Predicting circRNA-drug sensitivity associations by learning multimodal networks using graph auto-encoders and attention mechanism.

Recent studies have shown that the expression of circRNAs would affect drug sensitivity of cells and thus significantly influence the efficacy of drugs. Traditional biomedical experiments to validate such relationships are time-consuming and costly. Therefore, developing effective computational methods to predict potential associations between circRNAs and drug sensitivity is an important and urgent task. In this study, we propose a novel method, called MNGACDA, to predict possible circRNA-drug sensitivity associations for further biomedical screening. First, MNGACDA uses multiple sources of information from circRNAs and drugs to construct multimodal networks. It then employs node-level attention graph auto-encoders to obtain low-dimensional embeddings for circRNAs and drugs from the multimodal networks. Finally, an inner product decoder is applied to predict the association scores between circRNAs and drug sensitivity based on the embedding representations of circRNAs and drugs. Extensive experimental results based on cross-validations show that MNGACDA outperforms six other state-of-the-art methods. Furthermore, excellent performance in case studies demonstrates that MNGACDA is an effective tool for predicting circRNA-drug sensitivity associations in real situations. These results confirm the reliable prediction ability of MNGACDA in revealing circRNA-drug sensitivity associations.

Predicting miRNA-disease associations based on lncRNA-miRNA interactions and graph convolution networks.

Increasing studies have proved that microRNAs (miRNAs) are critical biomarkers in the development of human complex diseases. Identifying disease-related miRNAs is beneficial to disease prevention, diagnosis and remedy. Based on the assumption that similar miRNAs tend to associate with similar diseases, various computational methods have been developed to predict novel miRNA-disease associations (MDAs). However, selecting proper features for similarity calculation is a challenging task because of data deficiencies in biomedical science. In this study, we propose a deep learning-based computational method named MAGCN to predict potential MDAs without using any similarity measurements. Our method predicts novel MDAs based on known lncRNA-miRNA interactions via graph convolution networks with multichannel attention mechanism and convolutional neural network combiner. Extensive experiments show that the average area under the receiver operating characteristic values obtained by our method under 2-fold, 5-fold and 10-fold cross-validations are 0.8994, 0.9032 and 0.9044, respectively. When compared with five state-of-the-art methods, MAGCN shows improvement in terms of prediction accuracy. In addition, we conduct case studies on three diseases to discover their related miRNAs, and find that all the top 50 predictions for all the three diseases have been supported by established databases. The comprehensive results demonstrate that our method is a reliable tool in detecting new disease-related miRNAs.

Predicting miRNA-disease associations based on graph attention networks and dual Laplacian regularized least squares.

Increasing biomedical evidence has proved that the dysregulation of miRNAs is associated with human complex diseases. Identification of disease-related miRNAs is of great importance for disease prevention, diagnosis and remedy. To reduce the time and cost of biomedical experiments, there is a strong incentive to develop efficient computational methods to infer potential miRNA-disease associations. Although many computational approaches have been proposed to address this issue, the prediction accuracy needs to be further improved. In this study, we present a computational framework MKGAT to predict possible associations between miRNAs and diseases through graph attention networks (GATs) using dual Laplacian regularized least squares. We use GATs to learn embeddings of miRNAs and diseases on each layer from initial input features of known miRNA-disease associations, intra-miRNA similarities and intra-disease similarities. We then calculate kernel matrices of miRNAs and diseases based on Gaussian interaction profile (GIP) with the learned embeddings. We further fuse the kernel matrices of each layer and initial similarities with attention mechanism. Dual Laplacian regularized least squares are finally applied for new miRNA-disease association predictions with the fused miRNA and disease kernels. Compared with six state-of-the-art methods by 5-fold cross-validations, our method MKGAT receives the highest AUROC value of 0.9627 and AUPR value of 0.7372. We use MKGAT to predict related miRNAs for three cancers and discover that all the top 50 predicted results in the three diseases are confirmed by existing databases. The excellent performance indicates that MKGAT would be a useful computational tool for revealing disease-related miRNAs.

Enhancing Aboveground Biomass Prediction through Integration of the SCDR Paradigm into the U-Like Hierarchical Residual Fusion Model.

