Topological interactions between ring polymers: Implications for chromatin loops

Chromatin looping is a major epigenetic regulatory mechanism in higher eukaryotes. Besides its role in transcriptional regulation, chromatin loops have been proposed to play a pivotal role in the segregation of entire chromosomes. The detailed topological and entropic forces between loops still remain elusive. Here, we quantitatively determine the potential of mean force between the centers of mass of two ring polymers, i.e. loops. We find that the transition from a linear to a ring polymer induces a strong increase in the entropic repulsion between these two polymers. On top, topological interactions such as the non-catenation constraint further reduce the number of accessible conformations of close-by ring polymers by about 50%, resulting in an additional effective repulsion. Furthermore, the transition from linear to ring polymers displays changes in the conformational and structural properties of the system. In fact, ring polymers adopt a markedly more ordered and aligned state than linear ones. The forces and accompanying changes in shape and alignment between ring polymers suggest an important regulatory function of such a topology in biopolymers. We conjecture that dynamic loop formation in chromatin might act as a versatile control mechanism regulating and maintaining different local states of compaction and order.Comment: 12 pages, 11 figures. The article has been accepted by The Journal Of Chemical Physics. After it is published, it will be found at http://jcp.aip.or

Investigation of the aerodynamic characteristics and wing-deployment transients of the NASA DL-4 body with a sailwing landing aid Final report

Aerodynamic characteristics and wing deployment transients of NASA DL-4 lifting body fitted with sailwing landing ai

Pair Wave Functions in Atomic Fermi Condensates

Recent experiments have observed condensation behavior in a strongly interacting system of fermionic atoms. We interpret these observations in terms of a mean-field version of resonance superfluidity theory. We find that the objects condensed are not bosonic molecules composed of bound fermion pairs, but are rather spatially correlated Cooper pairs whose coherence length is comparable to the mean spacing between atoms. We propose experiments that will help to further probe these novel pairs

Asymmetric Load Balancing on a Heterogeneous Cluster of PCs

In recent years, high performance computing with commodity clusters of personal computers has become an active area of research. Many organizations build them because they need the computational speedup provided by parallel processing but cannot afford to purchase a supercomputer. With commercial supercomputers and homogenous clusters of PCs, applications that can be statically load balanced are done so by assigning equal tasks to each processor. With heterogeneous clusters, the system designers have the option of quickly adding newer hardware that is more powerful than the existing hardware. When this is done, the assignment of equal tasks to each processor results in suboptimal performance. This research addresses techniques by which the size of the tasks assigned to processors is a suitable match to the processors themselves, in which the more powerful processors can do more work, and the less powerful processors perform less work. We find that when the range of processing power is narrow, some benefit can be achieved with asymmetric load balancing. When the range of processing power is broad, dramatic improvements in performance are realized our experiments have shown up to 92% improvement when asymmetrically load balancing a modified version of the NAS Parallel Benchmarks\u27 LU application

Improving Long Term Stock Market Prediction with Text Analysis

The task of forecasting stock performance is well studied with clear monetary motivations for those wishing to invest. A large amount of research in the area of stock performance prediction has already been done, and multiple existing results have shown that data derived from textual sources related to the stock market can be successfully used towards forecasting. These existing approaches have mostly focused on short term forecasting, used relatively simple sentiment analysis techniques, or had little data available. In this thesis, we prepare over ten years worth of stock data and propose a solution which combines features from textual yearly and quarterly filings with fundamental factors for long term stock performance forecasting. Additionally, we develop a method of text feature extraction and apply feature selection aided by a novel evaluation function. We work with investment company Highstreet Inc. and create a set of models with our technique allowing us to compare the performance to their own models. Our results show that feature selection is able to greatly improve the validation and test performance when compared to baseline models. We also show that for 2015, our method produces models which perform comparably to Highstreet\u27s hand-made models while requiring no expert knowledge beyond data preparation, making the model an attractive aid for constructing investment portfolios. Highstreet has decided to continue to work with us on this research, and our machine learning models can potentially be used in actual portfolio selection in the near future

Chaotic Orbits in Thermal-Equilibrium Beams: Existence and Dynamical Implications

Phase mixing of chaotic orbits exponentially distributes these orbits through their accessible phase space. This phenomenon, commonly called ``chaotic mixing'', stands in marked contrast to phase mixing of regular orbits which proceeds as a power law in time. It is operationally irreversible; hence, its associated e-folding time scale sets a condition on any process envisioned for emittance compensation. A key question is whether beams can support chaotic orbits, and if so, under what conditions? We numerically investigate the parameter space of three-dimensional thermal-equilibrium beams with space charge, confined by linear external focusing forces, to determine whether the associated potentials support chaotic orbits. We find that a large subset of the parameter space does support chaos and, in turn, chaotic mixing. Details and implications are enumerated.Comment: 39 pages, including 14 figure

Mol. Cell. Proteomics

Chemical cross-linking in combination with mass spectrometric analysis offers the potential to obtain low-resolution structural information from proteins and protein complexes. Identification of peptides connected by a cross-link provides direct evidence for the physical interaction of amino acid side chains, information that can be used for computational modeling purposes. Despite impressive advances that were made in recent years, the number of experimentally observed cross-links still falls below the number of possible contacts of cross-linkable side chains within the span of the cross-linker. Here, we propose two complementary experimental strategies to expand cross-linking data sets. First, enrichment of cross-linked peptides by size exclusion chromatography selects cross-linked peptides based on their higher molecular mass, thereby depleting the majority of unmodified peptides present in proteolytic digests of cross-linked samples. Second, we demonstrate that the use of proteases in addition to trypsin, such as Asp-N, can additionally boost the number of observable cross-linking sites. The benefits of both SEC enrichment and multiprotease digests are demonstrated on a set of model proteins and the improved workflow is applied to the characterization of the 20S proteasome from rabbit and Schizosaccharomyces pombe

Could Stannern-trend eucrites be crustal-contaminated melts?

In this paper, we show that the composition of Stannern trend eucrites can be satisfactorily explained by contamination of normal main group eucrites by a crustal partial melt