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Aoyama, Y.

Publications and source records attributed to Aoyama, Y..

2 recordsLinked to original sources

Geographic divergence and the genomic basis of reproductive diapause in Drosophila triauraria

Adjusting reproduction timing to environmental cues is essential for lifetime fitness. In many insects, reproductive diapause shows clinal variation along environmental gradients such as photoperiod and temperature. How such continuous trait variation may be encoded at the molecular level and maintained in the presence of gene flow remains largely elusive. The fruit fly Drosophila triauraria is distributed across a wide latitudinal range of the Japanese archipelago. Northern strains exhibit a strong photoperiodic reproductive diapause in females, whereas southern strains fail to arrest ovarian development even under short-day conditions at low temperatures. These distinct phenotypes and the presumable clinal variation in between, provide an ideal opportunity to examine the molecular basis of latitudinal divergence. We first investigated diapause induction in both females and males from previously reported and newly tested strains collected from the regions spanning [~]26-43{degrees}N along the Japanese archipelago. The assessment revealed continuous geographic variation in sensitivity to photoperiod and temperature. We then analyzed the whole-genome sequences of 21 strains, including 14 newly sequenced, to identify genomic regions underlying this divergence. In addition to the conventional FST analysis, we applied a "monophyletic window" approach suitable for limited sample sizes. The analysis identified a candidate region containing putative E-box and TER-box sequence motifs of the timeless (tim) gene, which has been previously implicated in diapause regulation in multiple insect species. The quantitative PCR analysis further supported a partial association between the tim expression and the incidence of female diapause. These findings reinforce the growing evidence for a role of circadian clock genes in the adaptive regulation of reproductive diapause and demonstrate the utility of tree-based approaches for detecting genomic regions of geographic divergence.

genetics↗

Diagnosis of central serous chorioretinopathy by deep learning analysis of en face images of choroidal vasculature

PurposeTo classify central serous chorioretinopathy (CSC) by deep learning (DL) analyses of en face images of choroidal vasculature obtained by optical coherence tomography (OCT) and to analyze the regions of interest for DL from heatmaps. MethodsOne-hundred eyes were studied; 53 eyes with CSC and 47 normal eyes. Volume scans of 12x12 mm square were obtained at the same time as the OCT angiographic (OCTA) scans (Plex Elite 9000 Swept-Source OCT(R), Zeiss). High-quality en face images of the choroidal vasculature of the segmentation slab of one-half of the subfoveal choroidal thickness were created for the analyses. The entire 100 en face images were divided into 80 for training (100 times) and 20 for validation. The Neural Network Console (NNC) developed by Sony and the Keras-Tensorflow backend developed by Google were used as the software for the classification with 16 layers of convolutional neural networks. The active region of the heatmap based on the feature quantity extracted by DL was also evaluated as the percentages with gradient-weighted class activation mapping implementation in Keras. ResultsIn the 20 eyes used for validation including 8 eyes with CSC, the accuracy rate of the validation was 100% (20/20) for NNC and 95% (19/20) for Keras. This difference was not significant (P=0.33). The mean active region in the heatmap image was 12.5% in CSC eyes which was significantly lower than the 79.8% in normal eyes (P<0.01). ConclusionsCSC can be automatically classified with high accuracy from en face images of the choroidal vasculature by DLs with different programs, convolutional layer structures, and small data sets. Heatmap analyses showed that DL focused on the area occupied by the choroidal vessels and their uniformity. We conclude that DL can help in the diagnosis of CSC.

biophysics↗