Enhancement of Hydration Activity and Microstructure Analysis of γ-C2S.

This paper investigated the combined effect of chemical activators and nano-SiO2 on the hydration reaction and the microstructure of γ-C2S. The hydration reaction of γ-C2S slurry activated with chemical activators (NaHCO3, NaOH, K2CO3, and KOH at 1 mol/L) was enhanced by 1% nano-SiO2. The hydrate reaction rate was determined by isothermal calorimetry, and the hydrated samples were characterized by XRD, TGA/DTG, SEM-EDS, and 29Si MAS/NMR. The results revealed a substantial enhancement in the hydration activity of γ-C2S due to the presence of the alkaline activator. Furthermore, nano-SiO2 did not alter the composition of γ-C2S hydration products, instead providing nucleation sites for the growth of hydration products. Incorporating nano-SiO2 promoted the formation of C-(R)-S-H gel with a low calcium-to-silica ratio and increased its polymerization levels, resulting in more favorable structures. Among all the activators used in this study, potassium salts had a better activation effect than sodium salts. After 28 days of curing, the degree of hydration reaction in the KC+Si group was 48% and about 37% for the NHC+Si group. Whereas, the KH+Si and NH+Si groups only reached approximately 20% after the same hydration duration.

Integrated fragmentomic profile and 5-Hydroxymethylcytosine of capture-based low-pass sequencing data enables pan-cancer detection via cfDNA.

BACKGROUND: Using epigenetic markers and fragmentomics of cell-free DNA for cancer detection has been proven applicable. METHODS: We further investigated the diagnostic potential of combining two features (epigenetic markers and fragmentomic information) of cell-free DNA for detecting various types of cancers. To do this, we extracted cfDNA fragmentomic features from 191 whole-genome sequencing data and studied them in 396 low-pass 5hmC sequencing data, which included four common cancer types and control samples. RESULTS: In our analysis of 5hmC sequencing data from cancer samples, we observed aberrant ultra-long fragments (220-500 bp) that differed from normal samples in terms of both size and coverage profile. These fragments played a significant role in predicting cancer. Leveraging the ability to detect cfDNA hydroxymethylation and fragmentomic markers simultaneously in low-pass 5hmC sequencing data, we developed an integrated model that incorporated 63 features representing both fragmentomic features and hydroxymethylation signatures. This model achieved high sensitivity and specificity for pan-cancer detection (88.52% and 82.35%, respectively). CONCLUSION: We showed that fragmentomic information in 5hmC sequencing data is an ideal marker for cancer detection and that it shows high performance in low-pass sequencing data.

Noninvasive detection of brain gliomas using plasma cell-free DNA 5-hydroxymethylcytosine sequencing.

Liquid biopsy techniques based on deep sequencing of plasma cell-free DNA (cfDNA) could detect the low-frequency somatic mutations and provide an accurate diagnosis for many cancers. However, for brain gliomas, reliable performance of these techniques currently requires obtaining cfDNA from patients' cerebral spinal fluid, which is cumbersome and risky. Here we report a liquid biopsy method based on sequencing of plasma cfDNA fragments carrying 5-hydroxymethylcytosine (5hmC) using selective chemical labeling (hMe-Seal). We first constructed a dataset including 180 glioma patients and 229 non-glioma controls. We found marked concordance between cfDNA hydroxymethylome and the aberrant transcriptome of the underlying gliomas. Functional analysis also revealed overrepresentation of the differentially hydroxymethylated genes (DhmGs) in oncogenic and neural pathways. After splitting our dataset into training and test cohort, we showed that a penalized logistic model constructed with training set DhmGs could distinguish glioma patients from healthy controls in both our test set (AUC = 0.962) and an independent dataset (AUC = 0.930) consisting of 111 gliomas and 111 controls. Additionally, the DhmGs between gliomas with mutant and wild-type isocitrate dehydrogenase (IDH) could be used to train a cfDNA predictor of the IDH mutation status of the underlying tumor (AUC = 0.816), and patients with predicted IDH mutant gliomas had significantly better outcome (P = .01). These results indicate that our plasma cfDNA 5hmC sequencing method could obtain glioma-specific signals, which may be used to noninvasively detect these patients and predict the aggressiveness of their tumors.

A Weighted Fidelity and Regularization-Based Method for Mixed or Unknown Noise Removal from Images on Graphs.

Image denoising technologies in a Euclidean domain have achieved good results and are becoming mature. However, in recent years, many real-world applications encountered in computer vision and geometric modeling involve image data defined in irregular domains modeled by huge graphs, which results in the problem on how to solve image denoising problems defined on graphs. In this paper, we propose a novel model for removing mixed or unknown noise in images on graphs. The objective is to minimize the sum of a weighted fidelity term and a sparse regularization term that additionally utilizes wavelet frame transform on graphs to retain feature details of images defined on graphs. Specifically, the weighted fidelity term with ℓ1-norm and ℓ2-norm is designed based on a analysis of the distribution of mixed noise. The augmented Lagrangian and accelerated proximal gradient methods are employed to achieve the optimal solution to the problem. Finally, some supporting numerical results and comparative analyses with other denoising algorithms are provided. It is noted that we investigate image denoising with unknown noise or a wide range of mixed noise, especially the mixture of Poisson, Gaussian, and impulse noise. Experimental results reported for synthetic and real images on graphs demonstrate that the proposed method is effective and efficient, and exhibits better performance for the removal of mixed or unknown noise in images on graphs than other denoising algorithms in the literature. The method can effectively remove mixed or unknown noise and retain feature details of images on graphs. It delivers a new avenue for denoising images in irregular domains.