Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition

Spatial and temporal modeling is one of the most core aspects of few-shot action recognition. Most previous works mainly focus on long-term temporal relation modeling based on high-level spatial representations, without considering the crucial low-level spatial features and short-term temporal relations. Actually, the former feature could bring rich local semantic information, and the latter feature could represent motion characteristics of adjacent frames, respectively. In this paper, we propose SloshNet, a new framework that revisits the spatial and temporal modeling for few-shot action recognition in a finer manner. First, to exploit the low-level spatial features, we design a feature fusion architecture search module to automatically search for the best combination of the low-level and high-level spatial features. Next, inspired by the recent transformer, we introduce a long-term temporal modeling module to model the global temporal relations based on the extracted spatial appearance features. Meanwhile, we design another short-term temporal modeling module to encode the motion characteristics between adjacent frame representations. After that, the final predictions can be obtained by feeding the embedded rich spatial-temporal features to a common frame-level class prototype matcher. We extensively validate the proposed SloshNet on four few-shot action recognition datasets, including Something-Something V2, Kinetics, UCF101, and HMDB51. It achieves favorable results against state-of-the-art methods in all datasets

Boosting Few-shot Action Recognition with Graph-guided Hybrid Matching

Class prototype construction and matching are core aspects of few-shot action recognition. Previous methods mainly focus on designing spatiotemporal relation modeling modules or complex temporal alignment algorithms. Despite the promising results, they ignored the value of class prototype construction and matching, leading to unsatisfactory performance in recognizing similar categories in every task. In this paper, we propose GgHM, a new framework with Graph-guided Hybrid Matching. Concretely, we learn task-oriented features by the guidance of a graph neural network during class prototype construction, optimizing the intra- and inter-class feature correlation explicitly. Next, we design a hybrid matching strategy, combining frame-level and tuple-level matching to classify videos with multivariate styles. We additionally propose a learnable dense temporal modeling module to enhance the video feature temporal representation to build a more solid foundation for the matching process. GgHM shows consistent improvements over other challenging baselines on several few-shot datasets, demonstrating the effectiveness of our method. The code will be publicly available at https://github.com/jiazheng-xing/GgHM.Comment: Accepted by ICCV202

Altered surface hydrophilicity on copolymer scaffolds stimulate the osteogenic differentiation of human mesenchymal stem cells

Background: Recent studies have suggested that both poly(l-lactide-co-1,5-dioxepan-2-one) (or poly(LLA-co-DXO)) and poly(l-lactide-co-ε-caprolactone) (or poly(LLA-co-CL)) porous scaffolds are good candidates for use as biodegradable scaffold materials in the field of tissue engineering; meanwhile, their surface properties, such as hydrophilicity, need to be further improved. Methods: We applied several different concentrations of the surfactant Tween 80 to tune the hydrophilicity of both materials. Moreover, the modification was applied not only in the form of solid scaffold as a film but also a porous scaffold. To investigate the potential application for tissue engineering, human bone marrow mesenchymal stem cells (hMSCs) were chosen to test the effect of hydrophilicity on cell attachment, proliferation, and differentiation. First, the cellular cytotoxicity of the extracted medium from modified scaffolds was investigated on HaCaT cells. Then, hMSCs were seeded on the scaffolds or films to evaluate cell attachment, proliferation, and osteogenic differentiation. The results indicated a significant increasing of wettability with the addition of Tween 80, and the hMSCs showed delayed attachment and spreading. PCR results indicated that the differentiation of hMSCs was stimulated, and several osteogenesis related genes were up-regulated in the 3% Tween 80 group. Poly(LLA-co-CL) with 3% Tween 80 showed an increased messenger Ribonucleic acid (mRNA) level of late-stage markers such as osteocalcin (OC) and key transcription factor as runt related gene 2 (Runx2). Conclusion: A high hydrophilic scaffold may speed up the osteogenic differentiation for bone tissue engineering.publishedVersio

APTw combined with mDixon−Quant imaging to distinguish the differentiation degree of cervical squamous carcinoma

