Parvin cox biography channels

Abstract

Proper data transformation is an essential part of analysis. Choosing appropriate transformations for variables can enhance visualization, improve efficacy of analytical methods, and increase data interpretability. However determining appropriate transformations of variables from high-content imaging data poses new challenges. Imaging data produces hundreds of covariates from each of thousands of images in a corpus. Each of these covariates will have a different distribution and need a potentially different transformation. As such imaging data produces hundreds of covariates, determining an appropriate transformation for each of them is infeasible by hand. In this paper we explore simple, robust, and automatic transformations of high-content image data. A central application of our work is to microenvironment microarray bio-imaging data from the NIH LINCS program. We show that our robust transformations enhance visualization and improve the discovery of substantively relevant latent effects. These transformations enhance analysis of image features individually and also improve data integration approaches when combining together multiple features. We anticipate that the advantages of this work will likely also be realized in the analysis of data from other high-content and highly-multiplexed technologies like Cell Painting or Cyclic Immunofluorescence. Software and further analysis can be found at

Keywords: imaging, latent variables, visualization, automatic transformation, data integration, PCA

1. Introduction

Transformation of data is an essential component in many areas of analysis. Consider principal components analysis (PCA), one of the primary statistical techniques for visualization and recovery of latent variables. PCA is well-known to be sensitive to skewed distributions and outliers (Hubert et al., ; Maadooliat et al., ). Usi

MSC spheroids self-organize in a hierarchical manner, resulting in a spatial patterning of the function of individual cells.

Abstract

Organoids that recapitulate the functional hallmarks of anatomic structures comprise cell populations able to self-organize cohesively in 3D. However, the rules underlying organoid formation in vitro remain poorly understood because a correlative analysis of individual cell fate and spatial organization has been challenging. Here, we use a novel microfluidics platform to investigate the mechanisms determining the formation of organoids by human mesenchymal stromal cells that recapitulate the early steps of condensation initiating bone repair in vivo. We find that heterogeneous mesenchymal stromal cells self-organize in 3D in a developmentally hierarchical manner. We demonstrate a link between structural organization and local regulation of specific molecular signaling pathways such as NF-κB and actin polymerization, which modulate osteo-endocrine functions. This study emphasizes the importance of resolving spatial heterogeneities within cellular aggregates to link organization and functional properties, enabling a better understanding of the mechanisms controlling organoid formation, relevant to organogenesis and tissue repair.

INTRODUCTION

In recent years, organoids have emerged as powerful tools for basic research, drug screening, and tissue engineering. The organoids formed in vitro show many features of the structural organization and the functional hallmarks of adult or embryonic anatomical structures (1). In addition, the formation of organoids alleviates the need to perform animal studies and provides an attractive platform for robust quantitative studies on the mechanisms regulating organ homeostasis and tissue repair in vivo (1). The formation of organoids usually starts with populations of stem cells. They are therefore expected to be heterogeneous because pluripotent stem cells [induced pluripotent stem cells (p

British TV series ()

This article is about the television series. For the film, see Undeclared War. For a type of military conflict, see Undeclared war.

The Undeclared War is a British near-future thriller television mini-series, aired from 30 June on Channel 4. The series is written by Peter Kosminsky. Channel 4 announced on 12 February that a second second consisting of six episodes would be produced.

Plot

The series follows two main characters, Saara Parvin in the UK and Vadim Trusov in Russia, during a cyber and misinformation attack upon the UK.

Parvin has just started a one-year student-placement at GCHQ when a cyber-attack takes down some of the UK-internet and she joins the team examining the code of the malware. She is praised when she discovers a second attack within the code and a diligent search for a third attack doesn't find one.

Meanwhile, she feels alienated within GCHQ but makes friends with John Yeabsley who spends his lunch-time correcting the grammar of other people's blogs. He, in turn, says how alienating it is to not be able to talk about his work outside. We later find that Parvin hasn't told her family where she is working and her brother is appalled when she finally tells him.

Trusov had attended a class with Parvin in London and when he returns to Russia he starts working for Russia's twitter-misinformation campaign but when the UK crash the facility as reprisal for the malware he reluctantly joins the offensive malware department.

Russia escalates the attack and incites unrest in the UK by interfering with the reporting of a general election whereupon the UK remotely destroys some Russian arms dumps. Russia exaggerate the damage and uses it as a pretext for isolating GCHQ from NSA by leaking NSA software from a UK site.

Trusov eventually reveals that this was all planned by Russia and he deliberately and openly leaks all the Russian software t