Genome plasticity and systems evolution in Streptomyces

Background Streptomycetes are filamentous soil-dwelling bacteria. They are best known as the producers of a great variety of natural products such as antibiotics, antifungals, antiparasitics, and anticancer agents and the decomposers of organic substances for carbon recycling. They are also model organisms for the studies of gene regulatory networks, morphological differentiation, and stress response. The availability of sets of genomes from closely related Streptomyces strains makes it possible to assess the mechanisms underlying genome plasticity and systems adaptation. Results We present the results of a comprehensive analysis of the genomes of five Streptomyces species with distinct phenotypes. These streptomycetes have a pan-genome comprised of 17,362 orthologous families which includes 3,096 components in the core genome, 5,066 components in the dispensable genome, and 9,200 components that are uniquely present in only one species. The core genome makes up about 33%-45% of each genome repertoire. It contains important genes for Streptomyces biology including those involved in gene regulation, secretion, secondary metabolism and morphological differentiation. Abundant duplicate genes have been identified, with 4%-11% of the whole genomes composed of lineage-specific expansions (LSEs), suggesting that frequent gene duplication or lateral gene transfer events play a role in shaping the genome diversification within this genus. Two patterns of expansion, single gene expansion and chromosome block expansion are observed, representing different scales of duplication. Conclusions Our results provide a catalog of genome components and their potential functional roles in gene regulatory networks and metabolic networks. The core genome components reveal the minimum requirement for streptomycetes to sustain a successful lifecycle in the soil environment, reflecting the effects of both genome evolution and environmental stress acting upon the expressed phenotypes. A better understanding of the LSE gene families will, on the other hand, bring a wealth of new insights into the mechanisms underlying strain-specific phenotypes, such as the production of novel antibiotics, pathogenesis, and adaptive response to environmental challenges

The -omics Era- Toward a Systems-Level Understanding of Streptomyces

Streptomyces is a group of soil bacteria of medicinal, economic, ecological, and industrial importance. It is renowned for its complex biology in gene regulation, antibiotic production, morphological differentiation, and stress response. In this review, we provide an overview of the recent advances in Streptomyces biology inspired by -omics based high throughput technologies. In this post-genomic era, vast amounts of data have been integrated to provide significant new insights into the fundamental mechanisms of system control and regulation dynamics of Streptomyces

H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps

Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging. Online RL agents trained in imperfect simulation environments can suffer from severe sim-to-real issues. Offline RL approaches although bypass the need for simulators, often pose demanding requirements on the size and quality of the offline datasets. The recently emerged hybrid offline-and-online RL provides an attractive framework that enables joint use of limited offline data and imperfect simulator for transferable policy learning. In this paper, we develop a new algorithm, called H2O+, which offers great flexibility to bridge various choices of offline and online learning methods, while also accounting for dynamics gaps between the real and simulation environment. Through extensive simulation and real-world robotics experiments, we demonstrate superior performance and flexibility over advanced cross-domain online and offline RL algorithms

Study on TCM Syndrome Differentiation of Primary Liver Cancer Based on the Analysis of Latent Structural Model

Primary liver cancer (PLC) is one of the most common malignant tumors because of its high incidence and high mortality. Traditional Chinese medicine (TCM) plays an active role in the treatment of PLC. As the most important part in the TCM system, syndrome differentiation based on the clinical manifestations from traditional four diagnostic methods has met great challenges and questions with the lack of statistical validation support. In this study, we provided evidences for TCM syndrome differentiation of PLC using the method of analysis of latent structural model from clinic data, thus providing basis for establishing TCM syndrome criteria. And also we obtain the common syndromes of PLC as well as their typical clinical manifestations, respectively

High prevalence of vitamin D deficiency among children aged 1 month to 16 years in Hangzhou, China

<p>Abstract</p> <p>Background</p> <p>Recent studies have suggested that vitamin D deficiency in children is widespread. But the vitamin D status of Chinese children is seldom investigated. The objective of the present study was to survey the serum levels of 25-hydroxyvitamin D [25(OH)D] in more than 6,000 children aged 1 month to 16 years in Hangzhou (latitude: 30°N), the capital of Zhejiang Province, southeast China.</p> <p>Methods</p> <p>The children aged 1 month to 16 years who came to the child health care department of our hospital, the children's hospital affiliated to Zhejiang university school of medicine, for health examination were taken blood for 25(OH) D measurement. Serum 25(OH) D levels were determined by direct enzyme-linked immunosorbent assay and categorized as < 25, < 50, and < 75 nmol/L.</p> <p>Results</p> <p>A total of 6,008 children aged 1 month to 16 years participated in this cross-sectional study. All the subjects were divided into subgroups according to their age: 0-1y, 2-5y, 6-11y and 12-16y representing infancy, preschool, school age and adolescence stages respectively. The highest mean level of serum 25(OH)D was found in the 0-1y stage (99 nmol/L) and the lowest one was found in 12-16y stage (52 nmol/L). Accordingly, the prevalence of serum 25(OH)D levels of < 75 nmol/L and < 50 nmol/L were at the lowest among infants (33.6% and 5.4% respectively) and rose to the highest among adolescents (89.6% and 46.4% respectively). The mean levels of serum 25(OH)D and the prevalence of vitamin D deficiency changed according to seasons. In winter and spring, more than 50% of school age children and adolescents had a 25(OH)D level at < 50 nmol/L. If the threshold is changed to < 75 nmol/L, all of the adolescents (100%) had low 25(OH)D levels in winter and 93.7% school age children as well.</p> <p>Conclusions</p> <p>The prevalence of vitamin D deficiency and insufficiency among children in Hangzhou Zhejiang province is high, especially among children aged 6-16 years. We suggest that the recommendation for vitamin D supplementation in Chinese children should be extended to adolescence.</p

Generative Adversarial Inverse Reinforcement Learning With Deep Deterministic Policy Gradient

Although the issue of sparse expert samples at the early stage of training in inverse reinforcement learning (IRL) is successfully resolved by the introduction of generative adversarial network (GAN), the inherent drawbacks of GAN result in ineffective generated samples. Therefore, we propose an algorithm for generative adversarial inverse reinforcement learning that is based on deep deterministic policy gradient (DDPG). We use the deterministic strategy to replace the random noise input of the initial GAN model and reconstruct the generator of the GAN based on the Actor-Critic mechanism in order to improve the quality of GAN-generated samples during adversarial training. Meanwhile, we mix the GAN-generated virtual samples with the original expert samples of IRL as the expert sample set of IRL. Our approach not only solves the problem of sparse expert samples at the early stage of training, but most importantly, it makes the decision-making process of IRL occurring under GAN more efficient. In the subsequent IRL decision-making process, we also analyze the differences between the mixed expert samples and the non-expert trajectory samples generated by the initial strategy to determine the best reward function. The learned reward function is used to drive the RL process positively for policy updating and optimization, on which further non-expert trajectory samples are generated. By comparing the differences between the new non-expert samples and the mixed expert sample set, we hope to iteratively arrive at the reward function and optimal policy. Performance tests in the MuJoCo physical simulation environment and trajectory prediction experiments in Grid World show that our model improves the quality of GAN-generated samples and reduces the computational cost of the network training by approximately 20&#x0025; for each given environment, applying to decision planning for autonomous driving