Cas-OPRAD: a one-pot RPA/PCR CRISPR/Cas12 assay for on-site Phytophthora root rot detection.

Phytophthora sojae is a devastating plant pathogen that causes soybean Phytophthora root rot worldwide. Early on-site and accurate detection of the causal pathogen is critical for successful management. In this study, we have developed a novel and specific one-pot RPA/PCR-CRISPR/Cas12 assay for on-site detection (Cas-OPRAD) of Phytophthora root rot (P. sojae). Compared to the traditional RPA/PCR detection methods, the Cas-OPRAD assay has significant detection performance. The Cas-OPRAD platform has excellent specificity to distinguish 33 P. sojae from closely related oomycetes or fungal species. The PCR-Cas12a assay had a consistent detection limit of 100 pg. µL-1, while the RPA-Cas12a assay achieved a detection limit of 10 pg. µL-1. Furthermore, the Cas-OPRAD assay was equipped with a lateral flow assay for on-site diagnosis and enabled the visual detection of P. sojae on the infected field soybean samples. This assay provides a simple, efficient, rapid (<1 h), and visual detection platform for diagnosing Phytophthora root rot based on the one-pot CRISPR/Cas12a assay. Our work provides important methods for early and accurate on-site detection of Phytophthora root rot in the field or customs fields.

Comparative Evaluation of PCR-Based, LAMP and RPA-CRISPR/Cas12a Assays for the Rapid Detection of Diaporthe aspalathi.

Southern stem canker (SSC) of soybean, attributable to the fungal pathogen Diaporthe aspalathi, results in considerable losses of soybean in the field and has damaged production in several of the main soybean-producing countries worldwide. Early and precise identification of the causal pathogen is imperative for effective disease management. In this study, we performed an RPA-CRISPR/Cas12a, as well as LAMP, PCR and real-time PCR assays to verify and compare their sensitivity, specificity and simplicity and the practicality of the reactions. We screened crRNAs targeting a specific single-copy gene, and optimized the reagent concentrations, incubation temperatures and times for the conventional PCR, real-time PCR, LAMP, RPA and Cas12a cleavage stages for the detection of D. aspalathi. In comparison with the PCR-based assays, two thermostatic detection technologies, LAMP and RPA-CRISPR/Cas12a, led to higher specificity and sensitivity. The sensitivity of the LAMP assay could reach 0.01 ng µL-1 genomic DNA, and was 10 times more sensitive than real-time PCR (0.1 ng µL-1) and 100 times more sensitive than conventional PCR assay (1.0 ng µL-1); the reaction was completed within 1 h. The sensitivity of the RPA-CRISPR/Cas12a assay reached 0.1 ng µL-1 genomic DNA, and was 10 times more sensitive than conventional PCR (1.0 ng µL-1), with a 30 min reaction time. Furthermore, the feasibility of the two thermostatic methods was validated using infected soybean leaf and seeding samples. The rapid, visual one-pot detection assay developed could be operated by non-expert personnel without specialized equipment. This study provides a valuable diagnostic platform for the on-site detection of SSC or for use in resource-limited areas.

A quasi-Monte Carlo based flocculation model for fine-grained cohesive sediments in aquatic environments.

The quasi-Monte Carlo (QMC) method was enhanced to solve the population balance model (PBM) including aggregation and fragmentation processes for simulating the temporal evolutions of characteristic sizes and floc size distributions (FSDs) of cohesive sediments. Ideal cases with analytical solutions were firstly adopted to validate this QMC model to illustrate selected pure aggregation, pure fragmentation, and combined aggregation and fragmentation systems. Two available laboratory data sets, one with suspended kaolinite and the other with a mixture of kaolinite and montmorillonite, were further used to monitor the FSDs of cohesive sediments in controlled shear conditions. The model results show reasonable agreements with both analytical solutions and laboratory experiments. Moreover, different QMC schemes were tested and compared with the standard Monte Carlo scheme and a Latin Hypercube Sampling scheme to optimize the model performance. It shows that all QMC schemes perform better in both accuracy and time consumption than standard Monte Carlo scheme. In particular, compared with other schemes, the QMC scheme using Halton sequence requires the least particle numbers in the simulated system to reach reasonable accuracy. In the sensitivity tests, we also show that the fractal dimension and the fragmentation distribution function have large impacts on the predicted FSDs. This study indicates a great advance in employing QMC schemes to solve PBM for simulating the flocculation of cohesive sediments.