Lipid Coated Gold Nanoparticle Cores: Synthesis and Characterization

Including environmental, industrial, and biomedical sciences, applications of gold nanoparticles are on the forefront of research in many areas. By altering the surface treatment of spherical gold nanoparticle cores, particularly those smaller than 100 nm (nanometers), one can influence their potential use in a number of ways. Lipid coated nanoparticles with specifically selected surface ligands can be used for multiple biomedical functions, including medical imaging, for use as colorimetric and plasmonic sensors within the body, and as cell or organelle specific targets for therapeutic drug delivery or cancer treatment. Here, spherical gold nanoparticles ranging in size from 8-40 nm (avg. diameter 23-48 nm) have been synthesized and coated with poly(allylamine hydrochloride) (PAH) and a mixed lipid solution of 1:1 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine (POPS) and lysophosphatidylcholine (LPC), two of the four major types of lipids found in the human body. Characterization was performed using a NanoSight LM10HS particle sizer, and shows a gradual increase in size after each step in the coating process for nanoparticle cores ranging in size from 16-27 nm. The thickness of these purified and lipid coated nanoparticles was consistently 2-3 times that of the PAH coated sample it was layered onto, suggesting a successful, multi-layered coat that ranges in size based on the PAH coated core size. UV-Vis spectroscopy shows a slight red shift, indicating an increase in size and change in refractive index, which supports the presence of lipid coating on the PAH coated gold nanoparticle cores

DE-STRESS:A user-friendly web application for the evaluation of protein designs

De novo protein design is a rapidly growing field, and there are now many interesting and useful examples of designed proteins in the literature. However, most designs could be classed as failures when characterised in the lab, usually as a result of low expression, misfolding, aggregation or lack of function. This high attrition rate makes protein design unreliable and costly. It is possible that some of these failures could be caught earlier in the design process if it were quick and easy to generate information and a set of high-quality metrics regarding designs, which could be used to make reproducible and data-driven decisions about which designs to characterise experimentally. We present DE-STRESS (DEsigned STRucture Evaluation ServiceS), a web application for evaluating structural models of designed and engineered proteins. DE-STRESS has been designed to be simple, intuitive to use and responsive. It provides a wealth of information regarding designs, as well as tools to help contextualise the results and formally describe the properties that a design requires to be fit for purpose

PDBench: Evaluating Computational Methods for Protein Sequence Design

Proteins perform critical processes in all living systems: converting solar energy into chemical energy, replicating DNA, as the basis of highly performant materials, sensing and much more. While an incredible range of functionality has been sampled in nature, it accounts for a tiny fraction of the possible protein universe. If we could tap into this pool of unexplored protein structures, we could search for novel proteins with useful properties that we could apply to tackle the environmental and medical challenges facing humanity. This is the purpose of protein design. Sequence design is an important aspect of protein design, and many successful methods to do this have been developed. Recently, deep-learning methods that frame it as a classification problem have emerged as a powerful approach. Beyond their reported improvement in performance, their primary advantage over physics-based methods is that the computational burden is shifted from the user to the developers, thereby increasing accessibility to the design method. Despite this trend, the tools for assessment and comparison of such models remain quite generic. The goal of this paper is to both address the timely problem of evaluation and to shine a spotlight, within the Machine Learning community, on specific assessment criteria that will accelerate impact. We present a carefully curated benchmark set of proteins and propose a number of standard tests to assess the performance of deep learning based methods. Our robust benchmark provides biological insight into the behaviour of design methods, which is essential for evaluating their performance and utility. We compare five existing models with two novel models for sequence prediction. Finally, we test the designs produced by these models with AlphaFold2, a state-of-the-art structure-prediction algorithm, to determine if they are likely to fold into the intended 3D shapes.Comment: 9 pages, 5 figure

Temporal changes in fruit production between recurrent prescribed burns in pine woodlands of the Ouachita Mountains

