Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser

Audio-visual learning has been a major pillar of multi-modal machine learning, where the community mostly focused on its modality-aligned setting, i.e., the audio and visual modality are both assumed to signal the prediction target. With the Look, Listen, and Parse dataset (LLP), we investigate the under-explored unaligned setting, where the goal is to recognize audio and visual events in a video with only weak labels observed. Such weak video-level labels only tell what events happen without knowing the modality they are perceived (audio, visual, or both). To enhance learning in this challenging setting, we incorporate large-scale contrastively pre-trained models as the modality teachers. A simple, effective, and generic method, termed Visual-Audio Label Elaboration (VALOR), is innovated to harvest modality labels for the training events. Empirical studies show that the harvested labels significantly improve an attentional baseline by 8.0 in average F-score (Type@AV). Surprisingly, we found that modality-independent teachers outperform their modality-fused counterparts since they are noise-proof from the other potentially unaligned modality. Moreover, our best model achieves the new state-of-the-art on all metrics of LLP by a substantial margin (+5.4 F-score for Type@AV). VALOR is further generalized to Audio-Visual Event Localization and achieves the new state-of-the-art as well. Code is available at: https://github.com/Franklin905/VALOR

A HRNet-based Rehabilitation Monitoring System

The rehabilitation treatment helps to heal minor sports and occupational injuries. In a traditional rehabilitation process, a therapist will assign certain actions to a patient to perform in between hospital visits, and it will rely on the patient to remember actions correctly and the schedule to perform them. Unfortunately, many patients forget to perform actions or fail to recall actions in detail. As a consequence, the rehabilitation treatment is hampered or, in the worst case, the patient may suffer from additional injury caused by performing incorrect actions. To resolve these issues, we propose a HRNet-based rehabilitation monitoring system, which can remind a patient when to perform the actions and display the actions for the patient to follow via the patient's smartphone. In addition, it helps the therapist to monitor the progress of the rehabilitation for the patient. Our system consists of an iOS app and several components at the server side. The app is in charge of displaying and collecting action videos. The server computes the similarity score between the therapist's actions and the patient's in the videos to keep track of the number of repetitions of each action. Theses stats will be shown to both of the patient and therapist. The extensive experiments show that the F1-Score of the similarity calculation is as high as 0.9 and the soft accuracy of the number of repetitions is higher than 90%

Study on the Correlation between Objective Evaluations and Subjective Speech Quality and Intelligibility

Subjective tests are the gold standard for evaluating speech quality and intelligibility, but they are time-consuming and expensive. Thus, objective measures that align with human perceptions are crucial. This study evaluates the correlation between commonly used objective measures and subjective speech quality and intelligibility using a Chinese speech dataset. Moreover, new objective measures are proposed combining current objective measures using deep learning techniques to predict subjective quality and intelligibility. The proposed deep learning model reduces the amount of training data without significantly impacting prediction performance. We interpret the deep learning model to understand how objective measures reflect subjective quality and intelligibility. We also explore the impact of including subjective speech quality ratings on speech intelligibility prediction. Our findings offer valuable insights into the relationship between objective measures and human perceptions

Drosophila eyes absent is a Novel mRNA Target of the Tristetraprolin (TTP) Protein DTIS11

The Tristetraprolin (TTP) protein family includes four mammalian members (TTP, TIS11b, TIS11d, and ZFP36L3), but only one in Drosophila melanogaster (DTIS11). These proteins bind target mRNAs with AU-rich elements (AREs) via two C3H zinc finger domains and destabilize the mRNAs. We found that overexpression of mouse TIS11b or DTIS11 in the Drosophila retina dramatically reduced eye size, similar to the phenotype of eyes absent (eya) mutants. The eya transcript is one of many ARE-containing mRNAs in Drosophila. We showed that TIS11b reduced levels of eya mRNA in vivo. In addition, overexpression of Eya rescued the TIS11b overexpression phenotype. RNA pull-down and luciferase reporter analyses demonstrated that the DTIS11 RNA-binding domain is required for DTIS11 to bind the eya 3′ UTR and reduce levels of eya mRNA. Moreover, ectopic expression of DTIS11 in Drosophila S2 cells decreased levels of eya mRNA and reduced cell viability. Consistent with these results, TTP proteins overexpressed in MCF7 human breast cancer cells were associated with eya homologue 2 (EYA2) mRNA, and caused a decrease in EYA2 mRNA stability and cell viability. Our results suggest that eya mRNA is a target of TTP proteins, and that downregulation of EYA by TTP may lead to reduced cell viability in Drosophila and human cells

