Class-Incremental Exemplar Compression for Class-Incremental Learning

Exemplar-based class-incremental learning (CIL) finetunes the model with all samples of new classes but few-shot exemplars of old classes in each incremental phase, where the "few-shot" abides by the limited memory budget. In this paper, we break this "few-shot" limit based on a simple yet surprisingly effective idea: compressing exemplars by downsampling non-discriminative pixels and saving "many-shot" compressed exemplars in the memory. Without needing any manual annotation, we achieve this compression by generating 0-1 masks on discriminative pixels from class activation maps (CAM). We propose an adaptive mask generation model called class-incremental masking (CIM) to explicitly resolve two difficulties of using CAM: 1) transforming the heatmaps of CAM to 0-1 masks with an arbitrary threshold leads to a trade-off between the coverage on discriminative pixels and the quantity of exemplars, as the total memory is fixed; and 2) optimal thresholds vary for different object classes, which is particularly obvious in the dynamic environment of CIL. We optimize the CIM model alternatively with the conventional CIL model through a bilevel optimization problem. We conduct extensive experiments on high-resolution CIL benchmarks including Food-101, ImageNet-100, and ImageNet-1000, and show that using the compressed exemplars by CIM can achieve a new state-of-the-art CIL accuracy, e.g., 4.8 percentage points higher than FOSTER on 10-Phase ImageNet-1000. Our code is available at https://github.com/xfflzl/CIM-CIL.Comment: Accepted to CVPR 202

Maintaining human fetal pancreatic stellate cell function and proliferation require β1 integrin and collagen I matrix interactions

Pancreatic stellate cells (PaSCs) are cells that are located around the acinar, ductal, and vasculature tissue of the rodent and human pancreas, and are responsible for regulating extracellular matrix (ECM) turnover and maintaining the architecture of pancreatic tissue. This study examines the contributions of integrin receptor signaling in human PaSC function and survival. Human PaSCs were isolated from pancreata collected during the 2nd trimester of pregnancy and identified by expression of stellate cell markers, ECM proteins and associated growth factors. Multiple integrins are found in isolated human PaSCs, with high levels of β1, a3 and a5. Cell adhesion and migration assays demonstrated that human PaSCs favour collagen I matrix, which enhanced PaSC proliferation and increased TGFβ1, CTGF and a3β1 integrin. Significant activation of FAK/ERK and AKT signaling pathways, and up-regulation of cyclin D1 protein levels, were observed within PaSCs cultured on collagen I matrix. Blocking β1 integrin significantly decreased PaSC adhesion, migration and proliferation, further complementing the aforementioned findings. This study demonstrates that interaction of β1 integrin with collagen I is required for the proliferation and function of human fetal PaSCs, which may contribute to the biomedical engineering of the ECM microenvironment needed for the efficient regulation of pancreatic development

Robust tracking with discriminative ranking middle-level patches

The appearance model has been shown to be essential for robust visual tracking since it is the basic criterion to locating targets in video sequences. Though existing tracking-by-detection algorithms have shown to be greatly promising, they still suffer from the drift problem, which is caused by updating appearance models. In this paper, we propose a new appearance model composed of ranking middle-level patches to capture more object distinctiveness than traditional tracking-by-detection models. Targets and backgrounds are represented by both low-level bottom-up features and high-level top-down patches, which can compensate each other. Bottom-up features are defined at the pixel level, and each feature gets its discrimination score through selective feature attention mechanism. In top-down feature extraction, rectangular patches are ranked according to their bottom-up discrimination scores, by which all of them are clustered into irregular patches, named ranking middle-level patches. In addition, at the stage of classifier training, the online random forests algorithm is specially refined to reduce drifting problems. Experiments on challenging public datasets and our test videos demonstrate that our approach can effectively prevent the tracker drifting problem and obtain competitive performance in visual tracking

Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions

Towards intelligent Human-Vehicle Interaction systems and innovative Human-Vehicle Interaction designs, in-vehicle drivers' physiological data has been explored as an essential data source. However, equipping multiple biosensors is considered the limited extent of user-friendliness and impractical during the driving procedure. The lack of a proper approach to access physiological data has hindered wider applications of advanced biosignal-driven designs in practice (e.g. monitoring systems and etc.). Hence, the demand for a user-friendly approach to measuring drivers' body statuses has become more intense. In this Work-In-Progress, we present Face2Multi-modal, an In-vehicle multi-modal Data Streams Predictors through facial expressions only. More specifically, we have explored the estimations of Heart Rate, Skin Conductance, and Vehicle Speed of the drivers. We believe Face2Multi-modal provides a user-friendly alternative to acquiring drivers' physiological status and vehicle status, which could serve as the building block for many current or future personalized Human-Vehicle Interaction designs. More details and updates about the project Face2Multi-modal is online at https://github.com/unnc-ucc/Face2Multimodal/