The use of prescribed fire is integral to the restoration of open woodlands and savannas, including shortleaf pine (Pinus echinata) woodlands in the Ouachita Mountains of Oklahoma and Arkansas. Fire offers many potential benefits to numerous wildlife; however, short-term implications for understory fruit production are not fully understood, especially in stands subjected to frequent, recurrent burns. We examined the effects of dormant season prescribed burns on woody fruit production (kg ha−1) and fruit producing vegetative cover in the understory of restored pine woodlands. We inventoried 32 stands during four temporal periods after dormant season prescribed fires: 1, 2, 3, and 5 growing seasons post-burn. We counted fruit (\u3c2 m above the ground) throughout the summer and visually estimated vegetative cover of fruit producing plants. Fruit production was greatest in the 3rd year (18.2 kg ha−1), followed by 5th (10.9 kg ha−1) and 2nd (9.8 kg ha−1) years after burns. Overall, 87% of total production consisted of three genera: American beautyberry (Callicarpa americana [38%]), Vitis spp. (summer grapes [Vitis aestivalis; 11%] and muscadine grape [V. rotundifolia; 10%]), and Rubus spp. (blackberry [20%] and dewberry [R. flagellaris; 8%]). Production was recorded in 13 of the 14 fruit producing species present during the 5th year post-burn, indicating that production diversity increased over time. Percent cover and species richness (26 taxa) of fruit producing taxa were greatest in the 3rd year post-burn. Taxa such as poison ivy (Toxicodendron radicans) and sumac (Rhus spp.) comprised a sizable percent of coverage (\u3e7% each), but this did not translate into substantial fruit production. American beautyberry and summer grape had both substantial coverage and production. Results suggest that burning on a 3-year rotation maximizes and prolongs fruit production; however, occasional burning on a 5-year rotation will promote a higher diversity of woody mast-producing understory species

Improving the LSST dithering pattern and cadence for dark energy studies

The Large Synoptic Survey Telescope (LSST) will explore the entire southern sky over 10 years starting in 2022 with unprecedented depth and time sampling in six filters, $ugrizy$. Artificial power on the scale of the 3.5 deg LSST field-of-view will contaminate measurements of baryonic acoustic oscillations (BAO), which fall at the same angular scale at redshift $z \sim 1$. Using the HEALPix framework, we demonstrate the impact of an "un-dithered" survey, in which $17\%$ of each LSST field-of-view is overlapped by neighboring observations, generating a honeycomb pattern of strongly varying survey depth and significant artificial power on BAO angular scales. We find that adopting large dithers (i.e., telescope pointing offsets) of amplitude close to the LSST field-of-view radius reduces artificial structure in the galaxy distribution by a factor of $\sim$10. We propose an observing strategy utilizing large dithers within the main survey and minimal dithers for the LSST Deep Drilling Fields. We show that applying various magnitude cutoffs can further increase survey uniformity. We find that a magnitude cut of $r < 27.3$ removes significant spurious power from the angular power spectrum with a minimal reduction in the total number of observed galaxies over the ten-year LSST run. We also determine the effectiveness of the observing strategy for Type Ia SNe and predict that the main survey will contribute $\sim$100,000 Type Ia SNe. We propose a concentrated survey where LSST observes one-third of its main survey area each year, increasing the number of main survey Type Ia SNe by a factor of $\sim$1.5, while still enabling the successful pursuit of other science drivers.Comment: 9 pages, 6 figures, published in SPIE proceedings; corrected typo in equation

Distributional Collision Modeling for Monte Carlo Simulations

Abstract. In this paper we present the initial results in our development of Distributional DSMC (DDSMC) methods. By modifying Nanbu&apos;s method to allow distributed velocities, we have shown that DSMC methods are not limited to convergence in probability measure alone, but can achieve strong convergence for L 1 solutions of the Boltzmann equation and pointwise convergence for bounded solutions. We also present an initial attempt at a general distributional method and apply these methods to the Bobylev, Krook, and Wu space homogeneous solution of the Boltzmann equation