BackgroundTo investigate the value of amide proton transfer weighted (APTw) imaging combined with modified Dixon fat quantification (mDixon-Quant) imaging in determining the degree of differentiation of cervical squamous carcinoma (CSC) against histopathologic.MethodsMagnetic resonance imaging (MRI) data were collected from 52 CSC patients. According to histopathologic results, patients were divided into the poorly differentiated group (37 cases) and the well/moderately differentiated group (15 cases). The APTw value by APTw imaging and the fat fraction (FF) and transverse relaxation rate R2* values by mDixon-Quant were independently measured by two radiologists. Intra-class correlation coefficients (ICCs) were used to test the consistency of APTw, FF, and R2* values measured by the two observers. The Mann-Whitney U test was used to analyze the difference in each parameter between the two groups. Logistic regression analysis was used to assess the association between the degree of differentiation on histopathology and imaging parameters by APTw and mDixon Quant. The ROC curve was used to evaluate the diagnostic efficacy of various parameters and their combination in distinguishing the degree of CSC differentiation on histopathology. The DeLong test was used to access the differences among the area under the ROC curves (AUCs). The Pearson correlation coefficient was used to evaluate the correlation between APTw and mDixon-Quant imaging parameters.ResultsThe APTw means were 2.95 ± 0.78% and 2.05 (1.85, 2.65)% in the poorly and well/moderately differentiated groups, respectively. The R2* values were 26.62 (21.99, 33.31)/s and 22.93 ± 6.09/s in the poorly and well/moderately differentiated groups, respectively (P < 0.05). The AUCs of APTw, R2*, and their combination were 0.762, 0.686, and 0.843, respectively. The Delong test suggested statistical significance between R2* and the combination of APTw and R2*. R2* values showed a significant correlation with APTw values in the poorly differentiated group.ConclusionsAPTw combined with mDixon-Quant can be used to efficiently distinguish the differention degrees of CSC diagnosed on histopathology

Revisiting the Spatial and Temporal Modeling for Few-Shot Action Recognition

Spatial and temporal modeling is one of the most core aspects of few-shot action recognition. Most previous works mainly focus on long-term temporal relation modeling based on high-level spatial representations, without considering the crucial low-level spatial features and short-term temporal relations. Actually, the former feature could bring rich local semantic information, and the latter feature could represent motion characteristics of adjacent frames, respectively. In this paper, we propose SloshNet, a new framework that revisits the spatial and temporal modeling for few-shot action recognition in a finer manner. First, to exploit the low-level spatial features, we design a feature fusion architecture search module to automatically search for the best combination of the low-level and high-level spatial features. Next, inspired by the recent transformer, we introduce a long-term temporal modeling module to model the global temporal relations based on the extracted spatial appearance features. Meanwhile, we design another short-term temporal modeling module to encode the motion characteristics between adjacent frame representations. After that, the final predictions can be obtained by feeding the embedded rich spatial-temporal features to a common frame-level class prototype matcher. We extensively validate the proposed SloshNet on four few-shot action recognition datasets, including Something-Something V2, Kinetics, UCF101, and HMDB51. It achieves favorable results against state-of-the-art methods in all datasets

The Fitting of a Fiber-Reinforced-Plastic Complex Curved Surface and Its Orbit Optimization Model with Belt Grinding Line Contact

The surface quality and profile accuracy of a radar fiberglass radome are determined by the manufacturing of the fiber-reinforced-plastic (FRP) complex curved mold. The surface quality, thickness uniformity, and shape accuracy of the mold seriously affect the temperature and deformation control during the manufacturing process of the radome, thus affecting the antenna’s serviceability, including its wave permeability and stability. Abrasive belt grinding is an effective method for processing FRP materials. However, issues regarding the profile fitting of the abrasive belt section line contact state and its influence on the precision of complex curved surfaces have not been solved, which seriously affects the processing quality. Here, an FRP complex curved surface mold surface based on the least-squares method was established. The local two-dimensional line contact and profile contour trajectory were obtained by the algorithm of optimal trajectory planning. Based on this, a grinding experiment was carried out. The experiments showed that the surface roughness based on this method was reduced from 0.503 to 0.289 μm, and the contour accuracy was improved by 16.9% compared with the conventional error. Through our analysis, the following conclusions can be drawn: the algorithm can effectively solve the problem of line contact surface fitting and significantly improve the precision of an FRP complex surface

Utilization of adipocyte-derived lipids and enhanced intracellular trafficking of fatty acids contribute to breast cancer progression

