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Waterlogging is a major abiotic stress affecting plant growth and productivity. Regardless of rainfall or irrigated environments, plants frequently face waterlogging, which may range from short-term to prolonged durations. Excessive precipitation and soil moisture disrupt crop growth, not because of the water itself but due to oxygen deficiency caused by water saturation. This lack of oxygen triggers a cascade of detrimental effects. Once the soil becomes saturated, oxygen depletion leads to anaerobic respiration in plant roots, weakening their respiratory processes. Waterlogging impacts plant morphology, growth, and metabolism, often increasing ethylene production and impairing vital physiological functions. Plants respond to waterlogging stress by altering their morphological structures, energy metabolism, hormone synthesis, and signal transduction pathways. This paper synthesizes findings from previous studies to systematically analyze the effects of waterlogging on plant yield, hormone regulation, signal transduction, and adaptive responses while exploring the mechanisms underlying plant tolerance to waterlogging. For instance, waterlogging reduces crop yield and disrupts key physiological and biochemical processes, such as hormone synthesis and nutrient absorption, leading to deficiencies of essential nutrients like potassium and calcium. Under waterlogged conditions, plants exhibit morphological changes, including the formation of adventitious roots and the development of aeration tissues to enhance oxygen transport. This review also highlighted effective strategies to improve plant tolerance to waterlogging. Examples include strengthening field management practices, applying exogenous hormones such as 6-benzylaminopurine (6-BA) and γ-aminobutyric acid (GABA), overexpressing specific genes (e.g., , , and ), and modifying root architecture. Lastly, we discuss future challenges and propose directions for advancing research in this field.

* Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD). * Results Representation vectors of all k -mers were obtained through word2vec based on k -mer co-existence information. The original peptide sequences were then divided into k -mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively. * Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design.

Background Using knowledge-based interpretation to analyze omics data can not only obtain essential information regarding various biological processes, but also reflect the current physiological status of cells and tissue. The major challenge to analyze gene expression data, with a large number of genes and small samples, is to extract disease-related information from a massive amount of redundant data and noise. Gene selection, eliminating redundant and irrelevant genes, has been a key step to address this problem. Results The modified method was tested on four benchmark datasets with either two-class phenotypes or multiclass phenotypes, outperforming previous methods, with relatively higher accuracy, true positive rate, false positive rate and reduced runtime. Conclusions This paper proposes an effective feature selection method, combining double RBF-kernels with weighted analysis, to extract feature genes from gene expression data, by exploring its nonlinear mapping ability.

Advances in sequencing technologies have empowered epitranscriptomic profiling at the single-base resolution. Putative RNA modification sites identified from a single high-throughput experiment may contain one type of modification deposited by different writers or different types of modifications, along with false positive results because of the challenge of distinguishing signals from noise. However, current tools are insufficient for subtyping, visualization, and denoising these signals. Here, we present iMVP, which is an interactive framework for epitranscriptomic analysis with a nonlinear dimension reduction technique and density-based partition. As exemplified by the analysis of mRNA m 5 C and ModTect variant data, we show that iMVP allows the identification of previously unknown RNA modification motifs and writers and the discovery of false positives that are undetectable by traditional methods. Using putative m 6 A/m 6 Am sites called from 8 profiling approaches, we illustrate that iMVP enables comprehensive comparison of different approaches and advances our understanding of the difference and pattern of true positives and artifacts in these methods. Finally, we demonstrate the ability of iMVP to analyze an extremely large human A-to-I editing dataset that was previously unmanageable. Our work provides a general framework for the visualization and interpretation of epitranscriptomic data. The current available tools lack the ability to accurately classify and visually represent epitranscriptomic profiling data. Here, the authors provide a framework that offers a general solution for the visualization and interpretation of such data.

Background Identification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods for identifying the DTIs are arduous, time-intensive, and financially burdensome. In addition, robust computational methods have been developed for predicting the DTIs and are widely applied in drug discovery research. However, advancing more precise algorithms for predicting DTIs is essential to meet the stringent standards demanded by drug discovery. Results We proposed a novel method called GSRF-DTI, which integrates networks with a deep learning algorithm to identify DTIs. Firstly, GSRF-DTI learned the embedding representation of drugs and targets by integrating multiple drug association information and target association information, respectively. Then, GSRF-DTI considered the influence of drug-target pair (DTP) association on DTI prediction to construct a drug-target pair network (DTP-NET). Next, we utilized GraphSAGE on DTP-NET to learn the potential features of the network and applied random forest (RF) to predict the DTIs. Furthermore, we conducted ablation experiments to validate the necessity of integrating different types of network features for identifying DTIs. It is worth noting that GSRF-DTI proposed three novel DTIs. Conclusions GSRF-DTI not only considered the influence of the interaction relationship between drug and target but also considered the impact of DTP association relationship on DTI prediction. We initially use GraphSAGE to aggregate the neighbor information of nodes for better identification. Experimental analysis on Luo’s dataset and the newly constructed dataset revealed that the GSRF-DTI framework outperformed several state-of-the-art methods significantly.

