New Localities in Ohio for Five Vascular Plant Species

Author Institution: Dept. of Biological, Geological and Environmental Sciences, Cleveland State University, OHFloristic studies are imperative for documentation of our biodiversity. We conducted floristic surveys within regions of Ohio that were ecologically interesting and contained a diverse flora. We report new records of five vascular plant species, Nigella damascena, Salix x sepulcralis, Spiraea x bumalda,Thermopsis villosa, and Veronica longifolia. Two of these species have not been reported in Ohio, while three are new to the county in which they were collected

Two-Dimensional Fluorescence Difference Spectroscopy to Characterize Nanoparticles and their Interactions

Citation: Hurst, M. N., & DeLong, R. K. (2016). Two-Dimensional Fluorescence Difference Spectroscopy to Characterize Nanoparticles and their Interactions. Scientific Reports, 6, 9. https://doi.org/10.1038/srep33287Two dimensional fluorescence difference spectroscopy (2D FDS) detects nanoparticle interactions following surface functionalization and biomolecule loading by generating a spectral signature of the fluorescent intensity per excitation and emission wavelengths. Comparing metal oxide nanoparticles revealed a unique spectral signature per material composition. 2D FDS showed to be sensitive to changes in surface properties between ZnO NPs synthesized by different methods. ZnO NP loaded with glycol chitosan, polyacrylic acid (PAA), or methoxy polyethylene glycol (mPEG) exhibited a distinct spectral signature shift. ZnO NP loaded with Torula Yeast RNA (TYRNA)(640 nm), polyinosinic: polycytidylic acid (pIC)(680 nm), or splice switching oligonucleotide (SSO)(650 nm) each revealed a shift in emission. Ras-Binding domain (RBD) at three concentrations (25, 37.5, 50 mu g/mL) showed that fluorescent intensity was inversely related to the concentration of protein loaded. These data support 2D FDS as a novel technique in identifying nanoparticles and their surface interactions as a quality assurance tool

Time-frequency Domain Analogues of Phase Space Sub-Planck Structures

We present experimental data of the frequency resolved optical gating (FROG) measurements of light pulses revealing interference features corresponding to sub-Planck structures in phase space. For superpositions of pulses a small, sub-Fourier shift in the carrier frequency leads to a state orthogonal to the initial one, although in the representation of standard time-frequency distributions these states seem to have a nonvanishing overlap.Comment: New title, minor change

Plant Communities of Highland Heights Community Park, Cuyahoga County, Ohio

Author Institution: Dept of Biological, Geological & Environmental Sciences, Cleveland State University, Cleveland, OHAuthor Institution: Dept of Biology, John Carroll University, University Heights, OHAuthor Institution: Normandeau Associates, Bedford, NHWe have described the vegetation structure with respect to various community types of Highland Heights Community Park and adjoining territory. High values of Shannon’s Diversity Indexes and Floristic Quality Assessment Indexes indicate a superior quality, species-rich habitat with several high-fidelity species. Based on our research, which reveals that the study site is worthy of conservation and preservation, we suggest recommendations to the city of Highland Heights for park management and land use planning

Global tectonic studies: Hotspots and anomalous topography

Volcanic activity on Earth and its secular variations are compared with that on other terrestrial planets. Activity at divergent, transform, and convergent plate margins is described with particular emphasis on hot spots and flood basalts. The timing and causing of uplifting above 500 meters, which in not associated with either plate boundaries or the normal nonplate margin edges of continents is considered with particular focus on the Guyana Highlands in southern Venezuela and western British Guiana, and the Brazilian Highlands in the central, eastern, and southern parts of the country. The mode and mechanism of plateau uplifting and the re-elevation of old mountain belts and subsidence of intra-continental basins are also discussed

Characterization and performance of nucleic acid nanoparticles combined with protamine and gold

Macromolecular nucleic acids such as DNA vaccines, siRNA, and splice-site switching oligomers (SSO) have vast chemotherapeutic potential. Nanoparticulate biomaterials hold promise for DNA and RNA delivery when a means for binding is identified that retains structure-function and provides stabilization by the nanoparticles. In order to provide these benefits of binding, we combined DNA and RNA with protamine-demonstrating association to gold microparticles by electrophoretic, gel shot, fluorescence, and dynamic laser light spectroscopy (DLLS). A pivotal finding in these studies is that the Au-protamine-DNA conjugates greatly stabilize the DNA; and DNA structure and vaccine activity are maintained even after exposure to physical, chemical, and temperature-accelerated degradation. Specifically, protamine formed nanoparticles when complexed to RNA. These complexes could be detected by gel shift and were probed by high throughput absorbance difference spectroscopy (HTADS). Biological activity of these RNA nanoparticles (RNPs) was demonstrated also by a human tumor cell splice-site switching assay and by siRNA delivery against B-Raf-a key cancer target. Finally, RNA:protamine particles inhibited growth of cultured human tumor cells and bacteria. These data provide new insights into DNA and RNA nanoparticles and prospects for their delivery and chemotherapeutic activity. (C) 2009 Elsevier Ltd. All rights reserved

Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions

Structured-output learning is a challenging problem; particularly so because of the difficulty in obtaining large datasets of fully labelled instances for training. In this paper we try to overcome this difficulty by presenting a multi-utility learning framework for structured prediction that can learn from training instances with different forms of supervision. We propose a unified technique for inferring the loss functions most suitable for quantifying the consistency of solutions with the given weak annotation. We demonstrate the effectiveness of our framework on the challenging semantic image segmentation problem for which a wide variety of annotations can be used. For instance, the popular training datasets for semantic segmentation are composed of images with hard-to-generate full pixel labellings, as well as images with easy-to-obtain weak annotations, such as bounding boxes around objects, or image-level labels that specify which object categories are present in an image. Experimental evaluation shows that the use of annotation-specific loss functions dramatically improves segmentation accuracy compared to the baseline system where only one type of weak annotation is used