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This special issue focuses on computational model for drug research regarding drug bioactivity prediction, drug-related interaction prediction, modelling for immunotherapy and modelling for treatment of a specific disease, as conveyed by the following six research and four review articles. Notably, these 10 papers described a wide variety of in-depth drug research from the computational perspective and may represent a snapshot of the wide research landscape. [ABSTRACT FROM AUTHOR]

In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug–drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions. [ABSTRACT FROM AUTHOR]

The recent discovery of superconductivity in infinite-layer nickelates generates tremendous research endeavors, but the ground state of their parent compounds is still under debate. Here, we report experimental evidence for the dominant role of Kondo scattering in the underdoped Nd1−xSrxNiO2 thin films. A resistivity minimum associated with logarithmic temperature dependence in both longitudinal and Hall resistivities are observed in the underdoped Nd1−xSrxNiO2 samples before the superconducting transition. At lower temperatures down to 0.04 K, the resistivities become saturated, following the prediction of the Kondo model. A linear scaling behavior |$\sigma _{{\boldsymbol{xy}}}^{{{\bf AHE}}}{\rm{\ }}\sim{\rm{\ }}{\sigma }_{{\boldsymbol{xx}}}$| between anomalous Hall conductivity |$\sigma _{{\boldsymbol{xy}}}^{{\bf{AHE}}}$| and conductivity |${\sigma }_{{\boldsymbol{xx}}}{\rm{\ }}$| is revealed, verifying the dominant Kondo scattering at low temperature. The effect of weak (anti-)localization is found to be secondary. Our experiments can help in clarifying the basic physics in the underdoped Nd1−xSrxNiO2 infinite-layer thin films. [ABSTRACT FROM AUTHOR]

Cerebral cavernous malformations (CCMs) and spinal cord cavernous malformations (SCCMs) are common vascular abnormalities of the CNS that can lead to seizure, haemorrhage and other neurological deficits. Approximately 85% of patients present with sporadic (versus congenital) CCMs. Somatic mutations in MAP3K3 and PIK3CA were recently reported in patients with sporadic CCM, yet it remains unknown whether MAP3K3 mutation is sufficient to induce CCMs. Here we analysed whole-exome sequencing data for patients with CCM and found that ∼40% of them have a single, specific MAP3K3 mutation [c.1323C>G (p.Ile441Met)] but not any other known mutations in CCM-related genes. We developed a mouse model of CCM with MAP3K3 I441M uniquely expressed in the endothelium of the CNS. We detected pathological phenotypes similar to those found in patients with MAP3K3 I441M. The combination of in vivo imaging and genetic labelling revealed that CCMs were initiated with endothelial expansion followed by disruption of the blood–brain barrier. Experiments with our MAP3K3 I441M mouse model demonstrated that CCM can be alleviated by treatment with rapamycin, the mTOR inhibitor. CCM pathogenesis has usually been attributed to acquisition of two or three distinct genetic mutations involving the genes CCM1 / 2 / 3 and/or PIK3CA. However, our results demonstrate that a single genetic hit is sufficient to cause CCMs. [ABSTRACT FROM AUTHOR]

Adverse drug–drug interactions (DDIs) have become an increasingly serious problem in the medical and health system. Recently, the effective application of deep learning and biomedical knowledge graphs (KGs) have improved the DDI prediction performance of computational models. However, the problems of feature redundancy and KG noise also arise, bringing new challenges for researchers. To overcome these challenges, we proposed a Multi-Channel Feature Fusion model for multi-typed DDI prediction (MCFF-MTDDI). Specifically, we first extracted drug chemical structure features, drug pairs' extra label features, and KG features of drugs. Then, these different features were effectively fused by a multi-channel feature fusion module. Finally, multi-typed DDIs were predicted through the fully connected neural network. To our knowledge, we are the first to integrate the extra label information into KG-based multi-typed DDI prediction; besides, we innovatively proposed a novel KG feature learning method and a State Encoder to obtain target drug pairs' KG-based features which contained more abundant and more key drug-related KG information with less noise; furthermore, a Gated Recurrent Unit-based multi-channel feature fusion module was proposed in an innovative way to yield more comprehensive feature information about drug pairs, effectively alleviating the problem of feature redundancy. We experimented with four datasets in the multi-class and the multi-label prediction tasks to comprehensively evaluate the performance of MCFF-MTDDI for predicting interactions of known-known drugs, known-new drugs and new-new drugs. In addition, we further conducted ablation studies and case studies. All the results fully demonstrated the effectiveness of MCFF-MTDDI. [ABSTRACT FROM AUTHOR]

MicroRNA (miR)-23b-3p is known to target various genes that are involved in cancer-related pathways. Exosomes are emerging intercellular communication agents. Exosomes secreted by cancer cells can deliver active molecules to the surrounding stromal cells, thereby influencing the recipient cells and promoting the development of cancers. However, the role of exosomal miR-23b-3p in salivary adenoid cystic carcinoma (SACC) is not yet clear. In this study, we set out to investigate the potential role of cancer-derived exosomal miR-23b-3p-related phosphatase and tensin homolog deleted on chromosome 10 in the alteration of angiogenesis and vascular permeability in SACC. We investigated the effect of exosomal miR-23b-3p on the progression of SACC. In vitro experiments indicated that exosomal miR-23b-3p led to an upregulation of vascular permeability, and reduced expression of tight junction proteins. In addition, exosomal miR-23b-3p also enhanced angiogenesis and migration. Next, the angiogenic effect of exosomal miR-23b-3p was validated in vivo , as it led to an increase in the tumor microvasculature. Furthermore, the growth rate of SACC was faster after injection of exosomes loaded with cholesterol-modified miR-23b-3p in mice. In conclusion, these results revealed that SACC cell-derived exosomes play an important role in promoting angiogenesis and local vascular microleakage of SACC by transporting miR-23b-3p, which suggests that miR-23b-3p in the exosomes may be a potential biomarker for distant metastasis of SACC. This suggests the potential of a novel therapeutic target by delivering anti-miR-23b-3p that focuses on exosomes. [ABSTRACT FROM AUTHOR]