Deep learning methodologies employed for biomass prediction often neglect the intricate relationships between labels and samples, resulting in suboptimal predictive performance. This paper introduces an advanced supervised contrastive learning technique, termed Improved Supervised Contrastive Deep Regression (SCDR), which is adept at effectively capturing the nuanced relationships between samples and labels in the feature space, thereby mitigating this limitation. Simultaneously, we propose the U-like Hierarchical Residual Fusion Network (BioUMixer), a bespoke biomass prediction network tailored for image data. BioUMixer enhances feature extraction from biomass image data, facilitating information exchange and fusion while considering both global and local features within the images. The efficacy of the proposed method is validated on the Pepper_Biomass dataset, which encompasses over 600 original images paired with corresponding biomass labels. The results demonstrate a noteworthy enhancement in deep regression tasks, as evidenced by performance metrics on the Pepper_Biomass dataset, including RMSE = 252.18, MAE = 201.98, and MAPE = 0.107. Additionally, assessment on the publicly accessible GrassClover dataset yields metrics of RMSE = 47.92, MAE = 31.74, and MAPE = 0.192. This study not only introduces a novel approach but also provides compelling empirical evidence supporting the digitization and precision improvement of agricultural technology. The research outcomes align closely with the identified problem and research statement, underscoring the significance of the proposed methodologies in advancing the field of biomass prediction through state-of-the-art deep learning techniques.

Causal optimal and optically transparent ultra-wideband microwave metamaterials absorber with high angular stability.

Wideband microwave absorbers, especially those with high optical transparency, are significantly used in civil and military fields. This paper proposes an ultra-wideband optically transparent metamaterial absorber (MMA) with causal optimal thickness and high angular stability. Based on the equivalent circuits model of the MMA, a genetic algorithm is adopted to identify the best circuit parameters that can realize broadband microwave absorption. High transparent indium tin oxide and poly-methyl methacrylate are utilized to realize the absorber. Optimization and simulation results show that the designed MMA presents a high microwave absorption above 90%, covering a wide frequency of 2.05-15.5 GHz with an impressive FBW of 153.3%. The proposed MMA exhibits extraordinary angular stability. For TM polarization, it can still maintain a fractional bandwidth (FBW) over 114.5% at an incidence angle of 70° and over 142% at an incidence angle of 60°, while the FBW of both TE polarization and TM polarization exceeds 150% when the incidence angle is below 45°. Furthermore, the proposed absorber has the advantages of high transparency and polarization insensitiveness. A prototype of the proposed MMA is fabricated and experimentally tested. The measured results are in excellent agreement with the optimized design and the full-wave simulation results, demonstrating its excellent performance. Most significantly, the overall thickness of the absorber is 0.102 λ at the lowest working frequency and only 1.08 times the causality-dictated minimum sample thickness. The MMA proposed herein provides methods to achieve high compatibility with wideband microwave absorption, optical transparency, and wide-angle incidence, thus enabling a wide range of applications in stealth, electromagnetic pollution reduction, and electromagnetic compatible facilities.

DUSP1 regulates the JAK2/STAT3 signaling pathway through targeting miR-21 in cervical cancer cells.

This study was to investigate the effect of DUSP1 on cervical cancer (CC) cells by targeting the miR-21 regulatory JAK2/STAT3 signaling pathway. For this purpose, fifteen CC patients treated at our hospital from January 2021 to February 2023 were selected. CC tissues and para-cancerous (PC) tissues were collected from the patients, and DUSP1 protein and mRNA expression levels were detected by Western blot and qPCR. The C33a control group (COG) and DUSP1 overexpression group (OVG) were set up: human cervical squamous carcinoma cells (CSCC) in the C33a COG were cultured without any treatment, while the DUSP1 OVG was cultured using DUSP1 gene overexpression lentivirus infection progeny. The proliferation ability of the three groups of cells was measured by CCK8, protein and mRNA expression by Western blot and qPCR, and cell migration and invasion ability by Transwell. It was found that DUSP1 protein and mRNA in CC tissues were reduced compared with those in PC tissues (P<0.05). The miR-21 in the DUSP1 OVG was reduced than those in the C33a COG (P<0.05). The expression of JAK2, STAT3 mRNA and protein in the DUSP1 OVG were reduced compared with those in the C33a COG (P<0.05). In conclusion, overexpression of DUSP1 can target and reduce the expression of miR-21, block the JAK2/STAT3 signaling pathway, reduce the viability of CC cells, inhibit the proliferation and migration and invasion ability of CC cells, and induce apoptosis of CC cells, thus providing a theoretical basis for the targeted treatment of clinical CC.

