Notes

Multivalent interactions help make adherens junction-cytoskeletal linkage strong through morphogenesis.
Hibernomas are rare benign tumors of brown fat (adipose tissue) that have been reported in several different species. The cytologic characterization of these tumors has not been described in dogs. In this case report, we describe two dogs with hibernomas, focusing on the cytologic appearance of these unique neoplasms. Both cytologic specimens were highly cellular and predominated by vacuolated neoplastic cells with no evidence of concurrent inflammation. The cells contained a moderate to large number of variably sized cytoplasmic vacuoles, with occasional, irregularly shaped pink granular material. Most cells contained a single nucleus; however, cells displayed moderate anisokaryosis. A biopsy with histologic examination was performed in both cases, confirming the cytologic suspicion of hibernoma. Immunohistochemistry revealed that both tumors were positive for UCP1 and vimentin, and negative for cytokeratin. Hibernoma is an important differential diagnosis in dogs with conjunctival and periocular swellings that exfoliate numerous, mildly atypical, vacuolated cells. © 2020 American Society for Veterinary Clinical Pathology.PURPOSE Spatial resolution is an important parameter for magnetic resonance imaging (MRI). High-resolution MR images provide detailed information and benefit subsequent image analysis. However, higher resolution MR images come at the expense of longer scanning time and lower signal-to-noise ratios (SNR). Using algorithms to improve image resolution can mitigate these limitations. Recently, some convolutional neural network (CNN)-based super-resolution (SR) algorithms have ourished on MR image reconstruction. However, most algorithms usually adopt deeper network structures to improve the performance. METHODS In this study, we propose a novel hybrid network (named HybridNet) to improve the quality of SR images by increasing the width of the network. Specifically, the proposed hybrid block combines a multi-path structure and variant dense blocks to extract abundant features from low-resolution images. Futhermore, we fully exploit the hierarchical features from diffierent hybrid blocks to reconstruct high-quality images. RESULTS All SR algorithms are evaluated using three MR image datasets and the proposed HybridNet outperformed the comparative methods with PSNR of 42.12 ± 0.92 dB, 38.60 ± 2.46 dB, 35.17 ± 2.96 dB and SSIM of 0.9949 ± 0.0015, 0.9892 ± 0.0034, 0.9740 ± 0.0064 respectively. Besides, our proposed network can reconstruct high-quality images on an unseen MR dataset with PSNR of 33.27 ± 1.56 and SSIM of 0.9581 ± 0.0068. CONCLUSIONS The results demonstrate that HybridNet can reconstruct high-quality SR images from degraded MR images and has good generalization ability. It also can be leveraged to assist the task of image analysis or processing. This article is protected by copyright. All rights reserved.DELAY OF GERMINATION1 (DOG1) is a primary regulator of seed dormancy. Accumulation of DOG1 in seeds lead to deep dormancy and delayed germination in Arabidopsis. B3 domain-containing transcriptional repressors HSI2/VAL1 and HSL1/VAL2 silence seed dormancy and enable the subsequent germination and seedling growth. However, the roles of HSI2 and HSL1 in regulation of DOG1 expression and seed dormancy remain elusive. Transmembrane Transporters inhibitor Seed dormancy was analyzed by measurement of maximum germination percentage of freshly harvested Arabidopsis seeds. In vivo protein-protein interaction analysis, ChIP-qPCR and EMSA were performed and suggested that HSI2 and HSL1 can form dimers to directly regulate DOG1. HSI2 and HSL1 dimers interact with RY elements at DOG1 promoter. Both B3 and PHD-like domains are required for enrichment of HSI2 and HSL1 at the DOG1 promoter. HSI2 and HSL1 recruit components of polycomb-group proteins, including CURLY LEAF (CLF) and LIKE HETERCHROMATIN PROTEIN 1 (LHP1), for consequent deposition of H3K27me3 marks, leading to repression of DOG1 expression. Our findings suggest that HSI2- and HSL1-dependent histone methylation plays critical roles in regulation of seed dormancy during seed germination and early seedling growth. This article is protected by copyright. All rights reserved.BACKGROUND The quality of fresh tea leaves after harvest determines, to some extent, the quality and price of commercial tea. A fast and accurate method to evaluate the quality of fresh tea leaves is required. RESULTS In this study, the potential of hyperspectral imaging in the range of 328-1115 nm for the rapid prediction of moisture, total nitrogen, crude fiber contents, and quality index value was investigated. A total of 90 samples of eight tea leaf varieties and two picking standards were tested. Quantitative partial least squares regression (PLSR) models were established using full spectrum, whereas multiple linear regression (MLR) models were developed using characteristic wavelengths selected by successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS). The results showed that optimal SPA-MLR models for moisture, total nitrogen, crude fiber contents, and quality index value yielded optimal performance with coefficients of determination for prediction (R2 p) of 0.9357, 0.8543, 0.8188, 0.9168; root mean square error (RMSEP) of 0.3437, 0.1097, 0.3795, 1.0358; and residual prediction deviation (RPD) of 4.00, 2.56, 2.31, and 3.51, respectively. CONCLUSION The results suggested that hyperspectral imaging technique coupled with chemometrics was a promising tool for rapid and nondestructive measurement of tea leaf quality and had the potential to develop multispectral imaging systems for future online detection of tea leaf quality. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.BACKGROUND In recent years, the interest in the use of natural compounds as possible substitutes of chemicals, to prevent microbial spoilage on food has grown. The antimicrobial activity of the essential oils (EOs) is well known and nowadays there is a renewed interest in their application as natural preservatives in postharvest management. The aims of this study were the characterization of the EO extracted from pompia leaves and the evaluation of its effectiveness for the control of the postharvest decay agent Penicillium digitatum, when applied as vapour contact in new airtight boxes, supplied with a heating system. RESULTS The fumigations were performed in vitro and on food by using two concentrations of the EO, heated at controlled temperature. The headspace analysis revealed that the heating of the EO favoured the evaporation of the volatile compounds, without altering their functionality. The treatments reduced the pathogen growth in vitro and rots on inoculated food by about 50 %. CONCLUSION The chemical analysis of the vapour composition demonstrated that the heating of the oil did not alter the components and thus the antimicrobial effect of the oil.
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