The number and stacking order of layers are two important degrees of freedom that can modulate the properties of 2D van der Waals (vdW) materials. However, the layers' structures are essentially limited to the known layered 3D vdW materials. Recently, a new 2D vdW material, MoSi2N4, without known 3D counterparts, was synthesized by passivating the surface dangling bonds of non-layered 2D molybdenum nitride with elemental silicon, whose monolayer can be viewed as a monolayer MoN (-N-Mo-N-) sandwiched between two Si-N layers. This unique sandwich structure endows the MoSi2N4 monolayer with many fascinating properties and intriguing applications, and the surface-passivating growth method creates the possibility of tuning the layer's structure of 2D vdW materials. Here we synthesized a series of MoSi2N4(MoN)4n structures confined in the matrix of multilayer MoSi2N4. These super-thick monolayers are the homologous compounds of MoSi2N4, which can be viewed as multilayer MoN (Mo4n+1N4n+2) sandwiched between two Si-N layers. First-principles calculations show that MoSi2N4(MoN)4 monolayers have much higher Young's modulus than MoN, which is attributed to the strong Si-N bonds on the surface. Importantly, different from the semiconducting nature of the MoSi2N4 monolayer, the MoSi2N4(MoN)4 monolayer is identified as a superconductor with a transition temperature of 9.02 K. The discovery of MoSi2N4(MoN)4n structures not only expands the family of 2D materials but also brings a new degree of freedom to tailor the structure of 2D vdW materials, which may lead to unexpected novel properties and applications. We discovered superconducting homologous compounds of 2D MoSi2N4, MoSi2N4(MoN)4n, which provides a new strategy to tailor the structure and properties of 2D materials by phase homology. [ABSTRACT FROM AUTHOR]

Aims Feathers are keratin-rich byproducts of poultry processing, but those are often frequently abandoned as garbage and thus polluting the environment. Therefore, the study focused on the efficient biodegradation, bioactivity, and high-value application of feather keratin. Methods and results Feather-degrading bacteria were identified, and the degradation properties were characterized. DPPH (1,1-Diphenyl-2-picrylhydrazyl radical) and ABTS (2,2′-Azino-bis (3-ethylbenzthiazoline-6-sulfonic acid))radical scavenging assays, cytotoxicity assays, intracellular reactive oxygen scavenging assays, and cell migration assays were used to examine the biological activities of the feather keratin hydrolysis peptides (FKHPs). The results showed that we screened a feather-degrading strain of Bacillus licheniformis 8-4, which achieved complete degradation of 2% (w/v) feathers within 48 h. Notably, the feather fermentation broth was particularly high in FKHPs, which exhibited good DPPH and ABTS radical scavenging ability. Further studies revealed that FKHPs had both the ability to scavenge H2O2-induced ROS from HaCat cells and the ability to promote HaCat cell migration, while remaining non-toxic. Conclusions The effective feather-degrading ability of B. licheniformis 8-4 allowed for the fermentation of feather medium to yield active peptides that were both antioxidants and cell-migration enhancers. [ABSTRACT FROM AUTHOR]

Synergistic drug combinations can improve the therapeutic effect and reduce the drug dosage to avoid toxicity. In previous years, an in vitro approach was utilized to screen synergistic drug combinations. However, the in vitro method is time-consuming and expensive. With the rapid growth of high-throughput data, computational methods are becoming efficient tools to predict potential synergistic drug combinations. Considering the limitations of the previous computational methods, we developed a new model named Siamese Network and Random Matrix Projection for AntiCancer Drug Combination prediction (SNRMPACDC). Firstly, the Siamese convolutional network and random matrix projection were used to process the features of the two drugs into drug combination features. Then, the features of the cancer cell line were processed through the convolutional network. Finally, the processed features were integrated and input into the multi-layer perceptron network to get the predicted score. Compared with the traditional method of splicing drug features into drug combination features, SNRMPACDC improved the interpretability of drug combination features to a certain extent. In addition, the introduction of convolutional networks can better extract the potential information in the features. SNRMPACDC achieved the root mean-squared error of 15.01 and the Pearson correlation coefficient of 0.75 in 5-fold cross-validation of regression prediction for response data. In addition, SNRMPACDC achieved the AUC of 0.91 ± 0.03 and the AUPR of 0.62 ± 0.05 in 5-fold cross-validation of classification prediction of synergistic or not. These results are almost better than all the previous models. SNRMPACDC would be an effective approach to infer potential anticancer synergistic drug combinations. [ABSTRACT FROM AUTHOR]

Aim The study systematically compared the N2O-reducing functional performances and the genomic features of two N2O-reducing isolates, aimed to screen out effective N2O-reducing bacteria with strong environmental adaption, and explore the possible regulation. Methods and Results Two N2O reducers, namely, Pseudomonas veronii DM15 (DM15) and Pseudomonas frederiksbergensis DM22 (DM22), isolated from paddy soil were selected. Their N2O-reducing abilities, and nosZ gene transcript abundance were determined under different temperatures (20°C, 30°C, 40°C) and oxygen concentrations (0%, 10%, 21%), and the whole genomes were sequenced by Illumina sequencing. The results showed that both DM15 and DM22 exhibited the strongest N2O reducing activity at 30°C and under anaerobic conditions. In comparison, DM15 generally exhibited significantly higher N2O reducing abilities and nosZ gene expression than DM22 under all tested conditions. In addition, DM15 possessed obviously higher expression potentials (codon adaptation index (CAI) value) of nos genes than DM22, and the nos cluster of the former contained a transcriptional regulator gene of dnr , while the latter did not. Conclusions The results indicate that DM15 showed obviously stronger N2O-reducing abilities than DM22 under various conditions, which might be closely associated with its dnr transcriptional regulator, and thus promoting the higher transcriptional activities of nos genes. Although anaerobic conditions were the optimal conditions for N2O reduction in both strains, DM15 still reduced a certain amount of N2O even under aerobic conditions. [ABSTRACT FROM AUTHOR]

