Zika infection of neural progenitor cells perturbs transcription in neurodevelopmental pathways

Background: A recent study of the gene expression patterns of Zika virus (ZIKV) infected human neural progenitor cells (hNPCs) revealed transcriptional dysregulation and identified cell cycle-related pathways that are affected by infection. However deeper exploration of the information present in the RNA-Seq data can be used to further elucidate the manner in which Zika infection of hNPCs affects the transcriptome, refining pathway predictions and revealing isoform-specific dynamics. Methodology/Principal findings: We analyzed data published by Tang et al. using state-of-the-art tools for transcriptome analysis. By accounting for the experimental design and estimation of technical and inferential variance we were able to pinpoint Zika infection affected pathways that highlight Zika’s neural tropism. The examination of differential genes reveals cases of isoform divergence. Conclusions: Transcriptome analysis of Zika infected hNPCs has the potential to identify the molecular signatures of Zika infected neural cells. These signatures may be useful for diagnostics and for the resolution of infection pathways that can be used to harvest specific targets for further study

A discriminative learning approach to differential expression analysis for single-cell RNA-seq

Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3′ single-cell RNA-seq that can identify previously undetectable marker genes

A direct comparison of genome alignment and transcriptome pseudoalignment

Motivation: Genome alignment of reads is the first step of most genome analysis workflows. In the case of RNA-Seq, transcriptome pseudoalignment of reads is a fast alternative to genome alignment, but the different coordinate systems of the genome and transcriptome have made it difficult to perform direct comparisons between the approaches. Results: We have developed tools for converting genome alignments to transcriptome pseudoalignments, and conversely, for projecting transcriptome pseudoalignments to genome alignments. Using these tools, we performed a direct comparison of genome alignment with transcriptome pseudoalignment. We find that both approaches produce similar quantifications. This means that for many applications genome alignment and transcriptome pseudoalignment are interchangeable. Availability and Implementation: bam2tcc is a C++14 software for converting alignments in SAM/BAM format to transcript compatibility counts (TCCs) and is available at https://github.com/pachterlab/bam2tcc. kallisto genomebam is a user option of kallisto that outputs a sorted BAM file in genome coordinates as part of transcriptome pseudoalignment. The feature has been released with kallisto v0.44.0, and is available at https://pachterlab.github.io/kallisto/

Statistical Methods for Gene Differential Expression Analysis of RNA-Sequencing

RNA-Sequencing ("RNA-Seq") is performed to measure gene expression, often to ask the question of what genes are differentially expressed across various biological conditions. Statistical methods have been used to model RNA-Seq quantifications in order to determine differential expression, and have traditionally be divided into gene-level methods and transcript-level methods. There has been little attempt to connect the statistical divide, although transcript expression and gene expression are biologically inextricably linked. In this thesis, we provide a case study of a comparative differential expression analysis, demonstrating that many differential expression events happen on the isoform-level, and that performing an analysis using only summarized gene quantifications would fail to capture these events. Furthermore, we develop statistical methods that unify the transcript-level and gene-level analysis. In bulk RNA-Seq, by using p-value aggregation methods, we are able to translate transcript-level results into gene-level results under a unified framework. For single cell RNA-Seq, we propose using multiple logistic regression, leveraging the high dimensionality of the data in order to determine if the transcript quantifications pertaining to a gene are able to constitute a linear discriminant for cell type. This method combines differential transcript expression analysis and differential gene expression analysis into a unified framework which we call “gene differential expression.” Lastly, we demonstrate that our methods could be used on transcript compatibility counts instead of transcript quantifications in order to bypass ambiguous read assignment and improve accuracy. We show that transcript compatibility counts obtained via transcriptome pseudoalignment are comparable in quantification accuracy to quantifications from genome alignment methods.</p

DESIGN, OPTIMIZATION AND IN VITRO CHARACTERIZATION OF SELF NANO EMULSIFYING DRUG DELIVERY SYSTEM OF OLMESARTAN MEDOXOMIL

Objective: The objective of the present study was to design, optimise and characterise self nano emulsifying drug delivery systems (SNEDDS) for a poorly water soluble drug, olmesartan medoxomil (OLM) by Formulation by Design (FbD) approach with an aim to improve its solubility and dissolution.Methods: The SNEDDS were systematically optimised using three factor Box-Behnken design. Concentration of formulation variables, namely, the oil phaseX1 (Capryol 90), the surfactant X2 (Cremophor EL), and the co-surfactant X3 (Transcutol P), was optimized for its impact on mean globule size (Y1), percentage drug release in 20 min (Y2) and turbidity (Y3) of the formulation. Ternary phase diagrams were constructed to select the areas of nanoemulsion and the amounts of oil, surfactant and cosurfactants as critical formulation variables. The prepared SNEDDS were characterised for globule size, dissolution studies, turbidity, and transmission electron microscopy (TEM).Results: Following optimisation, the values of formulation variables were found to be 142.276 mg (Capryol P), 399.999 mg (Cremophor EL) and 598.871 mg (Transcutol P) which produced a globule size of 12.64 nm, percentage drug release of 93.34% and a turbidity of 0.02 FNU. TEM studies demonstrated spherical droplet morphology.Conclusion: Thus, the present studies reveal that the SNEDDS is a promising drug delivery system approach for the enhancement of solubility and dissolution rate of OLM

Off-line Digital Cash Schemes Providing Unlinkability, Anonymity and Change

Several ecash systems have been proposed in the last twenty years or so, each offering features similar to real cash. One feature which to date has not been provided is that of a payee giving change to a payer for an e-coin in an off-line setting. In this paper, we indicate how an off-line ecash system can solve the change-giving problem. In addition, our protocol provides the usual expected features of anonymity and unlinkability of the payer, but can reveal the identity of an individual who illegally tries to spend ecash twice

Proton incorporations and superconductivity in a cobalt oxyhydrate

We report the evidence of proton incorporations in a newly-discovered cobalt oxyhydrate superconductor. During the hydration process for Na$_{0.32}$CoO$_{2}$ by the direct reaction with water liquid, it was shown that substantial NaOH was gradually liberated, indicating that H$^{+}$ is incorporated into the hydrated compound. Combined with the thermogravimetric analysis, the chemical composition of the typical sample is Na$_{0.22}$H$_{0.1}$CoO$_{2}\cdot 0.85$H$_{2}$O, which shows bulk superconductivity at 4.4 K.Comment: 9 pages, 4 figure