Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches.

With the rapid development of multi-omics technologies and accumulation of large-scale bio-datasets, many studies have conducted a more comprehensive understanding of human diseases and drug sensitivity from multiple biomolecules, such as DNA, RNA, proteins and metabolites. Using single omics data is difficult to systematically and comprehensively analyze the complex disease pathology and drug pharmacology. The molecularly targeted therapy-based approaches face some challenges, such as insufficient target gene labeling ability, and no clear targets for non-specific chemotherapeutic drugs.

2,3-Butanediol from the leachates of pine needles induces the resistance of to the leaf pathogen .

Compared with the use of monocultures in the field, cultivation of medicinal herbs in forests is an effective strategy to alleviate disease. Chemical interactions between herbs and trees play an important role in disease suppression in forests. We evaluated the ability of leachates from needles of to induce resistance in leaves, identified the components via gas chromatography-mass spectrometry (GC-MS), and then deciphered the mechanism of 2,3-Butanediol as the main component in the leachates responsible for resistance induction via RNA sequencing (RNA-seq).

Dynamic Meta-data Network Sparse PCA for Cancer Subtype Biomarker Screening.

Previous research shows that each type of cancer can be divided into multiple subtypes, which is one of the key reasons that make cancer difficult to cure. Under these circumstances, finding a new target gene of cancer subtypes has great significance on developing new anti-cancer drugs and personalized treatment. Due to the fact that gene expression data sets of cancer are usually high-dimensional and with high noise and have multiple potential subtypes' information, many sparse principal component analysis (sparse PCA) methods have been used to identify cancer subtype biomarkers and subtype clusters.

Appropriate Soil Heat Treatment Promotes Growth and Disease Suppression of by Interfering with the Bacterial Community.

In our greenhouse experiment, soil heat treatment groups (50, 80, and 121°C) significantly promoted growth and disease suppression of in consecutively cultivated soil (CCS) samples ( < 0.01), and 80°C worked better than 50°C and 121°C ( < 0.01).

Appropriate nitrogen application enhances saponin synthesis and growth mediated by optimizing root nutrient uptake ability.

Background: Cultivation of medicinal crops, which synthesize hundreds of substances for curative functions, was focused on the synthesis of secondary metabolites rather than biomass accumulation. Nutrition is an important restrict factor for plant growth and secondary metabolites, but little attention has been given to the plasticity of nutrient uptake and secondary metabolites synthesis response to soil nitrogen (N) change.

Methods: Two year-field experiments of Sanqi (), which can synthesize a high level of saponin in cells, were conducted to study the effects of N application on the temporal dynamics of biomass, nutrient absorption, root architecture and the relationships between these parameters and saponin synthesis.

[Effects of As stress on contents of saponin and flavonoid, key enzymes activities of Panax notoginseng and its proteomic analysis].

Field plot experiments were conducted to study the effect of two-year consecutive As stress [As(V): 0, 20, 80, 140, 200 and 260 mg·kg] on contents of As, saponin and flanovoids, the enzyme activities of phenylalanine ammonia lyase (PAL), chalcone synthase (CHS), and squalene synthase (SS) in main root, fibrous root and rhizome and shoot, and proteome of three-year old Panax notoginseng in Wenshan prefecture, Yunnan Province, China. The results showed that total saponin content of fibrous root decreased with increase in As treatment concentration. Total saponin contents of shoot and rhizome increased with 140 mg·kg As treatment compared with control.