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Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categorization~(FGVC). In the existing FGVC datasets used in computer vision, it is generally assumed that each collected instance has fixed characteristics and the distribution of different categories is relatively balanced. In contrast, the real world scenario reveals the fact that the characteristics of instances tend to vary with time and exhibit a long-tailed distribution. Hence, the collected datasets may mislead the optimization of the fine-grained classifiers, resulting in unpleasant performance in real applications. Starting from the real-world conditions and to promote the practical progress of fine-grained visual categorization, we present a Concept Drift and Long-Tailed Distribution dataset. Specifically, the dataset is collected by gathering 11195 images of 250 instances in different species for 47 consecutive months in their natural contexts. The collection process involves dozens of crowd workers for photographing and domain experts for labelling. Extensive baseline experiments using the state-of-the-art fine-grained classification models demonstrate the issues of concept drift and long-tailed distribution existed in the dataset, which require the attention of future researches.

Fine-grained visual categorization (FGVC) aims to discriminate similar subcategories, whose main challenge is the large intraclass diversities and subtle inter-class differences. Existing FGVC methods usually select discriminant regions found by a trained model, which is prone to neglect other potential discriminant information. On the other hand, the massive interactions between the sequence of image patches in ViT make the resulting class-token contain lots of redundant information, which may also impacts FGVC performance. In this paper, we present a novel approach for FGVC, which can simultaneously make use of partial yet sufficient discriminative information in environmental cues and also compress the redundant information in class-token with respect to the target. Specifically, our model calculates the ratio of high-weight regions in a batch, adaptively adjusts the masking threshold and achieves moderate extraction of background information in the input space. Moreover, we also use the Information Bottleneck~(IB) approach to guide our network to learn a minimum sufficient representations in the feature space. Experimental results on three widely-used benchmark datasets verify that our approach can achieve outperforming performance than other state-of-the-art approaches and baseline models.

Total knee arthroplasty (TKA) is one of the most common surgeries performed to relieve joint pain in patients with end-stage osteoarthritis or rheumatic arthritis of the knee. However, TKA is followed by moderate to severe postoperative pain that affects postoperative rehabilitation, patient satisfaction, and overall outcomes. Historically, opioids have been widely used for perioperative pain management of TKA. However, opioids are associated with undesirable adverse effects, such as nausea, respiratory depression, and retention of urine, which limit their application in daily clinical practice. The aim of this review was to discuss the current postoperative pain management regimens for TKA. Our review of the literature demonstrated that multimodal analgesia is considered the optimal regimen for perioperative pain management of TKA and improves clinical outcomes and patient satisfaction, through a combination of several types of medications and delivery routes, including preemptive analgesia, neuraxial anesthesia, peripheral nerve blockade, patient-controlled analgesia and local infiltration analgesia, and oral opioid/nonopioid medications. Multimodal analgesia provides superior pain relief, promotes recovery of the knee, and reduces opioid consumption and related adverse effects in patients undergoing TKA.

BACKGROUND AND PURPOSE: Vascular inflammation, including the expression of inflammatory cytokines in endothelial cells, plays a critical role in hyperhomocysteinaemia‐associated vascular diseases. Cathepsin V, specifically expressed in humans, is involved in vascular diseases through its elastolytic and collagenolytic activities. The aim of this study was to determine the effects of cathepsin V on l‐homocysteine‐induced vascular inflammation. EXPERIMENTAL APPROACH: A high methionine diet‐induced hyperhomocysteinaemic mouse model was used to assess cathepsin V expression and vascular inflammation. Cultures of HUVECs were challenged with l‐homocysteine and the cathepsin L/V inhibitor SID to assess the pro‐inflammatory effects of cathepsin V. Transfection and antisense techniques were utilized to investigate the effects of cathepsin V on the dual‐specificity protein phosphatases (DUSPs) and MAPK pathways. KEY RESULTS: Cathepsin L (human cathepsin V homologous) was increased in the thoracic aorta endothelial cells of hyperhomocysteinaemic mice; l‐homocysteine promoted cathepsin V expression in HUVECs. SID suppressed the activity of cathepsin V and reversed the up‐regulation of inflammatory cytokines (IL‐6, IL‐8 and TNF‐α), adhesion and chemotaxis of leukocytes and vascular inflammation induced by l‐homocysteine in vivo and in vitro. Increased cathepsin V promoted the degradation of DUSP6 and DUSP7, phosphorylation and subsequent nuclear translocation of ERK1/2, phosphorylation of STAT1 and expression of IL‐6, IL‐8 and TNF‐α. CONCLUSIONS AND IMPLICATIONS: This study has identified a novel mechanism, which shows that l‐homocysteine‐induced upregulation of cathepsin V mediates vascular endothelial inflammation under high homocysteine condition partly via ERK(1/2)/STAT1 pathway. This mechanism could represent a potential therapeutic target in hyperaemia‐associated vascular diseases. LINKED ARTICLES: This article is part of a themed section on Spotlight on Small Molecules in Cardiovascular Diseases. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.8/issuetoc

We investigate the mKdV hierarchy with integral type of source (mKdVHWS), which consist of the reduced AKNS eigenvalue problem with $r=q$ and the mKdV hierarchy with extra term of the integration of square eigenfunction. First we propose a method to find the explicit evolution equation for eigenfunction of the auxiliary linear problems of the mKdVHWS. Then we determine the evolution equations of scattering data corresponding to the mKdVHWS which allow us to solve the equation in the mKdVHWS by inverse scattering transformation., Comment: 10 pages, Latex, to be published in Journal of Physics A