delivered into nonhost plants (Allium cepa) as well as into mature and immature cells of host plants (Nicotiana tabacum, N. benthamiana) by biolistic bombardment for transient expression in planta. BC1 directed GFP to two different cellular sites. In the majority of nonhost cells as well as in mature cells of host leaves, BC1 was mainly localized in small punctate flecks at the cell periphery or, to a lesser extent, around the nucleus. In sink leaves of host plants, GFP:BC1 additionally developed disc-like structures in the cell periphery. Cobombardment of GFP:BC1 with its cognate infectious DNA A and B did not change their subcellular distribution patterns in source leaves but led to the formation of peculiar needle-like structures in sink leaves. The nuclear shuttle protein (BV1) of AbMV accumulated mainly inside the nuclei as shown by immunohistochemical staining and GFP tagging. In sink cells of host plants it was mobilized to the plasma membrane and to the nucleus of the neighboring cell by coexpressed BC1, GFP:BC1, BC1:GFP, or after cobombardment with the cognate viral DNA. Only under these conditions were GFP:BC1 and BC1:GFP also found in the recipient cell

Residual Attention Network for Image Classification

In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is built by stacking Attention Modules which generate attention-aware features. The attention-aware features from different modules change adaptively as layers going deeper. Inside each Attention Module, bottom-up top-down feedforward structure is used to unfold the feedforward and feedback attention process into a single feedforward process. Importantly, we propose attention residual learning to train very deep Residual Attention Networks which can be easily scaled up to hundreds of layers. Extensive analyses are conducted on CIFAR-10 and CIFAR-100 datasets to verify the effectiveness of every module mentioned above. Our Residual Attention Network achieves state-of-the-art object recognition performance on three benchmark datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and ImageNet (4.8% single model and single crop, top-5 error). Note that, our method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69% forward FLOPs comparing to ResNet-200. The experiment also demonstrates that our network is robust against noisy labels.Comment: accepted to CVPR201

Real-space construction of crystalline topological superconductors and insulators in 2D interacting fermionic systems

The construction and classification of crystalline symmetry protected topological (SPT) phases in interacting bosonic and fermionic systems have been intensively studied in the past few years. Crystalline SPT phases are not only of conceptual importance, but also provide great opportunities towards experimental realization since space group symmetries naturally exist for any realistic material. In this paper, we systematically classify the crystalline topological superconductors (TSC) and topological insulators (TI) in 2D interacting fermionic systems by using an explicit real-space construction. In particular, we discover an intriguing fermionic crystalline topological superconductor that can only be realized in interacting fermionic systems (i.e., not in free-fermion or bosonic SPT systems). Moreover, we also verify the recently conjectured crystalline equivalence principle for generic 2D interacting fermionic systems.Comment: 39+37 pages, 10+13 figures, 3+1 tables, all comments and suggestions are very welcom

Intrinsically Interacting Higher-Order Topological Superconductors

We propose a minimal interacting lattice model for two-dimensional class-D higher-order topological superconductors with no free-fermion realization. A Lieb-Schultz-Mattis-type constraint has been proposed and applied to guide our lattice model construction. Our model exhibits a trivial product ground state in the weakly interacting regime while increasing electron correlations provoke a novel topological quantum phase transition to a $D_4$-symmetric higher-order topological superconducting state. The symmetry-protected Majorana corner modes are numerically confirmed with the matrix-product-state technique. Our theory paves the way for studying correlated higher-order topology with explicit lattice model constructions.Comment: 11 pages, 6 figure

Explainable Recommendation with Personalized Review Retrieval and Aspect Learning

Explainable recommendation is a technique that combines prediction and generation tasks to produce more persuasive results. Among these tasks, textual generation demands large amounts of data to achieve satisfactory accuracy. However, historical user reviews of items are often insufficient, making it challenging to ensure the precision of generated explanation text. To address this issue, we propose a novel model, ERRA (Explainable Recommendation by personalized Review retrieval and Aspect learning). With retrieval enhancement, ERRA can obtain additional information from the training sets. With this additional information, we can generate more accurate and informative explanations. Furthermore, to better capture users' preferences, we incorporate an aspect enhancement component into our model. By selecting the top-n aspects that users are most concerned about for different items, we can model user representation with more relevant details, making the explanation more persuasive. To verify the effectiveness of our model, extensive experiments on three datasets show that our model outperforms state-of-the-art baselines (for example, 3.4% improvement in prediction and 15.8% improvement in explanation for TripAdvisor)

BRAF L485–P490 deletion mutant metastatic melanoma sensitive to BRAF and MEK inhibition: A case report and literature review

Background: The combination therapy of BRAF inhibitors (BRAFis) and MEK inhibitors (MEKis) has been approved as a first-line treatment for metastatic melanoma with BRAF V600 mutants. Recently, BRAF mutations have been divided into three subtypes based on biochemical and signaling characteristics. Unlike V600 mutants that show class I BRAF mutations, evidence of the effects of using BRAF inhibitors and MEK inhibitors in patients with non-V600 BRAF mutations remains unclear. The exploration of effective therapy for non-V600 BRAF mutations in melanoma has thus attracted much interest.Case presentation: We reported a case of a 64-year-old female metastatic melanoma patient with a novel BRAF p.L485–P490 deletion mutation. The patient received anti-PD1 agent pembrolizumab (100 mg) therapy as the first-line treatment for two cycles, which was terminated due to an intolerable adverse effect. Considering the p.L485–P490 deletion mutation signal as an active dimer which is akin to a class II BRAF mutation, the patient underwent dabrafenib and trametinib combination therapy as a second-line treatment. After two cycles of combination treatment, the patient achieved a partial response confirmed by radiological examinations. At the last follow-up date, the patient had obtained over 18 months of progression-free survival, and the treatment was well tolerated.Conclusion: The combination therapy of dabrafenib and trametinib has been proven to be an effective method as a later-line therapy for metastatic melanoma patients with class II BRAF in-frame deletion mutations