SOCR Analyses: Implementation and Demonstration of a New Graphical Statistics Educational Toolkit

The web-based, Java-written SOCR (Statistical Online Computational Resource) tools have been utilized in many undergraduate and graduate level statistics courses for seven years now (Dinov 2006; Dinov et al. 2008b). It has been proven that these resources can successfully improve students' learning (Dinov et al. 2008b). Being first published online in 2005, SOCR Analyses is a somewhat new component and it concentrate on data modeling for both parametric and non-parametric data analyses with graphical model diagnostics. One of the main purposes of SOCR Analyses is to facilitate statistical learning for high school and undergraduate students. As we have already implemented SOCR Distributions and Experiments, SOCR Analyses and Charts fulfill the rest of a standard statistics curricula. Currently, there are four core components of SOCR Analyses. Linear models included in SOCR Analyses are simple linear regression, multiple linear regression, one-way and two-way ANOVA. Tests for sample comparisons include t-test in the parametric category. Some examples of SOCR Analyses' in the non-parametric category are Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, Kolmogorov-Smirnoff test and Fligner-Killeen test. Hypothesis testing models include contingency table, Friedman's test and Fisher's exact test. The last component of Analyses is a utility for computing sample sizes for normal distribution. In this article, we present the design framework, computational implementation and the utilization of SOCR Analyses.

SOCR Analyses: Implementation and Demonstration of a New Graphical Statistics Educational Toolkit

The web-based, Java-written SOCR (Statistical Online Computational Resource) toolshave been utilized in many undergraduate and graduate level statistics courses for sevenyears now (Dinov 2006; Dinov et al. 2008b). It has been proven that these resourcescan successfully improve students' learning (Dinov et al. 2008b). Being rst publishedonline in 2005, SOCR Analyses is a somewhat new component and it concentrate on datamodeling for both parametric and non-parametric data analyses with graphical modeldiagnostics. One of the main purposes of SOCR Analyses is to facilitate statistical learn-ing for high school and undergraduate students. As we have already implemented SOCRDistributions and Experiments, SOCR Analyses and Charts fulll the rest of a standardstatistics curricula. Currently, there are four core components of SOCR Analyses. Linearmodels included in SOCR Analyses are simple linear regression, multiple linear regression,one-way and two-way ANOVA. Tests for sample comparisons include t-test in the para-metric category. Some examples of SOCR Analyses' in the non-parametric category areWilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, Kolmogorov-Smirno testand Fligner-Killeen test. Hypothesis testing models include contingency table, Friedman'stest and Fisher's exact test. The last component of Analyses is a utility for computingsample sizes for normal distribution. In this article, we present the design framework,computational implementation and the utilization of SOCR Analyses

Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data

The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis

Identification of embryonic stem cell-derived midbrain dopaminergic neurons for engraftment

Embryonic stem cells (ESCs) represent a promising source of midbrain dopaminergic (DA) neurons for applications in Parkinson disease. However, ESC-based transplantation paradigms carry a risk of introducing inappropriate or tumorigenic cells. Cell purification before transplantation may alleviate these concerns and enable identification of the specific DA neuron stage most suitable for cell therapy. Here, we used 3 transgenic mouse ESC reporter lines to mark DA neurons at 3 stages of differentiation (early, middle, and late) following induction of differentiation using Hes5::GFP, Nurr1::GFP, and Pitx3::YFP transgenes, respectively. Transplantation of FACS-purified cells from each line resulted in DA neuron engraftment, with the mid-stage and late-stage neuron grafts being composed almost exclusively of midbrain DA neurons. Mid-stage neuron cell grafts had the greatest amount of DA neuron survival and robustly induced recovery of motor deficits in hemiparkinsonian mice. Our data suggest that the Nurrl(+) stage (middle stage) of neuronal differentiation is particularly suitable for grafting ESC-derived DA neurons. Moreover, global transcriptome analysis of progeny from each of the ESC reporter lines revealed expression of known midbrain DA neuron genes and also uncovered previously uncharacterized midbrain genes. These data demonstrate remarkable fate specificity of ESC-derived DA neurons and outline a sequential stage-specific ESC reporter line paradigm for in vivo gene discovery

A safe and effective magnetic labeling protocol for MRI-based tracking of human adult neural stem cells

Magnetic resonance imaging (MRI) provides a unique tool for in vivo visualization and tracking of stem cells in the brain. This is of particular importance when assessing safety of experimental cell treatments in the preclinical or clinical setup. Yet, specific imaging requires an efficient and non-perturbing cellular magnetic labeling which precludes adverse effects of the tag, e.g., the impact of iron-oxide-nanoparticles on the critical differentiation and integration processes of the respective stem cell population investigated. In this study we investigated the effects of very small superparamagnetic iron oxide particle (VSOP) labeling on viability, stemness, and neuronal differentiation potential of primary human adult neural stem cells (haNSCs). Cytoplasmic VSOP incorporation massively reduced the transverse relaxation time T2, an important parameter determining MR contrast. Cells retained cytoplasmic label for at least a month, indicating stable incorporation, a necessity for long-term imaging. Using a clinical 3T MRI, 1 × 103 haNSCs were visualized upon injection in a gel phantom, but detection limit was much lower (5 × 104 cells) in layer phantoms and using an imaging protocol feasible in a clinical scenario. Transcriptional analysis and fluorescence immunocytochemistry did not reveal a detrimental impact of VSOP labeling on important parameters of cellular physiology with cellular viability, stemness and neuronal differentiation potential remaining unaffected. This represents a pivotal prerequisite with respect to clinical application of this method

