Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning

Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models. However, these models mainly rely on the classification loss to implicitly regularize the class-level data distributions, and they may face difficulties when handling classes with diverse visual patterns. We argue that the incorporation of the structural information between data samples may improve this situation. To achieve this goal, this paper presents a framework termed \textbf{C}lass-level Structural Relation Modeling and Smoothing for Visual Representation Learning (CSRMS), which includes the Class-level Relation Modelling, Class-aware Graph Sampling, and Relational Graph-Guided Representation Learning modules to model a relational graph of the entire dataset and perform class-aware smoothing and regularization operations to alleviate the issue of intra-class visual diversity and inter-class similarity. Specifically, the Class-level Relation Modelling module uses a clustering algorithm to learn the data distributions in the feature space and identify three types of class-level sample relations for the training set; Class-aware Graph Sampling module extends typical training batch construction process with three strategies to sample dataset-level sub-graphs; and Relational Graph-Guided Representation Learning module employs a graph convolution network with knowledge-guided smoothing operations to ease the projection from different visual patterns to the same class. Experiments demonstrate the effectiveness of structured knowledge modelling for enhanced representation learning and show that CSRMS can be incorporated with any state-of-the-art visual representation learning models for performance gains. The source codes and demos have been released at https://github.com/czt117/CSRMS

Clinical value of miR-23a-3p expression in early diagnosis of diabetic kidney disease

Introduction: The objective was to observe the expression of miR-23a-3p in the serum of patients with type 2 diabetic nephropathy (T2DN) and to explore its clinical significance. Materials and methods: 112 patients with type 2 diabetes were divided into a simple diabetes mellitus (NON) group, T2DN microalbuminuria (MIC) group, and T2DN macroalbuminuria (MAC) group, according to the urinary protein-creatinine ratio (uACR). Clinical data were collected, miR-23a-3p levels in serum were measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR), and clinical parameters were measured by an automatic biochemical analyser; the influencing factors of diabetic kidney disease (DKD) and the correlation between miR-23a-3p expression and clinical parameters were analysed. Results: The expression of miR-23a-3p in the serum of the DKD group was lower than that of the normal control (CON) and NON groups. Correlation analysis showed that miR-23a-3p was positively correlated with urinary albumin (Albu), glycosylated haemoglobin (HbA1c), total cholesterol (CHOL), glycated albumin (GA-L), serum creatinine (Scr), fasting blood glucose (GLU), and uric acid (UA), negatively correlated with uACR and high-density lipoprotein cholesterol (HDL-C), but not correlated with urinary creatinine (CREA). The area under the receiver operating characteristic (ROC) curve (AUC) of miR-23a-3p for the diagnosis of DKD was 0.686 [95% confidence interval (CI): 0.599–0.773], with a sensitivity of 64.5% and a specificity of 71.2%; the AUC for differentiating NON from DKD was 0.700 (95% CI: 0.598–0.802), with a sensitivity of 61.8% and a specificity of 77.8%. Multivariate logistic regression analysis showed that serum miR-23a-3plevels were not associated with the development of DKD after adjusting for other levels of influence and were not significant for the differentiation of NON and DKD. Conclusion: Serum miR-23a-3p levels are decreased in T2DN patients, and this change becomes more significant with the severity of the disease, which may be a marker for the early diagnosis and progression of T2DN

Observational constraints on cosmic neutrinos and dark energy revisited

Using several cosmological observations, i.e. the cosmic microwave background anisotropies (WMAP), the weak gravitational lensing (CFHTLS), the measurements of baryon acoustic oscillations (SDSS+WiggleZ), the most recent observational Hubble parameter data, the Union2.1 compilation of type Ia supernovae, and the HST prior, we impose constraints on the sum of neutrino masses (\mnu), the effective number of neutrino species (\neff) and dark energy equation of state ($w$), individually and collectively. We find that a tight upper limit on \mnu can be extracted from the full data combination, if \neff and $w$ are fixed. However this upper bound is severely weakened if \neff and $w$ are allowed to vary. This result naturally raises questions on the robustness of previous strict upper bounds on \mnu, ever reported in the literature. The best-fit values from our most generalized constraint read \mnu=0.556^{+0.231}_{-0.288}\rm eV, \neff=3.839\pm0.452, and $w=-1.058\pm0.088$ at 68% confidence level, which shows a firm lower limit on total neutrino mass, favors an extra light degree of freedom, and supports the cosmological constant model. The current weak lensing data are already helpful in constraining cosmological model parameters for fixed $w$. The dataset of Hubble parameter gains numerous advantages over supernovae when $w=-1$, particularly its illuminating power in constraining \neff. As long as $w$ is included as a free parameter, it is still the standardizable candles of type Ia supernovae that play the most dominant role in the parameter constraints.Comment: 39 pages, 15 figures, 7 tables, accepted to JCA