LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction

<div><p>Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs’ potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases’ statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a <i>L</i>₁-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model’s superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction.</p></div

Prediction of the top 50 potential Breast Neoplasms-related miRNAs based on known associations in the old version of HMDD, that is, HMDD v1.0.

<p>The first column records top 1–25 related miRNAs. The third column records the top 26–50 related miRNAs. The evidences for the associations were either HMDD v2.0, dbDEMC and miR2Disease or more recent experimental literatures with the corresponding PMIDs.</p

Prediction of the top 50 potential Esophageal Neoplasms-related miRNAs based on known associations in HMDD v2.0 database.

<p>All the known miRNAs related to this cancer were removed from the training samples, and LRSSLMDA was built solely from the disease perspective. The first column records top 1–25 related miRNAs. The third column records the top 26–50 related miRNAs. The evidences for the associations were dbDEMC, miR2Disease and HMDD v2.0.</p

Prediction of the top 50 potential Colon Neoplasms-related miRNAs based on known associations in HMDD v2.0 database.

<p>The first column records top 1–25 related miRNAs. The third column records the top 26–50 related miRNAs. The evidences for the associations were either dbDEMC and miR2Disease or more recent experimental literatures with the corresponding PMIDs.</p

Prediction of the top 50 potential Lymphoma-related miRNAs based on known associations in HMDD v2.0 database.

<p>The first column records top 1–25 related miRNAs. The third column records the top 26–50 related miRNAs. The evidences for the associations were either dbDEMC and miR2Disease or more recent experimental literatures with the corresponding PMIDs.</p

Selective Imaging of Gram-Negative and Gram-Positive Microbiotas in the Mouse Gut

The diverse gut microbial communities are crucial for host health. How the interactions between microbial communities and between host and microbes influence the host, however, is not well understood. To facilitate gut microbiota research, selective imaging of specific groups of microbiotas in the gut is of great utility but remains technically challenging. Here we present a chemical approach that enables selective imaging of Gram-negative and Gram-positive microbiotas in the mouse gut by exploiting their distinctive cell wall components. Cell-selective labeling is achieved by the combined use of metabolic labeling of Gram-negative bacterial lipopolysaccharides with a clickable azidosugar and direct labeling of Gram-positive bacteria with a vancomycin-derivatized fluorescent probe. We demonstrated this strategy by two-color fluorescence imaging of Gram-negative and Gram-positive gut microbiotas in the mouse intestines. This chemical method should be broadly applicable to different gut microbiota research fields and other bacterial communities studied in microbiology

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Monkey 2's performance, when stimulus eccentricity was identical to that used for monkey 1 (4.6°).

<p>The drop in performance upon addition of flankers, the gradual improvement during flanker training, and the subsequent return to pre-flanker levels, was similar to that previously seen with a stimulus eccentricity of 1.5°. A: <i>P_correct</i>; B: slope of the psychometric function; C: PSE. Red data points: pre-flankers; green data points (grey background): flankers; blue data points: post-flankers. Unfilled markers: 20% sample contrast conditions; medium-coloured filled markers: 30%; dark-coloured filled markers: 40%.</p

Near-Infrared Light Activation of Proteins Inside Living Cells Enabled by Carbon Nanotube-Mediated Intracellular Delivery

Light-responsive proteins have been delivered into the cells for controlling intracellular events with high spatial and temporal resolution. However, the choice of wavelength is limited to the UV and visible range; activation of proteins inside the cells using near-infrared (NIR) light, which has better tissue penetration and biocompatibility, remains elusive. Here, we report the development of a single-walled carbon nanotube (SWCNT)-based bifunctional system that enables protein intracellular delivery, followed by NIR activation of the delivered proteins inside the cells. Proteins of interest are conjugated onto SWCNTs via a streptavidin-desthiobiotin (SA-DTB) linkage, where the protein activity is blocked. SWCNTs serve as both a nanocarrier for carrying proteins into the cells and subsequently a NIR sensitizer to photothermally cleave the linkage and release the proteins. The released proteins become active and exert their functions inside the cells. We demonstrated this strategy by intracellular delivery and NIR-triggered nuclear translocation of enhanced green fluorescent protein, and by intracellular delivery and NIR-activation of a therapeutic protein, saporin, in living cells. Furthermore, we showed that proteins conjugated onto SWCNTs via the SA-DTB linkage could be delivered to the tumors, and optically released and activated by using NIR light in living mice