This copy is for personal use only. To order printed copies, contact reprints@rsna.org I n P r e s s Abbreviations: AUC = area under the receiver operating characteristic curve CI = confidence interval COVID-19 = coronavirus disease 2019 COVNet = COVID-19 detection neural network CAP = community acquired pneumonia DICOM = digital imaging and communications in medicine Key Results:A deep learning method was able to identify COVID-19 on chest CT exams (area under the receiver operating characteristic curve, 0.96).A deep learning method to identify community acquired pneumonia on chest CT exams (area under the receiver operating characteristic curve, 0.95).There is overlap in the chest CT imaging findings of all viral pneumonias with other chest diseases that encourages a multidisciplinary approach to the final diagnosis used for patient treatment. Summary Statement:Deep learning detects coronavirus disease 2019 (COVID-19) and distinguish it from community acquired pneumonia and other non-pneumonic lung diseases using chest CT. I n P r e s s Abstract:Background: Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT.Purpose: To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. Materials and Methods:In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results:The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). Conclusions:A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.

Sediment core ARC4-BN05 collected from the Canada Basin, Arctic Ocean, covers the late to middle Quaternary (Marine Isotope Stage -MIS -1-15, ca. 0.5-0.6 Ma) as estimated by correlation to earlier proposed Arctic Ocean stratigraphies and AMS 14 C dating of the youngest sediments. Detailed examination of clay and bulk mineralogy along with grain size, content of Ca and Mn, and planktic foraminiferal numbers in core ARC4-BN05 provides important new information about sedimentary environments and provenance. We use increased contents of coarse debris as an indicator of glacier collapse events at the margins of the western Arctic Ocean, and identify the provenance of these events from mineralogical composition. Notably, peaks of dolomite debris, including large dropstones, track the Laurentide Ice Sheet (LIS) discharge events to the Arctic Ocean. Major LIS inputs occurred during the stratigraphic intervals estimated as MIS 3, intra-MIS 5 and 7 events, MIS 8, and MIS 10. Inputs from the East Siberian Ice Sheet (ESIS) are inferred from peaks of smectite, kaolinite, and chlorite associated with coarse sediment. Major ESIS sedimentary events occurred in the intervals estimated as MIS 4, MIS 6 and MIS 12. Differences in LIS vs. ESIS inputs can be explained by ice-sheet configurations at different sea levels, sediment delivery mechanisms (iceberg rafting, suspension plumes, and debris flows), and surface circulation. A longterm change in the pattern of sediment inputs, with an apparent step change near the estimated MIS 7-8 boundary (ca. 0.25 Ma), presumably indicates an overall glacial expan-sion at the western Arctic margins, especially in North America.

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