In silico method for systematic analysis of feature importance in microRNA-mRNA interactions

<p>Abstract</p> <p>Background</p> <p>MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear. With increasing experimental data becoming available, research interest has shifted from higher prediction performance to uncovering the mechanism of microRNA-mRNA interactions.</p> <p>Results</p> <p>Systematic analysis of sequence, structural and positional features was carried out for two different data sets. The dominant functional features were distinguished from uninformative features in single and hybrid feature sets. Models were developed using only statistically significant sequence, structural and positional features, resulting in area under the receiver operating curves (AUC) values of 0.919, 0.927 and 0.969 for one data set and of 0.926, 0.874 and 0.954 for another data set, respectively. Hybrid models were developed by combining various features and achieved AUC of 0.978 and 0.970 for two different data sets. Functional miRNA information is well reflected in these features, which are expected to be valuable in understanding the mechanism of microRNA-mRNA interactions and in designing experiments.</p> <p>Conclusions</p> <p>Differing from previous approaches, this study focused on systematic analysis of all types of features. Statistically significant features were identified and used to construct models that yield similar accuracy to previous studies in a shorter computation time.</p

SimpleX: A Simple and Strong Baseline for Collaborative Filtering

Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction encoder, loss function, and negative sampling. While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored. In this work, we show that the choice of loss function as well as negative sampling ratio is equivalently important. More specifically, we propose the cosine contrastive loss (CCL) and further incorporate it to a simple unified CF model, dubbed SimpleX. Extensive experiments have been conducted on 11 benchmark datasets and compared with 29 existing CF models in total. Surprisingly, the results show that, under our CCL loss and a large negative sampling ratio, SimpleX can surpass most sophisticated state-of-the-art models by a large margin (e.g., max 48.5% improvement in NDCG@20 over LightGCN). We believe that SimpleX could not only serve as a simple strong baseline to foster future research on CF, but also shed light on the potential research direction towards improving loss function and negative sampling. Our source code will be available at https://reczoo.github.io/SimpleX.Comment: Accepted by CIKM 2021. Code available at https://reczoo.github.io/Simple

2D-nanomaterials for AKI treatment

Acute kidney injury has always been considered a sword of Damocles over hospitalized patients and has received increasing attention due to its high morbidity, elevated mortality, and poor prognosis. Hence, AKI has a serious detrimental impact not only on the patients, but also on the whole society and the associated health insurance systems. Redox imbalance caused by bursts of reactive oxygen species at the renal tubules is the key cause of the structural and functional impairment of the kidney during AKI. Unfortunately, the failure of conventional antioxidant drugs complicates the clinical management of AKI, which is limited to mild supportive therapies. Nanotechnology-mediated antioxidant therapies represent a promising strategy for AKI management. In recent years, two-dimensional (2D) nanomaterials, a new subtype of nanomaterials with ultrathin layer structure, have shown significant advantages in AKI therapy owing to their ultrathin structure, large specific surface area, and unique kidney targeting. Herein, we review recent progress in the development of various 2D nanomaterials for AKI therapy, including DNA origami, germanene, and MXene; moreover, we discuss current opportunities and future challenges in the field, aiming to provide new insights and theoretical support for the development of novel 2D nanomaterials for AKI treatment

Separation of fluorescently labeled phosphoinositides and sphingolipids by capillary electrophoresis

Phosphoinositides (PIs) and sphingolipids regulate many aspects of cell behavior and are often involved in disease processes such as oncogenesis. Capillary electrophoresis with laser induced fluorescence detection (CE-LIF) is emerging as an important tool for enzymatic assays of the metabolism of these lipids, particularly in cell-based formats. Previous separations of phosphoinositide lipids by CE required a complex buffer with polymer additives which had the disadvantages of high cost and/or short shelf life. Further a simultaneous separation of these classes of lipids has not been demonstrated in a robust buffer system. In the current work, a simple separation buffer based on NaH2PO4 and 1-propanol was optimized to separate two sphingolipids and multiple phosphoinositides by CE. The NaH2PO4 concentration, pH, 1-propanol fraction, and a surfactant additive to the buffer were individually optimized to achieve simultaneous separation of the sphingolipids and phosphoinositides. Fluorescein-labeled sphingosine (SFL) and sphingosine 1-phosphate (S1PFL), fluorescein-labeled phosphatidyl-inositol 4,5-bisphosphate (PIP2) and phosphatidyl-inositol 3,4,5-trisphosphate (PIP3), and bodipy-fluorescein (BFL)-labeled PIP2 and PIP3 were separated pairwise and in combination to demonstrate the generalizability of the method. Theoretical plate numbers achieved were as high as 2×105 in separating fluorophore-labeled PIP2 and PIP3. Detection limits for the 6 analytes were in the range of 10−18 to 10−20 mol. The method also showed high reproducibility, as the relative standard deviation of the normalized migration time for each analyte in the simultaneous separation of all 6 compounds was less than 1%. The separation of a mixture composed of diacylglycerol (DAG) and multiple phosphoinositides was also demonstrated. As a final test, fluorescent lipid metabolites formed within cells loaded with BFLPIP2 were separated from a cell lysate as well as a single cell. This simple and robust separation method for SFL and S1PFL and various metabolites of phosphoinositide-related signal transduction is expected to enable improved enzymatic assays for biological and clinical applications