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

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

Extended data and supplementary information are available at https://doi.org/10.1038/s41588-019-0522-8Melon 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

An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis

Nowadays, electricity theft has been a major problem worldwide. Although many single-classification algorithms or an ensemble of single learners (i.e., homogeneous ensemble learning) have proven able to automatically identify suspicious customers in recent years, after the accuracy of these methods reaches a certain level, it still cannot be improved even if it continues to be optimized. To break through this bottleneck, a heterogeneous ensemble learning method with stacking integrated structure of different strong individual learners for detection of electricity theft is presented in this paper. Firstly, we use the grey relation analysis (GRA) method to select the heterogeneous strong classifier combination of LG + LSTM + KNN as the base model layer of stacking structure based on the principle of the highest comprehensive evaluation index value. Secondly, the support vector machine (SVM) model with relatively good results of the stacking overall structure experiment is selected as the model of the meta-model layer. In this way, a heterogeneous integrated learning model for electricity theft detection of the stacking structure is constructed. Finally, the experiments of this model are conducted on electricity consumption data from State Grid Corporation of China, and the results show that the detection performance of the proposed method is better than that of the existing state-of-the-art detection method (where the area under receiver operating characteristic curve (AUC) value is 0.98675)

An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis

Nowadays, electricity theft has been a major problem worldwide. Although many single-classification algorithms or an ensemble of single learners (i.e., homogeneous ensemble learning) have proven able to automatically identify suspicious customers in recent years, after the accuracy of these methods reaches a certain level, it still cannot be improved even if it continues to be optimized. To break through this bottleneck, a heterogeneous ensemble learning method with stacking integrated structure of different strong individual learners for detection of electricity theft is presented in this paper. Firstly, we use the grey relation analysis (GRA) method to select the heterogeneous strong classifier combination of LG + LSTM + KNN as the base model layer of stacking structure based on the principle of the highest comprehensive evaluation index value. Secondly, the support vector machine (SVM) model with relatively good results of the stacking overall structure experiment is selected as the model of the meta-model layer. In this way, a heterogeneous integrated learning model for electricity theft detection of the stacking structure is constructed. Finally, the experiments of this model are conducted on electricity consumption data from State Grid Corporation of China, and the results show that the detection performance of the proposed method is better than that of the existing state-of-the-art detection method (where the area under receiver operating characteristic curve (AUC) value is 0.98675)

Daytime variations of tear osmolarity and tear meniscus volume

OBJECTIVES: To determine the pattern of variations in tear osmolarity and tear meniscus volume in dry eye patients and in healthy control subjects over an 8-hour daytime period. METHODS: Ten normal subjects (5 males and 5 females with a mean age of 27 ± 7 yrs) and 10 dry eye patients (4 males and 6 females with a mean age of 36 ± 12 yrs) who had been diagnosed on the basis of having an ocular surface discomfort index (OSDI) >12 and a tear breakup time of < 10 seconds or Schirmer’s test score of < 5 mm were included. The tear meniscus volumes of the participants were measured using ultra-high resolution optical coherence tomography (OCT), and tear osmolarity was measured using the TearLab Osmolarity System. Both measurements protocols were conducted on the right eye of each participant every two hours beginning at 8:30AM and ending at 4:30PM. OCT imaging was performed first and was followed by osmolarity testing. RESULTS: The mean tear osmolarity of the dry eye patients was 304.0 ± 10.8 mOsm/L, and the mean tear osmolarity of the normal subjects was 298.0 ±14.2 mOsm/L (P > 0.05). Over the course of 8 hours, the average measured osmolarities of the dry eye group varied by approximately 21.9 ± 13.5 mOsm/L (range, 6–43 mOsm/L), and the average measured tear osmolarities of the normal group varied by approximately 21.0 ± 9.2 mOsm/L (range, 8–35 mOsm/L). At 2:30 PM, the average volume of the tear menisci in the dry eye group was significantly lower than that of the subjects in the normal group (P < 0.05). No correlations between the tear meniscus volumes and tear osmolarities of either group were observed. CONCLUSIONS: Variations in the tear osmolarities of individual dry eye patients and healthy normal control subjects were documented over the course of 8 daytime hours. No relationships between tear osmolarities and tear meniscus volumes were observed

Repeatability and reproducibility of eight macular intra-retinal layer thicknesses determined by an automated segmentation algorithm using two SD-OCT instruments.

PURPOSE: To evaluate the repeatability, reproducibility, and agreement of thickness profile measurements of eight intra-retinal layers determined by an automated algorithm applied to optical coherence tomography (OCT) images from two different instruments. METHODS: Twenty normal subjects (12 males, 8 females; 24 to 32 years old) were enrolled. Imaging was performed with a custom built ultra-high resolution OCT instrument (UHR-OCT, ∼3 µm resolution) and a commercial RTVue100 OCT (∼5 µm resolution) instrument. An automated algorithm was developed to segment the macular retina into eight layers and quantitate the thickness of each layer. The right eye of each subject was imaged two times by the first examiner using each instrument to assess intra-observer repeatability and once by the second examiner to assess inter-observer reproducibility. The intraclass correlation coefficient (ICC) and coefficients of repeatability and reproducibility (COR) were analyzed to evaluate the reliability. RESULTS: The ICCs for the intra-observer repeatability and inter-observer reproducibility of both SD-OCT instruments were greater than 0.945 for the total retina and all intra-retinal layers, except the photoreceptor inner segments, which ranged from 0.051 to 0.643, and the outer segments, which ranged from 0.709 to 0.959. The CORs were less than 6.73% for the total retina and all intra-retinal layers. The total retinal thickness measured by the UHR-OCT was significantly thinner than that measured by the RTVue100. However, the ICC for agreement of the thickness profiles between UHR-OCT and RTVue OCT were greater than 0.80 except for the inner segment and outer segment layers. CONCLUSIONS: Thickness measurements of the intra-retinal layers determined by the automated algorithm are reliable when applied to images acquired by the UHR-OCT and RTVue100 instruments