Exiting computational models for drug–target binding affinity prediction have much room for improvement in prediction accuracy, robustness and generalization ability. Most deep learning models lack interpretability analysis and few studies provide application examples. Based on these observations, we presented a novel model named Molecule Representation Block-based Drug-Target binding Affinity prediction (MRBDTA). MRBDTA is composed of embedding and positional encoding, molecule representation block and interaction learning module. The advantages of MRBDTA are reflected in three aspects: (i) developing Trans block to extract molecule features through improving the encoder of transformer, (ii) introducing skip connection at encoder level in Trans block and (iii) enhancing the ability to capture interaction sites between proteins and drugs. The test results on two benchmark datasets manifest that MRBDTA achieves the best performance compared with 11 state-of-the-art models. Besides, through replacing Trans block with single Trans encoder and removing skip connection in Trans block, we verified that Trans block and skip connection could effectively improve the prediction accuracy and reliability of MRBDTA. Then, relying on multi-head attention mechanism, we performed interpretability analysis to illustrate that MRBDTA can correctly capture part of interaction sites between proteins and drugs. In case studies, we firstly employed MRBDTA to predict binding affinities between Food and Drug Administration-approved drugs and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication-related proteins. Secondly, we compared true binding affinities between 3C-like proteinase and 185 drugs with those predicted by MRBDTA. The final results of case studies reveal reliable performance of MRBDTA in drug design for SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Currently, there exist no generally accepted strategies of evaluating computational models for microRNA-disease associations (MDAs). Though K -fold cross validations and case studies seem to be must-have procedures, the value of K , the evaluation metrics, and the choice of query diseases as well as the inclusion of other procedures (such as parameter sensitivity tests, ablation studies and computational cost reports) are all determined on a case-by-case basis and depending on the researchers' choices. In the current review, we include a comprehensive analysis on how 29 state-of-the-art models for predicting MDAs were evaluated. Based on the analytical results, we recommend a feasible evaluation workflow that would suit any future model to facilitate fair and systematic assessment of predictive performance. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction. [ABSTRACT FROM AUTHOR]

Since the problem proposed in late 2000s, microRNA–disease association (MDA) predictions have been implemented based on the data fusion paradigm. Integrating diverse data sources gains a more comprehensive research perspective, and brings a challenge to algorithm design for generating accurate, concise and consistent representations of the fused data. After more than a decade of research progress, a relatively simple algorithm like the score function or a single computation layer may no longer be sufficient for further improving predictive performance. Advanced model design has become more frequent in recent years, particularly in the form of reasonably combing multiple algorithms, a process known as model fusion. In the current review, we present 29 state-of-the-art models and introduce the taxonomy of computational models for MDA prediction based on model fusion and non-fusion. The new taxonomy exhibits notable changes in the algorithmic architecture of models, compared with that of earlier ones in the 2017 review by Chen et al. Moreover, we discuss the progresses that have been made towards overcoming the obstacles to effective MDA prediction since 2017 and elaborated on how future models can be designed according to a set of new schemas. Lastly, we analysed the strengths and weaknesses of each model category in the proposed taxonomy and proposed future research directions from diverse perspectives for enhancing model performance. [ABSTRACT FROM AUTHOR]

Framework conditions (FCs) are widely recognised as having a significant effect on the uptake and implementation of innovation-orientated public procurement (PPI) that is viewed as a promising innovation policy instrument. This paper presents a more systemic definition of FCs for PPI at both the core and the peripheral level, building on the existing understanding of FCs for public procurement and innovation. Against the background of this definition and in the light of public organisation theory, this paper identifies three main channels—influencing performance (Channel 1), enhancing capabilities (Channel 2), and modifying missions (Channel 3)—to shape purchase decision-making of public organisations for innovation through the FCs. By comparing the case of the UK and China, it further explores and conceptualises the three channels of the FCs. With the conceptualisation, the paper provides a framework and some implications for how to design, improve and evaluate FCs for PPI. [ABSTRACT FROM AUTHOR]

This paper is devoted to analysing stability of a standby system with an unreliable server and switching failure, where both the time-to-repair of units and the time-to-repair of server follow general distributions. By employing $C_0$ semigroup theory, we show that time-dependent solution of the system converges to steady-state solution under a rather mild condition. Further, if component' repair rate and server's repair rate satisfy a reasonable assumption, then the system is exponentially stable. Based on the above theoretical foundation, some steady-state reliability indices are obtained and total expected profit function per unit time is developed. Finally, a sensitivity analysis with numerical illustrations is also provided. [ABSTRACT FROM AUTHOR]

In recent years, increasing biological experiments and scientific studies have demonstrated that microRNA (miRNA) plays an important role in the development of human complex diseases. Therefore, discovering miRNA–disease associations can contribute to accurate diagnosis and effective treatment of diseases. Identifying miRNA–disease associations through computational methods based on biological data has been proven to be low-cost and high-efficiency. In this study, we proposed a computational model named Stacked Autoencoder for potential MiRNA–Disease Association prediction (SAEMDA). In SAEMDA, all the miRNA–disease samples were used to pretrain a Stacked Autoencoder (SAE) in an unsupervised manner. Then, the positive samples and the same number of selected negative samples were utilized to fine-tune SAE in a supervised manner after adding an output layer with softmax classifier to the SAE. SAEMDA can make full use of the feature information of all unlabeled miRNA–disease pairs. Therefore, SAEMDA is suitable for our dataset containing small labeled samples and large unlabeled samples. As a result, SAEMDA achieved AUCs of 0.9210 and 0.8343 in global and local leave-one-out cross validation. Besides, SAEMDA obtained an average AUC and standard deviation of 0.9102 ± /−0.0029 in 100 times of 5-fold cross validation. These results were better than those of previous models. Moreover, we carried out three case studies to further demonstrate the predictive accuracy of SAEMDA. As a result, 82% (breast neoplasms), 100% (lung neoplasms) and 90% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by databases. Thus, SAEMDA could be a useful and reliable model to predict potential miRNA–disease associations. [ABSTRACT FROM AUTHOR]