High sensitivity temperature sensor based on enhanced Vernier effect through two parallel Fabry-Perot cavities.

In this paper, an enhanced Vernier effect temperature sensor based on two parallel Fabry-Perot interferometers (FPIs) is proposed and demonstrated experimentally. Among them, F P I 1 is composed of a single-mode fiber (SMF), a quartz capillary, and AB glue filled in the capillary. F P I 2 is formed by filling a capillary with polyimide (PI) solution and inserting two-segment SMF from both sides of the capillary. Since AB glue and PI have good thermal sensitivity, F P I 1 and F P I 2 are highly sensitive to temperature. Due to their different structures, the temperature sensitivity of F P I 1 is negative, and that of F P I 2 is positive. When F P I 1 and F P I 2 with similar free spectral range are connected in parallel, they will act as reference cavities for each other, resulting in an enhanced Vernier effect, which enlarges the sensitivity of the sensor more. In the temperature range of 40°C-58°C, the temperature sensitivity of the sensor is as high as -13.09n m/∘ C, and the fitting coefficient is 0.9974. The experimental results show that in the enhanced Vernier effect sensor structure, only two FPIs with opposite temperature sensitivity are required, which does not increase the difficulty and cost of sensor manufacturing. In addition, the sensor has good stability and repeatability.

Predicting potential miRNA-disease associations based on more reliable negative sample selection.

BACKGROUND: Increasing biomedical studies have shown that the dysfunction of miRNAs is closely related with many human diseases. Identifying disease-associated miRNAs would contribute to the understanding of pathological mechanisms of diseases. Supervised learning-based computational methods have continuously been developed for miRNA-disease association predictions. Negative samples of experimentally-validated uncorrelated miRNA-disease pairs are required for these approaches, while they are not available due to lack of biomedical research interest. Existing methods mainly choose negative samples from the unlabelled ones randomly. Therefore, the selection of more reliable negative samples is of great importance for these methods to achieve satisfactory prediction results. RESULTS: In this study, we propose a computational method termed as KR-NSSM which integrates two semi-supervised algorithms to select more reliable negative samples for miRNA-disease association predictions. Our method uses a refined K-means algorithm for preliminary screening of likely negative and positive miRNA-disease samples. A Rocchio classification-based method is applied for further screening to receive more reliable negative and positive samples. We implement ablation tests in KR-NSSM and find that the combination of the two selection procedures would obtain more reliable negative samples for miRNA-disease association predictions. Comprehensive experiments based on fivefold cross-validations demonstrate improvements in prediction accuracy on six classic classifiers and five known miRNA-disease association prediction models when using negative samples chose by our method than by previous negative sample selection strategies. Moreover, 469 out of 1123 selected positive miRNA-disease associations by our method are confirmed by existing databases. CONCLUSIONS: Our experiments show that KR-NSSM can screen out more reliable negative samples from the unlabelled ones, which greatly improves the performance of supervised machine learning methods in miRNA-disease association predictions. We expect that KR-NSSM would be a useful tool in negative sample selection in biomedical research.

Highly sensitive gas pressure sensor based on the enhanced Vernier effect through a cascaded Fabry-Perot and Mach-Zehnder interferometer.

A high sensitivity optical fiber gas pressure sensor based on the enhanced Vernier effect is proposed. The sensor is composed of a fiber Fabry-Perot interferometer (FPI) and Mach-Zehnder interferometer (MZI). Since the interference fringes of FPI and MZI drift in the opposite direction with the change of gas pressure, when their free spectral ranges are similar, the enhanced Vernier effect is formed after their cascading. Compared with the traditional Vernier effect gas pressure sensor, the enhanced Vernier effect gas pressure sensor realizes much higher sensitivity gas pressure measurement without complex manufacturing process or desensitized reference interferometer. The experimental results show that the sensitivity of the enhanced Vernier effect sensor is 241.87 nm/MPa. In the two traditional Vernier effect gas pressure sensors formed by cascading FPI and MZI, the sensitivity of sensor is 63.02 nm/MPa and 171.26 nm/MPa, respectively. Compared with the two traditional Vernier effect sensors, the sensitivity of the enhanced Vernier effect sensor is increased by 3.8 times and 1.4 times, respectively. The proposed sensor also has the advantages of good repeatability and stability, fast response, low cost and easy manufacture. Our structure also provides a new design scheme for a high sensitivity optical fiber gas pressure sensor.

