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## Effective machine-learning assembly for next-generation amplicon sequencing with very low coverage.

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## Endogenetic structure of filter bubble in social networks.

#### In Royal Society open science ; h5-index 0.0 The filter bubble is an intermediate structure to provoke polarization and echo chambers in social networks, and it has become one of today's most urgent issues for social media. Previous studies usually equated filter bubbles with community structures and emphasized this exogenous isolation effect, but there is a lack of full discussion of the internal organization of filter bubbles. Here, we design an experiment for analysing filter bubbles taking advantage of social bots. We deployed 128 bots to Weibo (the largest microblogging network in China), and each bot consumed a specific topic (entertainment or sci-tech) and ran for at least two months. In total, we recorded about 1.3 million messages exposed to these bots and their social networks. By analysing the text received by the bots and motifs in their social networks, we found that a filter bubble is not only a dense community of users with the same preferences but also presents an endogenetic unidirectional star-like structure. The structure could spontaneously exclude non-preferred information and cause polarization. Moreover, our work proved that the felicitous use of artificial intelligence technology could provide a useful experimental approach that combines privacy protection and controllability in studying social media.Min Yong, Jiang Tingjun, Jin Cheng, Li Qu, Jin Xiaogang2019-Novcontrolled experiment, echo chamber, polarization, privacy protection, social network

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## Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy.

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## Predicting Long-Term Health-Related Quality of Life after Bariatric Surgery Using a Conventional Neural Network: A Study Based on the Scandinavian Obesity Surgery Registry.

#### In Journal of clinical medicine ; h5-index 0.0 Severe obesity has been associated with numerous comorbidities and reduced health-related quality of life (HRQoL). Although many studies have reported changes in HRQoL after bariatric surgery, few were long-term prospective studies. We examined the performance of the convolution neural network (CNN) for predicting 5-year HRQoL after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg). CNN was used to predict the 5-year HRQoL after bariatric surgery in a training dataset and evaluated in a test dataset. In general, performance of the CNN model (measured as mean squared error, MSE) increased with more convolution layer filters, computation units, and epochs, and decreased with a larger batch size. The CNN model showed an overwhelming advantage in predicting all the HRQoL measures. The MSEs of the CNN model for training data were 8% to 80% smaller than those of the linear regression model. When the models were evaluated using the test data, the CNN model performed better than the linear regression model. However, the issue of overfitting was apparent in the CNN model. We concluded that the performance of the CNN is better than the traditional multivariate linear regression model in predicting long-term HRQoL after bariatric surgery; however, the overfitting issue needs to be mitigated using more features or more patients to train the model.Cao Yang, Raoof Mustafa, Montgomery Scott, Ottosson Johan, Näslund Ingmar2019-Dec-05bariatric surgery, conventional neural network, deep learning, health-related quality of life, prediction

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## SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells.

#### In Bioinformatics (Oxford, England) ; h5-index 0.0 MOTIVATION : Spatial transcriptomics technology is increasingly being applied because it enables the measurement of spatial gene expression in an intact tissue along with imaging morphology of the same tissue. However, current analysis methods for spatial transcriptomics data do not use image pixel information, thus missing the quantitative links between gene expression and tissue morphology.RESULTS : We developed a user-friendly deep learning software, SpaCell, to integrate millions of pixel intensity values with thousands of gene expression measurements from spatially-barcoded spots in a tissue. We show the integration approach outperforms the use of gene-count data alone or imaging data alone to build deep learning models to identify cell types or predict labels of tissue images with high resolution and accuracy.AVAILABILITY : The SpaCell package is open source under a MIT license and it is available at https://github.com/BiomedicalMachineLearning/SpaCell.SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.Tan Xiao, Su Andrew, Tran Minh, Nguyen Quan2019-Dec-06

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## Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks.

#### In Annals of biomedical engineering ; h5-index 52.0 Twin-to-Twin Transfusion Syndrome is commonly treated with minimally invasive laser surgery in fetoscopy. The inter-foetal membrane is used as a reference to find abnormal anastomoses. Membrane identification is a challenging task due to small field of view of the camera, presence of amniotic liquid, foetus movement, illumination changes and noise. This paper aims at providing automatic and fast membrane segmentation in fetoscopic images. We implemented an adversarial network consisting of two Fully-Convolutional Neural Networks. The former (the segmentor) is a segmentation network inspired by U-Net and integrated with residual blocks, whereas the latter acts as critic and is made only of the encoding path of the segmentor. A dataset of 900 images acquired in 6 surgical cases was collected and labelled to validate the proposed approach. The adversarial networks achieved a median Dice similarity coefficient of 91.91% with Inter-Quartile Range (IQR) of 4.63%, overcoming approaches based on U-Net (82.98%-IQR: 14.41%) and U-Net with residual blocks (86.13%-IQR: 13.63%). Results proved that the proposed architecture could be a valuable and robust solution to assist surgeons in providing membrane identification while performing fetoscopic surgery.Casella Alessandro, Moccia Sara, Frontoni Emanuele, Paladini Dario, De Momi Elena, Mattos Leonardo S2019-Dec-05Adversarial networks, Deep learning, Fetoscopy, Intraoperative-image segmentation

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