Highly Anisotropic Electronic and Mechanical Properties of Monolayer and Bilayer As2S3

Anisotropic materials, with orientation-dependent properties, have attracted more and more attention due to their compelling tunable and flexible performance in electronic and optomechanical devices. So far, two-dimensional (2D) black phosphorus shows the largest known anisotropic behavior, which is highly desired for synaptic and neuromorphic devices, multifunctional directional memories, and even polarization-sensitive photodetector, whereas it is unstable at ambient conditions. Recently, 2D few-layered As2S3 with superior chemical stability was successfully exfoliated in experiments. However, the electronic and mechanical properties of monolayer and bilayer As2S3 is still lacking. Here, we report the large anisotropic electronic and mechanical properties of As2S3 systems through first-principles calculations and general angle-dependent Hookes law. Monolayer and bilayer As2S3 exhibit anisotropic factors of Youngs modulus of 3.15 and 3.32, respectively, which are larger than the black phosphorous with experimentally confirmed and an anisotropic factor of 2. This study provides an effective route to flexible orientation-dependent nanoelectronics, nanomechanics, and offers implications in promoting related experimental investigations.

Calibrated Intervention and Containment of the COVID-19 Pandemic

Within a short period of time, COVID-19 grew into a world-wide pandemic. Transmission by pre-symptomatic and asymptomatic viral carriers rendered intervention and containment of the disease extremely challenging. Based on reported infection case studies, we construct an epidemiological model that focuses on transmission around the symptom onset. The model is calibrated against incubation period and pairwise transmission statistics during the initial outbreaks of the pandemic outside Wuhan with minimal non-pharmaceutical interventions. Mathematical treatment of the model yields explicit expressions for the size of latent and pre-symptomatic subpopulations during the exponential growth phase, with the local epidemic growth rate as input. We then explore reduction of the basic reproduction number R_0 through specific disease control measures such as contact tracing, testing, social distancing, wearing masks and sheltering in place. When these measures are implemented in combination, their effects on R_0 multiply. We also compare our model behaviour to the first wave of the COVID-19 spreading in various affected regions and highlight generic and less generic features of the pandemic development.

Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured information of protein entities and their relations, which can be encoded in entity and relation embeddings to help PPI extraction. However, the prior knowledge of protein-protein pairs must be selectively used so that it is suitable for different contexts. This paper proposes a Knowledge Selection Model (KSM) to fuse the selected prior knowledge and context information for PPI extraction. Firstly, two Transformers encode the context sequence of a protein pair according to each protein embedding, respectively. Then, the two outputs are fed to a mutual attention to capture the important context features towards the protein pair. Next, the context features are used to distill the relation embedding by a knowledge selector. Finally, the selected relation embedding and the context features are concatenated for PPI extraction. Experiments on the BioCreative VI PPI dataset show that KSM achieves a new state-of-the-art performance (38.08% F1-score) by adding knowledge selection.