Discriminative Feature Learning with Foreground Attention for Person Re-Identification

The performance of person re-identification (Re-ID) has been seriously effected by the large cross-view appearance variations caused by mutual occlusions and background clutters. Hence learning a feature representation that can adaptively emphasize the foreground persons becomes very critical to solve the person Re-ID problem. In this paper, we propose a simple yet effective foreground attentive neural network (FANN) to learn a discriminative feature representation for person Re-ID, which can adaptively enhance the positive side of foreground and weaken the negative side of background. Specifically, a novel foreground attentive subnetwork is designed to drive the network's attention, in which a decoder network is used to reconstruct the binary mask by using a novel local regression loss function, and an encoder network is regularized by the decoder network to focus its attention on the foreground persons. The resulting feature maps of encoder network are further fed into the body part subnetwork and feature fusion subnetwork to learn discriminative features. Besides, a novel symmetric triplet loss function is introduced to supervise feature learning, in which the intra-class distance is minimized and the inter-class distance is maximized in each triplet unit, simultaneously. Training our FANN in a multi-task learning framework, a discriminative feature representation can be learned to find out the matched reference to each probe among various candidates in the gallery. Extensive experimental results on several public benchmark datasets are evaluated, which have shown clear improvements of our method over the state-of-the-art approaches

CBNet: A Novel Composite Backbone Network Architecture for Object Detection

In existing CNN based detectors, the backbone network is a very important component for basic feature extraction, and the performance of the detectors highly depends on it. In this paper, we aim to achieve better detection performance by building a more powerful backbone from existing backbones like ResNet and ResNeXt. Specifically, we propose a novel strategy for assembling multiple identical backbones by composite connections between the adjacent backbones, to form a more powerful backbone named Composite Backbone Network (CBNet). In this way, CBNet iteratively feeds the output features of the previous backbone, namely high-level features, as part of input features to the succeeding backbone, in a stage-by-stage fashion, and finally the feature maps of the last backbone (named Lead Backbone) are used for object detection. We show that CBNet can be very easily integrated into most state-of-the-art detectors and significantly improve their performances. For example, it boosts the mAP of FPN, Mask R-CNN and Cascade R-CNN on the COCO dataset by about 1.5 to 3.0 percent. Meanwhile, experimental results show that the instance segmentation results can also be improved. Specially, by simply integrating the proposed CBNet into the baseline detector Cascade Mask R-CNN, we achieve a new state-of-the-art result on COCO dataset (mAP of 53.3) with single model, which demonstrates great effectiveness of the proposed CBNet architecture. Code will be made available on https://github.com/PKUbahuangliuhe/CBNet.Comment: 7 pages,6 figure

Elevated Serum Human Epididymis Protein 4 Is Associated With Disease Activity and Systemic Involvements in Primary Sjögren’s Syndrome

BackgroundWe aimed to investigate the clinical utility of human epididymis protein 4, a tumor biomarker being widely utilized in clinical practice in the diagnosis of ovarian cancer, in primary Sjögren’s Syndrome (pSS).MethodsA total of 109 pSS patients and 113 healthy controls (HCs) were included in the study. HE4 were determined by Roche Cobas E601 electrochemical luminescence analyzer. Clinical and laboratory findings were reviewed, and the relationships between HE4 and clinical parameters were determined by Spearman’s correlation test. The European league against rheumatism Sjögren’s syndrome disease activity index (ESSDAI) was utilized to evaluate disease activity.FindingsThe levels of HE4 were significantly elevated in patients with pSS compared to HCs (103.65 pmol/L vs. 46.52 pmol/L, p<0.001). The levels of HE4 were positively correlated with ESSDAI scores (r=0.462, p<0.001). Significant positive correlations between the levels of HE4 with pulmonary involvements (r=0.442, p<0.001) and renal involvements (r=0.320, p=0.001) were observed. Receiver operating curve (ROC) analysis revealed an optimal cut-off value of 104.90 pmol/L and 128.05 pmol/L for distinguishing patients with pulmonary and renal involvements, with the areas under the ROC curve (AUCs) of 0.778 (95%CI 0.685-0.870, p<0.001) and 0.768 (95%CI 0.646-0.891, p=0.001), respectively. Among patients with pulmonary involvement, the levels of HE4 were positively correlated with the semiquantitative HRCT grade (r=0.417, p=0.016), and negatively correlated with the percentage of forced vital capacity (FVC) (r= -0.460, p=0.047) and diffusing capacity of the lung for carbon monoxide (DLco) (r= -0.623, p=0.004). For patients with renal involvement, HE4 was positively correlated with creatinine (r=0.588, p=0.021) and negatively correlated with estimated glomerular filtration rate (r= -0.599, p=0.030).ConclusionsOur findings demonstrated a novel role of HE4 in clinical stratification of pSS, suggesting that introducing HE4 to the current pSS test panel may provide additional diagnostic value, particularly in evaluating disease activity and pulmonary/renal involvements

A general method to determine twinning elements

Based on the minimum shear criterion, a direct and simple method is proposed to calculate twinning elements from the experimentally determined twinning plane for Type I twins or the twinning direction for Type II twins. It is generic and applicable to any crystal structure

The ORF7a Protein of SARS-CoV-2 Initiates Autophagy and Limits Autophagosome-Lysosome Fusion via Degradation of SNAP29 To Promote Virus Replication

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is closely related to various cellular aspects associated with autophagy. However, how SARS-CoV-2 mediates the subversion of the macroautophagy/autophagy pathway remains largely unclear. In this study, we demonstrate that overexpression of the SARS-CoV-2 ORF7a protein activates LC3-II and leads to the accumulation of autophagosomes in multiple cell lines, while knockdown of the viral ORF7a gene via shRNAs targeting ORF7a sgRNA during SARS-CoV-2 infection decreased autophagy levels. Mechanistically, the ORF7a protein initiates autophagy via the AKT-MTOR-ULK1-mediated pathway, but ORF7a limits the progression of autophagic flux by activating CASP3 (caspase 3) to cleave the SNAP29 protein at aspartic acid residue 30 (D30), ultimately impairing complete autophagy. Importantly, SARS-CoV-2 infection-induced accumulated autophagosomes promote progeny virus production, whereby ORF7a downregulates SNAP29, ultimately resulting in failure of autophagosome fusion with lysosomes to promote viral replication. Taken together, our study reveals a mechanism by which SARS-CoV-2 utilizes the autophagic machinery to facilitate its own propagation via ORF7a.Abbreviations: 3-MA: 3-methyladenine; ACE2: angiotensin converting enzyme 2; ACTB/β-actin: actin beta; ATG7: autophagy related 7; Baf A1: bafilomycin A1; BECN1: beclin 1; CASP3: caspase 3; COVID-19: coronavirus disease 2019; GFP: green fluorescent protein; hpi: hour post-infection; hpt: hour post-transfection; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MERS: Middle East respiratory syndrome; MTOR: mechanistic target of rapamycin kinase; ORF: open reading frame; PARP: poly(ADP-ribose) polymerase; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; shRNAs: short hairpin RNAs; siRNA: small interfering RNA; SNAP29: synaptosome associated protein 29; SQSTM1/p62: sequestosome 1; STX17: syntaxin 17; TCID50: tissue culture infectious dose; TEM: transmission electron microscopy; TUBB, tubulin, beta; ULK1: unc-51 like autophagy activating kinase 1