Highly Efficient CRISPR-Mediated Base Editing in Sinorhizobium meliloti

Rhizobia are widespread gram-negative soil bacteria and indispensable symbiotic partners of leguminous plants that facilitate the most highly efficient biological nitrogen fixation in nature. Although genetic studies in Sinorhizobium meliloti have advanced our understanding of symbiotic nitrogen fixation (SNF), the current methods used for genetic manipulations in Sinorhizobium meliloti are time-consuming and labor-intensive. In this study, we report the development of a few precise gene modification tools that utilize the CRISPR/Cas9 system and various deaminases. By fusing the Cas9 nickase to an adenine deaminase, we developed an adenine base editor (ABE) system that facilitated adenine-to-guanine transitions at one-nucleotide resolution without forming double-strand breaks (DSB). We also engineered a cytidine base editor (CBE) and a guanine base editor (GBE) that catalyze cytidine-to-thymine substitutions and cytidine-to-guanine transversions, respectively, by replacing adenine deaminase with cytidine deaminase and other auxiliary enzymes. All of these base editors are amenable to the assembly of multiple synthetic guide RNA (sgRNA) cassettes using Golden Gate Assembly to simultaneously achieve multigene mutations or disruptions. These CRISPR-mediated base editing tools will accelerate the functional genomics study and genome manipulation of rhizobia

A pathogenic UFSP2 variant in an autosomal recessive form of pediatric neurodevelopmental anomalies and epilepsy

Purpose: Neurodevelopmental disabilities are common and genetically heterogeneous. We identified a homozygous variant in the gene encoding UFM1-specific peptidase 2 (UFSP2), which participates in the UFMylation pathway of protein modification. UFSP2 variants are implicated in autosomal dominant skeletal dysplasias, but not neurodevelopmental disorders. Homozygosity for the variant occurred in eight children from four South Asian families with neurodevelopmental delay and epilepsy. We describe the clinical consequences of this variant and its effect on UFMylation.Methods: Exome sequencing was used to detect potentially pathogenic variants and identify shared regions of homozygosity. Immunoblotting assessed protein expression and post-translational modifications in patient-derived fibroblasts.Results: The variant (c.344T\u3eA; p.V115E) is rare and alters a conserved residue in UFSP2. Immunoblotting in patient-derived fibroblasts revealed reduced UFSP2 abundance and increased abundance of UFMylated targets, indicating the variant may impair de-UFMylation rather than UFMylation. Reconstituting patient-derived fibroblasts with wild-type UFSP2 reduced UFMylation marks. Analysis of UFSP2\u27s structure indicated that variants observed in skeletal disorders localize to the catalytic domain, whereas V115 resides in an N-terminal domain possibly involved in substrate binding.Conclusion: Different UFSP2 variants cause markedly different diseases, with homozygosity for V115E causing a severe syndrome of neurodevelopmental disability and epilepsy

Transmission Roles of Symptomatic and Asymptomatic COVID-19 Cases: A Modelling Study

Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9–80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0–34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks

Time series analysis of dengue fever and weather in Guangzhou, China

<p>Abstract</p> <p>Background</p> <p>Monitoring and predicting dengue incidence facilitates early public health responses to minimize morbidity and mortality. Weather variables are potential predictors of dengue incidence. This study explored the impact of weather variability on the transmission of dengue fever in the subtropical city of Guangzhou, China.</p> <p>Methods</p> <p>Time series Poisson regression analysis was performed using data on monthly weather variables and monthly notified cases of dengue fever in Guangzhou, China for the period of 2001-2006. Estimates of the Poisson model parameters was implemented using the Generalized Estimating Equation (GEE) approach; the quasi-likelihood based information criterion (QICu) was used to select the most parsimonious model.</p> <p>Results</p> <p>Two best fitting models, with the smallest QICu values, are selected to characterize the relationship between monthly dengue incidence and weather variables. Minimum temperature and wind velocity are significant predictors of dengue incidence. Further inclusion of minimum humidity in the model provides a better fit.</p> <p>Conclusion</p> <p>Minimum temperature and minimum humidity, at a lag of one month, are positively associated with dengue incidence in the subtropical city of Guangzhou, China. Wind velocity is inversely associated with dengue incidence of the same month. These findings should be considered in the prediction of future patterns of dengue transmission.</p

Mesoscale Modeling Study on Mechanical Deterioration of Alkali–Aggregate Reaction-Affected Concrete

