Continual Learning for Image Segmentation with Dynamic Query

Image segmentation based on continual learning exhibits a critical drop of performance, mainly due to catastrophic forgetting and background shift, as they are required to incorporate new classes continually. In this paper, we propose a simple, yet effective Continual Image Segmentation method with incremental Dynamic Query (CISDQ), which decouples the representation learning of both old and new knowledge with lightweight query embedding. CISDQ mainly includes three contributions: 1) We define dynamic queries with adaptive background class to exploit past knowledge and learn future classes naturally. 2) CISDQ proposes a class/instance-aware Query Guided Knowledge Distillation strategy to overcome catastrophic forgetting by capturing the inter-class diversity and intra-class identity. 3) Apart from semantic segmentation, CISDQ introduce the continual learning for instance segmentation in which instance-wise labeling and supervision are considered. Extensive experiments on three datasets for two tasks (i.e., continual semantic and instance segmentation are conducted to demonstrate that CISDQ achieves the state-of-the-art performance, specifically, obtaining 4.4% and 2.9% mIoU improvements for the ADE 100-10 (6 steps) setting and ADE 100-5 (11 steps) setting.Comment: Code: https://github.com/weijiawu/CisD

Exosomes: Multiple-targeted multifunctional biological nanoparticles in the diagnosis, drug delivery, and imaging of cancer cells

Exosomes are biological nanoparticles (30−150 nm) secreted in the extracellular area from all of cells, that mediate intercellular message. Exosomes act as the carriers for numerous proteins, DNAs, RNAs and cell-signaling molecules. Therefore, exosomes secreted by the tumor cells are useful for diagnostic purposes because of their persistent presence in the blood and their provision of genetic cargo similar to those in tumor. Due to the risks of aggressive activity and ambiguity of biological activity in other tissues, the use of exosomes in drug delivery and imaging has been limited. However, their high loading, stability and longer circulation time, excellent targeting, high cell penetration performance, and optimal biodegradability have made them potential agents in targeted cancer treatment. Therefore, in addition to examining methods for isolating and loading exosomes, this paper discusses the applications of exosomes in biological measurement, imaging, and therapeutic activities. Also, this review describes the challenges of using exosomes compared to conventional methods and shows that it is very useful to use them due to less aggressive activities. Finally, this review attempts to provide an appropriate incentive by showing the performance of exosomes in cancer therapy through targeted drug delivery, gene therapy, imaging and diagnosis

ICDAR 2023 Video Text Reading Competition for Dense and Small Text

Recently, video text detection, tracking, and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenarios, while ignoring extreme video text challenges, i.e., dense and small text in various scenarios. In this competition report, we establish a video text reading benchmark, DSText, which focuses on dense and small text reading challenges in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, a new challenge for video text spotter. 2) High-proportioned small texts. 3) Various new scenarios, e.g., Game, sports, etc. The proposed DSText includes 100 video clips from 12 open scenarios, supporting two tasks (i.e., video text tracking (Task 1) and end-to-end video text spotting (Task 2)). During the competition period (opened on 15th February 2023 and closed on 20th March 2023), a total of 24 teams participated in the three proposed tasks with around 30 valid submissions, respectively. In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols and the results summaries of the ICDAR 2023 on DSText competition. Moreover, we hope the benchmark will promise video text research in the community

A novel FCTF evaluation and prediction model for food efficacy based on association rule mining

