KASP-IEva: an intelligent typing evaluation model for KASP primers

KASP marker technology has been used in molecular marker-assisted breeding because of its high efficiency and flexibility, and an intelligent evaluation model of KASP marker primer typing results is essential to improve the efficiency of marker development on a large scale. To this end, this paper proposes a gene population delineation method based on NTC identification module and data distribution judgment module to improve the accuracy of K-Means clustering, and introduces a decision tree to construct the KASP-IEva primer typing evaluation model. The model firstly designs the NTC identification module and data distribution judgment module to extract four types of data, grouping and categorizing to achieve the improvement of the distinguishability of amplification product signals; secondly, the K-Means algorithm is used to aggregate and classify the data, to visualize the five aggregated clusters and to obtain the morphology location eigenvalues; lastly, the evaluation criteria for the typing effect level are constructed, and the logical decision tree is used to make conditional discrimination on the eigenvalues in order to realize the score prediction. The performance of the model was tested by the KASP marker typing test results of 2519 groups of cotton varieties, and the following conclusions were obtained: the model is able to visualize the aggregation and classification effects of the amplification products of NTC, pure genotypes, heterozygous genotypes, and untyped genotypes, enabling rapid and accurate KASP marker typing evaluation. Comparing and analyzing the model evaluation results with the expert evaluation results, the average accuracy rate of the four grades evaluated by the model was 87%, and the overall evaluation results showed an uneven distribution of the grades with significant differential characteristics. When evaluating 2519 KASP fractal maps, the expert evaluation consumes 15 hours, and the model evaluation only uses 8min27.45s, which makes the model intelligent evaluation significantly better than the expert evaluation from the perspective of time. The establishment of the model will further enhance the application of KASP markers in molecular marker-assisted breeding and provide technical support for the large-scale screening and identification of excellent genotypes

Association between Aldehyde Dehydrogenase 2 Glu504Lys Polymorphism and Alcoholic Liver Disease

Background Only a subset of patients with excessive alcohol use develop alcoholic liver disease (ALD); though the exact mechanism is not completely understood. Once ingested, alcohol is metabolized by 2 key oxidative enzymes, alcohol (ADH) and aldehyde dehydrogenase (ALDH). There are 2 major ALDH isoforms, cytosolic and mitochondrial, encoded by the aldehyde ALDH1 and ALDH2 genes, respectively. The ALDH2 gene was hypothesized to alter genetic susceptibility to alcohol dependence and alcohol-induced liver diseases. The aim of this study is to determine the association between aldehyde dehydrogenase 2 (rs671) glu504lys polymorphism and ALD. Methods ALDH2 genotype was performed in 535 healthy controls and 281 patients with ALD. Results The prevalence of the common form of the SNP rs671, 504glu (glu/glu) was significantly higher in patients with ALD (95.4%) compared to that of controls (73.7%, p<0.0001). Among controls, 23.7% had heterozygous (glu/lys) genotype when compared to 4.6% in those with ALD (OR 0.16, 95%CI 0.09–0.28). The allele frequency for 504lys allele in patients with ALD was 2.3%; compared to 14.5% in healthy controls (OR 0.13, 95%CI 0.07–0.24). Conclusions Patients with ALDH2 504lys variant were less associated with ALD compared to those with ALDH2 504glu using both genotypic and allelic analyses

Insights into the reduction of antibiotic-resistant bacteria and mobile antibiotic resistance genes by black soldier fly larvae in chicken manure

The increasing prevalence of antibiotic-resistant bacteria (ARB) from animal manure has raised concerns about the potential threats to public health. The bioconversion of animal manure with insect larvae, such as the black soldier fly larvae (BSFL, Hermetia illucens [L.]), is a promising technology for quickly attenuating ARB while also recycling waste. In this study, we investigated BSFL conversion systems for chicken manure. Using metagenomic analysis, we tracked ARB and evaluated the resistome dissemination risk by investigating the co-occurrence of antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and bacterial taxa in a genetic context. Our results indicated that BSFL treatment effectively mitigated the relative abundance of ARB, ARGs, and MGEs by 34.9%, 53.3%, and 37.9%, respectively, within 28 days. Notably, the transferable ARGs decreased by 30.9%, indicating that BSFL treatment could mitigate the likelihood of ARG horizontal transfer and thus reduce the risk of ARB occurrence. In addition, the significantly positive correlation links between antimicrobial concentration and relative abundance of ARB reduced by 44.4%. Moreover, using variance partition analysis (VPA), we identified other bacteria as the most important factor influencing ARB, explaining 20.6% of the ARB patterns. Further analysis suggested that antagonism of other bacteria on ARB increased by 1.4 times, while nutrient competition on both total nitrogen and crude fat increased by 2.8 times. Overall, these findings provide insight into the mechanistic understanding of ARB reduction during BSFL treatment of chicken manure and provide a strategy for rapidly mitigating ARB in animal manure.This work was funding by the National Natural Science Foundation of China (41977279), the Fundamental Research Funds for the Central Universities (2662020SKPY002 and 2662022SKYJ006), the Key Technology R & D Program of Hubei Province (2021BBA258) and the Major Project of Hubei Hongshan Laboratory (2022hszd013).Peer ReviewedPostprint (published version

