PLncWX: A Machine-Learning Algorithm for Plant lncRNA Identification Based on WOA-XGBoost

Long noncoding RNAs (lncRNAs) are a class of RNAs longer than 200 nt and cannot encode the protein. Studies have shown that lncRNAs can regulate gene expression at the epigenetic, transcriptional, and posttranscriptional levels, which are not only closely related to the occurrence, development, and prevention of human diseases, but also can regulate plant flowering and participate in plant abiotic stress responses such as drought and salt. Therefore, how to accurately and efficiently identify lncRNAs is still an essential job of relevant researches. There have been a large number of identification tools based on machine-learning and deep learning algorithms, mostly using human and mouse gene sequences as training sets, seldom plants, and only using one or one class of feature selection methods after feature extraction. We developed an identification model containing dicot, monocot, algae, moss, and fern. After comparing 20 feature selection methods (seven filter and thirteen wrapper methods) combined with seven classifiers, respectively, considering the correlation between features and model redundancy at the same time, we found that the WOA-XGBoost-based model had better performance with 91.55%, 96.78%, and 91.68% of accuracy, AUC, and F1_score. Meanwhile, the number of elements in the feature subset was reduced to 23, which effectively improved the prediction accuracy and modeling efficiency.

A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles

The improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identification by genetic algorithm, and the model accuracies based on standard diving cycle are further discussed. Then, three fusion modeling approaches including Bayesian fusion, residual normalization fusion, and state of charge (SOC) fragment fusion are presented and compared. In order to further improve the accuracy of these models, a two-layer fusion model based on SOC fragments is proposed in this paper. Compared with other fusion models, the root mean square error (RMSE), maximum error, and mean error of the two-layer fusion model can be reduced by at least 23.04%, 8.70%, and 30.13%, respectively. Moreover, the two-layer fusion model is further verified at 10, 25, and 40 °C, and the RMSE can be correspondingly reduced by 60.41%, 47.26%, 23.04%. The results indicate that the two-layer fusion model proposed in this paper achieves better robustness and accuracy.

Redundancy-selection trade-off in phenotype-structured populations

Realistic fitness landscapes generally display a redundancy-fitness trade-off: highly fit trait configurations are inevitably rare, while less fit trait configurations are expected to be more redundant. The resulting sub-optimal patterns in the fitness distribution are typically described by means of effective formulations. However, the extent to which effective formulations are compatible with explicitly redundant landscapes is yet to be understood, as well as the consequences of a potential miss-match. Here we investigate the effects of such trade-off on the evolution of phenotype-structured populations, characterised by continuous quantitative traits. We consider a typical replication-mutation dynamics, and we model redundancy by means of two dimensional landscapes displaying both selective and neutral traits. We show that asymmetries of the landscapes will generate neutral contributions to the marginalised fitness-level description, that cannot be described by effective formulations, nor disentangled by the full trait distribution. Rather, they appear as effective sources, whose magnitude depends on the geometry of the landscape. Our results highlight new important aspects on the nature of sub-optimality. We discuss practical implications for rapidly mutant populations such as pathogens and cancer cells, where the qualitative knowledge of their trait and fitness distributions can drive disease management and intervention policies.

Composition and spatio-temporal variation of soil arthropods in the wetland of the Daxia River on the Qinghai-Tibet Plateau

The Daxia River Basin on the eastern edge of the Chinese Qinghai-Tibetan Plateau is one of the typical distribution regions of alpine wetlands and a global biodiversity hotspot. The aim of this study is to analyze the composition and spatio-temporal distribution of the soil arthropods, and to use them as an indicator for the soil environment in the wetland. The soil arthropods of 32 taxa and 7706 individuals were collected from the soil samples at two soil layers (0–10 and 10–20 cm depth) in each habitat of six habitats along the Daxia River in cold season and warm season between 2016 and 2017. The habitat of Tumenguan had a greater arthropod abundance than all other habitats in both cold season and warm season. A significant seasonal variation was observed in the composition, abundance and diversity of the soil arthropod community in each habitat. In cold season the dominant groups were Chironomidae larvae, Sejidae and Trombidiidae. In warm season the dominant groups were Chironomidae larva, Onychiurus, Pygmephorus and Tullbergia. The soil arthopod communities exhibited significant differences among the habitats at the 0–10 and 10–20 cm depth. The multivariate tests with the linear model redundancy analysis (RDA) revealed the significant impact of soil physical and chemical factors on the seasonal change of soil arthropod community. The results demonstrate that soil arthropods in the study area respond more actively to temporal changes than to habitat changes.

Meta Segmentation Network for Ultra-Resolution Medical Images

Despite recent great progress on semantic segmentation, there still exist huge challenges in medical ultra-resolution image segmentation. The methods based on multi-branch structure can make a good balance between computational burdens and segmentation accuracy. However, the fusion structure in these methods require to be designed elaborately to achieve desirable result, which leads to model redundancy. In this paper, we propose Meta Segmentation Network (MSN) to solve this challenging problem. With the help of meta-learning, the fusion module of MSN is quite simple but effective. MSN can fast generate the weights of fusion layers through a simple meta-learner, requiring only a few training samples and epochs to converge. In addition, to avoid learning all branches from scratch, we further introduce a particular weight sharing mechanism to realize a fast knowledge adaptation and share the weights among multiple branches, resulting in the performance improvement and significant parameters reduction. The experimental results on two challenging ultra-resolution medical datasets BACH and ISIC show that MSN achieves the best performance compared with the state-of-the-art approaches.

A multi-model control of nonlinear systems: A cascade decoupled design procedure based on stability and performance

Recently, the multi-model controllers design was proposed in the literature based on integrating of the stability and performance criteria. Although these methods overcome the redundancy problem, the decomposition step is very complex and time consuming. In this paper, a cascade design of multi-model control is presented that is made from two sequential steps. In the first step, the nonlinear system is decomposed into a set of linear subsystems by just considering the stability criterion. In this step, the gap metric is used as a smart tool to measure the distance between linear subsystems. While the closed-loop stability is gained through the first step, the performance is improved in the second step by adding internal model controllers in a cascade structure. Therefore, the proposed idea supports designing a multi-model controller in a simple way by integrating the stability and performance criteria in two independent cascade steps. As a result, the proposed method avoids the model redundancy problem, has a simple structure, guarantees the robust stability, and improves the performance. Two nonlinear chemical processes are simulated to evaluate the proposed multi-model controller approach.