A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.

An IA is an abnormal swelling of cerebral vessels, and a subset of these IAs can rupture causing aneurysmal subarachnoid hemorrhage (aSAH), often resulting in death or severe disability. Few studies have used an appropriate method of feature selection combined with machine learning by analyzing transcriptomic sequencing data to identify new molecular biomarkers. Following gene ontology (GO) and enrichment analysis, we found that the distinct status of IAs could lead to differential innate immune responses using all 913 differentially expressed genes, and considering that there are numerous irrelevant and redundant genes, we propose a mixed filter- and wrapper-based feature selection.

Improving ordered arrangement of the short-chain amylose-lipid complex by narrowing molecular weight distribution of short-chain amylose.

To investigate effect of molecular weight distribution on formation of the short-chain amylose-lipid complex, debranched waxy rice starch (Unf) was fractionated into F1, F2 and F3, and the four fractions were used to complex with palmitic acid (C16:0). The peak molecular weight was in the order of F1 < Unf < F2 < F3, and the distribution was in the order of Unf> F3 > F1 > F2. XRD and DSC analysis indicated that the Unf-C16:0 complex was amorphous with the melting temperature (T) < 100 °C while the F3-C16:0 complex was highly ordered with T > 100 °C.