Synergistic effects of neck circumference and metabolic risk factors on insulin resistance: the Cardiometabolic Risk in Chinese (CRC) study

Objectives: Recent studies have associated neck circumference (NC) with insulin resistance (IR). We examined whether such relation was modified by other metabolic risk factors. Methods: The study samples were from a community-based health examination survey in central China. A total of 2588 apparently healthy Chinese men and women were included. Results: Plasma levels of total cholesterol (TC), HDL-C, uric acid (UA) and diastolic blood pressure (DBP) were independently associated with NC after adjusted for age, sex, body mass index (BMI), waist circumference (WC) and hip circumference (HC) (P = 0.009, 0.001, 0.015 and 0.015, respectively). We observed significant interactions of NC with triglyceride (TG) and UA (all the p for interaction = 0.001) in relation to HOMA-IR. It appeared that the associations between NC and HOMA-IR were more evident in those with higher UA or TG level. Conclusions: Our data indicate that in apparently healthy Chinese adults, there were synergistic effects of UA, TG and neck circumference on insulin resistance

Circulating irisin in nonalcoholic fatty liver disease: an updated meta-analysis

Introduction: Exogenous administration of recombinant irisin may reverse hepatic steatosis and steatohepatitis. However, it remains controversial as to whether nonalcoholic fatty liver disease (NAFLD) shows reduced circulating (serum/plasma) irisin levels. A meta-analysis was conducted to address this issue. Material and methods: A literature search of databases was performed up to June 2021. Observational studies that reported circulating irisin in NAFLD ascertained by any methods (e.g. ultrasonography or magnetic resonance) and compared with any controls were eligible for inclusion. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) were obtained using a random-effects meta-analysis model. Results: Eleven studies enrolling 1277 NAFLD cases and 944 non-NAFLD controls were included. The approaches used for NAFLD ascertainment included ultrasonography (4 studies), magnetic resonance (3 studies), and liver biopsy (5 studies). Meta-analysis showed that circulating irisin in NAFLD was comparable to any non-NAFLD controls (10 studies with 11 datasets; SMD –0.09, 95% CI: –0.48 to 0.29), including the body mass index (BMI)-matched and lean controls (both p ≥ 0.80). Restricting studies to NAFLD ascertained by magnetic resonance or liver biopsy rather than ultrasonography showed that serum irisin was reduced in NAFLD (5 studies, SMD –0.63, 95% CI: –1.14 to –0.13). Meta-analysis also suggested that circulating irisin did not differ between mild and moderate-to-severe NAFLD (7 studies; SMD 0.02, 95% CI: –0.25 to 0.30), and this association was not significantly moderated by study location (Europe versus Asia). Conclusions: Circulating irisin in NAFLD did not differ from any non-NAFLD controls and was unlikely to be affected by disease severity or racial-ethnic difference

Pancreatic Stellate Cells: A Rising Translational Physiology Star as a Potential Stem Cell Type for Beta Cell Neogenesis

The progressive decline and eventual loss of islet β-cell function underlies the pathophysiological mechanism of the development of both type 1 and type 2 diabetes mellitus. The recovery of functional β-cells is an important strategy for the prevention and treatment of diabetes. Based on similarities in developmental biology and anatomy, in vivo induction of differentiation of other types of pancreatic cells into β-cells is a promising avenue for future diabetes treatment. Pancreatic stellate cells (PSCs), which have attracted intense research interest due to their effects on tissue fibrosis over the last decade, express multiple stem cell markers and can differentiate into various cell types. In particular, PSCs can successfully differentiate into insulin- secreting cells in vitro and can contribute to tissue regeneration. In this article, we will brings together the main concepts of the translational physiology potential of PSCs that have emerged from work in the field and discuss possible ways to develop the future renewable source for clinical treatment of pancreatic diseases

The Prevalence and Risk Factors of Diabetic Retinopathy: Screening and Prophylaxis Project in 6 Provinces of China.

