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Long noncoding RNAs (lncRNAs) have significant regulatory roles in gene expression. Interactions with proteins are one of the ways lncRNAs play their roles. Since experiments to determine lncRNA-protein interactions (LPIs) are expensive and time-consuming, many computational methods for predicting LPIs have been proposed as alternatives. In the LPIs prediction problem, there commonly exists the imbalance in the distribution of positive and negative samples. However, there are few existing methods that give specific consideration to this problem. In this paper, we proposed a new clustering-based LPIs prediction method using segmented k-mer frequencies and multi-space clustering (LPI-SKMSC). It was dedicated to handling the imbalance of positive and negative samples. We constructed segmented k-mer frequencies to obtain global and local features of lncRNA and protein sequences. Then, the multi-space clustering was applied to LPI-SKMSC. The convolutional neural network (CNN)-based encoders were used to map different features of a sample to different spaces. It used multiple spaces to jointly constrain the classification of samples. Finally, the distances between the output features of the encoder and the cluster center in each space were calculated. The sum of distances in all spaces was compared with the cluster radius to predict the LPIs. We performed cross-validation on 3 public datasets and LPI-SKMSC showed the best performance compared to other existing methods. Experimental results showed that LPI-SKMSC could predict LPIs more effectively when faced with imbalanced positive and negative samples. In addition, we illustrated that our model was better at uncovering potential lncRNA-protein interaction pairs., (© 2024. International Association of Scientists in the Interdisciplinary Areas.)

Circular RNAs (circRNAs) participate in the regulation of biological processes by binding to specific proteins and thus influence transcriptional processes. In recent years, circRNAs have become an emerging hotspot in RNA research. Due to powerful learning ability, the various deep learning frameworks have been used to predict the binding sites of RNA-binding protein (RPB) on circRNAs. These methods usually perform only single-level feature extraction of sequence information. However, the feature acquisition may be inadequate for single-level extraction. Generally, the features of deep and shallow layers of neural network can complement each other and are both important for binding site prediction tasks. Based on this concept, we propose a method that combines deep and shallow features, namely CRBP-HFEF. Specifically, features are first extracted and expanded for different levels of network. Then, the expanded deep and shallow features are fused and fed into the classification network, which finally determines whether they are binding sites. Compared to several existing methods, the experimental results on multiple datasets show that the proposed method achieves significant improvements in a number of metrics (with an average AUC of 0.9855). Moreover, much sufficient ablation experiments are also performed to verify the effectiveness of the hierarchical feature expansion strategy., (© 2023. International Association of Scientists in the Interdisciplinary Areas.)

The DNase I hypersensitive sites (DHSs) are active regions on chromatin that have been found to be highly sensitive to DNase I. These regions contain various cis-regulatory elements, including promoters, enhancers and silencers. Accurate identification of DHSs helps researchers better understand the transcriptional machinery of DNA and deepen the knowledge of functional DNA elements in non-coding sequences. Researchers have developed many methods based on traditional experiments and machine learning to identify DHSs. However, low prediction accuracy and robustness limit their application in genetics research. In this paper, a novel computational approach based on deep learning is proposed by feature fusion and local-global feature extraction network to identify DHSs in mouse, named iDHS-FFLG. First of all, multiple binary features of nucleotides are fused to better express sequence information. Then, a network consisting of the convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) and self-attention mechanism is designed to extract local features and global contextual associations. In the end, the prediction module is applied to distinguish between DHSs and non-DHSs. The results of several experiments demonstrate the superior performances of iDHS-FFLG compared to the latest methods., (© 2022. International Association of Scientists in the Interdisciplinary Areas.)

Due to the crucial role of interactions between microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in biological processes, the study of their biological functions is necessary. So far, the various computational methods have been employed to make predictions of the miRNA-lncRNA interaction, which compensate for the inadequacy of biological experiments. However, the existing methods do not consider the differences between miRNA and lncRNA in feature extraction. In this paper, we propose a hybrid feature mining network, named PmliHFM, for predicting plant miRNA-lncRNA interactions. Firstly, miRNA and lncRNA with different sequence lengths are encoded by different encodings, which can reduce the loss of information caused by using the same coding approach. Then, a hybrid feature mining network is designed to adapt to different encoding methods and extract more useful feature information than a single network. Finally, an ensemble module is utilized to integrate the training results of the hybrid feature mining network, while a prediction module is employed to determine whether there are interactions. By testing on multiple test sets, PmliHFM outperforms several state-of-the-art approaches. The results show that the AUC of PmliHFM achieves 0.8[Formula: see text], 3.1[Formula: see text] and 0.4[Formula: see text] improvement respectively on three balanced datasets, and achieves 2.1[Formula: see text] and 1.8[Formula: see text] improvement respectively on two imbalanced datasets. These experiments demonstrate the feasibility of the proposed method., (© 2022. International Association of Scientists in the Interdisciplinary Areas.)

