Biodiversity and activity of the gut microbiota across the life history of the insect herbivore Spodoptera littoralis

Microbes that live inside insects play critical roles in host nutrition, physiology, and behavior. Although Lepidoptera (butterflies and moths) are one of the most diverse insect taxa, their microbial symbionts are little-studied, particularly during metamorphosis. Here, using ribosomal tag pyrosequencing of DNA and RNA, we investigated biodiversity and activity of gut microbiotas across the holometabolous life cycle of Spodoptera littoralis, a notorious agricultural pest worldwide. Proteobacteria and Firmicutes dominate but undergo a structural “metamorphosis” in tandem with its host. Enterococcus, Pantoea and Citrobacter were abundant and active in early-instar, while Clostridia increased in late-instar. Interestingly, only enterococci persisted through metamorphosis. Female adults harbored high proportions of Enterococcus, Klebsiella and Pantoea, whereas males largely shifted to Klebsiella. Comparative functional analysis with PICRUSt indicated that early-instar larval microbiome was more enriched for genes involved in cell motility and carbohydrate metabolism, whereas in late-instar amino acid, cofactor and vitamin metabolism increased. Genes involved in energy and nucleotide metabolism were abundant in pupae. Female adult microbiome was enriched for genes relevant to energy metabolism, while an increase in the replication and repair pathway was observed in male. Understanding the metabolic activity of these herbivore-associated microbial symbionts may assist the development of novel pest-management strategies

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic

Taxonomy analysis of shotgun sequencing results

Taxonomy analysis of silkworm gut microbiota based on shotgun metagenomic sequencing data

OTU table of 16s rRNA sequencing

OTU table with taxonomy informations generated by QIIME software

Annotation results.xlsx

Shotgun metagenomic data of silkworm gut microbiota annotated with Nr, KEGG, COG, CAZy, ARDB, and SignalP database

non-redundent gene set.fa

non-redundent gene set of silkworm gut microbiota shotgun sequencing data, including sample MS1P50 and MS1QB

Influence of Medium Frequency Light/Dark Cycles on the Cultivation of Auxenochlorella pyrenoidosa

Light (wavelength, intensity, and light/dark cycle) have been considered as one of the most important parameters for microalgae cultivation. In this paper, the effect of medium frequency intermittent light on Auxenochlorella pyrenoidosa (formerly Chlorella pyrenoidosa) cultivation was investigated. Three parameters of intermittent light, light intensity, light/dark ratio, and light/dark cycle were employed and the influence of these parameters on the productivity of Auxenochlorella pyrenoidosa was studied. The biomass yield and growth rates were mainly affected by the light fraction and cycle time. Light with 220 μE m−2 s−1 light intensity was determined as the optimal light intensity for biomass production. At the light intensity of 420 μE m−2 s−1, the results indicated that the intermittent light improved the biomass production with larger light/dark ratio compared with the continuous light. At a lower mean light intensity over time, the intermittent light should be more suitable for biomass growth and the decrease in the light/dark ratio (L/D) will lead to a higher biomass productivity. The light/dark cycle time has little influence on the biomass yield

16S rRNA sequencing result: bacteria abundance in genus level

Bacteria abundance of silkworm (strain QB and P50) in genus leve

Dynamic behaviour of stainless steel investigated with a tension split Hopkinson bar

Stainless steel has been used in construction industry for some years as minor components, such as fixings, fasteners and cladding. In recent times, the whole-life cost for a structure has become an important concern for developers, contractors, architects, engineers and property owners. Although the initial high cost has greatly limited the structural use of stainless steel, it is believed that this steel material will be increasingly used in civil engineering and construction, owing to the benefits of stainless steel. This has been recently witnessed in a lot of projects around the world. In order to better design a structure, it is critical to understand the behaviour of its members and materials under extreme conditions. In the past, a lot of investigations have been conducted on the dynamic behaviour of conventional carbon steel, whilst only a few studies have been dedicated to that of stainless steel. Based on this research background, stainless steel coupon tests were conducted recently using a tension split Hopkinson bar by the authors. At the same time, carbon steel material was also tested for comparison. These coupons were tested under different strain rates ranged from 300 s-1 to 1000 s-1. It was found that the dynamic behaviour of stainless steel was different from that of carbon steel. Generally, higher strain rate sensitivity is expected for the stainless steel material