Explore the vast range of titles available.

In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.

Recently, microRNAs (miRNAs) are confirmed to be important molecules within many crucial biological processes and therefore related to various complex human diseases. However, previous methods of predicting miRNA–disease associations have their own deficiencies. Under this circumstance, we developed a prediction method called deep representations‐based miRNA–disease association (DRMDA) prediction. The original miRNA–disease association data were extracted from HDMM database. Meanwhile, stacked auto‐encoder, greedy layer‐wise unsupervised pre‐training algorithm and support vector machine were implemented to predict potential associations. We compared DRMDA with five previous classical prediction models (HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA) in global leave‐one‐out cross‐validation (LOOCV), local LOOCV and fivefold cross‐validation, respectively. The AUCs achieved by DRMDA were 0.9177, 08339 and 0.9156 ± 0.0006 in the three tests above, respectively. In further case studies, we predicted the top 50 potential miRNAs for colon neoplasms, lymphoma and prostate neoplasms, and 88%, 90% and 86% of the predicted miRNA can be verified by experimental evidence, respectively. In conclusion, DRMDA is a promising prediction method which could identify potential and novel miRNA–disease associations.

In the context of continuously increasing anthropogenic nitrogen inputs, knowledge of how ammonia oxidation (AO) in the ocean responds to warming is crucial to predicting future changes in marine nitrogen biogeochemistry. Here, we show divergent thermal response patterns for marine AO across a wide onshore/offshore trophic gradient. We find ammonia oxidizer community and ambient substrate co-regulate optimum temperatures (T opt ), generating distinct thermal response patterns with T opt varying from ≤14 °C to ≥34 °C. Substrate addition elevates T opt when ambient substrate is unsaturated. The thermal sensitivity of kinetic parameters allows us to predict responses of both AO rate and T opt at varying substrate and temperature below the critical temperature. A warming ocean promotes nearshore AO, while suppressing offshore AO. Our findings reconcile field inconsistencies of temperature effects on AO, suggesting that predictive biogeochemical models need to include such differential warming mechanisms on this key nitrogen cycle process. Microbial ammonia oxidation is important in marine nutrient cycling and greenhouse gas dynamics, but the responses to ocean warming are unclear. Here coast to open ocean incubations show that projected year 2100 temperatures might be too hot for these microbes in oligotrophic regions to handle, but may facilitate oxidation rates in coastal waters.

Circular RNA (CircRNA)–microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted.

Background Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been paid to the development of efficient and feasible computational methods to predict the potential associations between miRNA and disease. Methods In this work, we present a machine learning-based model called MLMDA for predicting the association of miRNAs and diseases. More specifically, we first use the k -mer sparse matrix to extract miRNA sequence information, and combine it with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information. Then, more representative features are extracted from them through deep auto-encoder neural network (AE). Finally, the random forest classifier is used to effectively predict potential miRNA–disease associations. Results The experimental results show that the MLMDA model achieves promising performance under fivefold cross validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. In addition, to further evaluate the prediction performance of MLMDA model, case studies are carried out with three Human complex diseases including Lymphoma , Lung Neoplasm , and Esophageal Neoplasms . As a result, 39, 37 and 36 out of the top 40 predicted miRNAs are confirmed by other miRNA–disease association databases. Conclusions These prominent experimental results suggest that the MLMDA model could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates. The source code and datasets explored in this work are available at http://220.171.34.3:81/ .

Lungs are the primary metastatic sites for osteosarcomas responsible for associated mortality. Recent data has documented role of long non-coding RNAs (lncRNAs) in proliferation and growth of osteosarcoma cells. We evaluated a role of lncRNAs in the lung metastasis of osteosarcoma with the goal of identifying a unique signature. Comparison of different lncRNAs in tumor samples from osteosarcoma with and without lung metastasis led to identification of MALAT1 as the most differentially upregulated lncRNA in the osteosarcoma patients with lung metastasis. MALAT1 was also high in osteosarcoma cells KRIB and MALAT1’s targeted downregulation in these cells led to decreased invasive potential and identification of miR-202 as the miRNA that is sponged by MALAT1. In the lung metastasis in vivo model, parental KRIB cells metastasized to lungs and such metastasis was significantly inhibited in KRIB cells with downregulated MALAT1. Ectopic miR-202 expression attenuated KRIB downregulation-mediated effects on lung metastasis. In yet another in vivo model involving parental SAOS-2 and lung-metastatic derivatives SAOS-2-LM, MALAT1 expression was found to be elevated in lung metastatic cells, which also correlated with reduced miR-202. In conclusion, MALAT1-miR-202 represents a potential lncRNA-miRNA signature that affects lung metastasis of osteosarcomas and could potentially be targeted for therapy.

