Green Fluorescent Protein in the sea urchin: new experimental approaches to transcriptional regulatory analysis in embryos and larvae

The use of Green Fluorescent Protein (GFP) as a reporter for expression transgenes opens the way to several new experimental strategies for the study of gene regulation in sea urchin development. A GFP coding sequence was associated with three different previously studied cis-regulatory systems, viz those of the SM50 gene, expressed in skeletogenic mesenchyme, the CyIIa gene, expressed in archenteron, skeletogenic and secondary mesenchyme, and the Endo16 gene, expressed in vegetal plate, archenteron and midgut. We demonstrate that the sensitivity with which expression can be detected is equal to or greater than that of whole-mount in situ hybridization applied to detection of CAT mRNA synthesized under the control of the same cis-regulatory systems. However, in addition to the important feature that it can be visualized nondestructively in living embryos, GFP has other advantages. First, it freely diffuses even within fine cytoplasmic cables, and thus reveals connections between cells, which in sea urchin embryos is particularly useful for observations on regulatory systems that operate in the syncytial skeletogenic mesenchyme. Second, GFP expression can be dramatically visualized in postembryonic larval tissues. This brings postembryonic larval developmental processes for the first time within the easy range of gene transfer analyses. Third, GFP permits identification and segregation of embryos in which the clonal incorporation of injected DNA has occurred in any particular desired region of the embryo. Thus, we show explicitly that, as expected, GFP transgenes are incorporated in the same nuclei together with other transgenes with which they are co-injected

1064 nm Dispersive Raman Microspectroscopy and Optical Trapping of Pharmaceutical Aerosols.

Raman spectroscopy is a powerful tool for investigating chemical composition. Coupling Raman spectroscopy with optical microscopy (Raman microspectroscopy) and optical trapping (Raman tweezers) allows microscopic length scales and, hence, femtolitre volumes to be probed. Raman microspectroscopy typically uses UV/visible excitation lasers, but many samples, including organic molecules and complex tissue samples, fluoresce strongly at these wavelengths. Here we report the development and application of dispersive Raman microspectroscopy designed around a near-infrared continuous wave 1064 nm excitation light source. We analyze microparticles (1-5 μm diameter) composed of polystyrene latex and from three real-world pressurized metered dose inhalers (pMDIs) used in the treatment of asthma: salmeterol xinafoate (Serevent), salbutamol sulfate (Salamol), and ciclesonide (Alvesco). For the first time, single particles are captured, optically levitated, and analyzed using the same 1064 nm laser, which permits a convenient nondestructive chemical analysis of the true aerosol phase. We show that particles exhibiting overwhelming fluorescence using a visible laser (514.5 nm) can be successfully analyzed with 1064 nm excitation, irrespective of sample composition and irradiation time. Spectra are acquired rapidly (1-5 min) with a wavelength resolution of 2 nm over a wide wavenumber range (500-3100 cm-1). This is despite the microscopic sample size and low Raman scattering efficiency at 1064 nm. Spectra of individual pMDI particles compare well to bulk samples, and the Serevent pMDI delivers the thermodynamically preferred crystal form of salmeterol xinafoate. 1064 nm dispersive Raman microspectroscopy is a promising technique that could see diverse applications for samples where fluorescence-free characterization is required with high spatial resolution

Gunrock: GPU Graph Analytics

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing (TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance Graph Processing Library on the GPU

Jobseekers Regime and Flexible New Deal, the Six Month Offer and Support for the Newly Unemployed evaluations: An early process study

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Altered Topological Structure of the Brain White Matter in Maltreated Children through Topological Data Analysis

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white-matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated

Structural insights in cell-type specific evolution of intra-host diversity by SARS-CoV-2

As the global burden of SARS-CoV-2 infections escalates, so does the evolution of viral variants with increased transmissibility and pathology. In addition to this entrenched diversity, RNA viruses can also display genetic diversity within single infected hosts with co-existing viral variants evolving differently in distinct cell types. The BriSΔ variant, originally identified as a viral subpopulation from SARS-CoV-2 isolate hCoV-19/England/02/2020, comprises in the spike an eight amino-acid deletion encompassing a furin recognition motif and S1/S2 cleavage site. We elucidate the structure, function and molecular dynamics of this spike providing mechanistic insight into how the deletion correlates to viral cell tropism, ACE2 receptor binding and infectivity of this SARS-CoV-2 variant. Our results reveal long-range allosteric communication between functional domains that differ in the wild-type and the deletion variant and support a view of SARS-CoV-2 probing multiple evolutionary trajectories in distinct cell types within the same infected host