Resistance to receptor-blocking therapies primes tumors as targets for HER3-homing nanobiologics

Resistance to anti-tumor therapeutics is an important clinical problem. Tumor-targeted therapies currently used in the clinic are derived from antibodies or small molecules that mitigate growth factor activity. These have improved therapeutic efficacy and safety compared to traditional treatment modalities but resistance arises in the majority of clinical cases. Targeting such resistance could improve tumor abatement and patient survival. A growing number of such tumors are characterized by prominent expression of the human epidermal growth factor receptor 3 (HER3) on the cell surface. This study presents a “Trojan-Horse” approach to combating these tumors by using a receptor-targeted biocarrier that exploits the HER3 cell surface protein as a portal to sneak therapeutics into tumor cells by mimicking an essential ligand. The biocarrier used here combines several functions within a single fusion protein for mediating targeted cell penetration and non-covalent self-assembly with therapeutic cargo, forming HER3-homing nanobiologics. Importantly, we demonstrate here that these nanobiologics are therapeutically effective in several scenarios of resistance to clinically approved targeted inhibitors of the human EGF receptor family. We also show that such inhibitors heighten efficacy of our nanobiologics on naïve tumors by augmenting HER3 expression. This approach takes advantage of a current clinical problem (i.e. resistance to growth factor inhibition) and uses it to make tumors more susceptible to HER3 nanobiologic treatment. Moreover, we demonstrate a novel approach in addressing drug resistance by taking inhibitors against which resistance arises and re-introducing these as adjuvants, sensitizing tumors to the HER3 nanobiologics described here

Doped Overoxidized Polypyrrole Microelectrodes as Sensors for the Detection of Dopamine Released from Cell Populations

A surface modification of interdigitated gold microelectrodes (IDEs) with a doped polypyrrole (PPy) film for detection of dopamine released from populations of differentiated PC12 cells is presented. A thin PPy layer was potentiostatically electropolymerized from an aqueous pyrrole solution onto electrode surfaces. The conducting polymer film was doped during electropolymerization by introducing counter-ions in the monomer solution. Several counter-ions were tested and the resulting electrode modifications were characterized electrochemically to find the optimal dopant that increases sensitivity in dopamine detection. Overoxidation of the PPy films was shown to contribute to a significant enhancement in sensitivity to dopamine. The changes caused by overoxidation in the electrochemical behavior and electrode morphology were investigated using cyclic voltammetry and SEM as well as AFM, respectively. The optimal dopant for dopamine detection was found to be polystyrene sulfonate anion (PSS-). Rat pheochromocytoma (PC12) cells, a suitable model to study exocytotic dopamine release, were differentiated on IDEs functionalized with an overoxidized PSS--doped PPy film. The modified electrodes were used to amperometrically detect dopamine released by populations of cells upon triggering cellular exocytosis with an elevated K+ concentration. A comparison between the generated current on bare gold electrodes and gold electrodes modified with overoxidized doped PPy illustrates the clear advantage of the modification, yielding 2.6-fold signal amplification. The results also illustrate how to use cell population based dopamine exocytosis measurements to obtain biologically significant information that can be relevant in, for instance, the study of neural stem cell differentiation into dopaminergic neurons

Characterization of NLRP12 during the Development of Allergic Airway Disease in Mice

Among the 22 members of the nucleotide binding-domain, leucine rich repeat-containing (NLR) family, less than half have been functionally characterized. Of those that have been well studied, most form caspase-1 activating inflammasomes. NLRP12 is a unique NLR that has been shown to attenuate inflammatory pathways in biochemical assays and mediate the lymph node homing of activated skin dendritic cells in contact hypersensitivity responses. Since the mechanism between these two important observations remains elusive, we further evaluated the contribution of NLRP12 to organ specific adaptive immune responses by focusing on the lung, which, like skin, is exposed to both exogenous and endogenous inflammatory agents. In models of allergic airway inflammation induced by either acute ovalbumin (OVA) exposure or chronic house dust mite (HDM) antigen exposure, Nlrp12−/− mice displayed subtle differences in eosinophil and monocyte infiltration into the airways. However, the overall development of allergic airway disease and airway function was not significantly altered by NLRP12 deficiency. Together, the combined data suggest that NLRP12 does not play a vital role in regulating Th2 driven airway inflammation using common model systems that are physiologically relevant to human disease. Thus, the allergic airway inflammation models described here should be appropriate for subsequent studies that seek to decipher the contribution of NLRP12 in mediating the host response to agents associated with asthma exacerbation

The Three Hundred project: a large catalogue of theoretically modelled galaxy clusters for cosmological and astrophysical applications

We introduce the The Three Hundred project, an endeavour to model 324 large galaxy clusters with full-physics hydrodynamical re-simulations. Here we present the dataset and study the differences to observations for fundamental galaxy cluster properties and scaling relations. We find that the modelled galaxy clusters are generally in reasonable agreement with observations with respect to baryonic fractions and gas scaling relations at redshift z = 0. However, there are still some (model-dependent) differences, such as central galaxies being too massive, and galaxy colours (g − r) being bluer (about 0.2 dex lower at the peak position) than in observations. The agreement in gas scaling relations down to 1013 h−1M⊙ between the simulations indicates that particulars of the sub-grid modelling of the baryonic physics only has a weak influence on these relations. We also include – where appropriate – a comparison to three semi-analytical galaxy formation models as applied to the same underlying dark matter only simulation. All simulations and derived data products are publicly available