Applicability and Utility of the Astromaterials X-Ray Computed Tomography Laboratory at Johnson Space Center

The Astromaterials Acquisition and Curation Office at NASAs Johnson Space Center is responsible for curating all of NASAs astromaterial sample collections (i.e. Apollo samples, Luna Samples, Antarctic Meteorites, Cosmic Dust Particles, Microparticle Impact Collection, Genesis solar wind atoms, Stardust comet Wild-2 particles, Stardust interstellar particles, and Hayabusa asteroid Itokawa particles) [1-3]. To assist in sample curation and distribution, JSC Curation has recently installed an X-ray computed tomography (XCT) scanner to visualize and characterize samples in 3D. [3] describes the instrumental set-up and the utility of XCT to astromaterials curation. Here we describe some of the current and future projects and illustrate the usefulness of XCT in studying astromaterials

A Synthesis of Rates and Controls on Elemental Mercury Evasion in the Great Lakes Basin

Rates of surface-air elemental mercury (Hgo) fluxes in the literature were synthesized for the Great Lakes Basin (GLB). For the majority of surfaces, fluxes were net positive (evasion). Digital land-cover data were combined with representative evasion rates and used to estimate annual Hgo evasion for the GLB (7.7 Mg/yr). This value is less than our estimate of total Hg deposition to the area (15.9 Mg/yr), suggesting the GLB is a net sink for atmospheric Hg. The greatest contributors to annual evasion for the basin are agricultural (~55%) and forest (~25%) land cover types, and the open water of the Great Lakes (~15%). Areal evasion rates were similar across most land cover types (range: 7.0 to 21.0 μg/m2-yr), with higher rates associated with urban (12.6 μg/m2-yr) and agricultural (21.0 μg/m2-yr) lands. Uncertainty in these estimates could be partially remedied through a unified methodological approach to estimating Hgo fluxes

A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence

Detecting changepoints in datasets with many variates is a data science challenge of increasing importance. Motivated by the problem of detecting changes in the incidence of terrorism from a global terrorism database, we propose a novel approach to multiple changepoint detection in multivariate time series. Our method, which we call SUBSET, is a model-based approach which uses a penalised likelihood to detect changes for a wide class of parametric settings. We provide theory that guides the choice of penalties to use for SUBSET, and that shows it has high power to detect changes regardless of whether only a few variates or many variates change. Empirical results show that SUBSET out-performs many existing approaches for detecting changes in mean in Gaussian data; additionally, unlike these alternative methods, it can be easily extended to non-Gaussian settings such as are appropriate for modelling counts of terrorist events

Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram.

It is increasingly being realised that many real world time series are not stationary and exhibit evolving second-order autocovariance or spectral structure. This article introduces a Bayesian approach for modelling the evolving wavelet spectrum of a locally stationary wavelet time series. Our new method works by combining the advantages of a Haar-Fisz transformed spectrum with a simple, but powerful, Bayesian wavelet shrinkage method. Our new method produces excellent and stable spectral estimates and this is demonstrated via simulated data and on differenced infant electrocardiogram data. A major additional benefit of the Bayesian paradigm is that we obtain rigorous and useful credible intervals of the evolving spectral structure. We show how the Bayesian credible intervals provide extra insight into the infant electrocardiogram data

High methylmercury in Arctic and subarctic ponds is related to nutrient levels in the warming eastern Canadian Arctic

Permafrost thaw ponds are ubiquitous in the eastern Canadian Arctic, yet little information exists on their potential as sources of methylmercury (MeHg) to freshwaters. They are microbially active and conducive to methylation of inorganic mercury, and are also affected by Arctic warming. This multiyear study investigated thaw ponds in a discontinuous permafrost region in the Subarctic taiga (Kuujjuarapik-Whapmagoostui, QC) and a continuous permafrost region in the Arctic tundra (Bylot Island, NU). MeHg concentrations in thaw ponds were well above levels measured in most freshwater ecosystems in the Canadian Arctic (>0.1 ng L−1). On Bylot, ice-wedge trough ponds showed significantly higher MeHg (0.3−2.2 ng L−1) than polygonal ponds (0.1−0.3 ng L−1) or lakes (<0.1 ng L−1). High MeHg was measured in the bottom waters of Subarctic thaw ponds near Kuujjuarapik (0.1−3.1 ng L−1). High water MeHg concentrations in thaw ponds were strongly correlated with variables associated with high inputs of organic matter (DOC, a320, Fe), nutrients (TP, TN), and microbial activity (dissolved CO2 and CH4). Thawing permafrost due to Arctic warming will continue to release nutrients and organic carbon into these systems and increase ponding in some regions, likely stimulating higher water concentrations of MeHg. Greater hydrological connectivity from permafrost thawing may potentially increase transport of MeHg from thaw ponds to neighboring aquatic ecosystems