Effect of Magnetic Nanoparticles on Hormone Level Changes During Perimenopausal Period and Regulation of Bone Metabolism.

This work aimed to explore the effect of nerve magnetic stimulation based on superparamagnetic Fe3O4 nanoparticle (NP) on bone metabolism during the perimenopausal period. First, the multifunctional water-soluble polymer PTMP-PMAA was utilized as the ligand. PTMP-MAA@ Fe3O4 NP with high magnetization was prepared by the co-precipitation method, and NPX diffraction pattern analysis and in vitro stability analysis were implemented. Then, NPs were co-cultured with 293T cells, and the cytotoxicity was detected by the CCK-8 method. Subsequently, 3-month-old female young SD rats and 11~15-month-old natural menopausal SD rats were taken as the research objects. According to the vaginal smear, the rats were randomly rolled into a young control, perimenopausal period model, estrogen treatment, and osteoporosis prevention groups. Rats in the estrogen treatment group were given Premarin suspension by gavage. Rats in the osteoporosis prevention group were injected stereotaxically with PTMP-MAA@ Fe3O4 NP suspension, and a rotating magnetic field was applied to the brain for nerve magnetic stimulation. The rats were sacrificed three days after treatment and brain tissues were taken for pathological analysis. Rat humerus was weighted and dual-energy X-ray was utilized to determine bone density and bone mineral content. Serum was collected and radioimmunoassay and ELISA were employed to detect estradiol (E2), osteocalcin (Boneglaprotein, BGP), oxytocin (OT), bone alkaline phosphatase (BALP), type I collagen carboxy-terminated cross-linked peptide (CTX-I), and tartrate-resistant acid phosphatase (TRACP-5b) in the serum of rats in each group. The results showed that PTMP-MAA@ Fe3O4 NP had good biocompatibility, and the CCK-8 test results showed that PTMP-MAA@ Fe3O4 NP had low cytotoxicity. Compared with the young control group, the humeral dry weight, wet weight, bone density, and bone mineral content, serum E2, OT, and BGP content in the perimenopausal period model group were reduced, while the serum BALP, CTX-I, and TRACP -5b content was increased (P<0.05). It was verified that nerve magnetic stimulation based on PTMP-MAA@ Fe3O4 NP increased the serum estrogen level of female rats during the perimenopausal period, increased the bone density of rats, promoted bone formation, and regulated bone metabolism.

In silico drug repositioning based on the integration of chemical, genomic and pharmacological spaces.

BACKGROUND: Drug repositioning refers to the identification of new indications for existing drugs. Drug-based inference methods for drug repositioning apply some unique features of drugs for new indication prediction. Complementary information is provided by these different features. It is therefore necessary to integrate these features for more accurate in silico drug repositioning. RESULTS: In this study, we collect 3 different types of drug features (i.e., chemical, genomic and pharmacological spaces) from public databases. Similarities between drugs are separately calculated based on each of the features. We further develop a fusion method to combine the 3 similarity measurements. We test the inference abilities of the 4 similarity datasets in drug repositioning under the guilt-by-association principle. Leave-one-out cross-validations show the integrated similarity measurement IntegratedSim receives the best prediction performance, with the highest AUC value of 0.8451 and the highest AUPR value of 0.2201. Case studies demonstrate IntegratedSim produces the largest numbers of confirmed predictions in most cases. Moreover, we compare our integration method with 3 other similarity-fusion methods using the datasets in our study. Cross-validation results suggest our method improves the prediction accuracy in terms of AUC and AUPR values. CONCLUSIONS: Our study suggests that the 3 drug features used in our manuscript are valuable information for drug repositioning. The comparative results indicate that integration of the 3 drug features would improve drug-disease association prediction. Our study provides a strategy for the fusion of different drug features for in silico drug repositioning.

Platelet transcriptome profiles provide potential therapeutic targets for elderly acute myelocytic leukemia patients.