Google Scholar Crossref Search ADS PubMed WorldCat 12 Moreau Y, Tranchevent L-C. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Computational analysis of vast public and private omics data [[1], [3], [5]] generated by high-throughput technologies [[6]] aids in deciphering complex mechanisms [[8], [10]] and relevant gene functions [[12], [14]] of various diseases ranging from viral infections to cancers. The tool contained automated analytical pipelines to match disease and gene IDs, conduct enrichment analysis, compare diseases' shared genes and variants, estimate background distribution, and examine the statistical significance of candidate disease mechanisms by calculate empirical I P i -values that quantified disease functional and genetic coherence. Google Scholar Crossref Search ADS PubMed WorldCat 20 Zhao X, Liu T, Wang G. Ensemble classification based signature discovery for cancer diagnosis in RNA expression profiles across different platforms. [Extracted from the article]

MicroRNAs (miRNAs) play crucial roles in multiple biological processes and human diseases and can be considered as therapeutic targets of small molecules (SMs). Because biological experiments used to verify SM–miRNA associations are time-consuming and expensive, it is urgent to propose new computational models to predict new SM–miRNA associations. Here, we proposed a novel method called Dual-network Collaborative Matrix Factorization (DCMF) for predicting the potential SM–miRNA associations. Firstly, we utilized the Weighted K Nearest Known Neighbors (WKNKN) method to preprocess SM–miRNA association matrix. Then, we constructed matrix factorization model to obtain two feature matrices containing latent features of SM and miRNA, respectively. Finally, the predicted SM–miRNA association score matrix was obtained by calculating the inner product of two feature matrices. The main innovations of this method were that the use of WKNKN method can preprocess the missing values of association matrix and the introduction of dual network can integrate more diverse similarity information into DCMF. For evaluating the validity of DCMF, we implemented four different cross validations (CVs) based on two distinct datasets and two different case studies. Finally, based on dataset 1 (dataset 2), DCMF achieved Area Under receiver operating characteristic Curves (AUC) of 0.9868 (0.8770), 0.9833 (0.8836), 0.8377 (0.7591) and 0.9836 ± 0.0030 (0.8632 ± 0.0042) in global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold CV, respectively. For case studies, plenty of predicted associations have been confirmed by published experimental literature. Therefore, DCMF is an effective tool to predict potential SM–miRNA associations. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) play crucial roles in human disease and can be targeted by small molecule (SM) drugs according to numerous studies, which shows that identifying SM–miRNA associations in human disease is important for drug development and disease treatment. We proposed the method of Ensemble of Kernel Ridge Regression-based Small Molecule–MiRNA Association prediction (EKRRSMMA) to uncover potential SM–miRNA associations by combing feature dimensionality reduction and ensemble learning. First, we constructed different feature subsets for both SMs and miRNAs. Then, we trained homogeneous base learners based on distinct feature subsets and took the average of scores obtained from these base learners as SM–miRNA association score. In EKRRSMMA, feature dimensionality reduction technology was employed in the process of construction of feature subsets to reduce the influence of noisy data. Besides, the base learner, namely KRR_avg, was the combination of two classifiers constructed under SM space and miRNA space, which could make full use of the information of SM and miRNA. To assess the prediction performance of EKRRSMMA, we conducted Leave-One-Out Cross-Validation (LOOCV), SM-fixed local LOOCV, miRNA-fixed local LOOCV and 5-fold CV based on two datasets. For Dataset 1 (Dataset 2), EKRRSMMA got the Area Under receiver operating characteristic Curves (AUCs) of 0.9793 (0.8871), 0.8071 (0.7705), 0.9732 (0.8586) and 0.9767 ± 0.0014 (0.8560 ± 0.0027). Besides, we conducted four case studies. As a result, 32 (5-Fluorouracil), 19 (17β-Estradiol), 26 (5-Aza-2′-deoxycytidine) and 11 (cyclophosphamide) out of top 50 predicted potentially associated miRNAs were confirmed by database or experimental literature. Above evaluation results demonstrated that EKRRSMMA is reliable for predicting SM–miRNA associations. [ABSTRACT FROM AUTHOR]

Mounting evidence has demonstrated the significance of taking microRNAs (miRNAs) as the target of small molecule (SM) drugs for disease treatment. Given the fact that exploring new SM–miRNA associations through biological experiments is extremely expensive, several computing models have been constructed to reveal the possible SM–miRNA associations. Here, we built a computing model of Bounded Nuclear Norm Regularization for SM–miRNA Associations prediction (BNNRSMMA). Specifically, we first constructed a heterogeneous SM–miRNA network utilizing miRNA similarity, SM similarity, confirmed SM–miRNA associations and defined a matrix to represent the heterogeneous network. Then, we constructed a model to complete this matrix by minimizing its nuclear norm. The Alternating Direction Method of Multipliers was adopted to minimize the nuclear norm and obtain predicted scores. The main innovation lies in two aspects. During completion, we limited all elements of the matrix within the interval of (0,1) to make sure they have practical significance. Besides, instead of strictly fitting all known elements, a regularization term was incorporated to tolerate the noise in integrated similarities. Furthermore, four kinds of cross-validations on two datasets and two types of case studies were performed to evaluate the predictive performance of BNNRSMMA. Finally, BNNRSMMA attained areas under the curve of 0.9822 (0.8433), 0.9793 (0.8852), 0.8253 (0.7350) and 0.9758 ± 0.0029 (0.8759 ± 0.0041) under global leave-one-out cross-validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and 5-fold cross-validation based on Dataset 1(Dataset 2), respectively. With regard to case studies, plenty of predicted associations have been verified by experimental literatures. All these results confirmed that BNNRSMMA is a reliable tool for inferring associations. [ABSTRACT FROM AUTHOR]

