We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. RESULTSAmong the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). CONCLUSIONSOn the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.) a bs tr ac t Early Transmission Dynamics

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ObjectiveTo study the GI symptoms in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients.DesignWe analysed epidemiological, demographic, clinical and laboratory data of 95 cases with SARS-CoV-2 caused coronavirus disease 2019. Real-time reverse transcriptase PCR was used to detect the presence of SARS-CoV-2 in faeces and GI tissues.ResultsAmong the 95 patients, 58 cases exhibited GI symptoms of which 11 (11.6%) occurred on admission and 47 (49.5%) developed during hospitalisation. Diarrhoea (24.2%), anorexia (17.9%) and nausea (17.9%) were the main symptoms with five (5.3%), five (5.3%) and three (3.2%) cases occurred on the illness onset, respectively. A substantial proportion of patients developed diarrhoea during hospitalisation, potentially aggravated by various drugs including antibiotics. Faecal samples of 65 hospitalised patients were tested for the presence of SARS-CoV-2, including 42 with and 23 without GI symptoms, of which 22 (52.4%) and 9 (39.1%) were positive, respectively. Six patients with GI symptoms were subjected to endoscopy, revealing oesophageal bleeding with erosions and ulcers in one severe patient. SARS-CoV-2 RNA was detected in oesophagus, stomach, duodenum and rectum specimens for both two severe patients. In contrast, only duodenum was positive in one of the four non-severe patients.ConclusionsGI tract may be a potential transmission route and target organ of SARS-CoV-2.

KRAS was recently identified to be potentially druggable by allele-specific covalent targeting of Cys-12 in vicinity to an inducible allosteric switch II pocket (S-IIP). Success of this approach requires active cycling of KRAS between its active-GTP and inactive-GDP conformations as accessibility of the S-IIP is restricted only to the GDP-bound state. This strategy proved feasible for inhibiting mutant KRAS in vitro; however, it is uncertain whether this approach would translate to in vivo. Here, we describe structure-based design and identification of ARS-1620, a covalent compound with high potency and selectivity for KRAS. ARS-1620 achieves rapid and sustained in vivo target occupancy to induce tumor regression. We use ARS-1620 to dissect oncogenic KRAS dependency and demonstrate that monolayer culture formats significantly underestimate KRAS dependency in vivo. This study provides in vivo evidence that mutant KRAS can be selectively targeted and reveals ARS-1620 as representing a new generation of KRAS-specific inhibitors with promising therapeutic potential.

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Graphene has the potential for high-speed, wide-band photodetection, but only with very low external quantum efficiency and no spectral selectivity. Here we report a dramatic enhancement of the overall quantum efficiency and spectral selectivity that enables multicolour photodetection, by coupling graphene with plasmonic nanostructures. We show that metallic plasmonic nanostructures can be integrated with graphene photodetectors to greatly enhance the photocurrent and external quantum efficiency by up to 1,500%. Plasmonic nanostructures of variable resonance frequencies selectively amplify the photoresponse of graphene to light of different wavelengths, enabling highly specific detection of multicolours. Being atomically thin, graphene photodetectors effectively exploit the local plasmonic enhancement effect to achieve a significant enhancement factor not normally possible with traditional planar semiconductor materials.

Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flowguided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong single-frame baselines in ImageNet VID [33], especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The proposed method, together with Deep Feature Flow [49], powered the winning entry of ImageNet VID challenges 2017 1 . The code is available at https://github.com/msracver/ Flow-Guided-Feature-Aggregation. * This work is done when Xizhou Zhu and Yujie Wang are interns at Microsoft Research Asia 1 http://image-net.org/challenges/LSVRC/2017/ results 1