BACKGROUND: Acute myeloid leukemia (AML) is the most common acute leukemia in adults, with a median age of 68 in clinical diagnosis. About 60% patients are over 60 years old. There are various treatment options for AML patients. But for elderly patients, the complete remission rates are disappointing due to genetic, molecular, and age-related factors. Development of next-generation sequencing technologies makes it possible to seek individual strategies for patients in different ages. This study analyzed transcriptome profiles in platelets of AML patients in different ages for the first time. METHODS: Platelet RNA sequencing in AML of ten elderly and seven young patients were performed with Illumina TruSeq Stranded mRNA library Prep Kit and Illumina HiSeq4000 sequencing instrument. With the FASTQ sequencing data obtained, statistical analyses between elderly with young AML patients were analyzed by R program. GO and KEGG enrichment analyses were performed via R package clusterProfiler. TOP 10 down-regulated/up-regulated genes in elderly patients compared to young patients were selected with the threshold of |L2FC| > 2 and padj ≤ 0.0001. The down-regulated gene ATF4 was chosen by GSEA analysis and ROC analysis with AUC > 0.95. RESULTS: We found 3059 genes with differential transcript levels (GDTLs) in AML patients of different age. Among them, 2048 genes are down-regulated and 651 genes are up-regulated in elderly patients. We found that gene transcript profiles in elderly patients is obviously different from those in young patients, including a collection of down-regulated genes related to proteins processing in endoplasmic reticulum and immunity. We further identified that genes of pathway in cancer and mitogen activated protein kinase (MAPK) pathway, involved in natural immunity and metabolism, are significantly down-regulated in elderly patients. Among all screened genes with decreased transcript levels, we believe that activating transcription factor 4 (ATF4) is a biomarker indicating different chemotherapy strategies for elderly patients. CONCLUSIONS: In summary, gene transcript profiles are different in platelets of elderly and young AML patients. And ATF4 can be a useful biomarker indicating different chemotherapy strategies for AML patients with different ages.

Anti-angiogenic therapy in ovarian cancer: current situation & prospects.

Ovarian cancer (OC) is one of five leading causes of cancer related death among women worldwide. Although treatment has been improving, the survival rate has barely improved over the past 30 years. The fatality rate is due to asymptomatic early signs and the lack of long-term effective treatment strategies for advanced disease. Angiogenesis is an important process in tumour growth and metastasis and is the creation of new blood vessels from existing blood vessels. It is a dynamic and complex process involving various molecular regulatory pathways and multiple mechanisms. The inhibition of angiogenesis has become a recognized therapeutic strategy for many solid tumours. While benefits in progression-free survival have been observed, the OS is far from satisfactory for OC patients who receive antiangiogenic therapy. In this article, the present research status of angiogenesis in OC was reviewed and the reasons for poor antiangiogenic therapeutic effects was explored with the aim to identify potential therapeutic targets that may improve the effect of antiangiogenic therapies.

Anti-inflammatory effects of paeoniflorin caused by regulation of the hif1a/miR-210/caspase1/GSDMD signaling pathway in astrocytes: a novel strategy for hypoxia-induced brain injury in rats.

Context: Hypoxia-induced injury is a classic symptom of obstructive sleep apnea hypopnea syndrome (OSAHS), which is a risk factor of various diseases, such as hypertension, heart failure and stroke. However, there is no effective therapy for hypoxia-induced injury or OSAHS due to the elusive mechanism involved.Objective: This study aimed to assess the effects of paeoniflorin on hypoxia-induced injury and explore the underlying mechanism.Materials and methods: Hypoxic models of SD rats and CTX-TNA2 cells were used to assess the effect of paeoniflorin, and the expressions of hif1a, miR-210, caspase1 and GSDMD were detected using western blots and RT-PCR. Plasmid transfection was performed to explore the role of miR-210 in the effect of paeoniflorin.Results: Firstly, we confirmed that hypoxia induced severe neuronal injury and an enhancement of inflammation in the rat brain, with elevated expression of caspase1, IL1b and IL18. In addition, the results showed an activation of astrocytes and an increased level of pyroptosis under hypoxic conditions, which suggested a critical role of pyroptosis in hypoxiainduced injury of the brain. Furthermore, we found that compared with the controls, paeoniflorin treatment improved hypoxia-induced pyroptosis in astrocytes. Moreover, we detected the activation of hif1a/miR-210 signaling in the effects of paeoniflorin on astrocytes. As expected, the expression of hif1a and miR-210 was significantly upregulated in astrocytes when exposed to hypoxia, while paeoniflorin treatment reversed these enhancements. After transfection of miR-210 mimics, the attenuation of pyroptosis induced by paeoniflorin was suppressed, which was accompanied by an increase of ROS levels, as well as LDH release, indicating a critical role of miR-210 in pyroptosis in astrocytes.Conclusions: Our findings demonstrated that paeoniflorin improved hypoxia-induced pyroptosis in astrocytes via depressing hif1a/miR-210/caspase1/GSDMD signaling, providing robust evidence for the treatment of hypoxic injury and OSAHS.HighlightsHypoxia induces severe injury and inflammatory response in the rat brain;Hypoxia enhanced pyroptotic level and led to an activation of astrocytes.;Paeoniflorin alleviates hypoxia-induced pyroptosis in astrocytes;Transfection of miR-210 mimics suppressed the effects of paeoniflorin on hypoxia-induced pyroptosis in astrocytes.

