Cytogenetic and Reproductive studies of Bulls Born Co-Twin to Freemartins

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Coronary Subclavian Steal Syndrome

INTRODUCTION Coronary subclavian steal (CSS) syndrome is a rare complication of coronary artery bypass graft surgery (CABG) involving the left internal mammary artery (LIMA) graft to the left anterior descending (LAD) artery. It results from stenosis of the left subclavian artery proximal to the LIMA, which compromises myocardial blood flow. The incidence of CSS syndrome is between 0.1-3.4% in the United States.1 Most cases occur as a result of long-standing subclavian stenosis due to progres-sion of the stenosis following CABG. We report a case of CSS syndrome, which presented as a non-ST elevation myocardial infarction (NSTEMI)

Irreversibility at macromolecular scales in the flake graphite of the lithium-ion battery anode.

Charging a commercial lithium-ion battery intercalates lithium into the graphite-based anode, creating various lithium carbide structures. Despite their economic importance, these structures and the dynamics of their charging-discharging transitions are not well-understood. We have videoed single microcrystals of high-quality, natural graphite undergoing multiple lithiation-delithiation cycles. Because the equilibrium lithium-carbide compounds corresponding to full, half, and one-third charge are gold, red, and blue respectively, video observations give direct insight into both the macromolecular structures and the kinematics of charging and discharging. We find that the transport during the first lithiation is slow and orderly, and follows the core-shell or shrinking annuli model with phase boundaries moving at constant velocities (i.e. non-diffusively). Subsequent lithiations are markedly different, showing transport that is both faster and disorderly, which indicates that the initially pristine graphite is irreversibly and considerably altered during the first cycle. In all cases deintercalation is not the time-reverse of intercalation. These findings both illustrate how lithium enters nearly defect-free host material, and highlight the differences between the idealized case and an actual, cycling graphite anode

Determining the Locations of Dust Sources in FeLoBAL Quasars

We conduct a spectroscopic search of quasars observed by the Sloan Digital Sky Survey (SDSS) with broad absorption line (BAL) troughs due to Mg II and troughs due to Fe II that simultaneously exhibit strong Balmer narrow emission lines (NELs). We find that in a redshift range of 0.4 less than or equal to z less than or equal to 0.9 approximately 23 of the 70 Mg II BALs and 4 of a subset of 15 Fe II BALs exhibit strong Balmer emission. We also find significant fractions of Mg II BALs (approximately 23%) and those Mg II BALs with Fe II troughs (approximately 27%) have strong continuum reddening, E(B - V) greater than or equal to 0.1. From measurements of the Balmer decrement in three objects, we find similarly significant reddening of the NEL region in three of the four objects; the NELs in the fourth object are not measurable. We also include one object in this study not taken from the SDSS sample that shows Fe II absorption and strong narrow emission, but due to measurement uncertainty and low continuum reddening the comparison is consistent but inconclusive. We find a trend in both the Mg II and Fe II BAL samples between the NEL reddening and continuum reddening. Because the narrow line reddening is consistent with the continuum reddening in every object in the two SDSS samples, it suggests that the reddening sources in these objects likely exist at larger radial distances than the narrow line regions from the central nucleus.Comment: 40 manuscript pages, accepted in ApJ (July

Boundary Segmentation For Fluorescence Microscopy Using Steerable Filters

Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation

Urban Renewal and Sustainable Development in Jamaica: Progress, Challenges and New Directions

The chapter discusses the history and context of urban renewal in Jamaica and shares the country’s integrated model for urban renewal, as well as the lessons learned from over two decades of implementation. As the urban planning landscape evolves there is a call to move in new directions, incorporating concepts which embody the development of human capital. One call is to re-position urban renewal as a public health tool to reduce crime and violence, communicable and non-communicable diseases, especially for the urban poor and urban youth who share a greater burden of Jamaica’s status as a Low/Middle Income Country (LMIC) and Small Island Developing State (SIDS). The call for the paradigm shift from gender-blind to gender-sensitive urban planning is expected to promote policy coherence between commitments to gender mainstreaming and gender equality and urban development modalities. There is also the need for a new governance framework to support the active participation of the average resident in the decision making process for land use management and other aspects of urban renewal to meet the goals of the New Urban Agenda and to realize Vision 2030 Jamaica, making “Jamaica, the place of choice to live, work, raise families and do business”

Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks

Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D

APJ1 and GRE3 Homologs Work in Concert to Allow Growth in Xylose in a Natural Saccharomyces sensu stricto Hybrid Yeast

Creating Saccharomyces yeasts capable of efficient fermentation of pentoses such as xylose remains a key challenge in the production of ethanol from lignocellulosic biomass. Metabolic engineering of industrial Saccharomyces cerevisiae strains has yielded xylose-fermenting strains, but these strains have not yet achieved industrial viability due largely to xylose fermentation being prohibitively slower than that of glucose. Recently, it has been shown that naturally occurring xylose-utilizing Saccharomyces species exist. Uncovering the genetic architecture of such strains will shed further light on xylose metabolism, suggesting additional engineering approaches or possibly even enabling the development of xylose-fermenting yeasts that are not genetically modified. We previously identified a hybrid yeast strain, the genome of which is largely Saccharomyces uvarum, which has the ability to grow on xylose as the sole carbon source. To circumvent the sterility of this hybrid strain, we developed a novel method to genetically characterize its xylose-utilization phenotype, using a tetraploid intermediate, followed by bulk segregant analysis in conjunction with high-throughput sequencing. We found that this strain’s growth in xylose is governed by at least two genetic loci, within which we identified the responsible genes: one locus contains a known xylose-pathway gene, a novel homolog of the aldo-keto reductase gene GRE3, while a second locus contains a homolog of APJ1, which encodes a putative chaperone not previously connected to xylose metabolism. Our work demonstrates that the power of sequencing combined with bulk segregant analysis can also be applied to a nongenetically tractable hybrid strain that contains a complex, polygenic trait, and identifies new avenues for metabolic engineering as well as for construction of nongenetically modified xylose-fermenting strains

Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation

Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets

DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data

The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The size and complexity of these volumes is such that quantitative analysis requires automated methods of image processing to identify and characterize individual cells. For many workflows, this process starts with segmentation of nuclei that, due to their ubiquity, ease-of-labeling and relatively simple structure, make them appealing targets for automated detection of individual cells. However, in the context of large, three-dimensional image volumes, nuclei present many challenges to automated segmentation, such that conventional approaches are seldom effective and/or robust. Techniques based upon deep-learning have shown great promise, but enthusiasm for applying these techniques is tempered by the need to generate training data, an arduous task, particularly in three dimensions. Here we present results of a new technique of nuclear segmentation using neural networks trained on synthetic data. Comparisons with results obtained using commonly-used image processing packages demonstrate that DeepSynth provides the superior results associated with deep-learning techniques without the need for manual annotation