In recent years, increasing microRNA (miRNA)–disease associations were identified through traditionally biological experiments. These associations contribute to revealing molecular mechanism of diseases and preventing and curing diseases. To improve the efficiency of miRNA–disease association discovery, some calculation methods were developed as auxiliary tools for researchers. In the current study, we raised a novel model named Bayesian Ranking for MiRNA–Disease Association prediction (BRMDA) by improving Bayesian Personalized Ranking from three aspects: (i) taking advantage of similarity of diseases and miRNAs; (ii) incorporating miRNA bias for miRNAs associated with different number of diseases; and (iii) implementing neighborhood-based approach for new miRNAs and diseases. For each investigated disease, BRMDA used the set of triples (i.e. disease, labeled miRNA, unlabeled miRNA) that reflected association preference of the disease to miRNAs as training set, which made full use of unknown samples rather than simply considering them as negative samples. To investigate the predictive performance of BRMDA, we employed leave-one-out cross-validation and obtained Area Under the Curve of 0.8697, which outperformed many classical methods. Besides, we further implemented three distinct classes of case studies for three common Neoplasms. As a result, there are 44 (Colon Neoplasms), 49 (Esophageal Neoplasms) and 49 (Lung Neoplasms) among the top 50 predicted miRNAs validated through experiments. In short, BRMDA would be a trustable tool for inferring valuable associations. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data. [ABSTRACT FROM AUTHOR]

Aims Myocardial fibrosis is associated with clinical ventricular tachyarrhythmia (VTA) events in patients with non-ischaemic dilated cardiomyopathy (DCM). Subepicardial or mid-wall ring-like late gadolinium enhancement (LGE) has received increasing attention in recent years. The aim of this study was to investigate the relationship between ring-like LGE and VTAs in DCM. Methods and results Patients diagnosed with non-ischaemic DCM who underwent cardiac magnetic resonance with LGE imaging at baseline were investigated. The composite outcome was the occurrence of VTAs defined as sustained ventricular tachycardia, ventricular fibrillation/flutter, aborted sudden cardiac death (SCD), SCD, and appropriate implantable cardioverter-defibrillator intervention. The final cohort comprised 157 patients, including 36 (22.9%) in no LGE group, 48 (30.6%) in focal LGE group, 40 (25.5%) in multi-focal LGE group, and 33 (21%) in ring-like LGE group. Ring-like LGE group patients were younger compared to focal and multi-focal LGE group (P  <   0.001) with higher left ventricular ejection fraction (33.0% vs. 24.4% vs. 22.1%, P  <   0.001). After a median of 13 ± 7 months follow-up, compared to patients with no LGE, the hazard ratios (HRs) with 95% confidence intervals (CIs) for VTAs were 2.90 (0.56–15.06), 5.55 (1.21–25.44), and 11.75 (2.66–51.92) for patients with focal LGE, multi-focal LGE, and ring-like LGE, respectively. After multivariable adjustment, ring-like LGE group remained associated with increased risk of VTAs (adjusted HR 10.00, 95% CI 1.54–64.98; P  =   0.016) independent of the global LGE burden Conclusion The ring-like pattern of LGE is independently associated with an increased risk of VTAs in patients with non-ischaemic DCM. [ABSTRACT FROM AUTHOR]

MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations. [ABSTRACT FROM AUTHOR]

Studies have shown that the number of microbes in humans is almost 10 times that of cells. These microbes have been proven to play an important role in a variety of physiological processes, such as enhancing immunity, improving the digestion of gastrointestinal tract and strengthening metabolic function. In addition, in recent years, more and more research results have indicated that there are close relationships between the emergence of the human noncommunicable diseases and microbes, which provides a novel insight for us to further understand the pathogenesis of the diseases. An in-depth study about the relationships between diseases and microbes will not only contribute to exploring new strategies for the diagnosis and treatment of diseases but also significantly heighten the efficiency of new drugs development. However, applying the methods of biological experimentation to reveal the microbe-disease associations is costly and inefficient. In recent years, more and more researchers have constructed multiple computational models to predict microbes that are potentially associated with diseases. Here, we start with a brief introduction of microbes and databases as well as web servers related to them. Then, we mainly introduce four kinds of computational models, including score function-based models, network algorithm-based models, machine learning-based models and experimental analysis-based models. Finally, we summarize the advantages as well as disadvantages of them and set the direction for the future work of revealing microbe–disease associations based on computational models. We firmly believe that computational models are expected to be important tools in large-scale predictions of disease-related microbes. [ABSTRACT FROM AUTHOR]

Effective drugs are urgently needed to overcome human complex diseases. However, the research and development of novel drug would take long time and cost much money. Traditional drug discovery follows the rule of one drug-one target, while some studies have demonstrated that drugs generally perform their task by affecting related pathway rather than targeting single target. Thus, the new strategy of drug discovery, namely pathway-based drug discovery, have been proposed. Obviously, identifying associations between drugs and pathways plays a key role in the development of pathway-based drug discovery. Revealing the drug-pathway associations by experiment methods would take much time and cost. Therefore, some computational models were established to predict potential drug-pathway associations. In this review, we first introduced the background of drug and the concept of drug-pathway associations. Then, some publicly accessible databases and web servers about drug-pathway associations were listed. Next, we summarized some state-of-the-art computational methods in the past years for inferring drug-pathway associations and divided these methods into three classes, namely Bayesian spare factor-based, matrix decomposition-based and other machine learning methods. In addition, we introduced several evaluation strategies to estimate the predictive performance of various computational models. In the end, we discussed the advantages and limitations of existing computational methods and provided some suggestions about the future directions of the data collection and the calculation models development. [ABSTRACT FROM AUTHOR]