Background B-box ( BBX ) genes play important roles in plant growth regulation and responses to abiotic stresses. The plant growth and yield production of allotetraploid rapeseed is usually hindered by diverse nutrient stresses. However, no systematic analysis of Brassicaceae BBXs and the roles of BBXs in the regulation of nutrient stress responses have not been identified and characterized previously. Results In this study, a total of 536 BBXs were identified from nine brassicaceae species, including 32 AtBBXs , 66 BnaBBXs , 41 BoBBXs , 43 BrBBXs , 26 CrBBXs , 81 CsBBXs , 52 BnBBXs , 93 BjBBXs , and 102 BcBBXs . Syntenic analysis showed that great differences in the gene number of Brassicaceae BBXs might be caused by genome duplication. The BBXs were respectively divided into five subclasses according to their phylogenetic relationships and conserved domains, indicating their diversified functions. Promoter cis -element analysis showed that BBXs probably participated in diverse stress responses. Protein-protein interactions between BnaBBXs indicated their functions in flower induction. The expression profiles of BnaBBXs were investigated in rapeseed plants under boron deficiency, boron toxicity, nitrate limitation, phosphate shortage, potassium starvation, ammonium excess, cadmium toxicity, and salt stress conditions using RNA-seq data. The results showed that different BnaBBXs showed differential transcriptional responses to nutrient stresses, and some of them were simultaneously responsive to diverse nutrient stresses. Conclusions Taken together, the findings investigated in this study provided rich resources for studying Brassicaceae BBX gene family and enriched potential clues in the genetic improvement of crop stress resistance.

Background Histone modification is an important epigenetic regulatory mechanism and essential for stress adaptation in plants. However, systematic analysis of histone modification genes ( HMs ) in Brassicaceae species is lacking, and their roles in response to abiotic stress have not yet been identified. Results In this study, we identified 102 AtHMs , 280 BnaHMs , 251 BcHMs , 251 BjHMs , 144 BnHMs , 155 BoHMs , 137 BrHMs , 122 CrHMs , and 356 CsHMs in nine Brassicaceae species, respectively. Their chromosomal locations, protein/gene structures, phylogenetic trees, and syntenies were determined. Specific domains were identified in several Brassicaceae HMs , indicating an association with diverse functions. Syntenic analysis showed that the expansion of Brassicaceae HMs may be due to segmental and whole-genome duplications. Nine key BnaHMs in allotetraploid rapeseed may be responsible for ammonium, salt, boron, cadmium, nitrate, and potassium stress based on co-expression network analysis. According to weighted gene co-expression network analysis (WGCNA), 12 BnaHMs were associated with stress adaptation. Among the above genes, BnaPRMT11 simultaneously responded to four different stresses based on differential expression analysis, while BnaSDG46 , BnaHDT10 , and BnaHDA1 participated in five stresses. BnaSDG46 was also involved in four different stresses based on WGCNA, while BnaSDG10 and BnaJMJ58 were differentially expressed in response to six different stresses. In summary, six candidate genes for stress resistance ( BnaPRMT11 , BnaSDG46 , BnaSDG10 , BnaJMJ58 , BnaHDT10 , and BnaHDA1 ) were identified. Conclusions Taken together, these findings help clarify the biological roles of Brassicaceae HMs . The identified candidate genes provide an important reference for the potential development of stress-tolerant oilseed plants.

In this study, an absorber with the characteristics of high absorptivity and ultra-wideband (UWB), which was ranged from the visible light range and near-infrared band, was designed and numerically analyzed using COMSOL Multiphysics® simulation software (version 6.0). The designed absorber was constructed by using two-layer square cubes stacked on the four-layer continuous plane films. The two-layer square cubes were titanium dioxide (TiO2) and titanium (Ti) (from top to bottom) and the four-layer continuous plane films were Poly(N-isopropylacrylamide) (PNIPAAm), Ti, silica (SiO2), and Ti. The analysis results showed that the first reason to cause the high absorptivity in UWB is the anti-reflection effect of top TiO2 layer. The second reason is that the three different resonances, including localized surface plasmon resonance, the propagating surface plasmon resonance, and the Fabry-Perot (FP) cavity resonance, are coexisted in the absorption peaks of the designed absorber and at least two of them can be excited at the same time. The third reason is that two FP resonant cavities were formed in the PNIPAAm and SiO2 dielectric layers. Because of the combination of the anti-reflection effect and the three different resonances, the designed absorber presented the properties of UWB and high absorptivity.