Watermelon, Citrullus lanatus, is an important cucurbit crop grown throughout the world. Here we report a high-quality draft genome sequence of the east Asia watermelon cultivar 97103 (2n = 2x = 22) containing 23,440 predicted protein-coding genes. Comparative genomics analysis provided an evolutionary scenario for the origin of the 11 watermelon chromosomes derived from a 7-chromosome paleohexaploid eudicot ancestor. Resequencing of 20 watermelon accessions representing three different C. lanatus subspecies produced numerous haplotypes and identified the extent of genetic diversity and population structure of watermelon germplasm. Genomic regions that were preferentially selected during domestication were identified. Many disease-resistance genes were also found to be lost during domestication. In addition, integrative genomic and transcriptomic analyses yielded important insights into aspects of phloem-based vascular signaling in common between watermelon and cucumber and identified genes crucial to valuable fruit-quality traits, including sugar accumulation and citrulline metabolism

Watermelon [Citrullus lanatus (Thunb.) Matsum. & Nakai] is an important vegetable crop world-wide. Watermelon fruit quality is a complex trait determined by various factors such as sugar content, flesh color and flesh texture. Fruit quality and developmental process of cultivated and wild watermelon are highly different. To systematically understand the molecular basis of these differences, we compared transcriptome profiles of fruit tissues of cultivated watermelon 97103 and wild watermelon PI296341-FR. We identified 2,452, 826 and 322 differentially expressed genes in cultivated flesh, cultivated mesocarp and wild flesh, respectively, during fruit development. Gene ontology enrichment analysis of these genes indicated that biological processes and metabolic pathways related to fruit quality such as sweetness and flavor were significantly changed only in the flesh of 97103 during fruit development, while those related to abiotic stress response were changed mainly in the flesh of PI296341-FR. Our comparative transcriptome profiling analysis identified critical genes potentially involved in controlling fruit quality traits including α-galactosidase, invertase, UDP-galactose/glucose pyrophosphorylase and sugar transporter genes involved in the determination of fruit sugar content, phytoene synthase, β-carotene hydroxylase, 9-cis-epoxycarotenoid dioxygenase and carotenoid cleavage dioxygenase genes involved in carotenoid metabolism, and 4-coumarate:coenzyme A ligase, cellulose synthase, pectinesterase, pectinesterase inhibitor, polygalacturonase inhibitor and α-mannosidase genes involved in the regulation of flesh texture. In addition, we found that genes in the ethylene biosynthesis and signaling pathway including ACC oxidase, ethylene receptor and ethylene responsive factor showed highly ripening-associated expression patterns, indicating a possible role of ethylene in fruit development and ripening of watermelon, a non-climacteric fruit. Our analysis provides novel insights into watermelon fruit quality and ripening biology. Furthermore, the comparative expression profile data we developed provides a valuable resource to accelerate functional studies in watermelon and facilitate watermelon crop improvement.

BackgroundCultivated watermelon [Citrullus lanatus (Thunb.) Matsum. & Nakai var. lanatus] is an important agriculture crop world-wide. The fruit of watermelon undergoes distinct stages of development with dramatic changes in its size, color, sweetness, texture and aroma. In order to better understand the genetic and molecular basis of these changes and significantly expand the watermelon transcript catalog, we have selected four critical stages of watermelon fruit development and used Roche/454 next-generation sequencing technology to generate a large expressed sequence tag (EST) dataset and a comprehensive transcriptome profile for watermelon fruit flesh tissues.ResultsWe performed half Roche/454 GS-FLX run for each of the four watermelon fruit developmental stages (immature white, white-pink flesh, red flesh and over-ripe) and obtained 577,023 high quality ESTs with an average length of 302.8 bp. De novo assembly of these ESTs together with 11,786 watermelon ESTs collected from GenBank produced 75,068 unigenes with a total length of approximately 31.8 Mb. Overall 54.9% of the unigenes showed significant similarities to known sequences in GenBank non-redundant (nr) protein database and around two-thirds of them matched proteins of cucumber, the most closely-related species with a sequenced genome. The unigenes were further assigned with gene ontology (GO) terms and mapped to biochemical pathways. More than 5,000 SSRs were identified from the EST collection. Furthermore we carried out digital gene expression analysis of these ESTs and identified 3,023 genes that were differentially expressed during watermelon fruit development and ripening, which provided novel insights into watermelon fruit biology and a comprehensive resource of candidate genes for future functional analysis. We then generated profiles of several interesting metabolites that are important to fruit quality including pigmentation and sweetness. Integrative analysis of metabolite and digital gene expression profiles helped elucidating molecular mechanisms governing these important quality-related traits during watermelon fruit development.ConclusionWe have generated a large collection of watermelon ESTs, which represents a significant expansion of the current transcript catalog of watermelon and a valuable resource for future studies on the genomics of watermelon and other closely-related species. Digital expression analysis of this EST collection allowed us to identify a large set of genes that were differentially expressed during watermelon fruit development and ripening, which provide a rich source of candidates for future functional analysis and represent a valuable increase in our knowledge base of watermelon fruit biology.

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Research on batteries’ State of Charge (SOC) estimation for equivalent circuit models based on the Kalman Filter (KF) framework and machine learning algorithms remains relatively limited. Most studies are focused on a few machine learning algorithms and do not present comprehensive analysis and comparison. Furthermore, most of them focus on obtaining the state space parameters of the Kalman filter frame algorithm models using machine learning algorithms and then substituting the state space parameters into the Kalman filter frame algorithm to estimate the SOC. Such algorithms are highly coupled, and present high complexity and low practicability. This study aims to integrate machine learning with the Kalman filter frame algorithm, and to estimate the final SOC by using different combinations of the input, output, and intermediate variable values of five Kalman filter frame algorithms as the input of the machine learning algorithms of six main streams. These are: linear regression, support vector Regression, XGBoost, AdaBoost, random forest, and LSTM; the algorithm coupling is lower for two-way parameter adjustment and is not applied between the machine learning and Kalman filtering framework algorithms. The results demonstrate that the integrated learning algorithm significantly improves the estimation accuracy when compared to the pure Kalman filter framework or the machine learning algorithms. Among the various integrated algorithms, the random forest and Kalman filter framework presents the highest estimation accuracy along with good real-time performance. Therefore, it can be implemented in various engineering applications.