Model selection of polynomial kernel regression

Polynomial kernel regression is one of the standard and state-of-the-art learning strategies. However, as is well known, the choices of the degree of polynomial kernel and the regularization parameter are still open in the realm of model selection. The first aim of this paper is to develop a strategy to select these parameters. On one hand, based on the worst-case learning rate analysis, we show that the regularization term in polynomial kernel regression is not necessary. In other words, the regularization parameter can decrease arbitrarily fast when the degree of the polynomial kernel is suitable tuned. On the other hand,taking account of the implementation of the algorithm, the regularization term is required. Summarily, the effect of the regularization term in polynomial kernel regression is only to circumvent the " ill-condition" of the kernel matrix. Based on this, the second purpose of this paper is to propose a new model selection strategy, and then design an efficient learning algorithm. Both theoretical and experimental analysis show that the new strategy outperforms the previous one. Theoretically, we prove that the new learning strategy is almost optimal if the regression function is smooth. Experimentally, it is shown that the new strategy can significantly reduce the computational burden without loss of generalization capability.Comment: 29 pages, 4 figure

Prediction of complex super-secondary structure βαβ motifs based on combined features

AbstractPrediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins. The strand-loop-helix-loop-strand (βαβ) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of βαβ motifs. Therefore, the accurate prediction of βαβ motifs is very important to recognizing protein tertiary structure and the study of protein function. In this study, the βαβ motif dataset was first constructed using the DSSP package. A statistical analysis was then performed on βαβ motifs and non-βαβ motifs. The target motif was selected, and the length of the loop-α-loop varies from 10 to 26 amino acids. The ideal fixed-length pattern comprised 32 amino acids. A Support Vector Machine algorithm was developed for predicting βαβ motifs by using the sequence information, the predicted structure and function information to express the sequence feature. The overall predictive accuracy of 5-fold cross-validation and independent test was 81.7% and 76.7%, respectively. The Matthew’s correlation coefficient of the 5-fold cross-validation and independent test are 0.63 and 0.53, respectively. Results demonstrate that the proposed method is an effective approach for predicting βαβ motifs and can be used for structure and function studies of proteins

A Chinese Medicine Formula “Xian-Jia-Tang” for Treating Bladder Outlet Obstruction by Improving Urodynamics and Inhibiting Oxidative Stress through Potassium Channels

The aim of this study is to investigate efficacy of a traditional Chinese medicine formula (named Xian-Jia-Tang, XJT) on bladder outlet obstruction (BOO) in rats and explore its mechanisms. Total 80 BOO model rats were established and randomly divided into 4 groups: physiological saline, XJT, Cesium Chloride (CC), and XJT and CC groups. Meanwhile, 12 rats were used as normal control. Bladder weight and urodynamics were measured. Oxidative stress level and mRNA expressions of potassium channels gene were detected in detrusor. The mRNA and protein levels of hypoxia inducible factor-α (HIF-α) in detrusor were detected by RT-PCR and Western blot. BOO model rats showed significantly higher bladder weight and abnormal urodynamics. XJT significantly improved the abnormal urodynamics and inhibited the oxidative stress and changes of mRNA levels of potassium channels genes in detrusor of BOO model rats. Moreover, KATP and SK2/3 mRNA were overexpressed in BOO model rats treated by XJT. Besides, the significantly increased levels of HIF-α mRNA and protein were also inhibited by XJT. However, these inhibition effects of XJT were weakened by CC. XJT could effectively improve the urodynamics and inhibit the oxidative stress caused by hypoxia through suppressing the role of potassium channels in BOO model rats

Assessment of Vegetation Dynamics and Ecosystem Resilience in the Context of Climate Change and Drought in the Horn of Africa

Understanding the response of vegetation and ecosystem resilience to climate variability and drought conditions is essential for ecosystem planning and management. In this study, we assessed the vegetation changes and ecosystem resilience in the Horn of Africa (HOA) since 2000 and detected their drivers based mainly on analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) products. We found that the annual and seasonal trends of NDVI (Normalized Difference Vegetation Index) generally increased during the last two decades over the Horn of Africa particularly in western parts of Ethiopia and Kenya. The weakest annual and seasonal NDVI trends were observed over the grassland cover and tropical arid agroecological zones. The NDVI variation negatively correlated with Land Surface Temperature (LST) and positively correlated with precipitation at a significant level (p < 0.05) account for 683,197 km2 and 533,385 km2 area, respectively. The ecosystem Water Use Efficiency (eWUE) showed overall increasing trends with larger values for the grassland biome. The precipitation had the most significant effect on eWUE variation compared to LST and annual SPEI (Standardized Evapotranspiration Index). There were about 54.9% of HOA resilient to drought disturbance, whereas 32.6% was completely not-resilient. The ecosystems in the humid agroecological zones, the cropland, and wetland were slightly not-resilient to severe drought conditions in the region. This study provides useful information for policy makers regarding ecosystem and dryland management in the context of climate change at both national and regional levels

Low-Rank Linear Dynamical Systems for Motor Imagery EEG

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from “BCI Competition III Dataset IVa” and “BCI Competition IV Database 2a.” The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP

Assessment of Vegetation Dynamics and Ecosystem Resilience in the Context of Climate Change and Drought in the Horn of Africa

Understanding the response of vegetation and ecosystem resilience to climate variability and drought conditions is essential for ecosystem planning and management. In this study, we assessed the vegetation changes and ecosystem resilience in the Horn of Africa (HOA) since 2000 and detected their drivers based mainly on analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) products. We found that the annual and seasonal trends of NDVI (Normalized Difference Vegetation Index) generally increased during the last two decades over the Horn of Africa particularly in western parts of Ethiopia and Kenya. The weakest annual and seasonal NDVI trends were observed over the grassland cover and tropical arid agroecological zones. The NDVI variation negatively correlated with Land Surface Temperature (LST) and positively correlated with precipitation at a significant level (p < 0.05) account for 683,197 km2 and 533,385 km2 area, respectively. The ecosystem Water Use Efficiency (eWUE) showed overall increasing trends with larger values for the grassland biome. The precipitation had the most significant effect on eWUE variation compared to LST and annual SPEI (Standardized Evapotranspiration Index). There were about 54.9% of HOA resilient to drought disturbance, whereas 32.6% was completely not-resilient. The ecosystems in the humid agroecological zones, the cropland, and wetland were slightly not-resilient to severe drought conditions in the region. This study provides useful information for policy makers regarding ecosystem and dryland management in the context of climate change at both national and regional levels

Low-Rank Linear Dynamical Systems for Motor Imagery EEG

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from &quot;BCI Competition III Dataset IVa&quot; and &quot;BCI Competition IV Database 2a.&quot; The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP