Multimodal Prototype-Enhanced Network for Few-Shot Action Recognition

Current methods for few-shot action recognition mainly fall into the metric learning framework following ProtoNet. However, they either ignore the effect of representative prototypes or fail to enhance the prototypes with multimodal information adequately. In this work, we propose a novel Multimodal Prototype-Enhanced Network (MORN) to use the semantic information of label texts as multimodal information to enhance prototypes, including two modality flows. A CLIP visual encoder is introduced in the visual flow, and visual prototypes are computed by the Temporal-Relational CrossTransformer (TRX) module. A frozen CLIP text encoder is introduced in the text flow, and a semantic-enhanced module is used to enhance text features. After inflating, text prototypes are obtained. The final multimodal prototypes are then computed by a multimodal prototype-enhanced module. Besides, there exist no evaluation metrics to evaluate the quality of prototypes. To the best of our knowledge, we are the first to propose a prototype evaluation metric called Prototype Similarity Difference (PRIDE), which is used to evaluate the performance of prototypes in discriminating different categories. We conduct extensive experiments on four popular datasets. MORN achieves state-of-the-art results on HMDB51, UCF101, Kinetics and SSv2. MORN also performs well on PRIDE, and we explore the correlation between PRIDE and accuracy

What Large Language Models Bring to Text-rich VQA?

Text-rich VQA, namely Visual Question Answering based on text recognition in the images, is a cross-modal task that requires both image comprehension and text recognition. In this work, we focus on investigating the advantages and bottlenecks of LLM-based approaches in addressing this problem. To address the above concern, we separate the vision and language modules, where we leverage external OCR models to recognize texts in the image and Large Language Models (LLMs) to answer the question given texts. The whole framework is training-free benefiting from the in-context ability of LLMs. This pipeline achieved superior performance compared to the majority of existing Multimodal Large Language Models (MLLM) on four text-rich VQA datasets. Besides, based on the ablation study, we find that LLM brings stronger comprehension ability and may introduce helpful knowledge for the VQA problem. The bottleneck for LLM to address text-rich VQA problems may primarily lie in visual part. We also combine the OCR module with MLLMs and pleasantly find that the combination of OCR module with MLLM also works. It's worth noting that not all MLLMs can comprehend the OCR information, which provides insights into how to train an MLLM that preserves the abilities of LLM

The Bcl-2/xL inhibitor ABT-263 increases the stability of Mcl-1 mRNA and protein in hepatocellular carcinoma cells

Background Hepatocellular carcinoma (HCC) is one of the major causes of mortality. ABT-263 is a newly synthesized, orally available Bcl-2/xL inhibitor that shows promising efficacy in HCC therapy. ABT-263 inhibits the anti-apoptotic activity of Bcl-2 and Bcl-xL, but not Mcl-1. Previous reports have shown that ABT-263 upregulates Mcl-1 in various cancer cells, which contributes to ABT-263 resistance in cancer therapy. However, the associated mechanisms are not well known. Methods Western blot, RNAi and CCK-8 assays were used to investigate the relationship between Mcl-1 upregulation and ABT-263 sensitivity in HCC cells. Real-time PCR and Western blot were used to detect Mcl-1 mRNA and protein levels. Luciferase reporter assay and RNA synthesis inhibition assay were adopted to analyze the mechanism of Mcl-1 mRNA upregulation. Western blot and the inhibition assays for protein synthesis and proteasome were used to explore the mechanisms of ABT-263-enhanced Mcl-1 protein stability. Trypan blue exclusion assay and flow cytometry were used to examine cell death and apoptosis. Results ABT-263 upregulated Mcl-1 mRNA and protein levels in HCC cells, which contributes to ABT-263 resistance. ABT-263 increased the mRNA level of Mcl-1 in HCC cells by enhancing the mRNA stability without influencing its transcription. Furthermore, ABT-263 increased the protein stability of Mcl-1 through promoting ERK- and JNK-induced phosphorylation of Mcl-1Thr163 and increasing the Akt-mediated inactivation of GSK-3β. Additionally, the inhibitors of ERK, JNK or Akt sensitized ABT-263-induced apoptosis in HCC cells. Conclusions ABT-263 increases Mcl-1 stability at both mRNA and protein levels in HCC cells. Inhibition of ERK, JNK or Akt activity sensitizes ABT-263-induced apoptosis. This study may provide novel insights into the Bcl-2-targeted cancer therapeutics.This study was supported in part by Chongqing Natural Science Foundation (cstc2011BB5030 and 2013jjB10015), the National Natural Science Foundation of China (31201068, 81273226 and 81228005) and the Scientific Funds of Third Military Medical University (2011XHG02 and 2012XZH01)