Simple and Specific Dual-Wavelength Excitable Dye Staining for Glycoprotein Detection in Polyacrylamide Gels and Its Application in Glycoproteomics

In this study, a commercially available fluorescent dye, Lissamine rhodamine B sulfonyl hydrazine (LRSH), was designed to specifically stain the glycoproteins in polyacrylamide gels. Through the periodate/Schiff base mechanism, the fluorescent dye readily attaches to glycoproteins and the fluorescence can be simultaneously observed under either 305 nm or 532 nm excitation therefore, the dye-stained glycoproteins can be detected under a regular UV transilluminator or a more elegant laser-based gel scanner. The specificity and detection limit were examined using a standard protein mixture in polyacrylamide gels in this study. The application of this glycoprotein stain dye was further demonstrated using pregnancy urine samples. The fluorescent spots were further digested in gel and their identities confirmed through LC-MS/MS analysis and database searching. In addition, the N-glycosylation sites of LRSH-labeled uromodulin were readily mapped via in-gel PNGaseF deglycosylation and LC-MS/MS analysis, which indicated that this fluorescent dye labeling does not interfere with enzymatic deglycosylation. Hence, the application of this simple and specific dual-wavelength excitable dye staining in current glycoproteome research is promising

Label-free quantitative proteomics of CD133-positive liver cancer stem cells

Abstract Background CD133-positive liver cancer stem cells, which are characterized by their resistance to conventional chemotherapy and their tumor initiation ability at limited dilutions, have been recognized as a critical target in liver cancer therapeutics. In the current work, we developed a label-free quantitative method to investigate the proteome of CD133-positive liver cancer stem cells for the purpose of identifying unique biomarkers that can be utilized for targeting liver cancer stem cells. Label-free quantitation was performed in combination with ID-based Elution time Alignment by Linear regression Quantitation (IDEAL-Q) and MaxQuant. Results Initially, IDEAL-Q analysis revealed that 151 proteins were differentially expressed in the CD133-positive hepatoma cells when compared with CD133-negative cells. We then analyzed these 151 differentially expressed proteins by MaxQuant software and identified 10 significantly up-regulated proteins. The results were further validated by RT-PCR, western blot, flow cytometry or immunofluorescent staining which revealed that prominin-1, annexin A1, annexin A3, transgelin, creatine kinase B, vimentin, and EpCAM were indeed highly expressed in the CD133-positive hepatoma cells. Conclusions These findings confirmed that mass spectrometry-based label-free quantitative proteomics can be used to gain insights into liver cancer stem cells.http://deepblue.lib.umich.edu/bitstream/2027.42/113089/1/12953_2012_Article_407.pd

Activations of Both Extrinsic and Intrinsic Pathways in HCT 116 Human Colorectal Cancer Cells Contribute to Apoptosis through p53-Mediated ATM/Fas Signaling by Emilia sonchifolia Extract, a Folklore Medicinal Plant

Emilia sonchifolia (L.) DC (Compositae), an herbaceous plant found in Taiwan and India, is used as folk medicine. The clinical applications include inflammation, rheumatism, cough, cuts fever, dysentery, analgesic, and antibacteria. The activities of Emilia sonchifolia extract (ESE) on colorectal cancer cell death have not been fully investigated. The purpose of this study explored the induction of apoptosis and its molecular mechanisms in ESE-treated HCT 116 human colorectal cancer cells in vitro. The methanolic ESE was characterized, and γ-humulene was formed as the major constituent (63.86%). ESE induced cell growth inhibition in a concentration- and time-dependent response by MTT assay. Apoptotic cells (DNA fragmentation, an apoptotic catachrestic) were found after ESE treatment by TUNEL assay and DNA gel electrophoresis. Alternatively, ESE stimulated the activities of caspase-3, -8, and -9 and their specific caspase inhibitors protected against ESE-induced cytotoxicity. ESE promoted the mitochondria-dependent and death-receptor-associated protein levels. Also, ESE increased ROS production and upregulated the levels of ATM, p53, and Fas in HCT 116 cells. Strikingly, p53 siRNA reversed ESE-reduced viability involved in p53-mediated ATM/Fas signaling in HCT 116 cells. In summary, our result is the first report suggesting that ESE may be potentially efficacious in the treatment of colorectal cancer