Dataset Condensation via Generative Model

Dataset condensation aims to condense a large dataset with a lot of training samples into a small set. Previous methods usually condense the dataset into the pixels format. However, it suffers from slow optimization speed and large number of parameters to be optimized. When increasing image resolutions and classes, the number of learnable parameters grows accordingly, prohibiting condensation methods from scaling up to large datasets with diverse classes. Moreover, the relations among condensed samples have been neglected and hence the feature distribution of condensed samples is often not diverse. To solve these problems, we propose to condense the dataset into another format, a generative model. Such a novel format allows for the condensation of large datasets because the size of the generative model remains relatively stable as the number of classes or image resolution increases. Furthermore, an intra-class and an inter-class loss are proposed to model the relation of condensed samples. Intra-class loss aims to create more diverse samples for each class by pushing each sample away from the others of the same class. Meanwhile, inter-class loss increases the discriminability of samples by widening the gap between the centers of different classes. Extensive comparisons with state-of-the-art methods and our ablation studies confirm the effectiveness of our method and its individual component. To our best knowledge, we are the first to successfully conduct condensation on ImageNet-1k.Comment: old work,done in 202

Is synthetic data from generative models ready for image recognition?

Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. Though the results are astonishing to human eyes, how applicable these generated images are for recognition tasks remains under-explored. In this work, we extensively study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks, and focus on two perspectives: synthetic data for improving classification models in data-scarce settings (i.e. zero-shot and few-shot), and synthetic data for large-scale model pre-training for transfer learning. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks. Code: https://github.com/CVMI-Lab/SyntheticData.Comment: ICLR 2023, spotligh

Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks

Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additional challenges due to concerns regarding data privacy. Recently, synthetic images generated by text-to-image diffusion models, have shown great potential for benefiting image recognition. Although promising, there has been inadequate exploration dedicated to unsupervised learning on diffusion-generated images. To address this, we start by uncovering that diffusion models' cross-attention layers inherently provide annotation-free attention masks aligned with corresponding text inputs on generated images. We then investigate the problems of three prevalent unsupervised learning techniques ( i.e., contrastive learning, masked modeling, and vision-language pretraining) and introduce customized solutions by fully exploiting the aforementioned free attention masks. Our approach is validated through extensive experiments that show consistent improvements in baseline models across various downstream tasks, including image classification, detection, segmentation, and image-text retrieval. By utilizing our method, it is possible to close the performance gap between unsupervised pretraining on synthetic data and real-world scenarios

Plasma Clusterin and the CLU Gene rs11136000 Variant Are Associated with Mild Cognitive Impairment in Type 2 Diabetic Patients

Objective: Type 2 diabetes mellitus (T2DM) is related to an elevated risk of mild cognitive impairment (MCI). Plasma clusterin is reported associated with the early pathology of Alzheimer's disease (AD) and longitudinal brain atrophy in subjects with MCI. The rs11136000 single nucleotide polymorphism within the clusterin (CLU) gene is also associated with the risk of AD. We aimed to investigate the associations among plasma clusterin, rs11136000 genotype and T2DM-associated MCI. Methods: A total of 231 T2DM patients, including 126 MCI and 105 cognitively healthy controls were enrolled in this study. Demographic parameters were collected and neuropsychological tests were conducted. Plasma clusterin and CLU rs11136000 genotype were examined.Results: Plasma clusterin was significantly higher in MCI patients than in control group (p=0.007). In subjects with MCI, plasma clusterin level was negatively correlated with Montreal cognitive assessment and auditory verbal learning test_delayed recall scores (p=0.027 and p=0.020, respectively). After adjustment for age, educational attainment, and gender, carriers of rs11136000 TT genotype demonstrated reduced risk for MCI compared with the CC genotype carriers (OR=0.158, χ2=4.113, p=0.043). Multivariable regression model showed that educational attainment, duration of diabetes, HDL-c, and plasma clusterin levels are associated with MCI in T2DM patients.Conclusions: Plasma clusterin was associated with MCI and may reflect a protective response in T2DM patients. TT genotype exhibited a reduced risk of MCI compared to CC genotype. Further investigations should be conducted to determine the role of clusterin in cognitive decline

Clinical and immunological characteristics of TGM3 in pan-cancer: A potential prognostic biomarker

