One for More: Selecting Generalizable Samples for Generalizable ReID Model

Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the performance on samples outside the batch. It will inevitably cause the model to over-fit the data in the dominant position (e.g., head data in imbalanced class, easy samples or noisy samples). %We call the sample that updates the model towards generalizing on more data a generalizable sample. The latest resampling methods address the issue by designing specific criterion to select specific samples that trains the model generalize more on certain type of data (e.g., hard samples, tail data), which is not adaptive to the inconsistent real world ReID data distributions. Therefore, instead of simply presuming on what samples are generalizable, this paper proposes a one-for-more training objective that directly takes the generalization ability of selected samples as a loss function and learn a sampler to automatically select generalizable samples. More importantly, our proposed one-for-more based sampler can be seamlessly integrated into the ReID training framework which is able to simultaneously train ReID models and the sampler in an end-to-end fashion. The experimental results show that our method can effectively improve the ReID model training and boost the performance of ReID models

SoccerNet 2023 Challenges Results

peer reviewedThe SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet

Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals

Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram (EEG) devices, we developed a fast and accurate sleep stage classification method in this study with single-channel EEG signals for practical applications. The original sleep recordings were collected from the Sleep-EDF database. The wavelet threshold denoising (WTD) method and wavelet packet transformation (WPT) method were applied as signal preprocessing to extract six kinds of characteristic waves. With a comprehensive feature system including time, frequency, and nonlinear dynamics, we obtained the sleep stage classification results with different Support Vector Machine (SVM) models. We proposed a novel classification method based on cascaded SVM models with various features extracted from denoised EEG signals. To enhance the accuracy and generalization performance of this method, nonlinear dynamics features were taken into consideration. With nonlinear dynamics features included, the average classification accuracy was up to 88.11% using this method. In addition, with cascaded SVM models, the classification accuracy of the non-rapid eye movement sleep stage 1 (N1) was enhanced from 41.5% to 55.65% compared with the single SVM model, and the overall classification time for each epoch was less than 1.7 s. Moreover, we demonstrated that it was possible to apply this method for long-term sleep stage monitor applications

β-Catenin Cooperates with CREB Binding Protein to Promote the Growth of Tumor Cells

Background/Aims: β-catenin is an integral component of the canonical Wnt signaling pathway, and its mutations are an autosomal recessive cause of colorectal cancer (CRC), medulloblastoma (MDB), and ovarian cancer. Nevertheless, little is known about its function in lung cancers. Methods: We first knocked down β-catenin by siRNA to investigate its effects on lung cancer cell proliferation, migration and apoptosis. Then we verified the interaction between β-catenin and CREB binding protein (CBP) by immunofluoresence and co-immunoprecipition assays. Finally, the expression of β-catenin and CBP in human lung adenocarcinoma specimens were analyzed by immunohistochemistry assay. Results: β-catenin knockdown inhibited cell proliferation, promoted apoptosis and suppressed cell migration in A549 and H460 cells accompanied by the decreased expression of Myc, PCNA, VEGF, CD44, MMP-9, MMP-13 and activated bax/caspase-3 pathway. Furthermore, co-immunoprecipition and immunofluoresence analyses revealed that CBP interacted with β-catenin and contributed to β-catenin-mediated lung cancer cell growth. Abolishment of their interaction by the Wnt/β-catenin inhibitor ICG-001 remarkably suppressed cell proliferation. Immunohistochemistry assay of tissue microarrays from patients with lung cancer indicated that both CBP and β-catenin were highly expressed in tumor tissues and predicted poor prognosis in lung adenocarcinoma patients. Conclusions: Our study has provided new evidence for the role of β-catenin in promoting the growth of lung cancer cells through cooperation with CBP, and suggested that dual targeting of β-catenin and CBP could be a potential therapeutic strategy in lung cancer treatment

XRCC5 cooperates with p300 to promote cyclooxygenase-2 expression and tumor growth in colon cancers.

