This indicated a reduction in metabolic activity that positively impacted quality and storage life extension. For example, GCA3 and GCA7 blueberries had a 25% longer storage life when compared to control, based on reduced decay incidence. In addition, GCA fruit were 27% firmer than control and CA fruit after 28 days of cold storage. GCA3 had a positive effect on maintaining individual sugars concentrations throughout the experiment, and both GCA treatments maintained ascorbic acid content close to initial values compared to a decrease of 44% in the control fruit at the end of the experiment. This work provides a paradigm shift in how CA could be applied and a better understanding of blueberry physiology and postharvest behavior. Copyright © 2020 Falagán, Miclo and Terry.DNA methylation is involved in many different biological processes in the development and well-being of crop plants such as transposon activation, heterosis, environment-dependent transcriptome plasticity, aging, and many diseases. Whole-genome bisulfite sequencing is an excellent technology for detecting and quantifying DNA methylation patterns in a wide variety of species, but optimized data analysis pipelines exist only for a small number of species and are missing for many important crop plants. This is especially important as most existing benchmark studies have been performed on mammals with hardly any repetitive elements and without CHG and CHH methylation. Pipelines for the analysis of whole-genome bisulfite sequencing data usually consists of four steps read trimming, read mapping, quantification of methylation levels, and prediction of differentially methylated regions (DMRs). Here we focus on read mapping, which is challenging because un-methylated cytosines are transformed to uracil during bisulfiwe validated our findings using real-world data of Glycine max and showed the influence of the mapping step on DMR calling in WGBS pipelines. We found that the conversion rate had only a minor impact on the mapping quality and the number of uniquely mapped reads, whereas the error rate and the maximum number of allowed mismatches had a strong impact and leads to differences of the performance of the eight read mappers. In conclusion, we recommend BSMAP which needs the shortest run time and yields the highest precision, and Bismark which requires the smallest amount of memory and yields precision and high numbers of uniquely mapped reads. Copyright © 2020 Grehl, Wagner, Lemnian, Glaser and Grosse.Image-based plant phenotyping has been steadily growing and this has steeply increased the need for more efficient image analysis techniques capable of evaluating multiple plant traits. Deep learning has shown its potential in a multitude of visual tasks in plant phenotyping, such as segmentation and counting. Here, we show how different phenotyping traits can be extracted simultaneously from plant images, using multitask learning (MTL). MTL leverages information contained in the training images of related tasks to improve overall generalization and learns models with fewer labels. We present a multitask deep learning framework for plant phenotyping, able to infer three traits simultaneously (i) leaf count, (ii) projected leaf area (PLA), and (iii) genotype classification. We adopted a modified pretrained ResNet50 as a feature extractor, trained end-to-end to predict multiple traits. We also leverage MTL to show that through learning from more easily obtainable annotations (such as PLA and genotype) we can predict a better leaf count (harder to obtain annotation). We evaluate our findings on several publicly available datasets of top-view images of Arabidopsis thaliana. Experimental results show that the proposed MTL method improves the leaf count mean squared error (MSE) by more than 40%, compared to a single task network on the same dataset. We also show that our MTL framework can be trained with up to 75% fewer leaf count annotations without significantly impacting performance, whereas a single task model shows a steady decline when fewer annotations are available. Code available at https//github.com/andobrescu/Multi_task_plant_phenotyping. Copyright © 2020 Dobrescu, Giuffrida and Tsaftaris."Autoinflammatory disease (AiD)" has first been introduced in 1999 when the responsible gene for the familial Hibernean fever or autosomal dominant-type familial Mediterranean fever-like periodic fever syndrome was reportedly identified as tumor necrosis factor receptor superfamily 1. Linked with the rapid research progress in the field of innate immunity, "autoinflammation" has been designated for dysregulated innate immunity in contrast to "autoimmunity" with dysregulated acquired immunity. As hereditary periodic fever syndromes represent the prototype of AiD, monogenic systemic diseases are the main members of AiD. However, skin manifestations provide important clinical information and there are even some AiDs originating from skin diseases.  https://www.selleckchem.com/GSK-3.html  Recently, AiD showing psoriasis and related keratinization diseases have specifically been designated as "autoinflammatory keratinization diseases (AiKD)" and CARD14-associated psoriasis and deficiency of interleukin-36 receptor antagonist previously called as generalized pustular psoriasis are included. Similarly, a number of autoinflammatory skin diseases can be proposed; autoinflamatory urticarial dermatosis (AiUD) such as cryopyrin-associated periodic syndrome; autoinflammatory neutrophilic dermatosis (AiND) such as pyogenic sterile arthritis, pyoderma gangrenosm, and acne syndrome; autoinflammatory granulomatosis (AiG) such as Blau syndrome; autoinflammatory chilblain lupus (AiCL) such as Aicardi-Goutieres syndrome; autoinflammatory lipoatrophy (AiL) such as Nakajo-Nishimura syndrome; autoinflammatory angioedema (AiAE) such as hereditary angioedema; and probable autoinflammatory bullous disease (AiBD) such as granular C3 dermatosis. With these designations, skin manifestations in AiD can easily be recognized and, even more importantly, autoinflammatory pathogenesis of common skin diseases are expected to be more comprehensive. Copyright © 2020 Kanazawa.