The appearance, active components, antioxidant activities, and their markers in fibrous roots of Bletilla striata.

This study explores the potential medicinal properties of Bletilla striata fibrous roots by comparing their active ingredient contents and antioxidant activity with those in tubers. The study further examines the effects of growth age and origin on the distribution characteristics of medicinal properties in fibrous roots and identifies biomarkers that distinguish these characteristics. The Δ E value of fibrous roots significantly increases with age and is significantly influenced by origin; both the age and origin also significantly affect militarine content and antioxidant activity; a positive correlation exists between appearance quality, active ingredients, and antioxidant activity.

A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks.

It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years.

Expression profiles analysis reveals an integrated miRNA-lncRNA signature to predict survival in ovarian cancer patients with wild-type BRCA1/2.

Emerging evidence shows that dysregulated expression of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) were closely linked with disease progression, including cancers. However, the joint predictive power of miRNAs and lncRNAs in prognosis for ovarian cancer (OV) patients with wild-type BRCA1/2 is as yet unknown. In this study, we sought to assess the joint predictive power of miRNAs and lncRNAs by integrating miRNA and lncRNA expression profiles and clinical data of 281 OV patients with wild-type BRCA1/2 from The Cancer Genome Atlas (TCGA) project.