Cyclooxygenase (COX) is the rate-limiting enzyme in prostaglandins (PGs) biosynthesis. Previous studies indicate that COX-2, one of the isoforms of COX, is highly expressed in colon cancers and plays a key role in colon cancer carcinogenesis. Thus, searching for novel transcription factors regulating COX-2 expression will facilitate drug development for colon cancer. In this study, we identified XRCC5 as a binding protein of the COX-2 gene promoter in colon cancer cells with streptavidin-agarose pulldown assay and mass spectrometry analysis, and found that XRCC5 promoted colon cancer growth through modulation of COX-2 signaling. Knockdown of XRCC5 by siRNAs inhibited the growth of colon cancer cells in vitro and of tumor xenografts in a mouse model in vivo by suppressing COX-2 promoter activity and COX-2 protein expression. Conversely, overexpression of XRCC5 promoted the growth of colon cancer cells by activating COX-2 promoter and increasing COX-2 protein expression. Moreover, the role of p300 (a transcription co-activator) in acetylating XRCC5 to co-regulate COX-2 expression was also evaluated. Immunofluorescence assay and confocal microscopy showed that XRCC5 and p300 proteins were co-located in the nucleus of colon cancer cells. Co-immunoprecipitation assay also proved the interaction between XRCC5 and p300 in nuclear proteins of colon cancer cells. Cell viability assay indicated that the overexpression of wild-type p300, but not its histone acetyltransferase (HAT) domain deletion mutant, increased XRCC5 acetylation, thereby up-regulated COX-2 expression and promoted the growth of colon cancer cells. In contrast, suppression of p300 by a p300 HAT-specific inhibitor (C646) inhibited colon cancer cell growth by suppressing COX-2 expression. Taken together, our results demonstrated that XRCC5 promoted colon cancer growth by cooperating with p300 to regulate COX-2 expression, and suggested that the XRCC5/p300/COX-2 signaling pathway was a potential target in the treatment of colon cancers

XRCC5 interacting with p300 to co-regulate COX-2 expression in colon cancer cells.

<p>(A) Immunofluorescence and confocal microscopy of XRCC5 and p300 in RKO and LoVo cells. XRCC5 is stained by TRITC-conjugated secondary antibodies (red), p300 is stained by FITC-conjugated secondary antibodies (green), and nuclei are stained with DAPI (blue). (B) Co-immunoprecipitation assay of p300 and XRCC5 in RKO, LoVo and SW480 cells.Left: Immunoprecipitation assay (IP) of p300 and XRCC5. Right: Western blot (WB) of XRCC5 and p300. (C) Bottom: The design of the flag-tagged plasmids with different domains of p300. Left: The interaction between XRCC5 and the different domains of p300 detected by immunoprecipitation assay and Western blot. (D)Western blot of XRCC5 with the nuclear extractsimmunoprecipitated by an anti-acetylation antibody in RKO, LoVo and SW480 cells. (E) Western blot of XRCC5 with the nuclear extracts immunoprecipitated by an anti-acetylation antibody in LoVo cells. (F) Western blot of XRCC5 and COX-2 in LoVo cells. (G) MTS cell viability assay in LoVo cells (Left) and RKO cells (Right). Cells treated with liposome negative control is used for data alignment. Data are presented as the meanen.D. (*<i>P</i><0.05). lacZ represents negative control vector, p300WT represents wild type p300 overexpression, Δp300 represents histone acetyltransferase (HAT) domain deletion mutant p300, C646 represents p300 HAT inhibitor C646, and siXRCC5 represents knockdown of XRCC5 with siRNAs.</p

Activator Protein-2β Promotes Tumor Growth and Predicts Poor Prognosis in Breast Cancer