Carbon dioxide supplementation is significant for cell growth in autotrophic cultures of microalgae. However, the CO 2 utilization efficiency is quite low in most processes. Aimed at this problem, six kinds of physical absorption enhancers were investigated to enhance the biological carbon sequestration of microalgae. By the addition of a small amount of CO 2 absorption enhancer, the total inorganic carbon concentration of the medium was significantly increased. In addition, the biomass productivity of Scenedesmus dimorphus was maximally increased by 63% by the addition of propylene carbonate in flask cultures. In cultures using an air-lift photobioreactor equipped with a pH-feedback control system to supply CO 2 , the CO 2 consumption was maximally reduced by 71% with added polyethylene glycol dimethyl ether. This study indicates that the incorporation of physical absorption enhancers could be a promising approach to overcome the problems of low CO 2 utilization efficiency and high carbon source cost in algal biomass production., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The present paper describes a novel two-dimensional liquid chromatography (2D-LC) system, which is comprised of a first-dimensional ion exchange chromatography (IEX1) column, trap column, and second-dimensional reversed-phase chromatography (RP2) column system. The biological sample is separated by the first-dimensional LC using an IEX column to remove interferences. The analytes are transferred to the trap column after heart-cutting. Then, the analytes are transferred to the second-dimensional LC using an RP2 column for further separation and ultraviolet detection. This 2D-LC system can offer a large injection volume to provide sufficient sensitivity and exhibits a strong capacity for removing interferences. Here, the determination of three monoterpene indole alkaloids (MIAs; gelsemine, koumine, and humantenmine) from Gelsemium in biological matrices (plasma, tissue, and urine) was used this 2D-LC system. After a rapid and easy sample preparation method based on protein precipitation, the sample was injected into the 2D-LC. The method was developed and validated in terms of the selectivity, LOD, LOQ, linearity, precision, accuracy, and stability. The sample preparation time for the three MIAs was 15 min. The LOD for these compounds was 10 ng/mL, which was lower than the developed HPLC methods. The results showed that this method had good quantitation performance and allowed the determination of gelsemine, koumine, and humantenmine in biological matrices. The method is rapid, exhibits high selectivity, has good sensitivity, and is low-cost, thus making it well-suited for application in the pharmaceutical and toxicological analysis of Gelsemium. Graphical abstract.

Acetylcholine, glutamate, dopamine, serotonin (5-HT), gamma-aminobutyric acid, substance P (SP), amyloid-β (Aβ) and neurotrophic protein S100B are arguably the most important cognition-related biomarkers in the brain. Among this list are five neurotransmitters that signal through postsynaptic receptors. Our knowledge of cognition-related biomarkers has been advanced through translational experiments and clinical case-study data. Although these biomarkers are widespread in the brain and pronounced individual variations exist, these biomarkers can be used to identify both acute and chronic abnormalities following traumatic brain injury. Changes in these biomarkers likely indicate damage to brain networks or to key brain cell types that support cognitive functions. Identification of such biomarker abnormalities could result in earlier diagnoses, improved prognoses and therapies that enable neurotransmitters to return to normal levels.

A new strategy using hybrid ion trap/time-of-flight mass spectrometry coupled with high-performance liquid chromatography and post-acquisition data mining techniques was developed and applied to the detection and characterization of degradation products of danofloxacin. The degradation products formed under different forced conditions were separated using an ODS-C18 column with gradient elution. Accurate full-scan MS data were acquired in the first run and processed with the combination of extracted ion chromatograms and LC-UV chromatograms. These processes were able to find accurate molecular masses of possible degradation products. Then, the accurate MS/MS data acquired through data-dependent analysis mode in another run facilitated the structural elucidations of degradation products. As a result, a total of 11 degradation products of danofloxacin were detected and characterized using the developed method. Overall, this analytical strategy enables the acquisition of accurate-mass LC/MS data, search of a variety of degradation products through the post-acquisition processes, and effective structural characterization based on elemental compositions of degradation product molecules and their product ions. The ability to measure degradation products via tandem mass spectrometry coupled with accurate mass measurement, all in only two experimental runs, is one of the most attractive features of this methodology. The results demonstrate that use of the LC/MS-IT-TOF approach appears to be rapid, efficient and reliable in structural characterization of drug degradation products.