m 5 C is one of the longest-known RNA modifications, however, its developmental dynamics, functions, and evolution in mRNAs remain largely unknown. Here, we generate quantitative mRNA m 5 C maps at different stages of development in 6 vertebrate and invertebrate species and find convergent and unexpected massive methylation of maternal mRNAs mediated by NSUN2 and NSUN6. Using Drosophila as a model, we reveal that embryos lacking maternal mRNA m 5 C undergo cell cycle delays and fail to timely initiate maternal-to-zygotic transition, implying the functional importance of maternal mRNA m 5 C. From invertebrates to the lineage leading to humans, two waves of m 5 C regulatory innovations are observed: higher animals gain cis-directed NSUN2-mediated m 5 C sites at the 5' end of the mRNAs, accompanied by the emergence of more structured 5'UTR regions; humans gain thousands of trans-directed NSUN6-mediated m 5 C sites enriched in genes regulating the mitotic cell cycle. Collectively, our studies highlight the existence and regulatory innovations of a mechanism of early embryonic development and provide key resources for elucidating the role of mRNA m 5 C in biology and disease. mRNAs are known to be decorated with m5C at a low-to-medium level. Here, the authors generate atlases of mRNA m5C during animal development in 6 species and identify convergent and unexpected massive methylation of maternal mRNAs by NSUN2 and NSUN6.

Background Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few cancer driver genes whose mutations cause tumor growth. However, the majority of existing computational approaches underuse the co-occurrence mutation information of the individuals, which are deemed to be important in tumorigenesis and tumor progression, resulting in high rate of false positive. Results To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas, DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. Besides, DriverRWH discovers several potential drivers, which are enriched in cancer-related pathways. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data. Conclusion DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.

Background Apoptosis is associated with some human diseases, including cancer, autoimmune disease, neurodegenerative disease and ischemic damage, etc. Apoptosis proteins subcellular localization information is very important for understanding the mechanism of programmed cell death and the development of drugs. Therefore, the prediction of subcellular localization of apoptosis protein is still a challenging task. Results In this paper, we propose a novel method for predicting apoptosis protein subcellular localization, called PsePSSM-DCCA-LFDA. Firstly, the protein sequences are extracted by combining pseudo-position specific scoring matrix (PsePSSM) and detrended cross-correlation analysis coefficient (DCCA coefficient), then the extracted feature information is reduced dimensionality by LFDA (local Fisher discriminant analysis). Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of the apoptosis proteins. The overall prediction accuracy of 99.7, 99.6 and 100% are achieved respectively on the three benchmark datasets by the most rigorous jackknife test, which is better than other state-of-the-art methods. Conclusion The experimental results indicate that our method can significantly improve the prediction accuracy of subcellular localization of apoptosis proteins, which is quite high to be able to become a promising tool for further proteomics studies. The source code and all datasets are available at https://github.com/QUST-BSBRC/PsePSSM-DCCA-LFDA/ .

Non-invasive/minimally invasive continuous monitoring of blood glucose and blood glucose administration have a high impact on chronic disease management in diabetic patients, but the existing technology is yet to achieve the above two purposes at the same time. Therefore, this study proposes a microfluidic microneedle patch based on 3D printing technology and an integrated control system design for blood glucose measurement, and a drug delivery control circuit based on a 555 chip. The proposed method provides an improved preparation of a PVA-PEG-MoS2 nanosheet hydrogel, making use of its dielectric properties to fabricate a microcapacitor and then embedding it in a microfluidic chip. When MoS2 nanosheets react with interstitial liquid glucose (and during the calibration process), the permittivity of the hydrogel is changed, resulting in changes in the capacitance of the capacitor. By converting the capacitance change into the square-wave period change in the output of the 555 chip with the control circuit design accordingly, the minimally invasive continuous measurement of blood glucose and the controlled release of hypoglycemic drugs are realized. In this study, the cross-linking structure of MoS2 nanosheets in hydrogel was examined using infrared spectroscopy and scanning electron microscopy (SEM) methods. Moreover, the critical doping mass fraction of MoS2 nanosheets was determined to be 2% via the measurement of the dielectric constant. Meanwhile, the circuit design and the relationship between the pulse cycle and glucose concentration is validated. The results show that, compared with capacitors in series, the microcapacitors embedded in microfluidic channels can be connected in parallel to obtain better linearized blood glucose measurement results.

Background The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods. To enhance interpretability and overcome this problem, we developed a novel feature selection algorithm. In the meantime, complex genomic data brought great challenges for the identification of biomarkers and therapeutic targets. The current some feature selection methods have the problem of low sensitivity and specificity in this field. Results In this article, we designed a multi-scale clustering-based feature selection algorithm named MCBFS which simultaneously performs feature selection and model learning for genomic data analysis. The experimental results demonstrated that MCBFS is robust and effective by comparing it with seven benchmark and six state-of-the-art supervised methods on eight data sets. The visualization results and the statistical test showed that MCBFS can capture the informative genes and improve the interpretability and visualization of tumor gene expression and single-cell sequencing data. Additionally, we developed a general framework named McbfsNW using gene expression data and protein interaction data to identify robust biomarkers and therapeutic targets for diagnosis and therapy of diseases. The framework incorporates the MCBFS algorithm, network recognition ensemble algorithm and feature selection wrapper. McbfsNW has been applied to the lung adenocarcinoma (LUAD) data sets. The preliminary results demonstrated that higher prediction results can be attained by identified biomarkers on the independent LUAD data set, and we also structured a drug-target network which may be good for LUAD therapy. Conclusions The proposed novel feature selection method is robust and effective for gene selection, classification, and visualization. The framework McbfsNW is practical and helpful for the identification of biomarkers and targets on genomic data. It is believed that the same methods and principles are extensible and applicable to other different kinds of data sets.