Visible‐light photocatalysis is a rapidly developing and powerful strategy to initiate organic transformations, as it closely adheres to the tenants of green and sustainable chemistry. Generally, ...most visible‐light‐induced photochemical reactions occur through single‐electron transfer (SET) pathways. Recently, visible‐light‐induced energy‐transfer (EnT) reactions have received considerable attentions from the synthetic community as this strategy provides a distinct reaction pathway, and remarkable achievements have been made in this field. In this Review, we highlight the most recent advances in visible‐light‐induced EnT reactions.
Making light of synthesis: The distinctive reaction pathways provided by visible‐light‐induced energy transfer (EnT) has resulted in it receiving considerable attention. This Review discusses the most recent advances in visible‐light‐induced EnT reactions.
Key message
We proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding.
As an ...important component of the CRISPR/Cas9 system, single-guide RNA (sgRNA) plays an important role in gene redirection and editing. sgRNA has played an important role in the improvement of agronomic species, but there is a lack of effective bioinformatics tools to identify the activity of sgRNA in agronomic species. Therefore, it is necessary to develop a method based on machine learning to identify sgRNA high on-target activity. In this work, we proposed a simple convolutional neural network method to identify sgRNA high on-target activity. Our study used one-hot encoding and k-mers for sequence data conversion and a voting algorithm for constructing the convolutional neural network ensemble model sgRNACNN for the prediction of sgRNA activity. The ensemble model sgRNACNN was used for predictions in four crops: Glycine max, Zea mays, Sorghum bicolor and Triticum aestivum. The accuracy rates of the four crops in the sgRNACNN model were 82.43%, 80.33%, 78.25% and 87.49%, respectively. The experimental results showed that sgRNACNN realizes the identification of high on-target activity sgRNA of agronomic data and can meet the demands of sgRNA activity prediction in agronomy to a certain extent. These results have certain significance for guiding crop gene editing and academic research. The source code and relevant dataset can be found in the following link:
https://github.com/nmt315320/sgRNACNN.git
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The 2 + 2 photocycloaddition is undisputedly the most important and most frequently used photochemical reaction. In this review, it is attempted to cover all recent aspects of 2 + 2 ...photocycloaddition chemistry with an emphasis on synthetically relevant, regio-, and stereoselective reactions. The review aims to comprehensively discuss relevant work, which was done in the field in the last 20 years (i.e., from 1995 to 2015). Organization of the data follows a subdivision according to mechanism and substrate classes. Cu(I) and PET (photoinduced electron transfer) catalysis are treated separately in sections and , whereas the vast majority of photocycloaddition reactions which occur by direct excitation or sensitization are divided within section into individual subsections according to the photochemically excited olefin.
Circular RNA (circRNA) is mainly generated by the splice donor of a downstream exon joining to an upstream splice acceptor, a phenomenon known as backsplicing. It has been reported that circRNA can ...function as microRNA (miRNA) sponges, transcriptional regulators, or potential biomarkers. The availability of massive non-polyadenylated transcriptomes data has facilitated the genome-wide identification of thousands of circRNAs. Several circRNA detection tools or pipelines have recently been developed, and it is essential to provide useful guidelines on these pipelines for users, including a comprehensive and unbiased comparison. Here, we provide an improved and easy-to-use circRNA read simulator that can produce mimicking backsplicing reads supporting circRNAs deposited in CircBase. Moreover, we compared the performance of 11 circRNA detection tools on both simulated and real datasets. We assessed their performance regarding metrics such as precision, sensitivity, F1 score, and Area under Curve. It is concluded that no single method dominated on all of these metrics. Among all of the state-of-the-art tools, CIRI, CIRCexplorer, and KNIFE, which achieved better balanced performance between their precision and sensitivity, compared favorably to the other methods.
Abstract
Motivation
Prediction of therapeutic peptides is critical for the discovery of novel and efficient peptide-based therapeutics. Computational methods, especially machine learning based ...methods, have been developed for addressing this need. However, most of existing methods are peptide-specific; currently, there is no generic predictor for multiple peptide types. Moreover, it is still challenging to extract informative feature representations from the perspective of primary sequences.
