Colorectal cancer (CRC) is the third most common cancer and has a high metastasis and reoccurrence rate. Long noncoding RNAs (lncRNAs) play an important role in CRC growth and metastasis. Recent studies revealed that lncRNAs participate in CRC progression by coordinating with microRNAs (miRNAs) and protein-coding mRNAs. LncRNAs function as competitive endogenous RNAs (ceRNAs) by competitively occupying the shared binding sequences of miRNAs, thus sequestering the miRNAs and changing the expression of their downstream target genes. Such ceRNA networks formed by lncRNA/miRNA/mRNA interactions have been found in a broad spectrum of biological processes in CRC, including liver metastasis, epithelial to mesenchymal transition (EMT), inflammation formation, and chemo-/radioresistance. In this review, we summarize typical paradigms of lncRNA-associated ceRNA networks, which are involved in the underlying molecular mechanisms of CRC initiation and progression. We comprehensively discuss the competitive crosstalk among RNA transcripts and the novel targets for CRC prognosis and therapy.

In this study Illumina MiSeq was performed to investigate microbial diversity in soil, leaves, grape, grape juice and wine. A total of 1,043,102 fungal Internal Transcribed Spacer (ITS) reads and 2,422,188 high quality bacterial 16S rDNA sequences were used for taxonomic classification, revealed five fungal and eight bacterial phyla. At the genus level, the dominant fungi were Ascomycota, Sordariales, Tetracladium and Geomyces in soil, Aureobasidium and Pleosporaceae in grapes leaves, Aureobasidium in grape and grape juice. The dominant bacteria were Kaistobacter, Arthrobacter, Skermanella and Sphingomonas in soil, Pseudomonas, Acinetobacter and Kaistobacter in grape and grapes leaves, and Oenococcus in grape juice and wine. Principal coordinate analysis showed structural separation between the composition of fungi and bacteria in all samples. This is the first study to understand microbiome population in soil, grape, grapes leaves, grape juice and wine in Xinjiang through High-throughput Sequencing and identify microorganisms like Saccharomyces cerevisiae and Oenococcus spp. that may contribute to the quality and flavor of wine.

Highlights The paper considers imbalance classification, feature selection, and sample weight in the same framework. This is the first study simultaneously detecting the severe cases and predicting the conversion time for early diagnosis of COVID-19.

Dispositions of deoxynivalenol (DON) in rats and chickens were investigated, using a radiotracer method coupled with a novel γ-accurate radioisotope counting (γ-ARC) radio-high-performance liquid chromatography ion trap time-of-flight tandem mass spectrometry (radio-HPLC-IT-TOF-MS/MS) system. 3β-(3)H-DON was chemically synthesized and orally administrated to both sexes of rats and chickens as single or multiple doses. The results showed that DON was widely distributed and quickly eliminated in all tissues. The highest concentration was found in the gastrointestinal tract at 6 h post-administration. Substantially lower levels were detected in the kidney, liver, heart, lung, spleen, and brain. Three new metabolites were identified tentatively as 10-deoxynivalenol-sulfonate, 10-deepoxy-deoxynivalenol (DOM-1)-sulfonate, and deoxynivalenol-3α-sulfate. Deoxynivalenol-3α-sulfate was a major metabolite in chickens, while the major forms in rats were DOM-1 and DON. Additionally, a higher excretion rate in urine was observed in female rats than in male rats. The differences in metabolite profiles and excretion rates, which suggested diverse ways to detoxify, may relate to the different tolerances in different genders or species.

In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject’s IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a single-kernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge.

Understanding the degradation of dissolved organic matter (DOM) is vital for optimizing DOM control. However, the microbe-mediated DOM transformation during wastewater treatment remains poorly characterized. Here, microbes and DOM along full-scale biotreatment processes were simultaneously characterized using comparative genomics and high-resolution mass spectrometry-based reactomics. Biotreatments significantly increased DOM’s aromaticity and unsaturation due to the overproduced lignin and polyphenol analogs. DOM was diversified by over five times in richness, with thousands of nitrogenous and sulfur-containing compounds generated through microbe-mediated oxidoreduction, functional group transfer, and C–N and C–S bond formation. Network analysis demonstrated microbial division of labor in DOM transformation. However, their roles were determined by their functional traits rather than taxa. Specifically, network and module hubs exhibited rapid growth potentials and broad substrate affinities but were deficient in xenobiotics-metabolism-associated genes. They were mainly correlated to liable DOM consumption and its transformation to recalcitrant compounds. In contrast, connectors and peripherals were potential degraders of recalcitrant DOM but slow in growth. They showed specialized associations with fewer DOM molecules and probably fed on metabolites of hub microbes. Thus, developing technologies (e.g., carriers) to selectively enrich peripheral degraders and consequently decouple the liable and recalcitrant DOM transformation processes may advance DOM removal.

The needs for high-resolution, well-defined and complex 3D microstructures in diverse fields call for the rapid development of novel 3D microfabrication techniques. Among those, two-photon polymerization (TPP) attracted extensive attention owing to its unique and useful characteristics. As an approach to implementing additive manufacturing, TPP has truly 3D writing ability to fabricate artificially designed constructs with arbitrary geometry. The spatial resolution of the manufactured structures via TPP can exceed the diffraction limit. The 3D structures fabricated by TPP could properly mimic the microenvironment of natural extracellular matrix, providing powerful tools for the study of cell behavior. TPP can meet the requirements of manufacturing technique for 3D scaffolds (engineering cell culture matrices) used in cytobiology, tissue engineering and regenerative medicine. In this review, we demonstrated the development in 3D microfabrication techniques and we presented an overview of the applications of TPP as an advanced manufacturing technique in complex 3D biomedical scaffolds fabrication. Given this multidisciplinary field, we discussed the perspectives of physics, materials science, chemistry, biomedicine and mechanical engineering. Additionally, we dived into the principles of tow-photon absorption (TPA) and TPP, requirements of 3D biomedical scaffolders, developed-to-date materials and chemical approaches used by TPP and manufacturing strategies based on mechanical engineering. In the end, we draw out the limitations of TPP on 3D manufacturing for now along with some prospects of its future outlook towards the fabrication of 3D biomedical scaffolds.

In this paper, we propose a novel framework for ASD diagnosis using structural magnetic resonance imaging (MRI). Our method deals explicitly with the distributional differences of gray matter (GM) and white matter (WM) features extracted from MR images. We project linearly the GM and WM features onto a canonical space where their correlations are mutually maximized. In this canonical space, features that are highly correlated with the class labels are selected for ASD diagnosis. In addition, graph matching is employed to preserve the geometrical relationships between samples when projected onto the canonical space. Our evaluations based on a public ASD dataset show that the proposed method outperforms all competing methods on all clinically important measures in differentiating ASD patients from healthy individuals.