The Upper Respiratory Tract of Felids Is Highly Susceptible to SARS-CoV-2 Infection

Natural or experimental infection of domestic cats and virus transmission from humans to captive predatory cats suggest that felids are highly susceptible to SARS-CoV-2 infection. However, it is unclear which cells and compartments of the respiratory tract are infected. To address this question, primary cell cultures derived from the nose, trachea, and lungs of cat and lion were inoculated with SARS-CoV-2. Strong viral replication was observed for nasal mucosa explants and tracheal air–liquid interface cultures, whereas replication in lung slices was less efficient. Infection was mainly restricted to epithelial cells and did not cause major pathological changes. Detection of high ACE2 levels in the nose and trachea but not lung further suggests that susceptibility of feline tissues to SARS-CoV-2 correlates with ACE2 expression. Collectively, this study demonstrates that SARS-CoV-2 can efficiently replicate in the feline upper respiratory tract ex vivo and thus highlights the risk of SARS-CoV-2 spillover from humans to felids.

Automatic Changes Detection between Outdated Building Maps and New VHR Images Based on Pre-Trained Fully Convolutional Feature Maps

Detecting changes between the existing building basemaps and newly acquired high spatial resolution remotely sensed (HRS) images is a time-consuming task. This is mainly because of the data labeling and poor performance of hand-crafted features. In this paper, for efficient feature extraction, we propose a fully convolutional feature extractor that is reconstructed from the deep convolutional neural network (DCNN) and pre-trained on the Pascal VOC dataset. Our proposed method extract pixel-wise features, and choose salient features based on a random forest (RF) algorithm using the existing basemaps. A data cleaning method through cross-validation and label-uncertainty estimation is also proposed to select potential correct labels and use them for training an RF classifier to extract the building from new HRS images. The pixel-wise initial classification results are refined based on a superpixel-based graph cuts algorithm and compared to the existing building basemaps to obtain the change map. Experiments with two simulated and three real datasets confirm the effectiveness of our proposed method and indicate high accuracy and low false alarm rate.

A New Approach to Detection of Changes in Multidimensional Patterns

Nowadays, unprecedented amounts of heterogeneous data collections are stored, processed and transmitted via the Internet. In data analysis one of the most important problems is to verify whether data observed or/and collected in time are genuine and stationary, i.e. the information sources did not change their characteristics. There is a variety of data types: texts, images, audio or video files or streams, metadata descriptions, thereby ordinary numbers. All of them changes in many ways. If the change happens the next question is what is the essence of this change and when and where the change has occurred. The main focus of this paper is detection of change and classification of its type. Many algorithms have been proposed to detect abnormalities and deviations in the data. In this paper we propose a new approach for abrupt changes detection based on the Parzen kernel estimation of the partial derivatives of the multivariate regression functions in presence of probabilistic noise. The proposed change detection algorithm is applied to oneand two-dimensional patterns to detect the abrupt changes.