Identification of a Novel Chromosomal Passenger Complex and Its Unique Localization during Cytokinesis in Trypanosoma brucei

Aurora B kinase is a key component of the chromosomal passenger complex (CPC), which regulates chromosome segregation and cytokinesis. An ortholog of Aurora B was characterized in Trypanosoma brucei (TbAUK1), but other conserved components of the complex have not been found. Here we identified four novel TbAUK1 associated proteins by tandem affinity purification and mass spectrometry. Among these four proteins, TbKIN-A and TbKIN-B are novel kinesin homologs, whereas TbCPC1 and TbCPC2 are hypothetical proteins without any sequence similarity to those known CPC components from yeasts and metazoans. RNAi-mediated silencing of each of the four genes led to loss of spindle assembly, chromosome segregation and cytokinesis. TbKIN-A localizes to the mitotic spindle and TbKIN-B to the spindle midzone during mitosis, whereas TbCPC1, TbCPC2 and TbAUK1 display the dynamic localization pattern of a CPC. After mitosis, the CPC disappears from the central spindle and re-localizes at a dorsal mid-point of the mother cell, where the anterior tip of the daughter cell is tethered, to start cell division toward the posterior end, indicating a most unusual CPC-initiated cytokinesis in a eukaryote

Sequential Assembly of Centromeric Proteins in Male Mouse Meiosis

The assembly of the mitotic centromere has been extensively studied in recent years, revealing the sequence and regulation of protein loading to this chromosome domain. However, few studies have analyzed centromere assembly during mammalian meiosis. This study specifically targets this approach on mouse spermatocytes. We have found that during prophase I, the proteins of the chromosomal passenger complex Borealin, INCENP, and Aurora-B load sequentially to the inner centromere before Shugoshin 2 and MCAK. The last proteins to be assembled are the outer kinetochore proteins BubR1 and CENP-E. All these proteins are not detected at the centromere during anaphase/telophase I and are then reloaded during interkinesis. The loading sequence of the analyzed proteins is similar during prophase I and interkinesis. These findings demonstrate that the interkinesis stage, regularly overlooked, is essential for centromere and kinetochore maturation and reorganization previous to the second meiotic division. We also demonstrate that Shugoshin 2 is necessary for the loading of MCAK at the inner centromere, but is dispensable for the loading of the outer kinetochore proteins BubR1 and CENP-E

A microscopy-based screen employing multiplex genome sequencing identifies cargo-specific requirements for dynein velocity

The timely delivery of membranous organelles and macromolecules to specific locations within the majority of eukaryotic cells depends on microtubule-based transport. Here, we describe a screening method to identify mutations that have a critical effect on intracellular transport and its regulation using mutagenesis, multicolor-fluorescence microscopy, and multiplex genome sequencing. This screen exploits the filamentous fungus Aspergillus nidulans, which has many of the advantages of yeast molecular genetics, but uses long-range microtubule-based transport in a manner more similar to metazoan cells. Using this method, we identified 7 mutants that represent novel alleles of components of the intracellular transport machinery: specifically, kinesin-1, cytoplasmic dynein, and the dynein regulators Lis1 and dynactin. The two dynein mutations identified in our screen map to dynein's AAA+ catalytic core. Single-molecule studies reveal that both mutations reduce dynein's velocity in vitro. In vivo these mutants severely impair the distribution and velocity of endosomes, a known dynein cargo. In contrast, another dynein cargo, the nucleus, is positioned normally in these mutants. These results reveal that different dynein functions have distinct velocity requirements

Pattern Recognition Software and Techniques for Biological Image Analysis

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays