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General General

Emerging SARS-CoV-2 diversity revealed by rapid whole genome sequence typing.

In bioRxiv : the preprint server for biology

Background : Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events.

Methods : We developed a tool (GNUVID) that integrates whole genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to each of 69,686 SARS-CoV-2 complete, high-quality genomes available from GISAID as of October 20 th 2020. STs were then clustered into clonal complexes (CCs), and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events, and to estimate effective viral diversity across locations and over time in 16 US states.

Results : GNUVID is a scalable tool for viral genotype classification (available at https://github.com/ahmedmagds/GNUVID ) that can be used to quickly process tens of thousands of genomes. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states. We detected an average of 20.6 putative introductions and 7.5 exportations for each state. Effective viral diversity dropped in all states as shelter-in-place travel-restrictions went into effect and increased as restrictions were lifted. Interestingly, our analysis showed correlation between effective diversity and the date that state-wide mask mandates were imposed.

Conclusions : Our classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. Combined with future genomic sampling the GNUVID system could be used to track circulating viral diversity and identify emerging clones and hotspots.

Moustafa Ahmed M, Planet Paul J

2020-Dec-28

Radiology Radiology

Automated detection of cerebral microbleeds in MR images: A two-stage deep learning approach.

In NeuroImage. Clinical

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. However, automated detection and identification of CMBs in Magnetic Resonance (MR) images is a very challenging task due to their wide distribution throughout the brain, small sizes, and the high degree of visual similarity between CMBs and CMB mimics such as calcifications, irons, and veins. In this paper, we propose a fully automated two-stage integrated deep learning approach for efficient CMBs detection, which combines a regional-based You Only Look Once (YOLO) stage for potential CMBs candidate detection and three-dimensional convolutional neural networks (3D-CNN) stage for false positives reduction. Both stages are conducted using the 3D contextual information of microbleeds from the MR susceptibility-weighted imaging (SWI) and phase images. However, we average the adjacent slices of SWI and complement the phase images independently and utilize them as a two-channel input for the regional-based YOLO method. This enables YOLO to learn more reliable and representative hierarchal features and hence achieve better detection performance. The proposed work was independently trained and evaluated using high and low in-plane resolution data, which contained 72 subjects with 188 CMBs and 107 subjects with 572 CMBs, respectively. The results in the first stage show that the proposed regional-based YOLO efficiently detected the CMBs with an overall sensitivity of 93.62% and 78.85% and an average number of false positives per subject (FPavg) of 52.18 and 155.50 throughout the five-folds cross-validation for both the high and low in-plane resolution data, respectively. These findings outperformed results by previously utilized techniques such as 3D fast radial symmetry transform, producing fewer FPavg and lower computational cost. The 3D-CNN based second stage further improved the detection performance by reducing the FPavg to 1.42 and 1.89 for the high and low in-plane resolution data, respectively. The outcomes of this work might provide useful guidelines towards applying deep learning algorithms for automatic CMBs detection.

Al-Masni Mohammed A, Kim Woo-Ram, Kim Eung Yeop, Noh Young, Kim Dong-Hyun

2020

CNNs, Cerebral microbleeds, Computer-aided detection, Deep learning, Susceptibility-weighted imaging, YOLO

Radiology Radiology

Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19.

In Diagnostics (Basel, Switzerland)

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.

Guiot Julien, Vaidyanathan Akshayaa, Deprez Louis, Zerka Fadila, Danthine Denis, Frix Anne-Noëlle, Thys Marie, Henket Monique, Canivet Gregory, Mathieu Stephane, Eftaxia Evanthia, Lambin Philippe, Tsoutzidis Nathan, Miraglio Benjamin, Walsh Sean, Moutschen Michel, Louis Renaud, Meunier Paul, Vos Wim, Leijenaar Ralph T H, Lovinfosse Pierre

2020-Dec-30

COVID-19, artificial intelligence, computed tomography, machine learning, radiomics

General General

DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes.

In Nature methods ; h5-index 152.0

Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 106 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console ; a persistent deployment is available at https://deepcell.org/ .

Bannon Dylan, Moen Erick, Schwartz Morgan, Borba Enrico, Kudo Takamasa, Greenwald Noah, Vijayakumar Vibha, Chang Brian, Pao Edward, Osterman Erik, Graf William, Van Valen David

2021-Jan

oncology Oncology

Liver metastasis restrains immunotherapy efficacy via macrophage-mediated T cell elimination.

In Nature medicine ; h5-index 170.0

Metastasis is the primary cause of cancer mortality, and cancer frequently metastasizes to the liver. It is not clear whether liver immune tolerance mechanisms contribute to cancer outcomes. We report that liver metastases diminish immunotherapy efficacy systemically in patients and preclinical models. Patients with liver metastases derive limited benefit from immunotherapy independent of other established biomarkers of response. In multiple mouse models, we show that liver metastases siphon activated CD8+ T cells from systemic circulation. Within the liver, activated antigen-specific Fas+CD8+ T cells undergo apoptosis following their interaction with FasL+CD11b+F4/80+ monocyte-derived macrophages. Consequently, liver metastases create a systemic immune desert in preclinical models. Similarly, patients with liver metastases have reduced peripheral T cell numbers and diminished tumoral T cell diversity and function. In preclinical models, liver-directed radiotherapy eliminates immunosuppressive hepatic macrophages, increases hepatic T cell survival and reduces hepatic siphoning of T cells. Thus, liver metastases co-opt host peripheral tolerance mechanisms to cause acquired immunotherapy resistance through CD8+ T cell deletion, and the combination of liver-directed radiotherapy and immunotherapy could promote systemic antitumor immunity.

Yu Jiali, Green Michael D, Li Shasha, Sun Yilun, Journey Sara N, Choi Jae Eun, Rizvi Syed Monem, Qin Angel, Waninger Jessica J, Lang Xueting, Chopra Zoey, El Naqa Issam, Zhou Jiajia, Bian Yingjie, Jiang Long, Tezel Alangoya, Skvarce Jeremy, Achar Rohan K, Sitto Merna, Rosen Benjamin S, Su Fengyun, Narayanan Sathiya P, Cao Xuhong, Wei Shuang, Szeliga Wojciech, Vatan Linda, Mayo Charles, Morgan Meredith A, Schonewolf Caitlin A, Cuneo Kyle, Kryczek Ilona, Ma Vincent T, Lao Christopher D, Lawrence Theodore S, Ramnath Nithya, Wen Fei, Chinnaiyan Arul M, Cieslik Marcin, Alva Ajjai, Zou Weiping

2021-Jan

General General

DeepSleep convolutional neural network allows accurate and fast detection of sleep arousal.

In Communications biology

Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with symptoms such as sympathetic activation, non-restorative sleep, and daytime sleepiness. Currently, sleep arousals are mainly annotated by human experts through looking at 30-second epochs (recorded pages) manually, which requires considerable time and effort. Here we present a deep learning approach for automatically segmenting sleep arousal regions based on polysomnographic recordings. Leveraging a specific architecture that 'translates' input polysomnographic signals to sleep arousal labels, this algorithm ranked first in the "You Snooze, You Win" PhysioNet Challenge. We created an augmentation strategy by randomly swapping similar physiological channels, which notably improved the prediction accuracy. Our algorithm enables fast and accurate delineation of sleep arousal events at the speed of 10 seconds per sleep recording. This computational tool would greatly empower the scoring process in clinical settings and accelerate studies on the impact of arousals.

Li Hongyang, Guan Yuanfang

2021-Jan-04