BackgroundDengue is an important medical problem, with symptoms ranging from mild dengue fever to severe forms of the disease, where vascular leakage leads to hypovolemic shock. Cytokines have been implicated to play a role in the progression of severe dengue disease; however, their profile in dengue patients and the synergy that leads to continued plasma leakage is not clearly understood. Herein, we investigated the cytokine kinetics and profiles of dengue patients at different phases of illness to further understand the role of cytokines in dengue disease.Methods and FindingsCirculating levels of 29 different types of cytokines were assessed by bead-based ELISA method in dengue patients at the 3 different phases of illness. The association between significant changes in the levels of cytokines and clinical parameters were analyzed. At the febrile phase, IP-10 was significant in dengue patients with and without warning signs. However, MIP-1β was found to be significant in only patients with warning signs at this phase. IP-10 was also significant in both with and without warning signs patients during defervescence. At this phase, MIP-1β and G-CSF were significant in patients without warning signs, whereas MCP-1 was noted to be elevated significantly in patients with warning signs. Significant correlations between the levels of VEGF, RANTES, IL-7, IL-12, PDGF and IL-5 with platelets; VEGF with lymphocytes and neutrophils; G-CSF and IP-10 with atypical lymphocytes and various other cytokines with the liver enzymes were observed in this study.ConclusionsThe cytokine profile patterns discovered between the different phases of illness indicate an essential role in dengue pathogenesis and with further studies may serve as predictive markers for progression to dengue with warning signs.

The rapid mutation of human immunodeficiency virus-type 1 (HIV-1) and the limited characterization of the composition and incidence of the variant population are major obstacles to the development of an effective HIV-1 vaccine. This issue was addressed by a comprehensive analysis of over 58,000 clade B HIV-1 protein sequences reported over at least 26 years. The sequences were aligned and the 2,874 overlapping nonamer amino acid positions of the viral proteome, each a possible core binding domain for human leukocyte antigen molecules and T-cell receptors, were quantitatively analyzed for four patterns of sequence motifs: (1) “index”, the most prevalent sequence; (2) “major” variant, the most common variant sequence; (3) “minor” variants, multiple different sequences, each with an incidence less than that of the major variant; and (4) “unique” variants, each observed only once in the alignment. The collective incidence of the major, minor, and unique variants at each nonamer position represented the total variant population for the position. Positions with more than 50% total variants contained correspondingly reduced incidences of index and major variant sequences and increased minor and unique variants. Highly diverse positions, with 80 to 98% variant nonamer sequences, were present in each protein, including 5% of Gag, and 27% of Env and Nef, each. The multitude of different variant nonamer sequences (i.e. nonatypes; up to 68%) at the highly diverse positions, represented by the major, multiple minor, and multiple unique variants likely supported variants function both in immune escape and as altered peptide ligands with deleterious T-cell responses. The patterns of mutational change were consistent with the sequences of individual HXB2 and C1P viruses and can be considered applicable to all HIV-1 viruses. This characterization of HIV-1 protein mutation provides a foundation for the design of peptide-based vaccines and therapeutics.

BackgroundThe ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently investigate the complex biology of a heterogeneous population of cells, one at a time. However, till date, there has not been a suitable computational methodology for the analysis of such intricate deluge of data, in particular techniques which will aid the identification of the unique transcriptomic profiles difference between the different cellular subtypes. In this paper, we describe the novel methodology for the analysis of single-cell RNA-seq data, obtained from neocortical cells and neural progenitor cells, using machine learning algorithms (Support Vector machine (SVM) and Random Forest (RF)).ResultsThirty-eight key transcripts were identified, using the SVM-based recursive feature elimination (SVM-RFE) method of feature selection, to best differentiate developing neocortical cells from neural progenitor cells in the SVM and RF classifiers built. Also, these genes possessed a higher discriminative power (enhanced prediction accuracy) as compared commonly used statistical techniques or geneset-based approaches. Further downstream network reconstruction analysis was carried out to unravel hidden general regulatory networks where novel interactions could be further validated in web-lab experimentation and be useful candidates to be targeted for the treatment of neuronal developmental diseases.ConclusionThis novel approach reported for is able to identify transcripts, with reported neuronal involvement, which optimally differentiate neocortical cells and neural progenitor cells. It is believed to be extensible and applicable to other single-cell RNA-seq expression profiles like that of the study of the cancer progression and treatment within a highly heterogeneous tumour.

Traffic forecasting is attracting considerable interest due to its widespread application in intelligent transportation systems. Given the complex and dynamic traffic data, many methods focus on how to establish a spatial-temporal model to express the non-stationary traffic patterns. Recently, the latest Graph Convolution Network (GCN) has been introduced to learn spatial features while the time neural networks are used to learn temporal features. These GCN based methods obtain state-of-the-art performance. However, the current GCN based methods ignore the natural hierarchical structure of traffic systems which is composed of the micro layers of road networks and the macro layers of region networks, in which the nodes are obtained through pooling method and could include some hot traffic regions such as downtown and CBD etc., while the current GCN is only applied on the micro graph of road networks. In this paper, we propose a novel Hierarchical Graph Convolution Networks (HGCN) for traffic forecasting by operating on both the micro and macro traffic graphs. The proposed method is evaluated on two complex city traffic speed datasets. Compared to the latest GCN based methods like Graph WaveNet, the proposed HGCN gets higher traffic forecasting precision with lower computational cost.The website of the code is https://github.com/guokan987/HGCN.git.

The identification of lower-performance amplicons will be informative to laboratories intending to use this panel. We have also demonstrated proof-of-concept that different libraries (TruSight Myeloid and Nextera XT) can be combined and sequenced on the same flow cell to generate additional reads for CEBPA.

