A comprehensive genome variation map of melon identifies multiple domestication events and loci influencing agronomic traits

Melon is an economically important fruit crop that has been cultivated for thousands of years; however, the genetic basis and history of its domestication still remain largely unknown. Here we report a comprehensive map of the genomic variation in melon derived from the resequencing of 1,175 accessions, which represent the global diversity of the species. Our results suggest that three independent domestication events occurred in melon, two in India and one in Africa. We detected two independent sets of domestication sweeps, resulting in diverse characteristics of the two subspecies melo and agrestis during melon breeding. Genome-wide association studies for 16 agronomic traits identified 208 loci significantly associated with fruit mass, quality and morphological characters. This study sheds light on the domestication history of melon and provides a valuable resource for genomics-assisted breeding of this important crop.This work was supported by funding from the Agricultural Science and Technology Innovation Program (to Yongyang Xu, S.H., Z.Z. and H.W.), the China Agriculture Research System (CARS-25 to Yongyang Xu and H.W.), the Leading Talents of Guangdong Province Program (00201515 to S.H.), the Shenzhen Municipal (The Peacock Plan KQTD2016113010482651 to S.H.), the Dapeng district government, National Natural Science Foundation of China (31772304 to Z.Z.), the Science and Technology Program of Guangdong (2018B020202007 to S.H.), the National Natural Science Foundation of China (31530066 to S.H.), the National Key R&D Program of China (2016YFD0101007 to S.H.), USDA National Institute of Food and Agriculture Specialty Crop Research Initiative (2015-51181-24285 to Z.F.), the European Research Council (ERC-SEXYPARTH to A.B.), the Spanish Ministry of Economy and Competitiveness (AGL2015–64625-C2-1-R to J.G.-M.), Severo Ochoa Programme for Centres of Excellence in R&D 2016–2010 (SEV-2015–0533 to J.G.-M.), the CERCA Programme/Generalitat de Catalunya to J.G.-M. and the German Science Foundation (SPP1991 Taxon-OMICS to H.S.)

A comprehensive genome variation map of melon identifies multiple domestication events and loci influencing agronomic traits

Melon is an economically important fruit crop that has been cultivated for thousands of years; however, the genetic basis and history of its domestication still remain largely unknown. Here we report a comprehensive map of the genomic variation in melon derived from the resequencing of 1,175 accessions, which represent the global diversity of the species. Our results suggest that three independent domestication events occurred in melon, two in India and one in Africa. We detected two independent sets of domestication sweeps, resulting in diverse characteristics of the two subspecies melo and agrestis during melon breeding. Genome-wide association studies for 16 agronomic traits identified 208 loci significantly associated with fruit mass, quality and morphological characters. This study sheds light on the domestication history of melon and provides a valuable resource for genomics-assisted breeding of this important crop.info:eu-repo/semantics/acceptedVersio

The Predicament of Rural Development under the Strategy of Rural Revitalization

Solving rural poverty is the most urgent problem to be solved in the current rural revitalization strategy. The "alienation" of farmer organizations makes it difficult to give full play to the function of farmer organizations. The institutionalized obstacle of urban-rural dual division restricts the urban integration of migrant workers. There is an institutional conflict between the construction of new countryside and rural urbanization. It is the best way for farmers to find a suitable model for local development according to local conditions, but the exploration and practice of rural development model will be a complex and tortuous process

Dilemma and Path for Rural Mutual Aid Elderly Care Model in Underdeveloped Areas — A Case Study of Dazhou City in Sichuan Province

Rural mutual aid elderly care, as an emerging elderly care model, has become an exploration and attempt to rebuild rural communities in the process of rural social transformation. At present, in the vast underdeveloped rural areas, the rural mutual aid elderly care model is faced with such problems as imperfect policies and regulations, insufficient funds, single service items, lack of professional services, and limited functions of civil organizations. It is necessary to strengthen the formulation and improvement of laws and regulations, and to ensure the stable source of funds, enhance the level of specialization, and give full play to the functions of social organizations in the rural mutual aid elderly care model, so as to promote the sustainable development of the rural mutual aid elderly care model

Analysis on Development Pathway of Farmer Organization under the Background of Rural Revitalization

The development of farmer organization will play an important role in rural revitalization. Basic pathways of farmer organization development are excavating common interest, playing the role of farmer elite, cultivating modern farmers and increasing government support. But due to the influences of rural culture tradition and current management system, there exists certain difficulty to meet these conditions to different extents, which decides that benign development of farmer organization will be a long persistence process