Comparative analysis of similarity measurements in miRNAs with applications to miRNA-disease association predictions.

BACKGROUND: As regulators of gene expression, microRNAs (miRNAs) are increasingly recognized as critical biomarkers of human diseases. Till now, a series of computational methods have been proposed to predict new miRNA-disease associations based on similarity measurements. Different categories of features in miRNAs are applied in these methods for miRNA-miRNA similarity calculation. Benchmarking tests on these miRNA similarity measures are warranted to assess their effectiveness and robustness. RESULTS: In this study, 5 categories of features, i.e. miRNA sequences, miRNA expression profiles in cell-lines, miRNA expression profiles in tissues, gene ontology (GO) annotations of miRNA target genes and Medical Subject Heading (MeSH) terms of miRNA-associated diseases, are collected and similarity values between miRNAs are quantified based on these feature spaces, respectively. We systematically compare the 5 similarities from multi-statistical views. Furthermore, we adopt a rule-based inference method to test their performance on miRNA-disease association predictions with the similarity measurements. Comprehensive comparison is made based on leave-one-out cross-validations and a case study. Experimental results demonstrate that the similarity measurement using MeSH terms performs best among the 5 measurements. It should be noted that the other 4 measurements can also achieve reliable prediction performance. The best-performed similarity measurement is used for new miRNA-disease association predictions and the inferred results are released for further biomedical screening. CONCLUSIONS: Our study suggests that all the 5 features, even though some are restricted by data availability, are useful information for inferring novel miRNA-disease associations. However, biased prediction results might be produced in GO- and MeSH-based similarity measurements due to incomplete feature spaces. Similarity fusion may help produce more reliable prediction results. We expect that future studies will provide more detailed information into the 5 feature spaces and widen our understanding about disease pathogenesis.

Neuropeptide S Displays as a Key Neuromodulator in Olfactory Spatial Memory.

Neuropeptide S (NPS) is an endogenous peptide recently recognized to be presented in the brainstem and believed to play an important role in maintaining memory. The deletion of NPS or NPS receptor (NPSR) in mice shows a deficit in memory formation. Our recent studies have demonstrated that central administration of NPS facilitates olfactory function and ameliorates olfactory spatial memory impairment induced by muscarinic cholinergic receptor antagonist and N-methyl-D-aspartate receptor antagonist. However, it remains to be determined if endogenous NPS is an indispensable neuromodulator in the control of the olfactory spatial memory. In this study, we examined the effects of NPSR peptidergic antagonist [D-Val5]NPS (10 and 20 nmol, intracerebroventricular) and nonpeptidergic antagonist SHA 68 (10 and 50 mg/kg, intraperitoneal) on the olfactory spatial memory using computer-assisted 4-hole-board olfactory spatial memory test in mice. Furthermore, immunofluorescence was employed to identify the distributions of c-Fos and NPSR immunoreactive (-ir) neurons in olfactory system and hippocampal formation known to closely relate to the olfactory spatial memory. [D-Val5]NPS dosing at 20 nmol and SHA 68 dosing at 50 mg/kg significantly decreased the number of visits to the 2 odorants interchanged spatially, switched odorants, in recall trial, and simultaneously reduced the percentage of Fos-ir in NPSR-ir neurons, which were densely distributed in the anterior olfactory nucleus, piriform cortex, subiculum, presubiculum, and parasubiculum. These findings suggest that endogenous NPS is a key neuromodulator in olfactory spatial memory.

Correction to: Enhancement of the catalytic activity of Isopentenyl diphosphate isomerase (IDI) from Saccharomyces cerevisiae through random and site-directed mutagenesis.