This report describes the identification and bioinformatics analysis of HLA-DR4-restricted HIV-1 Gag epitope peptides, and the application of dendritic cell mediated immunization of DNA plasmid constructs. BALB/c (H-2d) and HLA-DR4 (DRA1*0101, DRB1*0401) transgenic mice were immunized with immature dendritic cells transfected by a recombinant DNA plasmid encoding the lysosome-associated membrane protein-1/HIV-1 Gag (pLAMP/gag) chimera antigen. Three immunization protocols were compared: 1) primary subcutaneous immunization with 1×105 immature dendritic cells transfected by electroporation with the pLAMP/gag DNA plasmid, and a second subcutaneous immunization with the naked pLAMP/gag DNA plasmid; 2) primary immunization as above, and a second subcutaneous immunization with a pool of overlapping peptides spanning the HIV-1 Gag sequence; and 3) immunization twice by subcutaneous injection of the pLAMP/gag DNA plasmid. Primary immunization with pLAMP/gag-transfected dendritic cells elicited the greatest number of peptide specific T-cell responses, as measured by ex vivo IFN-γ ELISpot assay, both in BALB/c and HLA-DR4 transgenic mice. The pLAMP/gag-transfected dendritic cells prime and naked DNA boost immunization protocol also resulted in an increased apparent avidity of peptide in the ELISpot assay. Strikingly, 20 of 25 peptide-specific T-cell responses in the HLA-DR4 transgenic mice contained sequences that corresponded, entirely or partially to 18 of the 19 human HLA-DR4 epitopes listed in the HIV molecular immunology database. Selection of the most conserved epitope peptides as vaccine targets was facilitated by analysis of their representation and variability in all reported sequences. These data provide a model system that demonstrates a) the superiority of immunization with dendritic cells transfected with LAMP/gag plasmid DNA, as compared to naked DNA, b) the value of HLA transgenic mice as a model system for the identification and evaluation of epitope-based vaccine strategies, and c) the application of variability analysis across reported sequences in public databases for selection of historically conserved HIV epitopes as vaccine targets.

BackgroundViral vaccine target discovery requires understanding the diversity of both the virus and the human immune system. The readily available and rapidly growing pool of viral sequence data in the public domain enable the identification and characterization of immune targets relevant to adaptive immunity. A systematic bioinformatics approach is necessary to facilitate the analysis of such large datasets for selection of potential candidate vaccine targets.ResultsThis work describes a computational methodology to achieve this analysis, with data of dengue, West Nile, hepatitis A, HIV-1, and influenza A viruses as examples. Our methodology has been implemented as an analytical pipeline that brings significant advancement to the field of reverse vaccinology, enabling systematic screening of known sequence data in nature for identification of vaccine targets. This includes key steps (i) comprehensive and extensive collection of sequence data of viral proteomes (the virome), (ii) data cleaning, (iii) large-scale sequence alignments, (iv) peptide entropy analysis, (v) intra- and inter-species variation analysis of conserved sequences, including human homology analysis, and (vi) functional and immunological relevance analysis.ConclusionThese steps are combined into the pipeline ensuring that a more refined process, as compared to a simple evolutionary conservation analysis, will facilitate a better selection of vaccine targets and their prioritization for subsequent experimental validation.Electronic supplementary materialThe online version of this article (10.1186/s12920-017-0301-2) contains supplementary material, which is available to authorized users.

West Nile virus (WNV), a member of the genus Flavivirus, is a mosquito-borne RNA pathogen closely related to numerous other flaviviruses of the Japanese encephalitis (JE) group, and to a lesser degree to Dengue virus (DENV), Yellow fever virus (YFV), and several other flaviviruses of about 70 different species (5, 18). The genome is a positive-sense, single-stranded RNA of approximately 10,293 nucleotides encoding a polyprotein of approximately 3,430 amino acids (aa) that is cleaved to produce three structural proteins, capsid (C), precursor membrane (prM), and envelope (E), and seven nonstructural (NS) proteins, NS1, 2a, 2b,3, 4a, 4b, and 5 (24,37). Originally isolated in Africa in 1937 (62), WNV has become an increasingly important human pathogen widely endemic in Africa, Asia, Europe, and North America and a significant agent of viral encephalitis (28,38,45). WNV and several of the major human pathogen flaviviruses are known to cocirculate (28, 71); for example, WNV and St. Louis encephalitis virus (LEV) in North America; WNV, DENV, and Japanese encephalitis virus (JEV) around the Indian subcontinent and portions of Southeast Asia (SEA); and WNV, DENV, JEV, and Murray Valley encephalitis virus (MVE) in neighboring pockets of SEA and Australasia.The widespread colocalization of WNV with other flaviviruses calls for a greater understanding of the phylogenetic relatedness and possible pathophysiologic associations of flaviviruses. We previously reported a large-scale analysis of the conservation and variability of overlapping nonamers, the typical length of human leukocyte antigen (HLA) class I or class II binding cores of T-cell epitopes (47), of the 2,746 WNV protein sequences collected from the NCBI Entrez Protein Database (17). Notably, of the 88 completely conserved sequence fragments identified, representing 34% of the WNV proteome, 67 were also present in many other flaviviruses with identities of nine or more amino acids. These sequence homologies called attention to a broad inter-Flavivirus risk of altered peptide ligands (APL) (58), T-cell epitopes with one or more amino acid differences, that possibly would result in modified immune responses in the event of exposure to multiple Flavivirus pathogens.This study focused on analyses of the diversity and presence in