Background/Aims: Activator protein-2 (AP-2) transcription factors have been proved to be essential in maintaining cellular homeostasis and regulating the transformation from normal growth to neoplasia. However, the role of AP-2β, a key member of AP-2 family, in breast cancer is rarely reported. Methods: The effect of AP-2 on cell growth, migration and invasion in breast cancer cells were measured by MTT, colony formation, wound-healing and transwell assays, respectively. The expression levels of AP-2β and other specific markers in breast cancer cell lines and tissue microarrays from the patients were detected using RT-PCR, Western blot and immunohistochemical staining. The regulation of AP-2β on tumor growth in vivo was analyzed in a mouse xenograft model. Results: We demonstrated the tumor-promoting function of AP-2β in breast cancer. AP-2β was found to be highly expressed in breast cancer cell lines and tumor tissues of breast cancer patients. The shRNA-mediated silencing of AP-2β led to the dramatic inhibition of cell proliferation, colony formation ability, migration and invasiveness in breast cancer cells accompanied by the down-regulated expression of some key proteins involved in cancer progression, including p75, MMP-2, MMP-9, C-Jun, p-ERK and STAT3. Overexpression of AP-2β markedly up-regulated the levels of these proteins. Consistent with the in vitro study, the silencing or overexpression of AP-2β blocked or promoted tumor growth in the mice with xenografts of breast cancers. Notably, the high AP-2β expression levels was correlated with poor prognosis and advanced malignancy in patients with breast cancer. Conclusions: Our study demonstrates that AP-2β promotes tumor growth and predicts poor prognosis, and may represent a potential therapeutic target for breast cancer

RBFOX3 Regulates the Chemosensitivity of Cancer Cells to 5-Fluorouracil via the PI3K/AKT, EMT and Cytochrome-C/Caspase Pathways

Background/Aims: RBFOX3, an RNA-binding fox protein, plays an important role in the differentiation of neuronal development, but its role in the chemosensitivity of hepatocellular carcinoma (HCC) to 5-FU is unknown. Methods: In this study, we examined the biological functions of RBFOX3 and its effect on the chemosensitivity of HCC cells to 5-FU in vitro and in a mouse xenograft model. Results: RBFOX3 was found to have elevated expression in HCC cell lines and tissue samples, and its knockdown inhibited HCC cell proliferation. Moreover, knockdown of RBFOX3 improved the inhibitory effect of 5-fluorouracil (5-FU) on cell proliferation, migration and invasion, and enhanced the apoptosis induced by 5-FU. However, overexpression of RBFOX3 reduced the inhibitory effect of 5-fluorouracil (5-FU) on cell proliferation, migration and invasion, and decreased the apoptosis induced by 5-FU. We further elucidated that RBFOX3 knockdown synergized with 5-FU to inhibit the growth and invasion of HCC cells through PI3K/AKT and epithelial-mesenchymal transition (EMT) signaling, and promote apoptosis by activating the cytochrome-c/caspase signaling pathway. Finally, we validated that RBFOX3 regulated 5-FU-mediated cytotoxicity in HCC in mouse xenograft models. Conclusions: The findings from this study indicate that RBFOX3 regulates the chemosensitivity of HCC to 5-FU in vitro and in vivo. Therefore, targeting RBFOX3 may improve the inhibition of HCC growth and progression by 5-FU, and provide a novel potential therapeutic strategy for HCC

XRCC5 regulating colon cancer cell proliferation <i>in vitro</i>.

<p>(A) MTS cell viability assay of LoVo cells. Cells treated with BPS negative control are used for data alignment. Data are presented as the meaen.D. (*<i>P</i><0.05). (B) MTS cell viability assay of RKO cells. Cells treated with PBS negative control are used for data alignment. Data are presented as the meannt.D. (*<i>P</i><0.05). (C) Morphology observation of LoVo cells. (D) Colony formation assay of LoVo cells. Si1, Si2 and Si3 represent three sequences of siRNAs of XRCC5, Sictr represents negative control siRNA of XRCC5, PBS represents PBS negative control, XRCC5 represents overexpression of XRCC5, and LacZ represents negative control vector.</p