Identification and expression analysis of microRNAs and targets in the biofuel crop sugarcane

Background  

MicroRNAs (miRNAs) are small regulatory RNAs, some of which are conserved in diverse plant genomes. Therefore, computational identification and further experimental validation of miRNAs from non-model organisms is both feasible and instrumental for addressing miRNA-based gene regulation and evolution. Sugarcane (Saccharum spp.) is an important biofuel crop with publicly available expressed sequence tag and genomic survey sequence databases, but little is known about miRNAs and their targets in this highly polyploid species.     

Health effects in fish from the polluted Orlando Dam and Klipspruit wetland system,Soweto, South Africa

Pollution of aquatic ecosystems often results in adverse environmental disturbances, including physiological and/ or histomorphological changes in fish. The health of Clarias gariepinus sampled from two polluted water bodies, Orlando Dam and a pond in the Klipspruit wetland catchment, Soweto, was investigated in 2015–2016. Limited fish health-related data is available for this highly impacted freshwater ecosystem. Fish were collected between April 2015 and February 2016. A necropsy and a histological assessment were done on various target organs of each fish. Water and sediment samples were analysed for selected organic and inorganic compounds. Macroscopic assessment revealed abnormally shaped urogenital papillae, morphological alterations of the gonads, as well as discoloration of liver tissue. These observations were supported by microscopic evidence of hepatic histological alterations in fish from Orlando Dam, as well as the presence of both female and male gonadal tissue (intersex) in 13.6% and 50% of fish from the wetland pond and the Orlando Dam, respectively. Water analyses showed high levels of faecal coliform bacteria and metal concentrations and sediment analyses showed detectable levels of potential endocrine disrupting chemicals.     

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study

Background

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process.

Methods

In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity.

Results

Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy.

Conclusions

The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user.