Prognostic and Predictive Value of Three DNA Methylation Signatures in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response. The DNA methylation patterns of LUAD display a great potential as a specific biomarker that will complement invasive biopsy, thus improving early detection. Method: In this study, based on the whole-genome methylation datasets from The Cancer Genome Atlas (TCGA) and several machine learning methods, we evaluated the possibility of DNA methylation signatures for identifying lymph node metastasis of LUAD, differentiating between tumor tissue and normal tissue, and predicting the overall survival (OS) of LUAD patients. Using the regularized logistic regression, we built a classifier based on the 3616 CpG sites to identify the lymph node metastasis of LUAD. Furthermore, a classifier based on 14 CpG sites was established to differentiate between tumor and normal tissues. Using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we built a 16-CpG-based model to predict the OS of LUAD patients. Results: With the aid of 3616-CpG-based classifier, we were able to identify the lymph node metastatic status of patients directly by the methylation signature from the primary tumor tissues. The 14-CpG-based classifier could differentiate between tumor and normal tissues. The area under the receiver operating characteristic (ROC) curve (AUC) for both classifiers achieved values close to 1, demonstrating the robust classifier effect. The 16-CpG-based model showed independent prognostic value in LUAD patients. Interpretation: These findings will not only facilitate future treatment decisions based on the DNA methylation signatures but also enable additional investigations into the utilization of LUAD DNA methylation pattern by different machine learning methods

Amelioration of drought effects in wheat and cucumber by the combined application of super absorbent polymer and potential biofertilizer

Biofertilizer is a good substitute for chemical fertilizer in sustainable agriculture, but its effects are often hindered by drought stress. Super absorbent polymer (SAP), showing good capacity of water absorption and retention, can increase soil moisture. However, limited information is available about the efficiency of biofertilizer amended with SAP. This study was conducted to investigate the effects of synergistic application of SAP and biofertilizers (Paenibacillus beijingensis BJ-18 and Bacillus sp. L-56) on plant growth, including wheat and cucumber. Potted soil was treated with different fertilizer combinations (SAP, BJ-18 biofertilizer, L-56 biofertilizer, BJ-18 + SAP, L-56 + SAP), and pot experiment was carried out to explore its effects on viability of inoculants, seed germination rate, plant physiological and biochemical parameters, and expression pattern of stress-related genes under drought condition. At day 29 after sowing, the highest viability of strain P. beijingensis BJ-18 (264 copies ng−1 gDNA) was observed in BJ-18 + SAP treatment group of wheat rhizosphere soil, while that of strain Bacillus sp. L-56 (331 copies ng−1 gDNA) was observed in the L-56 + SAP treatment group of cucumber rhizosphere soil. In addition, both biofertilizers amended with SAP could promote germination rate of seeds (wheat and cucumber), plant growth, soil fertility (urease, sucrose, and dehydrogenase activities). Quantitative real-time PCR analysis showed that biofertilizer + SAP significantly down-regulated the expression levels of genes involved in ROS scavenging (TaCAT, CsCAT, TaAPX, and CsAPX2), ethylene biosynthesis (TaACO2, CsACO1, and CsACS1), stress response (TaDHN3, TaLEA, and CsLEA11), salicylic acid (TaPR1-1a and CsPR1-1a), and transcription activation (TaNAC2D and CsNAC35) in plants under drought stress. These results suggest that SAP addition in biofertilizer is a good tactic for enhancing the efficiency of biofertilizer, which is beneficial for plants in response to drought stress. To the best of our knowledge, this is the first report about the effect of synergistic use of biofertilizer and SAP on plant growth under drought stress

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic

Prognostic and Predictive Value of Three DNA Methylation Signatures in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response. The DNA methylation patterns of LUAD display a great potential as a specific biomarker that will complement invasive biopsy, thus improving early detection.Method: In this study, based on the whole-genome methylation datasets from The Cancer Genome Atlas (TCGA) and several machine learning methods, we evaluated the possibility of DNA methylation signatures for identifying lymph node metastasis of LUAD, differentiating between tumor tissue and normal tissue, and predicting the overall survival (OS) of LUAD patients. Using the regularized logistic regression, we built a classifier based on the 3616 CpG sites to identify the lymph node metastasis of LUAD. Furthermore, a classifier based on 14 CpG sites was established to differentiate between tumor and normal tissues. Using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we built a 16-CpG-based model to predict the OS of LUAD patients.Results: With the aid of 3616-CpG-based classifier, we were able to identify the lymph node metastatic status of patients directly by the methylation signature from the primary tumor tissues. The 14-CpG-based classifier could differentiate between tumor and normal tissues. The area under the receiver operating characteristic (ROC) curve (AUC) for both classifiers achieved values close to 1, demonstrating the robust classifier effect. The 16-CpG-based model showed independent prognostic value in LUAD patients.Interpretation: These findings will not only facilitate future treatment decisions based on the DNA methylation signatures but also enable additional investigations into the utilization of LUAD DNA methylation pattern by different machine learning methods

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Multi-Scale Graph-Based Feature Fusion for Few-Shot Remote Sensing Image Scene Classification

Remote sensing image scene classification has drawn extensive attention for its wide application in various scenarios. Scene classification in many practical cases faces the challenge of few-shot conditions. The major difficulty of few-shot remote sensing image scene classification is how to extract effective features from insufficient labeled data. To solve these issues, a multi-scale graph-based feature fusion (MGFF) model is proposed for few-shot remote sensing image scene classification. In the MGFF model, a graph-based feature construction model is developed to transform traditional image features into graph-based features, which aims to effectively represent the spatial relations among images. Then, a graph-based feature fusion model is proposed to integrate graph-based features of multiple scales, which aims to enhance sample discrimination based on different scale information. Experimental results on two public remote sensing datasets prove that the MGFF model can achieve superior accuracy than other few-shot scene classification approaches

Characteristics of the chloroplast genome and genetic divergence of Tamarix hispida Willd. 1816 (Tamaricaceae)

Tamarix hispida Willd. 1816, a crucial native plant species in the arid desert region of northwestern China, plays a significant role in maintaining ecological stability. It is instrumental in addressing soil salinity–alkalinity and heavy metal pollution. This research aims to analyze the phylogenetic divergence pattern and evolutionary history of T. hispida by comparing chloroplast genome structures across different populations. Despite the minimal differences in chloroplast genome structure due to conserved genes and junction regions, sequencing was conducted using the Illumina NovaSeq platform to verify the historical evolutionary processes between different populations, followed by assembly and annotation. The results revealed that the T. hispida chloroplast genome is approximately 156,164–156,186 bp in length, with a quadripartite structure and 131 annotated genes. Phylogenetic analysis indicated two lineages within T. hispida, with a divergence time of 3.15 Ma. These findings emphasize the low genetic diversity in T. hispida and offer valuable insights into its evolutionary past. To effectively protect and manage this species, increased scientific research and monitoring of its genetic diversity are necessary. This study underscores the importance of comprehending the genetic mechanisms behind species divergence to develop informed conservation strategies

Multi-Scale Graph-Based Feature Fusion for Few-Shot Remote Sensing Image Scene Classification

Remote sensing image scene classification has drawn extensive attention for its wide application in various scenarios. Scene classification in many practical cases faces the challenge of few-shot conditions. The major difficulty of few-shot remote sensing image scene classification is how to extract effective features from insufficient labeled data. To solve these issues, a multi-scale graph-based feature fusion (MGFF) model is proposed for few-shot remote sensing image scene classification. In the MGFF model, a graph-based feature construction model is developed to transform traditional image features into graph-based features, which aims to effectively represent the spatial relations among images. Then, a graph-based feature fusion model is proposed to integrate graph-based features of multiple scales, which aims to enhance sample discrimination based on different scale information. Experimental results on two public remote sensing datasets prove that the MGFF model can achieve superior accuracy than other few-shot scene classification approaches