Prioritization of potentially contaminated sites: A comparison between the application of a solute transport model and a risk-screening method in China.

Numerous potential contaminated sites in China pose a substantial risk to human health and the local ecology. Thus, there is an urgent need to prioritize and further investigate potential contaminated sites and determine those that pose a threat in this regard. Newly developed by the Ministry of Ecology and Environment, the Risk Screening Method (RSM) scoring system is employed to assess soil and groundwater risk across China. In this study, the RSM is tested at a screening level and compared with the EPACMTP model, a solute transport model developed for the risk assessment of land disposal sites. First, a regional sensitivity analysis is conducted for EPACMTP model parameters, and those with significant sensitivity are compared with the risk indicators in the RSM. Second, 28 sites are evaluated by both prioritization methods in order to compare RSM risk scores and EPACMTP model simulations. Our results show that the RSM have similar risk assessing factors as EPACMTP model and its promising capability of prioritizing high-risk sites with very little available data. However, it does provide a conservative assessment, as risks at some sites are over-estimated, so further investigation is recommended for sites with high RSM risk scores. In addition, the initial screening should be documented by additional investigations at sites in order to prove the potential risk. The length of the period considered in the assessment has a great influence on prioritization results for heavy metals. As longer time scale will result in higher risk, its selection reflects the balance of current cost and future risk. The EPACMTP model provides a range of possible risks and can assess them within different timeframes. It is suggested to conduct further comparisons between the RSM and the solute transport models for sites from other areas, types of industries and more mobile compounds.

Prioritizing complex disease risk genes by integrating multiple data.

Complex diseases, such as obesity, type II diabetes and chronic obstructive pulmonary disease (COPD) as metabolic disorder-related diseases are major concern for worldwide public health in the 21st century. The identification of these disease risk genes has attracted increasing interest in computational systems biology. In this paper, a novel method was proposed to prioritize disease risk genes (PDRG) by integrating functional annotations, protein interactions and gene expression information to assess similarity between genes in a disease-related metabolic network. The gene prioritization method was successfully carried out for obesity and COPD, the effectiveness of which was superior to those of ToppGene and ToppNet in both literature validation and recall rate by LOOCV. Our method could be applied broadly to other metabolism-related diseases, helping to prioritize novel disease risk genes, and could shed light on diagnosis and effective therapies.

Path planning and collision avoidance methods for distributed multi-robot systems in complex dynamic environments.

Multi-robot systems are experiencing increasing popularity in joint rescue, intelligent transportation, and other fields. However, path planning and navigation obstacle avoidance among multiple robots, as well as dynamic environments, raise significant challenges. We propose a distributed multi-mobile robot navigation and obstacle avoidance method in unknown environments. First, we propose a bidirectional alternating jump point search A* algorithm (BAJPSA*) to obtain the robot's global path in the prior environment and further improve the heuristic function to enhance efficiency. We construct a robot kinematic model based on the dynamic window approach (DWA), present an adaptive navigation strategy, and introduce a new path tracking evaluation function that improves path tracking accuracy and optimality. To strengthen the security of obstacle avoidance, we modify the decision rules and obstacle avoidance rules of the single robot and further improve the decision avoidance capability of multi-robot systems. Moreover, the mainstream prioritization method is used to coordinate the local dynamic path planning of our multi-robot systems to resolve collision conflicts, reducing the difficulty of obstacle avoidance and simplifying the algorithm. Experimental results show that this distributed multi-mobile robot motion planning method can provide better navigation and obstacle avoidance strategies in complex dynamic environments, which provides a technical reference in practical situations.

Prioritization of Microorganisms Isolated from the Indian Ocean Sponge Scopalina hapalia Based on Metabolomic Diversity and Biological Activity for the Discovery of Natural Products.

Despite considerable advances in medicine and technology, humanity still faces many deadly diseases such as cancer and malaria. In order to find appropriate treatments, the discovery of new bioactive substances is essential. Therefore, research is now turning to less frequently explored habitats with exceptional biodiversity such as the marine environment. Many studies have demonstrated the therapeutic potential of bioactive compounds from marine macro- and microorganisms. In this study, nine microbial strains isolated from an Indian Ocean sponge, Scopalina hapalia, were screened for their chemical potential. The isolates belong to different phyla, some of which are already known for their production of secondary metabolites, such as the actinobacteria. This article aims at describing the selection method used to identify the most promising microorganisms in the field of active metabolites production. The method is based on the combination of their biological and chemical screening, coupled with the use of bioinformatic tools. The dereplication of microbial extracts and the creation of a molecular network revealed the presence of known bioactive molecules such as staurosporin, erythromycin and chaetoglobosins. Molecular network exploration indicated the possible presence of novel compounds in clusters of interest. The biological activities targeted in the study were cytotoxicity against the HCT-116 and MDA-MB-231 cell lines and antiplasmodial activity against Plasmodium falciparum 3D7. Chaetomium globosum SH-123 and Salinispora arenicola SH-78 strains actually showed remarkable cytotoxic and antiplasmodial activities, while Micromonospora fluostatini SH-82 demonstrated promising antiplasmodial effects. The ranking of the microorganisms as a result of the different screening steps allowed the selection of a promising strain, Micromonospora fluostatini SH-82, as a premium candidate for the discovery of new drugs.