Results
In this study, we have developed PEPred-Suite, a bioinformatics tool for the generic prediction of therapeutic peptides. In PEPred-Suite, we introduce an adaptive feature representation strategy that can learn the most representative features for different peptide types. To be specific, we train diverse sequence-based feature descriptors, integrate the learnt class information into our features, and utilize a two-step feature optimization strategy based on the area under receiver operating characteristic curve to extract the most discriminative features. Using the learnt representative features, we trained eight random forest models for eight different types of functional peptides, respectively. Benchmarking results showed that as compared with existing predictors, PEPred-Suite achieves better and robust performance for different peptides. As far as we know, PEPred-Suite is currently the first tool that is capable of predicting so many peptide types simultaneously. In addition, our work demonstrates that the learnt features can reliably predict different peptides.
Availability and implementation
The user-friendly webserver implementing the proposed PEPred-Suite is freely accessible at http://server.malab.cn/PEPred-Suite.
Supplementary information
Supplementary data are available at Bioinformatics online.
Liquid organic hydrogen carriers (LOHCs) are powerful systems for the efficient unloading and loading molecular hydrogen. Herein, a liquid‐to‐liquid organic hydrogen carrier system based on ...reversible dehydrogenative coupling of ethylene glycol (EG) with ethanol catalysed by ruthenium pincer complexes is reported. Noticeable advantages of the current LOHC system is that both reactants (hydrogen‐rich components) and the produced esters (hydrogen‐lean components) are liquids at room temperature, and the dehydrogenation process can be performed under solvent and base‐free conditions. Moreover, the hydrogenation reaction proceeds under low hydrogen pressure (5 bar), and the LOHC system has a relatively high theoretical gravimetric hydrogen storage capacity (HSC>5.0 wt %), presenting an attractive hydrogen storage system.
Hydrogen carriers: A liquid‐to‐liquid organic hydrogen carrier system based on reversible dehydrogenation and hydrogenation reactions by using ethylene glycol and ethanol is reported. Both the reactants are abundant, cheap, and non‐toxic industrial raw materials. Moreover, this dehydrogenative esterification, as well as hydrogenation, can be conducted under base‐ and solvent‐free conditions, thus offering a promising approach for application in hydrogen storage.
MicroRNAs (miRNA) play critical roles in regulating gene expressions at the posttranscriptional levels. The prediction of disease-related miRNA is vital to the further investigation of miRNA's ...involvement in the pathogenesis of disease. In previous years, biological experimentation is the main method used to identify whether miRNA was associated with a given disease. With increasing biological information and the appearance of new miRNAs every year, experimental identification of disease-related miRNAs poses considerable difficulties (e.g. time-consumption and high cost). Because of the limitations of experimental methods in determining the relationship between miRNAs and diseases, computational methods have been proposed. A key to predict potential disease-related miRNA based on networks is the calculation of similarity among diseases and miRNA over the networks. Different strategies lead to different results. In this review, we summarize the existing computational approaches and present the confronted difficulties that help understand the research status. We also discuss the principles, efficiency and differences among these methods. The comprehensive comparison and discussion elucidated in this work provide constructive insights into the matter.
Gastric cancer (GC) is among the most fatal cancers in China. MicroRNAs (miRNAs) are versatile regulators during GC development and progression. miR‐491‐5p has been demonstrated to act as a tumor ...suppressor in several types of cancer. However, the role of miR‐491‐5p in GC metastasis remains unknown. Here, we found that miR‐491‐5p was significantly decreased in GC tissues compared with adjacent non‐cancerous tissues, and low miR‐491‐5p level was associated with large tumor size. Overexpression of miR‐491‐5p significantly suppressed GC cell epithelial‐to‐mesenchymal transition (EMT) and tumor metastasis in vitro and in vivo. Mechanistically, SNAIL was identified as a direct target of miR‐491‐5p. The silencing of SNAIL phenocopied the tumor suppressive function of miR‐491‐5p, whereas re‐expression of SNAIL in GC cells rescued the EMT markers and cell migratory ability that were inhibited by miR‐491‐5p. In addition, miR‐491‐5p inhibited FGFR4 indirectly. Inhibition of FGFR4 also decreased the SNAIL level and impaired EMT and cell migration. Taken together, these findings indicate that downregulation of miR‐491‐5p promoted GC metastasis by inducing EMT via regulation of SNAIL and FGFR4.
miR‐491‐5p is remarkably downregulated in gastric cancer. Restoration of miR‐491‐5p could effectively inhibited tumor growth and metastasis. We discovered that miR‐491‐5p directly targeted SNAIL and indirectly inhibited FGFR4, resulted in suppressed EMT and tumor metastasis.
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