The six-membered ring (SMR) is a common structure unit for numerous material systems. These materials include, but are not limited to, the typical two-dimensional materials such as graphene, h -BN, and transition metal dichalcogenides, as well as three-dimensional materials such as beryllium, magnesium, MgB2 and Bi2Se3. Although many of these materials have already become 'stars' in materials science and condensed-matter physics, little attention has been paid to the roles of the SMR unit across a wide range of compositions and structures. In this article, we systematically analyze these materials with respect to their very basic SMR structural unit, which has been found to play a deterministic role in the occurrence of many intriguing properties and phenomena, such as Dirac electronic and phononic spectra, superconductivity and topology. As a result, we have defined this group of materials as SMR inorganic materials, opening up a new perspective on materials research and development. With their unique properties, SMR materials deserve wide attention and in-depth investigation from materials design, new physical discoveries to target-wizard applications. It is expected that SMR materials will find niche applications in next-generation information technology, renewable energy, space, etc. [ABSTRACT FROM AUTHOR]

Emerging evidence shows that microRNAs (miRNAs) play a critical role in diverse fundamental and important biological processes associated with human diseases. Inferring potential disease related miRNAs and employing them as the biomarkers or drug targets could contribute to the prevention, diagnosis and treatment of complex human diseases. In view of that traditional biological experiments cost much time and resources, computational models would serve as complementary means to uncover potential miRNA–disease associations. In this study, we proposed a new computational model named Neighborhood Constraint Matrix Completion for MiRNA–Disease Association prediction (NCMCMDA) to predict potential miRNA–disease associations. The main task of NCMCMDA was to recover the missing miRNA–disease associations based on the known miRNA–disease associations and integrated disease (miRNA) similarity. In this model, we innovatively integrated neighborhood constraint with matrix completion, which provided a novel idea of utilizing similarity information to assist the prediction. After the recovery task was transformed into an optimization problem, we solved it with a fast iterative shrinkage-thresholding algorithm. As a result, the AUCs of NCMCMDA in global and local leave-one-out cross validation were 0.9086 and 0.8453, respectively. In 5-fold cross validation, NCMCMDA achieved an average AUC of 0.8942 and standard deviation of 0.0015, which demonstrated NCMCMDA's superior performance than many previous computational methods. Furthermore, NCMCMDA was applied to three different types of case studies to further evaluate its prediction reliability and accuracy. As a result, 84% (colon neoplasms), 98% (esophageal neoplasms) and 98% (breast neoplasms) of the top 50 predicted miRNAs were verified by recent literature. [ABSTRACT FROM AUTHOR]

Culture permeates across human mind and behavior. Cultural influence is reported even in economic decision making, which involves basic cognitive process, once believed to be invariant across all humans. The current study investigated the neurocognitive processes underlying economic decision making in East Asians and European Americans, with an aim to understand the cross-cultural differences in the discrete mental processes of decision making. Participants performed a risky gambling task that captures the gain maximizing and loss minimizing strategies, while electroencephalography was simultaneously collected. Event-related potentials (ERPs) associated with spontaneous emotional arousal (P2) and effortful attentional allocation (P3) were examined to determine the cultural effects on mental processes during pre-decisional and post-decisional stages. Behaviorally, Americans showed greater loss minimization than Asians. ERPs demonstrated significant cultural differences during post-decisional evaluation of outcomes, but not during pre-decisional processes. In Asians', ERP associated with emotional arousal (P2) was strongly modulated by gains, while in Americans', ERP associated with attentional allocation (P3) was strongly modulated by losses. These results suggest that Americans make conscious efforts to be self-reliant when facing financial losses, whereas Asians are more emotionally aroused by financial gains, which invites a refinement to the current theoretical propositions about cultural influence on decision making. [ABSTRACT FROM AUTHOR]

Clustered regularly interspaced short palindromic repeats (CRISPR) is described as RNA mediated adaptive immune system defense, which is naturally found in bacteria and archaea. CRISPR-Cas9 has shown great promise for cancer treatment in cancer immunotherapy, manipulation of cancer genome and epigenome and elimination or inactivation of carcinogenic viral infections. However, many challenges remain to be addressed to increase its efficacy, including off-target effects, editing efficiency, fitness of edited cells, immune response and delivery methods. Here, we explain CRISPR-Cas classification and its general function mechanism for gene editing. Then, we summarize these preclinical CRISPR-Cas9-based therapeutic strategies against cancer. Moreover, the challenges and improvements of CRISPR-Cas9 clinical applications will be discussed. [ABSTRACT FROM AUTHOR]

Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein–protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets. Several drug-like compound libraries were screened successfully against different miRNAs in cellular assays further demonstrating the possibility of targeting miRNAs with small molecules. In this review, we summarized the concept and functions of small molecule and miRNAs. Especially, five aspects of miRNA functions were exhibited in detail with individual examples. In addition, four disease states that have been linked to miRNA alterations were summed up. Then, small molecules related to four important miRNAs miR-21, 122, 4644 and 27 were selected for introduction. Some important publicly accessible databases and web servers of the experimentally validated or potential small molecule-miRNA associations were discussed. Identifying small molecule targeting miRNAs has become an important goal of biomedical research. Thus, several experimental and computational models have been developed and implemented to identify novel small molecule-miRNA associations. Here, we reviewed four experimental techniques used in the past few years to search for small-molecule inhibitors of miRNAs, as well as three types of models of predicting small molecule-miRNA associations from different perspectives. Finally, we summarized the limitations of existing methods and discussed the future directions for further development of computational models. [ABSTRACT FROM AUTHOR]

Proxies can help users to bypass the network filtering system, leaving the network open to banned content, and can also enable users to anonymize themselves for terminal security protection. Proxies are widely used in the current network environment. However, certain spy proxies record user information for privacy theft. In addition, attackers can use such technologies to anonymize malicious behaviors and hide identities. Such behaviors have posed serious challenges to the internal defense and security threat assessment of an organization; however, the anonymity of the proxy makes it consistent with normal network communication, and general network traffic identification methods are not able to detect it. To accurately and effectively discover proxy users in the organization based on s, a proxy user detection method based on communication behavior portrait offers the following: (1) analysis of the communication behavior from the perspective of the portrait. Based on not abandoning the effective information of the traffic itself, the label system is established by introducing exogenous data to identify the difference between proxy communication and normal communication. (2) Construction of the portrait feature set of proxy user detection based on the traffic file and external data by studying the differences between the attribute sets of communication behavior labels for proxy users and non-proxy users. (3) Design and implementation a data-driven machine learning method to supply guidance for automatic recognition of such behavior. The experimental results show that, compared with state-of-the-art methods, the detection accuracy for the proxy user exceeds 95%, and that of real network traffic environment exceeds 85%. These results indicate that the detection method proposed in this paper can accurately distinguish proxy communication and normal communication and thus achieves precise proxy user detection. [ABSTRACT FROM AUTHOR]

