The recent outbreak of novel coronavirus (SARS-CoV-2) causing COVID-19 disease spreads rapidly in the world. Rapid and early detection of SARS-CoV-2 facilitates early intervention and prevents the disease spread. Here, we present an All-In-One Dual CRISPR-Cas12a (AIOD-CRISPR) assay for one-pot, ultrasensitive, and visual SARS-CoV-2 detection. By targeting SARS-CoV-2’s nucleoprotein gene, two CRISPR RNAs without protospacer adjacent motif (PAM) site limitation are introduced to develop the AIOD-CRISPR assay and detect the nucleic acids with a sensitivity of few copies. We validate the assay by using COVID-19 clinical swab samples and obtain consistent results with RT-PCR assay. Furthermore, a low-cost hand warmer (~$0.3) is used as an incubator of the AIOD-CRISPR assay to detect clinical samples within 20 min, enabling an instrument-free, visual SARS-CoV-2 detection at the point of care. Thus, our method has the significant potential to provide a rapid, sensitive, one-pot point-of-care assay for SARS-CoV-2.

SummaryThe dynamics and coordination between autophagy machinery and selective receptors during mitophagy are unknown. Also unknown is whether mitophagy depends on pre-existing membranes or is triggered on the surface of damaged mitochondria. Using a ubiquitin-dependent mitophagy inducer, the lactone ivermectin, we have combined genetic and imaging experiments to address these questions. Ubiquitination of mitochondrial fragments is required the earliest, followed by auto-phosphorylation of TBK1. Next, early essential autophagy proteins FIP200 and ATG13 act at different steps, whereas ULK1 and ULK2 are dispensable. Receptors act temporally and mechanistically upstream of ATG13 but downstream of FIP200. The VPS34 complex functions at the omegasome step. ATG13 and optineurin target mitochondria in a discontinuous oscillatory way, suggesting multiple initiation events. Targeted ubiquitinated mitochondria are cradled by endoplasmic reticulum (ER) strands even without functional autophagy machinery and mitophagy adaptors. We propose that damaged mitochondria are ubiquitinated and dynamically encased in ER strands, providing platforms for formation of the mitophagosomes.

A recent outbreak of novel coronavirus (SARS-CoV-2), the causative agent of COVID-19, has spread rapidly all over the world. Human immunodeficiency virus (HIV) is another deadly virus and causes acquired immunodeficiency syndrome (AIDS). Rapid and early detection of these viruses will facilitate early intervention and reduce disease transmission risk. Here, we present an All-In-One Dual CRISPR-Cas12a (termed "AIOD-CRISPR") assay method for simple, rapid, ultrasensitive, one-pot, and visual detection of coronavirus SARS-CoV-2 and HIV virus. In our AIOD CRISPR assay, a pair of crRNAs was introduced to initiate dual CRISPR-Cas12a detection and improve detection sensitivity. The AIOD-CRISPR assay system was successfully utilized to detect nucleic acids (DNA and RNA) of SARS-CoV-2 and HIV with a sensitivity of few copies. Also, it was evaluated by detecting HIV-1 RNA extracted from human plasma samples, achieving a comparable sensitivity with real-time RT-PCR method. Thus, our method has a great potential for developing next-generation point-of-care molecular diagnostics.

Recently, CRISPR-Cas technology has opened a new era of nucleic acid-based molecular diagnostics. However, current CRISPR-Cas-based nucleic acid biosensing has a lack of the quantitative detection ability and typically requires separate manual operations. Herein, we reported a dynamic aqueous multiphase reaction (DAMR) system for simple, sensitive and quantitative onepot CRISPR-Cas12a based molecular diagnosis by taking advantage of density difference of sucrose concentration. In the DAMR system, recombinase polymerase amplification (RPA) and CRISPR-Cas12a derived fluorescent detection occurred in spatially separated but connected aqueous phases. Our DAMR system was utilized to quantitatively detect human papillomavirus (HPV) 16 and 18 DNAs with sensitivities of 10 and 100 copies within less than 1 h. Multiplex detection of HPV16/18 in clinical human swab samples were successfully achieved in the DAMR system using 3D-printed microfluidic device. Furthermore, we demonstrated that target DNA in real human plasma samples can be directly amplified and detected in the DAMR system without complicated sample pretreatment. As demonstrated, the DAMR system has shown great potential for development of next-generation point-of-care molecular diagnostics.

Quantifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical samples is crucial for early diagnosis and timely medical treatment of coronavirus disease 2019. Here, we describe a digital warm-start CRISPR (dWS-CRISPR) assay for sensitive quantitative detection of SARS-CoV-2 in clinical samples. The dWS-CRISPR assay is initiated at above 50 °C and overcomes undesired premature target amplification at room temperature, enabling accurate and reliable digital quantification of SARS-CoV-2. By targeting SARS-CoV-2's nucleoprotein gene, the dWS-CRISPR assay is able to detect down to 5 copies/μl SARS-CoV-2 RNA in the chip. It is clinically validated by quantitatively determining 32 clinical swab samples and three clinical saliva samples. Moreover, it has been demonstrated to directly detect SARS-CoV-2 in heat-treated saliva samples without RNA extraction. Thus, the dWS-CRISPR method, as a sensitive and reliable CRISPR assay, facilitates accurate SARS-CoV-2 detection toward digitized quantification.

Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

Abstract. Crop phenology provides essential information for monitoring and modeling land surface phenology dynamics and crop management and production. Most previous studies mainly investigated crop phenology at the site scale; however, monitoring and modeling land surface phenology dynamics at a large scale need high-resolution spatially explicit information on crop phenology dynamics. In this study, we produced a 1 km grid crop phenological dataset for three main crops from 2000 to 2015 based on Global Land Surface Satellite (GLASS) leaf area index (LAI) products, called ChinaCropPhen1km. First, we compared three common smoothing methods and chose the most suitable one for different crops and regions. Then, we developed an optimal filter-based phenology detection (OFP) approach which combined both the inflection- and threshold-based methods and detected the key phenological stages of three staple crops at 1 km spatial resolution across China. Finally, we established a high-resolution gridded-phenology product for three staple crops in China during 2000–2015. Compared with the intensive phenological observations from the agricultural meteorological stations (AMSs) of the China Meteorological Administration (CMA), the dataset had high accuracy, with errors of the retrieved phenological date being less than 10 d, and represented the spatiotemporal patterns of the observed phenological dynamics at the site scale fairly well. The well-validated dataset can be applied for many purposes, including improving agricultural-system or earth-system modeling over a large area (DOI of the referenced dataset: https://doi.org/10.6084/m9.figshare.8313530; Luo et al., 2019).

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