Author Correction: The flying spider-monkey tree fern genome provides insights into fern evolution and arborescence (Nature Plants, (2022), 8, 5, (500-512), 10.1038/s41477-022-01146-6)

Correction to: Nature Plantshttps://doi.org/10.1038/s41477-022-01146-6, published online 9 May 2022. In the version of the article initially published, Dipak Khadka, who collected the samples in Nepal, was thanked in the Acknowledgements instead of being listed as an author. His name and affiliation (GoldenGate International College, Tribhuvan University, Battisputali, Kathmandu, Nepal) have been added to the authorship in the HTML and PDF versions of the article

Underwater Topography Detection and Analysis of the Qilianyu Islands in the South China Sea Based on GF-3 SAR Images

Shallow sea underwater topography plays an important role in the development of islands and reefs. The Qilianyu Islands, located in Xisha, South China Sea, are a key area for the development and utilization of the South China Sea. Compared with traditional underwater topography detection methods, synthetic aperture radar (SAR) has the advantages of low cost, short time consumption, and the large-scale detection of shallow water topography. The GF-3 satellite is the first SAR satellite launched by China, and its ability to probe shallow sea topography has never been assessed. To detect the underwater topography of the Qilianyu Islands and test the application of GF-3 SAR data in shallow sea underwater topography detection, this paper implements the SAR shallow sea underwater topography detection model, the tidal information corresponding to the imaging time of the SAR image, and six GF-3 SAR images to detect the underwater topography of the Qilianyu island and reefs. The detection results have been analyzed from different imaging times, different water depths and different polarization modes, and the first four SAR images show promising detection results. The average absolute error (MAE) and average relative error (MRE) of the results are 1.5 m and 14.33%, respectively, which demonstrates that GF-3 SAR images have an impressive performance in underwater topography detection of South China Sea island reefs

Active Mask-Box Scoring R-CNN for Sonar Image Instance Segmentation

Instance segmentation of sonar images is an effective method for underwater target recognition. However, the mismatch among positioning accuracy found by boxIoU and classification confidence, which is used as NMS score in current instance segmentation models; and the high annotation cost of sonar images, are two major problems in the task. To tackle these problems, in this paper, we present a novel instance segmentation method called Mask-Box Scoring R-CNN and embedded it in our proposed deep active learning framework. For the mismatch problem between boxIoU and NMS score, Mask-Box Scoring R-CNN uses a boxIoU head to predict the quality of the bounding boxes. We amend the non-maximum suppression (NMS) score predicted by BoxIoU to preserve high-quality bounding boxes in inference flow. To deal with the annotating problem, we propose a triplets-measure-based active learning (TBAL) method and a balanced-sampling method applicable for deep learning. The TBAL method evaluates the amount of information of unlabeled samples from the aspects of classification confidence, positioning accuracy, and mask quality. The balanced-sampling method selects hard samples from the dataset to train the model to improve performance. The experimental results show that Mask-Box Scoring R-CNN achieves improvements of 1% in boxAP and 1.3% boxAP on our sonar image dataset compared with Mask Scoring R-CNN and Mask R-CNN, respectively. The active learning framework with TBAL and balanced sampling can achieve a competitive performance with less labeled samples than other frameworks, which can better facilitate underwater target recognition

Enhancing Insulating Performances of Presspaper by Introduction of Nanofibrillated Cellulose

This study explores the possibility of enhancing both mechanical and breakdown properties of insulating presspaper by the introduction of an organic nano additive. Four different concentrations of nanofibrillated cellulose (NFC) were taken into account: 0.5 wt %, 2.5 wt %, 5 wt %, and 10 wt %. Presspaper containing no NFC was also prepared as a reference. Obtained samples were characterized by scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). Mechanical properties and breakdown behaviors were measured. Results show that the addition of 10 wt % NFC to softwood fibers can achieve the best performance. Tensile strength of reference presspaper is 109 MPa, whereas that of presspaper modified by 10 wt % NFC is 136 MPa, resulting in a 25% increase. The improved tensile strength can be attributed to the increased density and inter fiber bond strength. More importantly, presspaper reinforced by 10 wt % NFC can also achieve enhanced AC and DC breakdown strengths, which are 19% and 21% higher than those of the reference presspaper. It is concluded that NFC is likely to be a promising nano additive for cellulose insulation