MicroRNAs (miRNAs) offer great potential as cancer biomarkers. The importance of miRNAs profiling in tissue and body fluids in colorectal cancer (CRC) have been addressed respectively in many studies. The purpose of our study is to systematically assess the expression of miRNAs in cancer tissue and matched plasma samples and to evaluate their usefulness as minimally invasive diagnostic biomarkers for the detection of CRC. The study was divided into two phases: firstly, qRT-PCR based TaqMan Low Density MiRNA Arrays (TLDAs) was used to screen the differentially expressed miRNAs in 6 plasma samples of CRC patients and 6 healthy controls. Secondly, marker validation by stem-loop reverse transcription real-time PCR using an independent set of paired cancer tissues (n = 88) and matched plasma samples (CRC, n = 88; control, n = 40). Correlation analysis was determined by Pearson’s test. Receiver operating characteristic curve analyses were applied to obtain diagnostic utility of the differentially expressed miRNAs. Target gene prediction and signal pathway analyses were used to predict the function of miRNAs. TLDAs identified 42 miRNAs, which were differentially expressed in patients and healthy individuals. Five of them (miR-375, miR-150, miR-206, miR-125b and miR-126*) were chosen to be validated in plasma and tissue samples. The results indicated that for plasma sample, miR-375 (p < 0.0001) and miR-206 (p = 0.0002) were dysregulated and could discriminate CRC patients from healthy controls. For tissue samples, miR-375 (p < 0.0001), miR-150 (p < 0.0001), miR-125b (p = 0.0065) and miR-126*(p = 0.0009) were down-regulated. miR-375 was significantly down-regulated and positively correlated in both tissue and plasma samples (r = 0.4663, p = 0.0007). Gene ontology and signal pathway analyses showed that most of the target genes that were regulated by miR-375 were involved in some critical pathways in the development and progression of cancer. Our results indicate that the down-regulation of miR-375 in plasma and tissue is matched in CRC. Moreover, bioinformatics prediction revealed miR-375 association with some critical signal pathways in the development and progression of CRC. Therefore, plasma miR-375 holds great promise to be an alternative tissue biomarker for CRC detection.

Previous studies have shown that sirtuin 1 (SIRT1) reduces the production of neuronal amyloid beta (Aβ) and inhibits the inflammatory response of glial cells, thereby generating a neuroprotective effect against Aβ neurotoxicity in animal models of Alzheimer’s disease. However, the protective effect of SIRT1 on astrocytes is still under investigation. This study established a time point model for the clearance of Aβ in primary astrocytes. Results showed that 12 hours of culture was sufficient for endocytosis of oligomeric Aβ, and 36 hours sufficient for effective degradation. Immunofluorescence demonstrated that Aβ degradation in primary astrocytes relies on lysosome function. Enzymatic agonists or SIRT1 inhibitors were used to stimulate cells over a concentration gradient. Aβ was co-cultured for 36 hours in medium. Western blot assay results under different conditions revealed that SIRT1 relies on its deacetylase activity to promote intracellular Aβ degradation. The experiment further screened SIRT1 using quantitative proteomics to investigate downstream, differentially expressed proteins in the Aβ degradation pathway and selected the ones related to enzyme activity of SIRT1. Most of the differentially expressed proteins detected are close to the primary astrocyte lysosomal pathway. Immunofluorescence staining demonstrated that SIRT1 relies on its deacetylase activity to upregulate lysosome number in primary astrocytes. Taken together, these findings confirm that SIRT1 relies on its deacetylase activity to upregulate lysosome number, thereby facilitating oligomeric Aβ degradation in primary astrocytes.

In this letter, a robust minimum error entropy based cubature information filter is proposed for state estimation in non-Gaussian measurement noise. A new combined optimization cost is defined based on the error entropy. Through cubature transform, a statistical linearization regression model is constructed, and a new information filter is then developed by minimizing the error entropy based cost. The fixed-point iteration approach is used to compute the state estimate. Further, the convergence of the proposed information filter is analyzed, and the convergence conditions are derived. Simulations are performed to demonstrate the effectiveness of the proposed algorithm. It is shown that the estimation performance of the proposed filter is more robust than that of traditional methods against the complicated non-Gaussian noises, such as outliers and noises from multimodal distributions.

A labeled multi-Bernoulli (LMB) filter is presented to perform multi-target tracking (MTT) for the glint noise. The measurement noise is modeled as a multivariate Student-t process. The variational Bayesian method is applied in the LMB framework with the augmented state. The predictive likelihood is calculated via minimizing the Kullback-Leibler divergence by the variational lower bound. Simulation results show that our approach is effective in MTT with the glint noise

Considerable progress has recently been made in leveraging CLIP (Contrastive Language-Image Pre-Training) models for text-guided image manipulation. However, all existing works rely on additional generative models to ensure the quality of results, because CLIP alone cannot provide enough guidance information for fine-scale pixel-level changes. In this paper, we introduce CLIPVG, a text-guided image manipulation framework using differentiable vector graphics, which is also the first CLIP-based general image manipulation framework that does not require any additional generative models. We demonstrate that CLIPVG can not only achieve state-of-art performance in both semantic correctness and synthesis quality, but also is flexible enough to support various applications far beyond the capability of all existing methods.

This paper proposes a new solution to multi-target joint detection, tracking and classification based on labeled random finite set (RFS) and belief function theory. A class dependent multi-model marginal generalized labeled multi-Bernoulli (MGLMB) filter is developed to analytically calculate the multi-target number, state estimates and model probabilities. In addition, a two-level classifier based on continuous transferable belief model (cTBM) is designed for target classification. To make full use of the kinematic characteristics for classification, both the dynamic modes and states are considered in the classifier, the model dependent class beliefs are computed on the continuous state feature subspace corresponding to different dynamic modes and then fused. As a result that the uncertainty about the classes is well described for decision, the classification results are more reasonable and robust. Moreover, as the estimation and classification problems are jointly solved, the tracking and classification performance are both improved. In the simulation, a scenario contains multi-target with miss detection and dense clutter is used. The performance of multi-target detection, tracking and classification is better than traditional methods based on Bayesian classifier or single model. Simulation results are illustrated to demonstrate the effectiveness and superiority of the proposed algorithm.