Abstract Background To determine whether adipocyte-derived lipids could be transferred into breast cancer cells and investigate the underlying mechanisms of subsequent lipolysis and fatty acid trafficking in breast cancer cells. Methods A Transwell co-culture system was used in which human breast cancer cells were cultured in the absence or presence of differentiated murine 3 T3-L1 adipocytes. Migration/invasion and proliferation abilities were compared between breast cancer cells that were cultivated alone and those co-cultivated with mature adipocytes. The ability of lipolysis in breast cancer cells were measured, as well as the expression of the rate-limiting lipase ATGL and fatty acid transporter FABP5. ATGL and FABP5 were then ablated to investigate their impact on the aggressiveness of breast cancer cells that were surrounded by adipocytes. Further, immunohistochemistry was performed to detect differential expression of ATGL and FABP5 in breast cancer tissue sections. Results The migration and invasion abilities of cancer cells were significantly enhanced after co-culture with adipocytes, accompanied by elevated lipolysis and expression of ATGL and FABP5. Abrogation of ATGL and FABP5 sharply attenuated the malignancy of co-cultivated breast cancer cells. However, this phenomenon was not observed if a lipid emulsion was added to the culture medium to substitute for adipocytes. Furthermore, epithelial-mesenchymal transaction was induced in co-cultivated breast cancer cells. That may partially due to the stimulation of PPARβ/δ and MAPK, which was resulted from upregulation of FABP5. As evidenced by immunohistochemistry, ATGL and FABP5 also had higher expression levels at the invasive front of the breast tumor, in where the adipocytes abound, compared to the central area in tissue specimens. Conclusions Lipid originating from tumor-surrounding adipocytes could be transferred into breast cancer cells. Adipocyte-cancer cell crosstalk rather than lipids alone induced upregulation of lipases and fatty acid transport protein in cancer cells to utilize stored lipids for tumor progression. The increased expression of the key lipase ATGL and intracellular fatty acid trafficking protein FABP5 played crucial roles in this process via fueling or signaling

Impact of Resolvin D1 on the inflammatory phenotype of periodontal ligament cell response to hypoxia

Objective - Periodontal ligament cells (PDLCs) are critical for wound healing and regenerative capacity of periodontal diseases. Within an inflammatory periodontal pocket, a hypoxic environment can aggravate periodontal inflammation, where PDLCs response to the inflammation would change. Resolvin D1 (RvD1) is an endogenous lipid mediator, which can impact intracellular inflammatory pathways of periodontal/oral cells and periodontal regeneration. It is not clear how hypoxia and RvD1 impact the inflammatory responses of pro-inflammatory PDLCs phenotype. Therefore, this study aimed to test hypoxia could induce changes in pro-inflammatory phenotype of PDLCs and RvD1 could reverse it. Methods - Human PDLCs were cultured from periodontal tissues from eight healthy individuals and were characterized by immunofluorescence staining of vimentin and cytokeratin. Cell viability was examined by Methyl-thiazolyl-tetrazolium (MTT) assay. To examine the effects of hypoxia and RvD1 on the inflammatory responses of pro-inflammatory PDLCs phenotype, protein levels and gene expressions of inflammatory cytokines and signal transduction molecules were measured by enzyme-linked immunosorbent assay (ELISA), western blotting (WB), and real-time quantitative reverse transcription PCR (real-time qRT-PCR). Alizarin red S staining and real-time qRT-PCR were employed to study the effects of hypoxia and RvD1 on the osteogenic differentiation of pro-inflammatory PDLCs phenotype. Results - It was found that hypoxia increases the expression of inflammatory factors at the gene level (p  Conclusion - Our results indicate that hypoxia up-regulated the inflammatory level of PDLCs. RvD1 can reduce under-hypoxia-induced pro-inflammatory cytokines in the inflammatory phenotype of PDLCs. Moreover, RvD1 promotes the calcium nodules in PDLCs, possibly by affecting the p38 MAPK signaling pathway through Akt and HIF-1α

Additional file 1: of Utilization of adipocyte-derived lipids and enhanced intracellular trafficking of fatty acids contribute to breast cancer progression

Table S1. List of primers used in this study. (PDF 63 kb