Building The Sugarcane Genome For Biotechnology And Identifying Evolutionary Trends

Background: Sugarcane is the source of sugar in all tropical and subtropical countries and is becoming increasingly important for bio-based fuels. However, its large (10 Gb), polyploid, complex genome has hindered genome based breeding efforts. Here we release the largest and most diverse set of sugarcane genome sequences to date, as part of an on-going initiative to provide a sugarcane genomic information resource, with the ultimate goal of producing a gold standard genome.Results: Three hundred and seventeen chiefly euchromatic BACs were sequenced. A reference set of one thousand four hundred manually-annotated protein-coding genes was generated. A small RNA collection and a RNA-seq library were used to explore expression patterns and the sRNA landscape. In the sucrose and starch metabolism pathway, 16 non-redundant enzyme-encoding genes were identified. One of the sucrose pathway genes, sucrose-6-phosphate phosphohydrolase, is duplicated in sugarcane and sorghum, but not in rice and maize. A diversity analysis of the s6pp duplication region revealed haplotype-structured sequence composition. Examination of hom(e)ologous loci indicate both sequence structural and sRNA landscape variation. A synteny analysis shows that the sugarcane genome has expanded relative to the sorghum genome, largely due to the presence of transposable elements and uncharacterized intergenic and intronic sequences.Conclusion: This release of sugarcane genomic sequences will advance our understanding of sugarcane genetics and contribute to the development of molecular tools for breeding purposes and gene discovery. © 2014 de Setta et al.; licensee BioMed Central Ltd.151European Commission: Agriculture and Rural Development: Sugar http://ec.europa.eu/agriculture/sugar/index_en.htmKellogg, E.A., Evolutionary history of the grasses (2001) Plant Physiol, 125, pp. 1198-1205Grivet, L., Arruda, P., Sugarcane genomics: depicting the complex genome of an important tropical crop (2001) Curr Opin Plant Biol, 5, pp. 122-127Piperidis, G., Piperidis, N., D'Hont, A., Molecular cytogenetic investigation of chromosome composition and transmission in sugarcane (2010) Mol Genet Genomics, 284, pp. 65-73D'Hont, A., 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Parametric analysis of a desiccant cooling system: effect of hygrothermal interactions with building envelope - Part 1

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Polyphenol-enriched Cocoa Protects The Diabetic Retina From Glial Reaction Through The Sirtuin Pathway

Cocoa is rich in flavonoids, which are potent antioxidants with established benefits for cardiovascular health but unproven effects on neurodegeneration. Sirtuins (SIRTs), which make up a family of deacetylases, are thought to be sensitive to oxidation. In this study, the possible protective effects of cocoa in the diabetic retina were assessed. Rat Mu¨ller cells (rMCs) exposed to normal or high glucose (HG) or H2O2 were submitted to cocoa treatment in the presence or absence of SIRT-1 inhibitor and small interfering RNA The experimental animal study was conducted in streptozotocin-induced diabetic rats randomized to receive low-, intermediate-, or high-polyphenol cocoa treatments via daily gavage for 16weeks (i.e., 0.12, 2.9 or 22.9mg/kg/day of polyphenols). The rMCs exposed to HG or H2O2 exhibited increased glial fibrillary acidic protein (GFAP) and acetyl-RelA/p65 and decreased SIRT1 activity/expression. These effects were cancelled out by cocoa, which decreased reactive oxygen species production and PARP-1 activity, augmented the intracellular pool of NAD+, and improved SIRT1 activity. The rat diabetic retinas displayed the early markers of retinopathy accompanied by markedly impaired electroretinogram. The presence of diabetes activated PARP-1 and lowered NAD+ levels, resulting in SIRT1 impairment. This augmented acetyl RelA/p65 had the effect of up-regulated GFAP. Oral administration of polyphenol cocoa restored the above alterations in a dose-dependent manner. This study reveals that cocoa enriched with polyphenol improves the retinal SIRT-1 pathway, thereby protecting the retina from diabetic milieu insult

Interpretable deep learning approach for oral cancer classification using guided attention inference network

Significance: Convolutional neural networks (CNNs) show the potential for automated classification of different cancer lesions. However, their lack of interpretability and explainability makes CNNs less than understandable. Furthermore, CNNs may incorrectly concentrate on other areas surrounding the salient object, rather than the network's attention focusing directly on the object to be recognized, as the network has no incentive to focus solely on the correct subjects to be detected. This inhibits the reliability of CNNs, especially for biomedical applications. Aim: Develop a deep learning training approach that could provide understandability to its predictions and directly guide the network to concentrate its attention and accurately delineate cancerous regions of the image. Approach: We utilized Selvaraju et al.'s gradient-weighted class activation mapping to inject interpretability and explainability into CNNs. We adopted a two-stage training process with data augmentation techniques and Li et al.'s guided attention inference network (GAIN) to train images captured using our customized mobile oral screening devices. The GAIN architecture consists of three streams of network training: classification stream, attention mining stream, and bounding box stream. By adopting the GAIN training architecture, we jointly optimized the classification and segmentation accuracy of our CNN by treating these attention maps as reliable priors to develop attention maps with more complete and accurate segmentation. Results: The network's attention map will help us to actively understand what the network is focusing on and looking at during its decision-making process. The results also show that the proposed method could guide the trained neural network to highlight and focus its attention on the correct lesion areas in the images when making a decision, rather than focusing its attention on relevant yet incorrect regions. Conclusions: We demonstrate the effectiveness of our approach for more interpretable and reliable oral potentially malignant lesion and malignant lesion classification. © The Authors.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]