Direct-ink-writable nanocellulose ternary hydrogels via one-pot gelation with alginate and calcium montmorillonite.

Nanocellulose hydrogels are promising to replace synthetic ones for direct ink writing (DIW)-based 3D printing biobased applications. However, less gelation strength and low solid content of the hydrogels limit the printability and subsequent fidelity of the dried object. Herein, a biobased, ternary DIW hydrogel ink is developed by one-pot gelation of cellulose nanofibrils (CNF), sodium alginate (SA), and Ca-montmorillonite (Ca-MMT) via in situ ionic crosslinking.

Preparation and Hydration Properties of Sodium Silicate-Activated Municipal Solid Waste Incineration Bottom Ash Composite Ground-Granulated Blast Furnace Slag Cementitious Materials.

The production of municipal solid waste incineration bottom ash (MSWIBA) is substantial and has the potential to replace cement, despite challenges such as complex composition, uneven particle size distribution, and low reactivity. This paper employs sodium silicate activation of MSWIBA composite Ground-granulated Blast Furnace slag (GGBS) to improve the reactivity in preparing composite cementitious materials. It explores the hydration performance of the composite cementitious materials using isothermal calorimetric analysis, Fourier-transform infrared (FTIR) spectroscopy, XRD physical diffraction analysis, and SEM tests.

TAGET: a toolkit for analyzing full-length transcripts from long-read sequencing.

Single-molecule Real-time Isoform Sequencing (Iso-seq) of transcriptomes by PacBio can generate very long and accurate reads, thus providing an ideal platform for full-length transcriptome analysis. We present an integrated computational toolkit named TAGET for Iso-seq full-length transcript data analyses, including transcript alignment, annotation, gene fusion detection, and quantification analyses such as differential expression gene analysis and differential isoform usage analysis. We evaluate the performance of TAGET using a public Iso-seq dataset and newly sequenced Iso-seq datasets from tumor patients.

High-throughput screening of spike variants uncovers the key residues that alter the affinity and antigenicity of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has elicited a worldwide pandemic since late 2019. There has been ~675 million confirmed coronavirus disease 2019 (COVID-19) cases, leading to more than 6.8 million deaths as of March 1, 2023.

Patient-derived tumor-like cell clusters for drug testing in cancer therapy.

Several patient-derived tumor models emerged recently as robust preclinical drug-testing platforms. However, their potential to guide clinical therapy remained unclear. Here, we report a model called patient-derived tumor-like cell clusters (PTCs).

CNV-BAC: Copy number Variation Detection in Bacterial Circular Genome.

Motivation: Whole-genome sequencing (WGS) is widely used for copy number variation (CNV) detection. However, for most bacteria, their circular genome structure and high replication rate make reads more enriched near the replication origin. CNV detection based on read depth could be seriously influenced by such replication bias.

Detecting virus integration sites based on multiple related sequencing data by VirTect.

Background: Since tumor often has a high level of intra-tumor heterogeneity, multiple tumor samples from the same patient at different locations or different time points are often sequenced to study tumor intra-heterogeneity or tumor evolution. In virus-related tumors such as human papillomavirus- and Hepatitis B Virus-related tumors, virus genome integrations can be critical driving events. It is thus important to investigate the integration sites of the virus genomes.

Somatic mutations in renal cell carcinomas from Chinese patients revealed by whole exome sequencing.

Background: While the somatic mutation profiles of renal cell carcinoma (RCC) have been revealed by several studies worldwide, the overwhelming majority of those were not derived from Chinese patients. The landscape of somatic alterations in RCC from Chinese patients still needs to be elucidated to determine whether discrepancies exist between Chinese patients and sufferers from other countries and regions.

Methods: We collected specimens from 26 Chinese patients with primary RCC, including 15 clear cell renal cell carcinoma (ccRCC) samples, 5 papillary renal cell carcinoma (PRCC) samples and 6 chromophobe renal cell carcinoma (ChRCC) samples.

SVmine improves structural variation detection by integrative mining of predictions from multiple algorithms.

Motivation: Structural variation (SV) is an important class of genomic variations in human genomes. A number of SV detection algorithms based on high-throughput sequencing data have been developed, but they have various and often limited level of sensitivity, specificity and breakpoint resolution. Furthermore, since overlaps between predictions of algorithms are low, SV detection based on multiple algorithms, an often-used strategy in real applications, has little effect in improving the performance of SV detection.

Genomic analysis of oesophageal squamous-cell carcinoma identifies alcohol drinking-related mutation signature and genomic alterations.

Approximately half of the world's 500,000 new oesophageal squamous-cell carcinoma (ESCC) cases each year occur in China. Here, we show whole-genome sequencing of DNA and RNA in 94 Chinese individuals with ESCC. We identify six mutational signatures (E1-E6), and Signature E4 is unique in ESCC linked to alcohol intake and genetic variants in alcohol-metabolizing enzymes.

Pysim-sv: a package for simulating structural variation data with GC-biases.

Background: Structural variations (SVs) are wide-spread in human genomes and may have important implications in disease-related and evolutionary studies. High-throughput sequencing (HTS) has become a major platform for SV detection and simulation serves as a powerful and cost-effective approach for benchmarking SV detection algorithms. Accurate performance assessment by simulation requires the simulator capable of generating simulation data with all important features of real data, such GC biases in HTS data and various complexities in tumor data.

Cell Culture System for Analysis of Genetic Heterogeneity Within Hepatocellular Carcinomas and Response to Pharmacologic Agents.

Background & Aims: No targeted therapies have been found to be effective against hepatocellular carcinoma (HCC), possibly due to the large degree of intratumor heterogeneity. We performed genetic analyses of different regions of HCCs to evaluate levels of intratumor heterogeneity and associate alterations with responses to different pharmacologic agents.

Methods: We obtained samples of HCCs (associated with hepatitis B virus infection) from 10 patients undergoing curative resection, before adjuvant therapy, at hospitals in China.

A microscopic landscape of the invasive breast cancer genome.

Histologic grade is one of the most important microscopic features used to predict the prognosis of invasive breast cancer and may serve as a marker for studying cancer driving genomic abnormalities in vivo. We analyzed whole genome sequencing data from 680 cases of TCGA invasive ductal carcinomas of the breast and correlated them to corresponding pathology information. Ten genetic abnormalities were found to be statistically associated with histologic grade, including three most prevalent cancer driver events, TP53 and PIK3CA mutations and MYC amplification.

Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants.

Whole-genome sequencing data allow detection of copy number variation (CNV) at high resolution. However, estimation based on read coverage along the genome suffers from bias due to GC content and other factors. Here, we develop an algorithm called BIC-seq2 that combines normalization of the data at the nucleotide level and Bayesian information criterion-based segmentation to detect both somatic and germline CNVs accurately.

PDEGEM: Modeling non-uniform read distribution in RNA-Seq data.

Background: RNA-Seq is a powerful new technology to comprehensively analyze the transcriptome of any given cells. An important task in RNA-Seq data analysis is quantifying the expression levels of all transcripts. Although many methods have been introduced and much progress has been made, a satisfactory solution remains be elusive.