Dynamics of clusters: From elementary to biological structures

Between isolated atoms or molecules and bulk materials there lies a class of unique structures, known as clusters, that consist of a few to hundreds of atoms or molecules. Within this range of "nanophase," many physical and chemical properties of the materials evolve as a function of cluster size, and materials may exhibit novel properties due to quantum confinement effects. Understanding these phenomena is in its own rights fundamental, but clusters have the additional advantage of being controllable model systems for unraveling the complexity of condensed-phase and biological structures, not to mention their vanguard role in defining nanoscience and nanotechnology. Over the last two decades, much progress has been made, and this short overview highlights our own involvement in developing cluster dynamics, from the first experiments on elementary systems to model systems in the condensed phase, and on to biological structures

Electrochemical reductions of diphenyldiazomethane and azobenzene: the effect of electroinactive proton donors

Call number: LD2668 .T4 1986 C443Master of ScienceChemistr

Customer Concentration of Targets in Mergers and Acquisitions

We study how customer base concentration at a target firm impacts the occurrence and structure of M&A deals. We hypothesize that customer concentration increases information asymmetry and adverse selection between bidders and targets, such that (1) firms with greater customer concentration are less likely to receive a bid and (2) bidders for targets with greater customer concentration share the risk by using more stock payment in their offer. Using data on customer concentration and M&A deals from 1985 to 2016, we find consistent evidence supporting our predictions. Our findings extend the literature by systematically documenting an important factor in M&A decisions and by quantifying the economic consequences of customer concentration

The Mean Sum of Squared Linking Numbers of Random Piecewise-Linear Embeddings of KnK_n

DNA and other polymer chains in confined spaces behave like closed loops. Arsuaga et al. \cite{AB} introduced the uniform random polygon model in order to better understand such loops in confined spaces using probabilistic and knot theoretical techniques, giving some classification on the mean squared linking number of such loops. Flapan and Kozai \cite{flapan2016linking} extended these techniques to find the mean sum of squared linking numbers for random linear embeddings of complete graphs $K_n$ and found it to have order $\Theta(n(n!))$. We further these ideas by inspecting random piecewise-linear embeddings of complete graphs and give introductory-level summaries of the ideas throughout. In particular, we give a model of random piecewise-linear embeddings of complete graphs where the number of line segments between vertices is given by a random variable. We find further that in our model of the random piecewise-linear embeddings, the order of the expected sum of squared linking numbers is still $\Theta(n (n!))$

Energy stability analysis for a hybrid fluid-kinetic plasma model

In plasma physics, a hybrid fluid-kinetic model is composed of a magnetohydrodynamics (MHD) part that describes a bulk fluid component and a Vlasov kinetic theory part that describes an energetic plasma component. While most hybrid models in the plasma literature are non-Hamiltonian, this paper investigates a recent Hamiltonian variant in its two-dimensional configuration. The corresponding Hamiltonian structure is described along with its Casimir invariants. Then, the energy-Casimir method is used to derive explicit sufficient stability conditions, which imply a stable spectrum and suggest nonlinear stability

Prolog implementation of a graphic tool for generation of Ada language specifications

Call number: LD2668 .R4 CMSC 1987 C533Master of ScienceComputing and Information Science

DeepSF: deep convolutional neural network for mapping protein sequences to folds

Motivation Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a tar get protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice. Results We develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein se quence into one of 1195 known folds, which is useful for both fold recognition and the study of se quence-structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and map it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding a classification accuracy of 80.4%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 77.0%. We compare our method with a top profile profile alignment method - HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 14.5%-29.1% higher than HHSearch on template-free modeling targets and 4.5%-16.7% higher on hard template-based modeling targets for top 1, 5, and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking.Comment: 28 pages, 13 figure

Flavonoid-Rich Mixed Berries Maintain and Improve Cognitive Function Over a 6h Period in Young Healthy Adults

Research with young adults has previously indicated flavonoid-rich berry interventions facilitate improved executive function (EF) and positive affect 20 min–2 h post-dosing. There has been little consideration of the impact of a berry intervention over a working day and interventions have also tended to consider only a single berry type. This study investigated the temporal profile of EF and mood changes over a 6 h period following a mixed-berry intervention. We hypothesized berry-related benefits would be most evident when participants were cognitively compromised on demanding elements of the task or during periods of fatigue. The study employed a single-blind, randomized, placebo controlled, between-subjects design. Forty participants aged 20–30 years consumed a 400 mL smoothie containing equal blueberry, strawberry, raspberry, and blackberry (n = 20) or matched placebo (n = 20). Mood was assessed using the Positive and Negative Affect Schedule; EF was tested using the Modified Attention Network (MANT) and Task Switching (TST) Tasks. Testing commenced at baseline then 2, 4 and 6 h post-dosing. As expected, following placebo intervention, performance decreased across the day as participants became cognitively fatigued. However, following berry intervention, participants maintained accuracy on both cognitive tasks up to and including 6 h, and demonstrated quicker response times on the MANT at 2 and 4 h, and TST at 6 h. This study demonstrates the efficacy of flavonoid rich berries in maintaining or improving cognitive performance across the 6 h day