Variation in Sex Allocation and Floral Morphology in an Expanding Distylous Plant Hybrid Complex

Premise of research. Sex allocation, the relative energy devoted to producing pollen, ovules, and floral displays, can significantly affect reproductive output and population dynamics. In this study, we investigated floral morphology and gamete production in bisexual, distylous plants from a self-incompatible hybrid complex (Piriqueta cistoides ssp. caroliniana Walter [Arbo]; Turneraceae). Sampling focused on two parent types (C, V) and their stable hybrid derivative (H). Since H morphotypes are heterotic for growth and fruit production, we hypothesized that they would produce larger flowers with more gametes. We also anticipated that plants with long styles (long morphs) would produce less pollen than short morphs, since long-morph pollen is larger. Methodology. Over two consecutive summers, flowers were collected from 1465 individual plants in 28 field populations. Floral parameters were measured digitally, and each flower’s pollen number, ovule number, and stigma-anther separation was quantified under a dissecting microscope. Gamete production (n = 332) and stigma-anther separation (n = 119) were also quantified for plants from a greenhouse accession. Pivotal results. Floral display differed among morphotypes, with H plants producing the largest flowers and C plants displaying the least petal separation. Hybrid morphotypes produced significantly more pollen than parental morphotypes, and pollen quantity was significantly greater for long morphs. Ovule production, however, was greatest for V flowers. Stigma-anther separation differed between years and style morphs (greater for short morphs) but not among morphotypes or within a single season. Conclusions. Differences in pollen production between morphs were not consistent with trade-offs in pollen size and number or selection for increased male function in short morphs. Greater stigma-anther separation in short morphs supported the hypothesis of selection to reduce pollen interference. Enhanced floral display and pollen production followed other heterotic traits observed in H morphotypes. The superior ability of H morphotypes to attract pollinators and sire seeds might partially explain this hybrid zone’s continuing expansion

Design principles in housing for people with complex physical and cognitive disability: towards an integrated framework for practice

To develop a research-based environmental framework to guide the design and construction of suitable residential dwellings for individuals with complex disability. An environmental approach to housing design and development recognises that there are physical, psychological and social components relating to housing design, dwelling location and the neighbourhood context, and that these elements interact to affect the physical, psychological, and social wellness of individuals. Following theoretical review and synthesis, a comprehensive set of design features that are conducive to residents’ wellness and quality of life are described. It is clear that housing design and development for people with complex disability ought to consider the physical, social, natural, symbolic, and care environment in relation to housing design, dwelling location, and the neighbourhood context for improved housing outcomes. An integrated housing design and development framework is presented. It is hoped this practical matrix/evaluative tool will inform future inclusive housing design and development decisions in Australia and internationally. The application of this framework is especially relevant to political climates striving to achieve design innovation to increase housing choice for people with complex disability

Extragalactic Star Cluster Science with the Nancy Grace Roman Space Telescope's High Latitude Wide Area Survey and the Vera C. Rubin Observatory

The Nancy Grace Roman Telescope's High Latitude Wide Area Survey will have a number of synergies with the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST), particularly for extragalactic star clusters. Understanding the nature of star clusters and star cluster systems are key topics in many areas of astronomy, chief among them stellar evolution, high energy astrophysics, galaxy assembly/dark matter, the extragalactic distance scale, and cosmology. One of the challenges will be disentangling the age/metallicity degeneracy because young ($\sim$Myr) metal-rich clusters have similar SEDs to old ($\sim$Gyr) metal-poor clusters. Rubin will provide homogeneous, $ugrizy$ photometric coverage, and measurements in the red Roman filters will help break the age-metallicity and age-extinction degeneracies, providing the first globular cluster samples that cover wide areas while essentially free of contamination from Milky Way stars. Roman's excellent spatial resolution will also allow measurements of cluster sizes. We advocate for observations of a large sample of galaxies with a range of properties and morphologies in the Rubin/LSST footprint matching the depth of the LSST Wide-Fast-Deep field $i$ band limit (26.3 mag), and recommend adding the F213 filter to the survey.Comment: white paper submitted for Roman CCS inpu

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training

Ginger inhibits cell growth and modulates angiogenic factors in ovarian cancer cells

<p>Abstract</p> <p>Background</p> <p>Ginger (<it>Zingiber officinale </it>Rosc) is a natural dietary component with antioxidant and anticarcinogenic properties. The ginger component [6]-gingerol has been shown to exert anti-inflammatory effects through mediation of NF-κB. NF-κB can be constitutively activated in epithelial ovarian cancer cells and may contribute towards increased transcription and translation of angiogenic factors. In the present study, we investigated the effect of ginger on tumor cell growth and modulation of angiogenic factors in ovarian cancer cells <it>in vitro</it>.</p> <p>Methods</p> <p>The effect of ginger and the major ginger components on cell growth was determined in a panel of epithelial ovarian cancer cell lines. Activation of NF-κB and and production of VEGF and IL-8 was determined in the presence or absence of ginger.</p> <p>Results</p> <p>Ginger treatment of cultured ovarian cancer cells induced profound growth inhibition in all cell lines tested. We found that <it>in vitro</it>, 6-shogaol is the most active of the individual ginger components tested. Ginger treatment resulted in inhibition of NF-kB activation as well as diminished secretion of VEGF and IL-8.</p> <p>Conclusion</p> <p>Ginger inhibits growth and modulates secretion of angiogenic factors in ovarian cancer cells. The use of dietary agents such as ginger may have potential in the treatment and prevention of ovarian cancer.</p

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts

Great Lake Review Fall 2013

Great Lake Review is SUNY Oswego's student-edited literary and art magazine. Great Lake Review is published, in general, every semester, and contains primarily student art, poetry, fiction, and other literary works.SUNY Oswego Student Associatio