Biomechanical and histological changes associated with riboflavin ultraviolet-A-induced CXL with different irradiances in young human corneal stroma.

Keratoconus (KC) is a degenerative condition affecting the cornea, characterized by progressive thinning and bulging, which can ultimately result in serious visual impairment. The onset and progression of KC are closely tied to the gradual weakening of the cornea's biomechanical properties. KC progression can be prevented with corneal cross-linking (CXL), but this treatment has shortcomings, and evaluating its tissue stiffening effect is important for determining its efficacy. In this field, the shortage of human corneas has made it necessary for most previous studies to rely on animal corneas, which have different microstructure and may be affected differently from human corneas. In this research, we have used the lenticules obtained through small incision lenticule extraction (SMILE) surgeries as a source of human tissue to assess CXL. And to further improve the results' reliability, we used inflation testing, personalized finite element modeling, numerical optimization and histology microstructure analysis. These methods enabled determining the biomechanical and histological effects of CXL protocols involving different irradiation intensities of 3, 9, 18, and 30 mW/cm2, all delivering the same total energy dose of 5.4 J/cm2. The results showed that the CXL effect did not vary significantly with protocols using 3-18 mW/cm2 irradiance, but there was a significant efficacy drop with 30 mW/cm2 irradiance. This study validated the updated algorithm and provided guidance for corneal lenticule reuse and the effects of different CXL protocols on the biomechanical properties of the human corneal stroma

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

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.)

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