The novel coronavirus outbreak (COVID-19) in mainland China has rapidly spread across the globe. Within 2 mo since the outbreak was first reported on December 31, 2019, a total of 566 Severe Acute Respiratory Syndrome (SARS CoV-2) cases have been confirmed in 26 other countries. Travel restrictions and border control measures have been enforced in China and other countries to limit the spread of the outbreak. We estimate the impact of these control measures and investigate the role of the airport travel network on the global spread of the COVID-19 outbreak. Our results show that the daily risk of exporting at least a single SARS CoV-2 case from mainland China via international travel exceeded 95% on January 13, 2020. We found that 779 cases (95% CI: 632 to 967) would have been exported by February 15, 2020 without any border or travel restrictions and that the travel lockdowns enforced by the Chinese government averted 70.5% (95% CI: 68.8 to 72.0%) of these cases. In addition, during the first three and a half weeks of implementation, the travel restrictions decreased the daily rate of exportation by 81.3% (95% CI: 80.5 to 82.1%), on average. At this early stage of the epidemic, reduction in the rate of exportation could delay the importation of cases into cities unaffected by the COVID-19 outbreak, buying time to coordinate an appropriate public health response.

The realization of graphene gapped states with large on/off ratios over wide doping ranges remains challenging. Here, we investigate heterostructures based on Bernal-stacked bilayer graphene (BLG) atop few-layered CrOCl, exhibiting an over-1-GΩ-resistance insulating state in a widely accessible gate voltage range. The insulating state could be switched into a metallic state with an on/off ratio up to 10⁷by applying an in-plane electric field, heating, or gating. We tentatively associate the observed behavior to the formation of a surface state in CrOCl under vertical electric fields, promoting electron–electron (e–e) interactions in BLG via long-range Coulomb coupling. Consequently, at the charge neutrality point, a crossover from single particle insulating behavior to an unconventional correlated insulator is enabled, below an onset temperature. We demonstrate the application of the insulating state for the realization of a logic inverter operating at low temperatures. Our findings pave the way for future engineering of quantum electronic states based on interfacial charge coupling.

Umbelopsis ramanniana is one of the most commonly reported species within the genus and an important oleaginous fungus. The morphology of the species varies remarkably in sporangiospores, columellae and chlamydospores. However, phylogenetic analyses based on ITS and nLSU rDNA had previously shown insufficiency in achieving species level identification in the genus Umbelopsis. In this study, by applying a polyphasic approach involving multi-gene (nSSU, ITS, nLSU, act1, MCM7 and cox1) phylogeny, morphology and maximum growth temperature, U. ramanniana sensu lato was revealed as a polyphyletic group and resolved with five novel taxa, namely U. curvata, U. dura, U. macrospora, U. microsporangia and U. oblongielliptica. Additionally, a key for all currently accepted species in Umbelopsis was also updated.

The coverless steganography methods based on the GAN satisfy the need of large image set which is a shortcoming for traditional coverless methods. But most of GAN based methods have either low hiding capacity or weak robustness. In this paper, to solve these problems we design a coverless image steganography framework based on multi-domain image translation called MDI. The target label used to translation is built a relationship with secret message. This framework includes a generator and a multi-label classifier. Generator is used to translate cover image into stego image according to target label. Classifier is adopted to extract label in the image and obtain secret message. Moreover, common types of image attacks are considered in advance to improve the robustness of classifier. Finally, our experiments and discussion show our method has more robustness and higher hiding capacity than state of art coverless image steganography methods.

During studies on the diversity of Mucorales associated with traditional fermentation food from China, two Mucor strains were isolated from the surface of Mao-tofu, a fermented soybean food. Phylogenetic analyses of the internal transcribed spacer (ITS) and the large subunit (LSU) rDNA sequences revealed that the new isolates belong to the M. mucedo group and are closely related to M. mucedo. However, they differ from other species of this group by producing swollen, tall sporangiophores, sporangia measuring 40–260 µm and irregular sporangiospores. The maximum growth temperature of the new isolates is 33 °C, which is higher than other species in the M. mucedo group. Based on the combination of morphological characteristics, growth experiments and molecular phylogenetics, a new species Mucor tofus is proposed. Detailed phylogenetic trees, descriptions and illustrations are provided.

Aiming at the classification of indoor scene images, a multi-modal fusion model is proposed. Firstly, based on the scene image and its semantic description, a single-modal classification model is constructed. For scene images, a convolutional neural network is used to extract features and train classification models. For scene semantic descriptions, a recurrent neural network is used to extract text features. A scene feature space is then constructed and the semantic descriptions are embedded to it to obtain classification results. Secondly, these two kinds of single-modal features are fused and input to a deep neural network after dimensionality reduction, a feature-level fusion model is trained. Finally, two single-modal models and the feature-level fusion model are given different weights to construct a hybrid fusion model, the weights are constantly adjusted to get the best classification accuracy.