Motivation Recent studies have shown that microRNAs (miRNAs) play a critical part in several biological processes and dysregulation of miRNAs is related with numerous complex human diseases. Thus, in-depth research of miRNAs and their association with human diseases can help us to solve many problems. Results Due to the high cost of traditional experimental methods, revealing disease-related miRNAs through computational models is a more economical and efficient way. Considering the disadvantages of previous models, in this paper, we developed adaptive boosting for miRNA-disease association prediction (ABMDA) to predict potential associations between diseases and miRNAs. We balanced the positive and negative samples by performing random sampling based on k -means clustering on negative samples, whose process was quick and easy, and our model had higher efficiency and scalability for large datasets than previous methods. As a boosting technology, ABMDA was able to improve the accuracy of given learning algorithm by integrating weak classifiers that could score samples to form a strong classifier based on corresponding weights. Here, we used decision tree as our weak classifier. As a result, the area under the curve (AUC) of global and local leave-one-out cross validation reached 0.9170 and 0.8220, respectively. What is more, the mean and the standard deviation of AUCs achieved 0.9023 and 0.0016, respectively in 5-fold cross validation. Besides, in the case studies of three important human cancers, 49, 50 and 50 out of the top 50 predicted miRNAs for colon neoplasms, hepatocellular carcinoma and breast neoplasms were confirmed by the databases and experimental literatures. Availability and implementation The code and dataset of ABMDA are freely available at https://github.com/githubcode007/ABMDA. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Aims: Left bundle branch pacing (LBBP) recently emerges as a novel pacing modality. We aimed to evaluate the feasibility and cardiac synchrony of permanent LBBP in bradycardia patients.Methods and Results: Left bundle branch pacing was successfully performed in 56 pacemaker-indicated patients with normal cardiac function. Left bundle branch pacing was achieved by penetrating the interventricular septum (IVS) into the left side sub-endocardium with the pacing lead. His-bundle pacing (HBP) was successfully performed in another 29 patients, 19 of whom had right ventricular septal pacing (RVSP) for backup pacing. The QRS duration, left ventricular (LV) activation time (LVAT), and mechanical synchrony using phase analysis of gated SPECT myocardial perfusion imaging were evaluated. Paced QRS duration in LBBP group was significantly shorter than that in RVSP group (117.8 ± 11.0 ms vs. 158.1 ± 11.1 ms, P < 0.0001) and wider than that in HBP group (99.7 ± 15.6 ms, P < 0.0001). Left bundle branch potential was recorded during procedure in 37 patients (67.3%). Left bundle branch pacing patients with potential had shorter LVAT than those without potential (73.1 ± 11.3 ms vs. 83.2 ± 16.8 ms, P = 0.03). Left bundle branch pacing patients with potential had similar LV mechanical synchrony to those in HBP group. R-wave amplitude and capture threshold of LBBP were 17.0 ± 6.7 mV and 0.5 ± 0.1 V, respectively at implant and remained stable during a mean follow-up of 4.5 months without lead-related complications.Conclusion: Permanent LBBP through IVS is safe and feasible in bradycardia patients. Left bundle branch pacing could achieve favourable cardiac electrical and LV mechanical synchrony. [ABSTRACT FROM AUTHOR]

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Ribonucleic acid (RNA) methylation is a type of posttranscriptional modifications occurring in all kingdoms of life. It is strongly related to important biological process, thus making it linked to a number of human diseases. Owing to the development of high-throughput sequencing technology, plenty of achievement had been obtained in RNA methylation research recently. Meanwhile, various computational models have been developed to analyze and mining increasing RNA methylation data. In this review, we first made a brief introduction about eight types of most popular RNA methylation, the biological functions of RNA methylation, the relationship between RNA methylation and disease and five important RNA methylation-related diseases. The research of RNA methylation is based on sequencing data processing, and effective bioinformatics techniques can benefit better understanding of RNA methylation. We further introduced seven publicly available RNA methylation-related databases, and some important publicly available RNA-methylation-related Web servers and software for RNA methylation site identification, differential analysis and so on. Furthermore, we provided detailed analysis of the state-of-the-art computational models used in these Web servers and software. We also analyzed the limitations of these models and discussed the future directions of developing computational models for RNA methylation research. [ABSTRACT FROM AUTHOR]

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA–disease associations. In this review, the functions of miRNAs, miRNA–target interactions, miRNA–disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA–disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA–disease association identification, which could select the most promising miRNA–disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA–disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA–disease associations including five feasible and important research schemas, and future directions for further development of computational models. [ABSTRACT FROM AUTHOR]

From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function–related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future. [ABSTRACT FROM AUTHOR]