IntroductionFood-components-target-function (FCTF) is an evaluation and prediction model based on association rule mining (ARM) and network interaction analysis, which is an innovative exploration of interdisciplinary integration in the food field.MethodsUsing the components as the basis, the targets and functions are comprehensively explored in various databases and platforms under the guidance of the ARM concept. The focused active components, key targets and preferred efficacy are then analyzed by different interaction calculations. The FCTF model is particularly suitable for preliminary studies of medicinal plants in remote and poor areas.ResultsThe FCTF model of the local medicinal food Laoxianghuang focuses on the efficacy of digestive system cancers and neurological diseases, with key targets ACE, PTGS2, CYP2C19 and corresponding active components citronellal, trans-nerolidol, linalool, geraniol, α-terpineol, cadinene and α-pinene.DiscussionCenturies of traditional experience point to the efficacy of Laoxianghuang in alleviating digestive disorders, and our established FCTF model of Laoxianghuang not only demonstrates this but also extends to its possible adjunctive efficacy in neurological diseases, which deserves later exploration. The FCTF model is based on the main line of components to target and efficacy and optimizes the research level from different dimensions and aspects of interaction analysis, hoping to make some contribution to the future development of the food discipline

DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models

Current deep networks are very data-hungry and benefit from training on largescale datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data can be generated infinitely using generative models such as DALL-E and diffusion models, with minimal effort and cost. In this paper, we present DatasetDM, a generic dataset generation model that can produce diverse synthetic images and the corresponding high-quality perception annotations (e.g., segmentation masks, and depth). Our method builds upon the pre-trained diffusion model and extends text-guided image synthesis to perception data generation. We show that the rich latent code of the diffusion model can be effectively decoded as accurate perception annotations using a decoder module. Training the decoder only needs less than 1% (around 100 images) manually labeled images, enabling the generation of an infinitely large annotated dataset. Then these synthetic data can be used for training various perception models for downstream tasks. To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation. Notably, it achieves 1) state-of-the-art results on semantic segmentation and instance segmentation; 2) significantly more robust on domain generalization than using the real data alone; and state-of-the-art results in zero-shot segmentation setting; and 3) flexibility for efficient application and novel task composition (e.g., image editing). The project website and code can be found at https://weijiawu.github.io/DatasetDM_page/ and https://github.com/showlab/DatasetDM, respectivel

Kaempferol as a flavonoid induces osteoblastic differentiation via estrogen receptor signaling

<p>Abstract</p> <p>Background</p> <p>Flavonoids, a group of compounds mainly derived from vegetables and herbal medicines, chemically resemble estrogen and some have been used as estrogen substitutes. Kaempferol, a flavonol derived from the rhizome of <it>Kaempferia galanga </it>L., is a well-known phytoestrogen possessing osteogenic effects that is also found in a large number of plant foods.</p> <p>The herb <it>K. galanga </it>is a popular traditional aromatic medicinal plant that is widely used as food spice and in medicinal industries. In the present study, both the estrogenic and osteogenic properties of kaempferol are evaluated.</p> <p>Methods</p> <p>Kaempferol was first evaluated for its estrogenic properties, including its effects on estrogen receptors. The osteogenic properties of kaempferol were further determined its induction effects on specific osteogenic enzymes and genes as well as the mineralization process in cultured rat osteoblasts.</p> <p>Results</p> <p>Kaempferol activated the transcriptional activity of pERE-Luc (3.98 ± 0.31 folds at 50 μM) and induced estrogen receptor α (ERα) phosphorylation in cultured rat osteoblasts, and this ER activation was correlated with induction and associated with osteoblast differentiation biomarkers, including alkaline phosphatase activity and transcription of osteoblastic genes, <it>e.g</it>., type I collagen, osteonectin, osteocalcin, Runx2 and osterix. Kaempferol also promoted the mineralization process of osteoblasts (4.02 ± 0.41 folds at 50 μM). ER mediation of the kaempferol-induced effects was confirmed by pretreatment of the osteoblasts with an ER antagonist, ICI 182,780, which fully blocked the induction effect.</p> <p>Conclusion</p> <p>Our results showed that kaempferol stimulates osteogenic differentiation of cultured osteoblasts by acting through the estrogen receptor signaling.</p

