From structure to function - Ligand recognition by myeloid C-type lectin receptors.

The relevance of protein-glycan interactions in immunity has long been underestimated. Yet, the immune system possesses numerous classes of glycan-binding proteins, so-called lectins. Of specific interest is the group of myeloid C-type lectin receptors (CLRs) as they are mainly expressed by myeloid cells and play an important role in the initiation of an immune response. Myeloid CLRs represent a major group amongst pattern recognition receptors (PRRs), placing them at the center of the rapidly growing field of glycoimmunology. CLRs have evolved to encompass a wide range of structures and functions and to recognize a large number of glycans and many other ligands from different classes of biopolymers. This review aims at providing the reader with an overview of myeloid CLRs and selected ligands, while highlighting recent insights into CLR-ligand interactions. Subsequently, methodological approaches in CLR-ligand research will be presented. Finally, this review will discuss how CLR-ligand interactions culminate in immunological functions, how glycan mimicry favors immune escape by pathogens, and in which way immune responses can be affected by CLR-ligand interactions in the long term.

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.