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Conclusions Our data (i) reveal that cp genomes can be used for the identification and classification of Rosa species; (ii) can aid studies on molecular identification, genetic transformation, expression of secondary metabolic pathways and resistant proteins; (iii) can lay a theoretical foundation for the discovery of disease-resistance genes and cultivation of Rosa species. © The Author(s) 2020.Gout is a common arthritis condition due to disorders of purine metabolism and decreased uric acid excretion. Although researchers have carried out various studies on this disease, there are no effective drugs for patients with gout. In traditional Chinese medicine (TCM), gout pertains the category of Bi pattern due to qi stagnation in the meridians and collaterals. Dihydroartemisinin chemical structure Chinese herbal medicinals has been employed to treat Bi patterns since the ancient China. In recent decades, classical TCM formulas and agents isolated from some Chinese herbal medicinals have been applied to treat gout and have achieved satisfactory effect. In this review, we focus on recent studies of gout in which TCM formulas were applied to treat animal models or to treat patients, and summarize the mechanism of gout from TCM perspective, the clinical application, pharmacological mechanism and the chemical compounds of TCM formulas in treating gout. In conclusion, through this study, we summarized the application principle of TCM formulas in gout treatment and some key issues of current research, and we hope this study will provide some references for applying TCM formulas to treat gout and will lay a foundation for the development of novel formulas for gout treatments. © The Author(s) 2020.Background Advances in molecular biology have resulted in big and complicated data sets, therefore a clustering approach that able to capture the actual structure and the hidden patterns of the data is required. Moreover, the geometric space may not reflects the actual similarity between the different objects. As a result, in this research we use clustering-based space that convert the geometric space of the molecular to a categorical space based on clustering results. Then we use this space for developing a new classification algorithm. Results In this study, we propose a new classification method named GrpClassifierEC that replaces the given data space with categorical space based on ensemble clustering (EC). The EC space is defined by tracking the membership of the points over multiple runs of clustering algorithms. Different points that were included in the same clusters will be represented as a single point. Our algorithm classifies all these points as a single class. The similarity between two objects is defined as the number of times that these objects were not belong to the same cluster. In order to evaluate our suggested method, we compare its results to the k nearest neighbors, Decision tree and Random forest classification algorithms on several benchmark datasets. The results confirm that the suggested new algorithm GrpClassifierEC outperforms the other algorithms. Conclusions Our algorithm can be integrated with many other algorithms. In this research, we use only the k-means clustering algorithm with different k values. In future research, we propose several directions (1) checking the effect of the clustering algorithm to build an ensemble clustering space. (2) Finding poor clustering results based on the training data, (3) reducing the volume of the data by combining similar points based on the EC. Availability and implementation The KNIME workflow, implementing GrpClassifierEC, is available at https//malikyousef.com. © The Author(s) 2020.Objective Although the relative risk from a prospective cohort study is numerically approximate to the odds ratio from a case-control study for a low-probability event, a definite relationship between case-control and cohort studies cannot be confirmed. In this study, we established a different model to determine the relationship between case-control and cohort studies. Methods Two analysis models (the cross-sectional model and multiple pathogenic factor model) were established. Incidences in both the exposure group and the nonexposure group in a cohort study were compared with the frequency of the observed factor in each group (diseased and nondiseased) in a case-control study. Results The relationship between the results of a case-control study and a cohort study is as follows Pe=(Pd∗m)/(Pc∗m)/(Pd∗m)/(Pn=(m)/(∗PdPc∗m)/(Pd∗m)/(Pe and Pn represent the incidence in the exposed group and nonexposed group, respectively, from the cohort study, while Pd and Pc represent the observed frequencies in the disease group and the control group, respectively, for the case-control study; finally, m)/(. Conclusions There is a definite relationship between the results of case-control and cohort studies assessing the same exposure. The outcomes of case-control studies can be translated into cohort study data. Copyright © 2019 Hui Liu.Pulse signals are widely used to evaluate the status of the human cardiovascular, respiratory, and circulatory systems. In the process of being collected, the signals are usually interfered by some factors, such as the spike noise and the poor-sensor-contact noise, which have severely affected the accuracy of the subsequent detection models. In recent years, some methods have been applied to processing the above noisy signals, such as dynamic time warping, empirical mode decomposition, autocorrelation, and cross-correlation. Effective as they are, those methods are