Fusing Multilevel Deep Features for Fabric Defect Detection Based NTV-RPCA

© 2013 IEEE. Fabric defect detection plays an important role in automated inspection and quality control in textile manufacturing. As the fabric images have complex and diverse textures and defects, traditional detection methods show a poor adaptability and low detection accuracy. Robust principal component analysis (RPCA) model that can be used to separate the image into object and background have proven applicable in fabric defect detection. However, how to represent texture feature of the fabric image more effectively is still problematic in this kind of method. In addition, the use of the traditional RPCA may result in low accuracy and more noises in sparse part. In this article, a novel fabric defect detection method based on multilevel deep features fusion and non-convex total variation regularized RPCA (NTV-RPCA) is proposed. Firstly, the image representation ability is well enhanced through multilevel deep features extracted by a convolutional neural network. Then, the non-convex total variation regularized RPCA is proposed in which total variation constraint significantly reduces the noises in sparse part and non-convex solution is more approximate to the authentic one. Next, multilevel saliency maps generated by the sparse matrixes are fused via RPCA to produce a more reliable detection result. Finally, the defect region is located by segmenting the fused saliency map via a threshold segmentation algorithm. Qualitative and quantitative experiments conducted on two public fabric image databases demonstrate that the proposed method improves the adaptability and detection accuracy comparing to the state-of-the-arts

Wavelet Scattering Transform for ECG Beat Classification

An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k=4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation

Effect of one comprehensive education course to lower anxiety and depression among Chinese breast cancer patients during the postoperative radiotherapy period - one randomized clinical trial

Abstract Background We investigated the effectiveness of one education course to lower the severity of anxiety and depression symptoms among breast cancer (BC) patients during radiotherapy (RT). Methods All 290 one-sided BC patients were evenly randomized into intervention or control arm. “Intervention” patient was additionally provided with one three-hour course on psychological stresses and management skills. Changes of anxiety and depression score and their 3-level severity category (‘normal’, ‘borderline’ and ‘abnormal’ scored 0–7, 8–10 and 11–21, respectively) from HADS questionnaire over RT were evaluated by multivariable linear and ordinal logistic regressions. Results Response rates were 94 and 100% by “intervention” and “control” arm, respectively. Means of score changes by “intervention” and “control” (n = 145) were + 0.59 (SD = 2.47) and + 0.11 (SD = 2.55) for anxiety and + 0.81 (SD = 2.81) and + 0.45 (SD = 2.77) for depression scores, respectively. ‘Abnormal’ anxiety and depression patients were 4.1 and 6.9% at baseline and 4.8 and 6.9% at end of RT at ‘control’ arm; those rates were 6.6 and 7.4%, and 8.8 and 10.3% at ‘intervention’ arm, respectively. Both changes on anxiety and depression measurements between two arms were all insignificant (p > 0.20). Conclusions One education course did not reduce the score and severity of anxiety and depression symptoms over RT period. Trial registration Chinese Clinical Trial Registry #: ChiCTR-IIR-16008818 at www.chictr.org.cn

Effect of Gas Flow Rate and Ratio on Structure and Properties of Nitrogen-Doped Diamond-like Carbon Films

Diamond-like carbon (DLC) has attracted much attention due to its unique properties such as high chemical inertness, optical transparency, and high biocompatibility. In this study, the total gas flow rate was kept constant, while the ratio of reactive gases was varied to deposit nitrogen-doped diamond-like carbon thin films on glass substrates using radiofrequency plasma-enhanced chemical vapor deposition. The effects of the gas flow ratio on the composition, microstructure, surface morphology, and optical properties of the thin films were investigated through extended deposition times. It was found that with an increase in the nitrogen-to-methane gas flow ratio, the film surface became smoother and more compact. The maximum transmittance in the visible range reached 90%, and the highest and lowest transmittance in the same ultraviolet wavelength region differed by up to 25.62% among several sample groups. The optical bandgap decreased from 3.58 eV to 3.46 eV, contrary to the trend of the sp2 fraction variation. Compared with other studies, this study considered the preparation of nitrogen-doped diamondoids using a chemical vapor deposition method with a lesser total gas flow rate passed into it, which provides practical data reference value for the preparation of N-DLC