Biology subjects

Xiao, S.

Publications and source records attributed to Xiao, S..

130 records · Page 8Linked to original sources

bioRxiv2018

Genome-wide characterization, evolutionary analysis of WRKY genes in Cucurbitaceae species and assessment of its roles in resisting to powdery mildew disease

The WRKY proteins constitute a large family of transcription factors that have been known to play a wide range of regulatory roles in multiple biological processes. Over the past few years, many reports have focused on analysis of evolution and biological function of WRKY genes at the whole genome level in different plant species. However, little information is known about WRKY genes in melon (Cucumis melo L.). In the present study, a total of 56 putative WRKY genes were identified in melon, which were randomly distributed on their respective chromosomes. A multiple sequence alignment and phylogenetic analysis using melon, cucumber and watermelon predicted WRKY domains indicated that melon WRKY proteins could be classified into three main groups (I-III). Our analysis indicated that no recent duplication events of WRKY genes were detected in melon, and strong purifying selection was observed among the 85 orthologous pairs of Cucurbitaceae species. Expression profiles of CmWRKY derived from RNA-seq data and quantitative RT-PCR (qRT-PCR) analyses showed distinct expression patterns in various tissues, and the expression of 16 CmWRKY were altered following powdery mildew infection in melon. Besides, we also found that a total of 24 WRKY genes were co-expressed with 11 VQ family genes in melon. Our comparative genomic analysis provides a foundation for future functional dissection and understanding the evolution of WRKY genes in cucurbitaceae species, and will promote powdery mildew resistance study in melon.

plant biology↗

bioRxiv2018

Forecasting Future Humphrey Visual Fields Using Deep Learning

PurposeTo determine if deep learning networks could be trained to forecast a future 24-2 Humphrey Visual Field (HVF).\n\nDesignRetrospective database study.\n\nParticipantsAll patients who obtained a HVF 24-2 at the University of Washington.\n\nMethodsAll datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington database. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction.\n\nMain outcome measuresMean absolute error (MAE) and difference in Mean Deviation (MD) between predicted and actual future HVF.\n\nResultsMore than 1.7 million perimetry points were extracted to the hundredth decibel from 32,443 24-2 HVFs. The best performing model with 20 million trainable parameters, CascadeNet-5, was selected. The overall MAE for the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB). The 100 fully trained models were able to successfully predict progressive field loss in glaucomatous eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of predicted and actual future HVF (p < 2.2 = 10-16) and an average difference of 0.41 dB.\n\nConclusionsUsing unfiltered real-world datasets, deep learning networks show an impressive ability to not only learn spatio-temporal HVF changes but also to generate predictions for future HVFs up to 5.5 years, given only a single HVF.

bioinformatics↗

bioRxiv2018

MRI2MRI: A deep convolutional network that accurately transforms between brain MRI contrasts

Different brain MRI contrasts represent different tissue properties and are sensitive to different artifacts. The relationship between different contrasts is therefore complex and nonlinear. We developed a deep convolutional network that learns the mapping between different MRI contrasts. Using a publicly available dataset, we demonstrate that this algorithm accurately transforms between T1- and T2-weighted images, proton density images, time-of-flight angiograms, and diffusion MRI images. We demonstrate that these transformed images can be used to improve spatial registration between MR images of different contrasts.

neuroscience↗

bioRxiv2018

Generating perfusion maps from structural optical coherence tomography with artificial intelligence

Despite advances in artificial intelligence (AI), its application in medical imaging has been burdened and limited by expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures retinal blood flow, to train an AI algorithm to generate flow maps from standard optical coherence tomography (OCT) images, exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer flow from single structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (P < 0.00001). Our model allows generating flow maps from large volumes of previously collected OCT data in existing clinical trials and clinical practice. This finding demonstrates a novel application of AI to medical imaging, whereby subtle regularities between different modalities are used to image the same body part and AI is used to generate detailed inferences of tissue function from structure imaging.

neuroscience↗