complex and difficult to implement. It is also found that the noisy signals are tightly related to gross errors. The Chauvenet criterion, one of the gross error discrimination criterions, is highly efficient and widely applicable for being without the complex calculations like decomposition and reconstruction. Therefore, in this study, based on the Chauvenet criterion, a new pulse signal preprocessing method is proposed, in which adaptive thresholdection models with the same network structure and parameters were established, respectively. Through comparing the recognition rate and the prediction rate of the models, higher rates were obtained by using the proposed method. To prove the efficiency, the comparison experiment between the proposed Chauvenet-based method and a Romanovsky-based method was conducted, and the execution time of the proposed method is much shorter than that of the Romanovsky method. The results suggest that the superiority in execution time of the Chauvenet-based method becomes more significant as the date size increases. Copyright © 2019 Weiguang Ni et al.Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). AD considered as an irreversible neurodegenerative disorder with progressive memory impairment moreover cognitive functions, while MCI would be considered as a transitional phase amongst age-related cognitive weakening. Numerous machine learning approaches have been examined, aiming at AD computer-aided diagnosis through employing MR image analysis. Conversely, MR brain image changes could be caused by different effects such as aging and dementia. It is still a challenging difficulty to extract the relevant imaging features and classify the subjects of different groups. This paper would propose an automatic classification technique based on feature decomposition and kernel discriminant analysis (KDA) for classifications of progressive MCI (pMCI) vs. normal control (NC), AD vs. NC, and pMCI vs. stable MCI (sMCI). link2 Feature decomposition would be based on dictionary learning, which is used for separation of class-specific components from the non-class-specific components in the features, while KDA would be applied for mapping original nonlinearly separable feature space to the separable features that are linear. The proposed technique would be evaluated by employing T1-weighted MR brain images from 830 subjects comprising 198 AD patients, 167 pMCI, 236 sMCI, and 229 NC from the Alzheimer's disease neuroimaging initiative (ADNI) dataset. Experimental results demonstrate that classification accuracy (ACC) of 90.41%, 84.29%, and 65.94% can be achieved for classification of AD vs. NC, pMCI vs. NC, and pMCI vs. link3 sMCI, respectively, indicating the promising performance of the proposed method. Copyright © 2019 Farzaneh Elahifasaee et al.Background Forward genetic screens are a powerful approach for identifying the genes contributing to a trait of interest. However, mutants arising in genes already known can obscure the identification of new genes contributing to the trait. Here, we describe a strategy called Candidate gene-Sequencing (Can-Seq) for rapidly identifying and filtering out mutants carrying new alleles of known and candidate genes. Results We carried out a forward genetic screen and identified 40 independent Arabidopsis mutants with defects in systemic spreading of RNA interference (RNAi), or more specifically in root-to-shoot transmission of post-transcriptional gene silencing (rtp). To classify the mutants as either representing a new allele of a known or candidate gene versus carrying a mutation in an undiscovered gene, bulk genomic DNA from up to 23 independent mutants was used as template to amplify a collection of 47 known or candidate genes. These amplified sequences were combined into Can-Seq libraries and deep sequenced. used in forward genetic screens for gene discovery. Using Can-Seq in conjunction with map-based gene cloning is a cost-effective approach towards identifying the full complement of genes contributing to a trait of interest. © The Author(s) 2020.Background Improving abiotic stress tolerance in wheat requires large scale screening of yield components such as seed weight, seed number and single seed weight, all of which is very laborious, and a detailed analysis of seed morphology is time-consuming and visually often impossible. Computed tomography offers the opportunity for much faster and more accurate assessment of yield components. Results An X-ray computed tomographic analysis was carried out on 203 very diverse wheat accessions which have been exposed to either drought or combined drought and heat stress. Results demonstrated that our computed tomography pipeline was capable of evaluating grain set with an accuracy of 95-99%. Most accessions exposed to combined drought and heat stress developed smaller, shrivelled seeds with an increased seed surface. As expected, seed weight and seed number per ear as well as single seed size were significantly reduced under combined drought and heat compared to drought alone. Seed weight along the ear was significantly reduced at the top and bottom of the wheat spike. Conclusions We were able to establish a pipeline with a higher throughput with scanning times of 7 min per ear and accuracy than previous pipelines predicting a set of agronomical important seed traits and to visualize even more complex traits such as seed deformations. The pipeline presented here could be scaled up to use for high throughput, high resolution phenotyping of tens of thousands of heads, greatly accelerating breeding efforts to improve abiotic stress tolerance. © The Author(s) 2020.