Background: Recent studies have identified that transglutaminases (TGMs) are involved in a widespread epigenetic modification in tumorigenesis. However, it remains unclear how transglutaminase 3 (TGM3) affects in pan-cancer. The present study aimed to explore the clinical and prognostic function of TGM3 in pan-cancer as well as to explore the relationship of TGM3 expression with clinical stage, survival rate, prognosis condition, immune infiltration and mutation indicators.Methods: The relevant data of tumors were obtained from The Cancer Genome Atlas (TCGA), TARGET, Cancer Cell Line Encyclopedia (CCLE) and Genotype-Tissue Expression (GTEx) databases. According to the Human Protein Atlas (HPA) and TIMER databases, we evaluated the protein expression levels of TGM3 in different organs and tissues as well as their association with immune cell infiltration and immunotherapeutic response in pan-cancers. Expression differences between normal and tumor tissues as well as survival and prognosis situation, clinical data characteristics, tumor mutational burden (TMB), microsatellite instability (MSI), and RNA methylation were also assessed. Oncogenic analyses were also evaluated by GSEA.Results: Compared to normal tissues, some tumor tissues had a lower expression level of TGM3, while other tumor tissues had a high expression level of TGM3. Further studies showed that high TGM3 expression had a certain risk impact on pan-cancer as high TGM3 expression levels were detrimental to the survival of several cancers, except for pancreatic cancer (PAAD). High expression level of TGM3 was also related to higher clinical stages in most cancers. The expression level of TGM3 was significantly negatively correlated with the expression of immune infiltration-related cells, including B cells, CD8+ T cells, CD4+ T cells, neutrophils, macrophages and dendritic cells (DCs). Furthermore, in most cancer types, TGM3 was inversely correlated with TMB, MSI, and methylation, suggesting that TGM3 expression can be used to assess potential therapeutic response, especially immune-related targeted therapy. GSEA analysis elucidated the biological and molecular function of TGM3 in various cancer types. Taken together, these bioinformatic analyses identified TGM3 as an important biomarker for clinical tumor prognosis and evaluation of treatment efficacy.Conclusion: We comprehensively analyzed the clinical characteristics, tumor stages, immune infiltration, methylation level, gene mutation, functional enrichment analysis and immunotherapeutic value of TGM3 in pan-cancer, providing implications for the function of TGM3 and its role in clinical treatment

Interferon regulatory factor 2 binding protein 2b regulates neutrophil versus macrophage fate during zebrafish definitive myelopoiesis

International audienceInterferon regulatory factor 2 binding protein 2b regulates neutrophil versus macrophage fate during zebrafish definitive myelopoiesis

Steam explosion pretreatment enhancing enzymatic digestibility of overground tubers of tiger nut (Cyperus esculentus L.)

IntroductionTiger nut (TN) is recognized as a high potential plant which can grow in well-drained sandy or loamy soils and provide food nutrients. However, the overground tubers of TN remain unutilized currently, which limits the value-added utilization and large-area cultivation of this plant.MethodsIn the present study, the overground tubers of TN were subjected to enzymatic hydrolysis to produce fermentable sugars for biofuels production. Steam explosion (SE) was applied to modify the physical-chemical properties of the overground tubers of TN for enhancing its saccharification.Results and discussionResults showed that SE broke the linkages of hemicellulose and lignin in the TN substrates and increased cellulose content through removal of hemicellulose. Meanwhile, SE cleaved inner linkages within cellulose molecules, reducing the degree of polymerization by 32.13–77.84%. Cellulose accessibility was significantly improved after SE, which was revealed visibly by the confocal laser scanning microscopy imaging techniques. As a result, enzymatic digestibility of the overground tubers of TN was dramatically enhanced. The cellulose conversion of the SE treated TN substrates reached 38.18–63.97%, which was 2.5–4.2 times higher than that without a SE treatment.ConclusionTherefore, SE pretreatment promoted saccharification of the overground tubers of TN, which paves the way for value-added valorization of the TN plants

Clinical efficacy analysis of paxlovid in children with hematological diseases infected with the omicron SARS-CoV-2 new variant

ObjectiveTo summarize the clinical characteristics of children with hematological malignancies co-infected with novel coronavirus and explore the safety and effectiveness of Paxlovid treatment.MethodsFrom December 10, 2022, to January 20, 2023, the clinical data of children with hematological diseases diagnosed with novel coronavirus infection in the outpatient and emergency department of the Seventh Affiliated Hospital of Sun Yat-sen University were retrospectively analyzed.ResultsAccording to whether to give paxlovid or not, it is divided into group A (paxlovid group) and group B (non-paxlovid group). The length of fever was 1–6 days in group A and 0–3 days in group B. The viral clearance time was shorter in group A than in group B. The inflammatory indexes CRP and PCT were significantly higher in group A than in group B (P < 0.05). Twenty patients were followed up for 1 month after leaving the hospital, and there were 5 cases of reappearance of fever, 1 case of increased sleep, 1 case of physical fatigue and 1 case of loss of appetite within 2 weeks.ConclusionsPaxlovid has no apparent adverse reactions in children 12 years old and younger with underlying hematological diseases infected with the new coronavirus. Focusing on the interaction between paxlovid and other drugs is necessary during the treatment

A review on electrospun magnetic nanomaterials:methods, properties and applications

Magnetic materials display attractive properties for a wide range of applications. More recently, interest has turned to significantly enhancing their behaviour for advanced technologies, by exploiting the remarkable advantages that nanoscale materials offer over their bulk counterparts. Electrospinning is a high-throughput method that can continuously produce nanoscale fibres, providing a versatile way to prepare novel magnetic nanomaterials. This article reviews 20 years of magnetic nanomaterials fabricated via electrospinning and introduces their two primary production methods: electrospinning polymer-based magnetic fibres directly from solution and electrospinning fibrous templates for post-treatment. Continual advances in electrospinning have enabled access to a variety of morphologies, which has led to magnetic materials having desirable flexibility, anisotropy and high specific surface area. Post-treatment methods, such as surface deposition, carbonization and calcination, further improve or even create unique magnetic properties in the materials. This renders them useful in broad ranging applications, including electromagnetic interference shielding (EMS), magnetic separation, tissue engineering scaffolding, hyperthermia treatment, drug delivery, nanogenerators and data storage. The processing methods of electrospun magnetic nanofibres, their properties and related applications are discussed throughout this review. Key areas for future research have been highlighted with the aim of stimulating advances in the development of electrospun magnetic nanomaterials for a wide range of applications