The alkali–aggregate reaction (AAR) is a harmful chemical reaction that reduces the mechanical properties and weakens the durability of concrete. Different types of activated aggregates may result in various AAR modes, which affect the mechanical deterioration of concrete. In this paper, the aggregate expansion model and the gel pocket model are considered to represent the two well-recognized AAR modes. The mesoscale particle model of concrete was presented to model the AAR expansion process and the splitting tensile behavior of AAR-affected concrete. The numerical results show that different AAR modes have a great influence on the development of AAR in terms of expansion and microcracks and the deterioration of concrete specimens. The AAR mode of the gel pocket model causes slight expansion, but generates microcracks in the concrete at the early stage of AAR. This means there is difficulty in achieving early warning and timely maintenance of AAR-affected concrete structures based on the monitoring expansion. Compared with the aggregate expansion model, more severe cracking can be observed, and a greater loss of tensile strength is achieved at the same AAR expansion in the gel pocket model. AAR modes determine the subsequent reaction process and deterioration, and thus, it is necessary to develop effective detection methods and standards for large concrete projects according to different reactive aggregates

Analysis of Heavy Metals in Foodstuffs and an Assessment of the Health Risks to the General Public via Consumption in Beijing, China

Consumption of foodstuffs is the most likely route for human exposure to heavy metals. This study was designed to investigate the toxic metals (cadmium (Cd), lead (Pb), chromium (Cr), arsenic (As), and mercury (Hg)) concentrations in different foodstuffs (cereals, vegetables, fruits, fish, and meat) and then estimate the potential health risks of toxic metals via consumption to the local residents in Beijing, China. Most of the selected toxic metal levels in the foodstuffs were lower than the maximum allowable concentrations of Pb, Cr, Cd, As, and Hg for Chinese foodstuffs recommended in the China National Food Safety Standard. The health risks associated with the toxic metals Pb, Cr, Cd, As, and Hg were assessed based on the target hazard quotients (THQs) proposed by the United States Environmental Protection Agency (US EPA). The THQ values of the foodstuffs varied and were 0.03&ndash;0.29 for Cr, 0.02&ndash;0.23 for Pb, 0.01&ndash;0.33 for Cd, 0.01&ndash;0.06 for As, and 0.00&ndash;0.04 for Hg, not exceeding the maximum level of 1. The total THQ (TTHQ) values were 0.88 for vegetables, 0.57 for cereals, 0.46 for meat, 0.32 for fish, and 0.07 for fruits. This indicates that the risk contribution from vegetable intake (38.8%) was significant in comparison to that from other foodstuffs. The TTHQ values were 0.96 for Cr, 0.54 for Pb, 0.50 for Cd, 0.19 for As, and 0.09 for Hg, suggesting that Cr was a major risk contributor (41.7%) for the local residents of Beijing, which should attract great attention. However, the THQ/TTHQ values were all below 1, suggesting no health risks to the local population through consumption. Furthermore, dietary weekly intakes (WIs) were also calculated and the values were all lower than the proposed limit of Provisional Tolerable Weekly Intakes (PTWI) established by the the Food and Agriculture Organization of the United Nations (FAO) and the World Health Organization (WHO). This suggests no additional health risks as well as consistency with the THQ results

An SAR Ship Object Detection Algorithm Based on Feature Information Efficient Representation Network

In the synthetic aperture radar (SAR) ship image, the target size is small and dense, the background is complex and changeable, the ship target is difficult to distinguish from the surrounding background, and there are many ship-like targets in the image. This makes it difficult for deep-learning-based target detection algorithms to obtain effective feature information, resulting in missed and false detection. The effective expression of the feature information of the target to be detected is the key to the target detection algorithm. How to improve the clear expression of image feature information in the network has always been a difficult point. Aiming at the above problems, this paper proposes a new target detection algorithm, the feature information efficient representation network (FIERNet). The algorithm can extract better feature details, enhance network feature fusion and information expression, and improve model detection capabilities. First, the convolution transformer feature extraction (CTFE) module is proposed, and a convolution transformer feature extraction network (CTFENet) is built with this module as a feature extraction block. The network enables the model to obtain more accurate and comprehensive feature information, weakens the interference of invalid information, and improves the overall performance of the network. Second, a new effective feature information fusion (EFIF) module is proposed to enhance the transfer and fusion of the main information of feature maps. Finally, a new frame-decoding formula is proposed to further improve the coincidence between the predicted frame and the target frame and obtain more accurate picture information. Experiments show that the method achieves 94.14% and 92.01% mean precision (mAP) on SSDD and SAR-ship datasets, and it works well on large-scale SAR ship images. In addition, FIERNet greatly reduces the occurrence of missed detection and false detection in SAR ship detection. Compared to other state-of-the-art object detection algorithms, FIERNet outperforms them on various performance metrics on SAR images