Purpose: To investigate the prevalence and associated factors of diabetic retinopathy (DR) and advanced DR in Chinese adults with diabetes mellitus (DM). Patients and Methods: A cross-sectional study was performed on 4831 diabetic patients from 24 hospitals from April 2018 to July 2020. Non-mydriatic fundus of patients were interpreted by an artificial intelligence (AI) system. Fundus photos that were unsuitable for AI interpretation were interpreted by two ophthalmologists trained by one expert ophthalmologist at Beijing Tongren Hospital. Medical history, height, weight, body mass index (BMI), glycosylated hemoglobin (HbA1c), blood pressure, and laboratory examinations were recorded. Results: A total of 4831 DM patients were included in this study. The prevalence of DR and advanced DR in the diabetic population was 31.8% and 6.6%, respectively. In multiple logistic regression analysis, male (odds ratio [OR], 1.39), duration of diabetes (OR, 1.05), HbA1c (OR, 1.11), farmer (OR, 1.39), insulin treatment (OR, 1.61), region (northern, OR, 1.78; rural, OR, 6.96), and presence of other diabetic complications (OR: 2.03) were associated with increased odds of DR. The factors associated with increased odds of advanced DR included poor glycemic control (HbA1c > 7.0%) (OR, 2.58), insulin treatment (OR, 1.73), longer duration of diabetes (OR, 3.66), rural region (OR, 4.84), and presence of other diabetic complications (OR, 2.36), but overweight (BMI > 25 kg/m2) (OR, 0.61) was associated with reduced odds of advanced DR. Conclusion: This study shows that the prevalence of DR is very high in Chinese adults with DM, highlighting the necessity of early diabetic retinal screening

Muscle strength and prediabetes progression and regression in middle‐aged and older adults: a prospective cohort study

Background: Prediabetes progression is associated with increased mortality while its regression decreases it. It is unclear whether muscle strength is related to prediabetes progression or regression. This study investigated the associations of muscle strength, assessed by grip strength and chair‐rising time, with prediabetes progression and regression based on the China Health and Retirement Longitudinal Study (CHARLS) enrolling middle‐aged and older adults. Methods: We included 2623 participants with prediabetes from CHARLS, who were followed up 4 years later with blood samples collected for measuring fasting plasma glucose and haemoglobin A1c. Grip strength (normalized by body weight) and chair‐rising time were assessed at baseline and categorized into tertiles (low, middle, and high groups). Prediabetes at baseline and follow‐up was defined primarily using the American Diabetes Association (ADA) criteria and secondarily using the World Health Organization (WHO) and International Expert Committee (IEC) criteria. Multinomial logistic regression analysis was applied to obtain the odds ratios (ORs) and 95% confidence intervals (CIs). Results: The mean age of included participants was 59.0 ± 8.6 years, and 46.6% of them were males. During follow‐up, 1646 participants remained as prediabetes, 379 progressed to diabetes, and 598 regressed to normoglycaemia based on ADA criteria. Participants who progressed to diabetes had lower normalized grip strength than those who remained as prediabetes (0.49 ± 0.15 vs. 0.53 ± 0.15, P < 0.001), but participants who regressed to normoglycaemia showed the opposite (0.55 ± 0.16 vs. 0.53 ± 0.15, P = 0.003). However, chair‐rising time was comparable across different groups (P overall = 0.17). Compared with participants in low normalized grip strength or high chair‐rising time group, those in high normalized grip strength or low chair‐rising time group had decreased odds of progression to diabetes (OR 0.62, 95% CI 0.44 to 0.87; and OR 0.69, 95% CI 0.51 to 0.93, respectively) after multivariable adjustment. However, both were unrelated to the odds of regression to normoglycaemia (OR 0.94, 95% CI 0.71 to 1.25; and OR 0.84, 95% CI 0.65 to 1.07, respectively). These outcomes remained generally comparable when prediabetes was defined by WHO or IEC criteria. Higher normalized grip strength but not lower chair‐rising time was prospectively associated with lower blood pressure, better glycaemic condition, and lower inflammation (all P ≤ 0.04). Conclusions: High muscle strength is associated with reduced odds of progression to diabetes but does not predict regression to normoglycaemia in prediabetes. Future studies are warranted to assess whether increases in muscle strength promote prediabetes regression

Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain

The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL https://github.com/zhaoyw456/DIPRAS