No abstract

Highlights The disruption of the host immune system is the characteristic of the outbreak of COVID-19. Identifying the regulating mechanism of virus behavior will help design antiviral vaccine candidates. SARS-CoV-2 vaccines is critical to reduce morbidity and mortality.

Visceral leishmaniasis (VL) is endemic in Northwest and southern Iran. Reports of cutaneous leishmaniasis (CL) in Northwest areas are rare, and its etiological agents are unknown. In the current study, we report six CL and two post kala-azar dermal leishmaniasis (PKDL) cases caused by Leishmania infantum from endemic areas of VL in the Northwest. Smears were made from skin lesions of 30 suspected patients in 2002-2011, and CL was determined by microscopy or culture. Leishmania spp. were identified by nested-PCR assay. The disease was confirmed in 20 out of 30 (66%) suspected patients by parasitological examinations. L. infantum was identified in eight and Leishmania major in 12 CL cases by nested-PCR. Cutaneous leishmaniasis patients infected with L. major had the history of travel to CL endemic areas. L. infantum antibodies were detected by direct agglutination test (DAT) at titers of 1:3200 in two cases with history of VL. Results of this study indicated that L. infantum is a causative agent of CL as well as PKDL in the VL endemic areas.

There are many real-world complex systems with multi-type interacting entities that can be regarded as heterogeneous networks including human connections and biological evolutions. One of the main issues in such networks is to predict information diffusion such as shape, growth and size of social events and evolutions in the future. While there exist a variety of works on this topic mainly using a threshold-based approach, they suffer from the local viewpoint on the network and sensitivity to the threshold parameters. In this paper, information diffusion is considered through a latent representation learning of the heterogeneous networks to encode in a deep learning model. To this end, we propose a novel meta-path representation learning approach, Heterogeneous Deep Diffusion(HDD), to exploit meta-paths as main entities in networks. At first, the functional heterogeneous structures of the network are learned by a continuous latent representation through traversing meta-paths with the aim of global end-to-end viewpoint. Then, the wellknown deep learning architectures are employed on our generated features to predict diffusion processes in the network. The proposed approach enables us to apply it on different information diffusion tasks such as topic diffusion and cascade prediction. We demonstrate the proposed approach on benchmark network datasets through the well-known evaluation measures. The experimental results show that our approach outperforms the earlier state-of-the-art methods.

This Time series mining is a new area of research in temporal databases and has been an active area of research with its notable recent progress. Event prediction is one of the main goals of time series mining which have important roles for appropriate decision making in different application area. So far, different studies have been presented in the field of time series mining for meaningful events prediction, which have ample challenges. Lack of systematic identification of challenges causes some obstacles for the development of methods. In this paper, due to the abundance and diversity of challenges in event prediction system on time series, lack of a perfect platform for their systematic identification and removing barriers for the development of methods, a classification is proposed for challenging problems of event prediction system on time series. Also, reviewed and analyzed the application of data mining techniques for solving different challenges in event prediction system on time series. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a comparison structure that can map data mining techniques into the event prediction challenges. The proposed classification of this paper by introducing systematic challenges can help create different research pivots for the elimination of challenges in different areas of applying and developing methods. We think that this paper can help researchers in event prediction systems on time series for the future activities.