Toughened (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2–SiC composites fabricated by one-step reactive sintering with a unique SiB6 additive

High-entropy diboride has been arousing considerable interest in recent years. However, the low toughness and damage tolerance limit its applications as ultra-high-temperature structural materials. Here we report that a unique SiB6 additive has been first incorporated as boron and silicon sources to fabricate a high-entropy boride (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2–SiC composite though one-step boro/carbothermal reduction reactive sintering. A synergetic effect of high-entropy sluggish diffusion and SiC secondary phase retarded the grain growth of the (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2–51SiC composites. The small grain size was beneficial to shorten the diffusion path for mass transport, thereby enhancing the relative density to ~99.3%. These results in an increase of fracture toughness from ~5.2 in HEBS-1900 to ~7.7 MPa·m1/2 in HEBS-2000, which corresponded to a large improvement of 48%. The improvement was attributed to a mixed mode of intergranular and transgranular cracking for offering effective pinning in crack propagation, resulting from balanced grain boundary strength collectively affected by improved densification, solid solution strengthening, and incorporation of SiC secondary phase

The impact of lookback windows on the prevalence and incidence of chronic diseases among people living with HIV: an exploration in administrative health data in Canada

Background We described the impact of different lengths of lookback window (LW), a retrospective time period to observe diagnoses in administrative data, on the prevalence and incidence of eight chronic diseases. Methods Our study populations included people living with HIV (N = 5151) and 1:5 age-sex-matched HIV-negative individuals (N = 25,755) in British Columbia, Canada, with complete follow-up between 1996 and 2012. We measured period prevalence and incidence of diseases in 2012 using LWs ranging from 1 to 16 years. Cases were deemed prevalent if identified in 2012 or within a defined LW, and incident if newly identified in 2012 with no previous cases detected within a defined LW. Chronic disease cases were ascertained using published case-finding algorithms applied to population-based provincial administrative health datasets. Results Overall, using cases identified by the full 16-year LW as the reference, LWs ≥8 years and ≥ 4 years reduced the proportion of misclassified prevalent and incidence cases of most diseases to < 20%, respectively. The impact of LWs varied across diseases and populations. Conclusions This study underscored the importance of carefully choosing LWs and demonstrated data-driven approaches that may inform these choices. To improve comparability of prevalence and incidence estimates across different settings, we recommend transparent reporting of the rationale and limitations of chosen LWs.Medicine, Faculty ofOther UBCMedicine, Department ofPopulation and Public Health (SPPH), School ofReviewedFacultyResearcherOthe

AKT-mediated phosphorylation of ATG4B impairs mitochondrial activity and enhances the Warburg effect in hepatocellular carcinoma cells

<p>Phosphorylation is a major type of post-translational modification, which can influence the cellular physiological function. ATG4B, a key macroautophagy/autophagy-related protein, has a potential effect on the survival of tumor cells. However, the role of ATG4B phosphorylation in cancers is still unknown. In this study, we identified a novel phosphorylation site at Ser34 of ATG4B induced by AKT in HCC cells. The phosphorylation of ATG4B at Ser34 had little effect on autophagic flux, but promoted the Warburg effect including the increase of L-lactate production and glucose consumption, and the decrease of oxygen consumption in HCC cells. The Ser34 phosphorylation of ATG4B also contributed to the impairment of mitochondrial activity including the inhibition of F₁Fo-ATP synthase activity and the elevation of mitochondrial ROS in HCC cells. Moreover, the phosphorylation of ATG4B at Ser34 enhanced its mitochondrial location and the subsequent colocalization with F₁Fo-ATP synthase in HCC cells. Furthermore, recombinant human ATG4B protein suppressed the activity of F₁Fo-ATP synthase in MgATP submitochondrial particles from patient-derived HCC tissues in vitro. In brief, our results demonstrate for the first time that the phosphorylation of ATG4B at Ser34 participates in the metabolic reprogramming of HCC cells via repressing mitochondrial function, which possibly results from the Ser34 phosphorylation-induced mitochondrial enrichment of ATG4B and the subsequent inhibition of F₁Fo-ATP synthase activity. Our findings reveal a noncanonical working pattern of ATG4B under pathological conditions, which may provide a scientific basis for developing novel strategies for HCC treatment by targeting ATG4B and its Ser34 phosphorylation.</p

A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging

Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community