No abstract

S evere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread globally and, as of May 2, 2020, had caused >3 million confirmed coronavirus disease cases (1). Although SARS-CoV-2 transmission through respiratory droplets and direct contact is clear, the potential for transmission through contact with surfaces or objects contaminated with SARS-CoV-2 is poorly understood (2). The virus can be detected on various surfaces in the contaminated environment from symptomatic and paucisymptomatic patients (3,4). Moreover, we recently reported detection of SARS-CoV-2 RNA on environmental surfaces of a symptomatic patient's household (5). Because SARS-CoV-2 remains viable and infectious from hours to days on surfaces (6,7), contact with a contaminated surface potentially could be a medium for virus transmission. In addition, high viral load in throat swab specimens at symptom onset (8,9) and peak infectiousness at 0-2 days for presymptomatic patients (8) suggest that presymptomatic patients may easily contaminate the environment. However, data are limited on environmental contamination of SARS-CoV-2 by patients who may be presymptomatic. Therefore, to test this hypothesis, we examined the presence of SARS-CoV-2 RNA in collected environmental surface swab specimens from 2 rooms of a centralized quarantine hotel where 2 presymptomatic patients had stayed. The Study Two Chinese students studying overseas returned to China on March 19 (patient A) and March 20 (patient B), 2020 (Table 1). On the day of their arrival in China, neither had fever or clinical symptoms, and they were transferred to a hotel for a 14-day quarantine. They had normal body temperatures (patient A, 36.3°C; patient B, 36.5°C) and no symptoms when they checked into the hotel. During the quarantine period, local medical staff were to monitor their body temperature and symptoms each morning and afternoon. On the morning of the second day of quarantine, they had no fever (patient A, 36.2°C; patient B, 36.7°C) or symptoms. At the same time their temperatures were taken, throat swab samples were collected; both tested positive for SARS-CoV-2 RNA by real-time reverse transcription PCR (rRT-PCR). The students were transferred to a local hospital for treatment. At admission, they remained presymptomatic, but nasopharyngeal swab, sputum, and fecal samples were positive for SARS-CoV-2 RNA with high viral loads (Table 1). In patient A, fever (37.5°C) and cough developed on day 2 of hospitalization, but his chest computed tomography images showed no significant abnormality during hospitalization. In patient B, fever (37.9°C) and cough developed on day 6 of

Provided here is evidence showing that the stacking between triplet chromophores plays a critical role in ultralong organic phosphorescence (UOP) generation within a crystal. By varying the structure of a functional unit, and different on‐off UOP behavior was observed for each structure. Remarkably, 24CPhCz, having the strongest intermolecular interaction between carbazole units exhibited the most impressive UOP with a long lifetime of 1.06 s and a phosphorescence quantum yield of 2.5 %. 34CPhCz showed dual‐emission UOP and thermally activated delayed fluorescence (TADF) with a moderately decreased phosphorescence lifetime of 770 ms, while 35CPhCz only displayed TADF owing to the absence of strong electronic coupling between triplet chromophores. This study provides an explanation for UOP generation in crystal and new guidelines for obtaining UOP materials.

Portable cameras record dynamic first-person video footage and these videos contain information on the motion of the individual to whom the camera is mounted, defined as ego. We address the task of discovering ego-motion from the video itself, without other external calibration information. We investigate the use of similarity transformations between successive video frames to extract signals reflecting ego-motions and their frequencies. We use novel graph-based unsupervised and semi-supervised learning algorithms to segment the video frames into different ego-motion categories. Our results show very accurate results on both choreographed test videos and ego-motion videos provided by the Los Angeles Police Department.

Hyperspectral imagery is a challenging modality due to the dimension of the pixels which can range from hundreds to over a thousand frequencies depending on the sensor. Most methods in the literature reduce the dimension of the data using a method such as principal component analysis, however this procedure can lose information. More recently methods have been developed to address classification of large datasets in high dimensions. This paper presents two classes of graph-based classification methods for hyperspectral imagery. Using the full dimensionality of the data, we consider a similarity graph based on pairwise comparisons of pixels. The graph is segmented using a pseudospectral algorithm for graph clustering that requires information about the eigenfunctions of the graph Laplacian but does not require computation of the full graph. We develop a parallel version of the Nyström extension method to randomly sample the graph to construct a low rank approximation of the graph Laplacian. With at most a few hundred eigenfunctions, we can implement the clustering method designed to solve a variational problem for a graph-cut-based semi-supervised or unsupervised classification problem. We implement OpenMP directive-based parallelism in our algorithms and show performance improvement and strong, almost ideal, scaling behavior. The method can handle very large datasets including a video sequence with over a million pixels, and the problem of segmenting a data set into a pre-determined number of classes. Source CodeThe reviewed source code and documentation for this algorithm are available from the web page of this article 1 . Compilation and usage instructions are included in the README.txt file of the archive.