Motivation It has been shown that microRNAs (miRNAs) play key roles in variety of biological processes associated with human diseases. In Consideration of the cost and complexity of biological experiments, computational methods for predicting potential associations between miRNAs and diseases would be an effective complement. Results This paper presents a novel model of Inductive Matrix Completion for MiRNA–Disease Association prediction (IMCMDA). The integrated miRNA similarity and disease similarity are calculated based on miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. The main idea is to complete the missing miRNA–disease association based on the known associations and the integrated miRNA similarity and disease similarity. IMCMDA achieves AUC of 0.8034 based on leave-one-out-cross-validation and improved previous models. In addition, IMCMDA was applied to five common human diseases in three types of case studies. In the first type, respectively, 42, 44, 45 out of top 50 predicted miRNAs of Colon Neoplasms, Kidney Neoplasms, Lymphoma were confirmed by experimental reports. In the second type of case study for new diseases without any known miRNAs, we chose Breast Neoplasms as the test example by hiding the association information between the miRNAs and Breast Neoplasms. As a result, 50 out of top 50 predicted Breast Neoplasms-related miRNAs are verified. In the third type of case study, IMCMDA was tested on HMDD V1.0 to assess the robustness of IMCMDA, 49 out of top 50 predicted Esophageal Neoplasms-related miRNAs are verified. Availability and implementation The code and dataset of IMCMDA are freely available at https://github.com/IMCMDAsourcecode/IMCMDA. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation A large number of resources have been devoted to exploring the associations between microRNAs (miRNAs) and diseases in the recent years. However, the experimental methods are expensive and time-consuming. Therefore, the computational methods to predict potential miRNA–disease associations have been paid increasing attention. Results In this paper, we proposed a novel computational model of Bipartite Network Projection for MiRNA–Disease Association prediction (BNPMDA) based on the known miRNA–disease associations, integrated miRNA similarity and integrated disease similarity. We firstly described the preference degree of a miRNA for its related disease and the preference degree of a disease for its related miRNA with the bias ratings. We constructed bias ratings for miRNAs and diseases by using agglomerative hierarchical clustering according to the three types of networks. Then, we implemented the bipartite network recommendation algorithm to predict the potential miRNA–disease associations by assigning transfer weights to resource allocation links between miRNAs and diseases based on the bias ratings. BNPMDA had been shown to improve the prediction accuracy in comparison with previous models according to the area under the receiver operating characteristics (ROC) curve (AUC) results of three typical cross validations. As a result, the AUCs of Global LOOCV, Local LOOCV and 5-fold cross validation obtained by implementing BNPMDA were 0.9028, 0.8380 and 0.8980 ± 0.0013, respectively. We further implemented two types of case studies on several important human complex diseases to confirm the effectiveness of BNPMDA. In conclusion, BNPMDA could effectively predict the potential miRNA–disease associations at a high accuracy level. Availability and implementation BNPMDA is available via http://www.escience.cn/system/file?fileId=99559. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Chronic arsenic exposure is associated with the development of several cardiovascular (CV) diseases, including hypertension, carotid atherosclerosis and microvascular abnormalities. Upregulation of systemic and aortic angiotensin II (ANGII) signaling has been proposed to contribute to arsenic-induced vascular dysfunction. However, the underlying mechanisms of ANGII signaling augmentation and of the attendant pathological effects on the CV system induced by arsenic remain largely unknown. Here, we reported that exposure of human umbilical vein endothelial cells (HUVECs) to arsenite resulted in elevation of angiotensinogen (AGT, the precursor of ANGII), angiotensin-converting enzyme (ACE, the enzyme critical for ANGII generation), and ANGII type I receptor (AT1R) synthesis as well as increased ANGII production. Further investigations showed that endoplasmic reticulum (ER) stress was induced and activation of the IRE1a/XBP1s arm of the unfolded protein response was responsible for the augmented ACE/ANGII/AT1R axis components in arsenite-treated HUVECs. Moreover, XBP1s promoted HIF1α accumulation, and inducible XBP1s/HIF1a complex formation was required to drive the transcription of AGT, ACE, and AT1R under arsenite exposure. Ablation of IRE1α/XBP1s/HIF1γ-dependent ANGII signaling activation inhibited oxidative stress and proinflammatory response induced in HUVECs by arsenite. These results thus have revealed the novel role of ER stress-coupled HIF1a pathway activation in mediating ANGII-dependent endothelial cell dysfunction upon arsenite exposure. Therefore, searching for strategies to alleviate endothelial ER stress or ANGII signaling might be helpful for managing arsenite-induced CV disorders. [ABSTRACT FROM AUTHOR]

Ag2Se quantum dots (QDs) are novel fluorescent probes in the second near-infrared window with great imaging quality and biocompatibility. Surface modification is an essential step to disperse Ag2Se QDs into biological fluids, and endow Ag2Se QDs with diverse surface chemistry. However, the effect of surface chemistry on the biological behaviors and chemical fates of Ag2Se QDs has not been studied, which hinders the design of suitable Ag2Se QDs for biomedical applications. Here, the distribution, degradation, excretion and toxicity of 2-aminoethanethiol and mercaptopropionic acid coated Ag2Se QDs (denoted as QDs-MEA and QDs-MPA, respectively) were systematically investigated in mice for a 28-day observation period after a single intravenous injection. Ag2Se QDs with different surface chemistries displayed similar trends in all observations, such as fast blood clearance, main uptake in the liver and spleen, severe biotransformation, Ag excretion through feces, and low toxicity. The major different behaviors observed were the partially pulmonary deposition, the faster transformation at the initial stage, the lower excretion percentage, and the more obvious damage to the liver by QDs-MEA compared to QDs-MPA. The surface chemistry of Ag2Se QDs regulated their biological behaviors and chemical fates in vivo, and surface chemistry should be fully regarded when designing Ag2Se QDs for biomedical applications from the biosafety perspective. [ABSTRACT FROM AUTHOR]

Yeast-like symbionts (YLSs) are transovarially transmitted between brown planthopper generations, but the transmission mechanism is poorly understood. In the present study, we found that YLSs do not enter the brown planthopper oocyte until it develops to late vitellogenesis. During oocyte vitellogenesis, the diversification and morphological changes of the follicular cells surrounding the oocyte are associated with the accumulation and polarization of vitellogenin (Vg) and lipids in the oocyte, indirectly affecting the entry of YLSs into the oocyte. Additionally, the posterior follicular cells, one of the diversified follicular cell populations, form the epithelial plug that is the only structure for YLSs to enter the oocyte. In addition, actin is rearranged within the follicular cells to build an actin bridge for YLSs to move from the epithelial plug to the oocyte, indicating that actin mediates the entry of YLSs into the brown planthopper oocyte during oocyte vitellogenesis. Taken together, our results demonstrate that the entry of YLSs into the brown planthopper oocyte is triggered by oocyte vitellogenesis. [ABSTRACT FROM AUTHOR]