The authors of this article [1] wish to draw the readers' attention to their closely related paper, published in RSC Advances [2] which should have been cited in this article. The authors regret that there is unattributed overlap in text describing the construction of the plasmid coding for the biosynthetic pathway because of the commonly used research strategies between this article [1] and similar work presented in RSC Advances, although this does not affect the main scientific conclusion in this study.

Potential circRNA-disease association prediction using DeepWalk and network consistency projection.

A growing body of experimental studies have reported that circular RNAs (circRNAs) are of interest in pathogenicity mechanism research and are becoming new diagnostic biomarkers. As experimental techniques for identifying disease-circRNA interactions are costly and laborious, some computational predictors have been advanced on the basis of the integration of biological features about circRNAs and diseases. However, the existing circRNA-disease relationships are not well exploited. To solve this issue, a novel method named DeepWalk and network consistency projection for circRNA-disease association prediction (DWNCPCDA) is proposed. Specifically, our method first reveals features of nodes learned by the deep learning method DeepWalk based on known circRNA-disease associations to calculate circRNA-circRNA similarity and disease-disease similarity, and then these two similarity networks are further employed to feed to the network consistency projection method to predict unobserved circRNA-disease interactions. As a result, DWNCPCDA shows high-accuracy performances for disease-circRNA interaction prediction: an AUC of 0.9647 with leave-one-out cross validation and an average AUC of 0.9599 with five-fold cross validation. We further perform case studies to prioritize latent circRNAs related to complex human diseases. Overall, this proposed method is able to provide a promising solution for disease-circRNA interaction prediction, and is capable of enhancing existing similarity-based prediction methods.

Prediction and interpretation of miRNA-disease associations based on miRNA target genes using canonical correlation analysis.

BACKGROUND: It has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been proposed for predicting miRNA-disease associations. However, the existing methods rarely investigated the cause-and-effect mechanism behind these associations, which hindered further biomedical follow-ups. RESULTS: In this study, we presented a CCA-based model in which the possible molecular causes of miRNA-disease associations were comprehensively revealed by extracting correlated sets of genes and diseases based on the co-occurrence of miRNAs in target gene profiles and disease profiles. Our method directly suggested the underlying genes involved, which could be used for experimental tests and confirmation. The inference of associated diseases of a new miRNA was made by taking into account the weight vectors of the extracted sets. We extracted 60 pairs of correlated sets from 404 miRNAs with two profiles for 2796 target genes and 362 diseases. The extracted diseases could be considered as possible outcomes of miRNAs regulating the target genes which appeared in the same set, some of which were supported by independent source of information. Furthermore, we tested our method on the 404 miRNAs under the condition of 5-fold cross validations and received an AUC value of 0.84606. Finally, we extensively inferred miRNA-disease associations for 100 new miRNAs and some interesting prediction results were validated by established databases. CONCLUSIONS: The encouraging results demonstrated that our method could provide a biologically relevant prediction and interpretation of associations between miRNAs and diseases, which were of great usefulness when guiding biological experiments for scientific research.

Improvement of isoprene production in Escherichia coli by rational optimization of RBSs and key enzymes screening.

BACKGROUND: As an essential platform chemical mostly used for rubber synthesis, isoprene is produced in industry through chemical methods, derived from petroleum. As an alternative, bio-production of isoprene has attracted much attention in recent years. Previous researches were mostly focused on key enzymes to improve isoprene production. In this research, besides screening of key enzymes, we also paid attention to expression intensity of non-key enzymes. RESULTS: Firstly, screening of key enzymes, IDI, MK and IspS, from other organisms and then RBS optimization of the key enzymes were carried out. The strain utilized IDIsa was firstly detected to produce more isoprene than other IDIs. IDIsa expression was improved after RBS modification, leading to 1610-fold increase of isoprene production. Secondly, RBS sequence optimization was performed to reduce translation initiation rate value of non-key enzymes, ERG19 and MvaE. Decreased ERG19 and MvaE expression and increased isoprene production were detected. The final strain showed 2.6-fold increase in isoprene production relative to the original strain. Furthermore, for the first time, increased key enzyme expression and decreased non-key enzyme expression after RBS sequence optimization were obviously detected through SDS-PAGE analysis. CONCLUSIONS: This study prove that desired enzyme expression and increased isoprene production were obtained after RBS sequence optimization. RBS optimization of genes could be a powerful strategy for metabolic engineering of strain. Moreover, to increase the production of engineered strain, attention should not only be focused on the key enzymes, but also on the non-key enzymes.