A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification

In this paper, we propose two techniques, namely joint modeling and data augmentation, to improve system performances for audio-visual scene classification (AVSC). We employ pre-trained networks trained only on image data sets to extract video embedding; whereas for audio embedding models, we decide to train them from scratch. We explore different neural network architectures for joint modeling to effectively combine the video and audio modalities. Moreover, data augmentation strategies are investigated to increase audio-visual training set size. For the video modality the effectiveness of several operations in RandAugment is verified. An audio-video joint mixup scheme is proposed to further improve AVSC performances. Evaluated on the development set of TAU Urban Audio Visual Scenes 2021, our final system can achieve the best accuracy of 94.2% among all single AVSC systems submitted to DCASE 2021 Task 1b.Comment: 5 pages, 1 figure, submitted to INTERSPEECH 202

Mir-382 Promotes Differentiation of Rat Liver Progenitor Cell WB-F344 by Targeting Ezh2

Background/Aims: Liver progenitor cells (LPCs) were considered as a promising hepatocyte source of cell therapy for liver disease due to their self-renewal and differentiation capacities, while little is known about the mechanism of LPC differentiate into hepatocytes. This study aims to explore the effect of miR-382, a member of Dlk1-Dio3 microRNA cluster, during hepatic differentiation from LPCs. Methods: In this study, we used rat liver progenitor cell WB-F344 as LPC cell model and HGF as inducer to simulate the process of LPCs hepatic differentiation, then microRNAs were quantified by qPCR. Next, WB-F344 cell was transfected with miR-382 mimics, then hepatocyte cell trait was characterized by multiple experiments, including that periodic acid schiff staining and cellular uptake and excretion of indocyanine green to evaluate the hepatocellular function, qPCR and Western Blotting analysis to detect the hepatocyte-specific markers (ALB, Ttr, Apo E and AFP) and transmission electron microscopy to observe the hepatocellular morphology. Moreover, Luciferase reporter assay was used to determine whether Ezh2 is the direct target of miR-382. Results: We found that miR-382 increased gradually and was inversely correlated with the potential target, Ezh2, during WB-F344 hepatic differentiation. In addition, functional studies indicated that miR-382 increased the level of hepatocyte-specific genes. Conclusions: This study demonstrates that miR-382 may be a novel regulator of LPCs differentiation by targeting Ezh2

The mechanisms of Yu Ping Feng San in tracking the cisplatin-resistance by regulating ATP-binding cassette transporter and glutathione S-transferase in lung cancer cells

Cisplatin is one of the first line anti-cancer drugs prescribed for treatment of solid tumors; however, the chemotherapeutic drug resistance is still a major obstacle of cisplatin in treating cancers. Yu Ping Feng San (YPFS), a well-known ancient Chinese herbal combination formula consisting of Astragali Radix, Atractylodis Macrocephalae Rhizoma and Saposhnikoviae Radix, is prescribed as a herbal decoction to treat immune disorders in clinic. To understand the fast-onset action of YPFS as an anti-cancer drug to fight against the drug resistance of cisplatin, we provided detailed analyses of intracellular cisplatin accumulation, cell viability, and expressions and activities of ATP-binding cassette transporters and glutathione S-transferases (GSTs) in YPFS-treated lung cancer cell lines. In cultured A549 or its cisplatin-resistance A549/DDP cells, application of YPFS increased accumulation of intracellular cisplatin, resulting in lower cell viability. In parallel, the activities and expressions of ATP-binding cassette transporters and GSTs were down-regulated in the presence of YPFS. The expression of p65 subunit of NF-κB complex was reduced by treating the cultures with YPFS, leading to a high ratio of Bax/Bcl-2, i.e. increasing the rate of cell death. Prim-O-glucosylcimifugin, one of the abundant ingredients in YPFS, modulated the activity of GSTs, and then elevated cisplatin accumulation, resulting in increased cell apoptosis. The present result supports the notion of YPFS in reversing drug resistance of cisplatin in lung cancer cells by elevating of intracellular cisplatin, and the underlying mechanism may be down regulating the activities and expressions of ATP-binding cassette transporters and GSTs