Scaling of human behavior during portal browsing

We investigate transitions of portals users between different subpages. A weighted network of portals subpages is reconstructed where edge weights are numbers of corresponding transitions. Distributions of link weights and node strengths follow power laws over several decades. Node strength increases faster than linearly with node degree. The distribution of time spent by the user at one subpage decays as power law with exponent around 1.3. Distribution of numbers P(z) of unique subpages during one visit is exponential. We find a square root dependence between the average z and the total number of transitions n during a single visit. Individual path of portal user resembles of self-attracting walk on the weighted network. Analytical model is developed to recover in part the collected data.Comment: 6 pages, 7 figure

Endometrial Cancer

This chapter in Cancer Concepts: A Guidebook for the Non-Oncologist is about cancers of the endometrium and uterus, including the epidemiology, risk factors, diagnosis, genetic risk, histology, grading and type categorization, management, and prognosis.https://escholarship.umassmed.edu/cancer_concepts/1013/thumbnail.jp

The Primarily Undergraduate Nanomaterials Cooperative: A New Model for Supporting Collaborative Research at Small Institutions on a National Scale

The Primarily Undergraduate Nanomaterials Cooperative (PUNC) is an organization for research-active faculty studying nanomaterials at Primarily Undergraduate Institutions (PUIs), where undergraduate teaching and research go hand-in-hand. In this perspective, we outline the differences in maintaining an active research group at a PUI compared to an R1 institution. We also discuss the work of PUNC, which focuses on community building, instrument sharing, and facilitating new collaborations. Currently consisting of 37 members from across the United States, PUNC has created an online community consisting of its Web site (nanocooperative.org), a weekly online summer group meeting program for faculty and students, and a Discord server for informal conversations. Additionally, in-person symposia at ACS conferences and PUNC-specific conferences are planned for the future. It is our hope that in the years to come PUNC will be seen as a model organization for community building and research support at primarily undergraduate institutions

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.</p> <p>Results</p> <p>This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|H_i), which is used as confidence level. The unit network with higher P(D|H_i) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|H_i), which is a unique property of the proposed algorithm.</p> <p>The algorithm is evaluated with synthetic and <it>Saccharomyces cerevisiae </it>expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.</p> <p>The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and <it>Saccharomyces cerevisiae </it>expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.</p> <p>Conclusion</p> <p>From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.</p

Top Dipole Form Factors and Loop-induced CP-violation in Supersymmetry

The one-loop Minimal Supersymmetric Standard Model (MSSM) contributions to the weak and electromagnetic dipole form factors of the top quark are presented. Far from the Z peak, they are not sufficient to account for all the new physics effects. In the context of the calculation of the process e^+e^- -> t tbar to one loop in the MSSM, we compare the impact on the phenomenology of the CP-violating dipole form factors of the top quark with the contribution from CP-violating box graphs. Some exemplificative observables are analyzed and the relevance of both the contributions is pointed out. The one-loop expressions for the electromagnetic and weak dipole form factors in a general renormalizable theory and the SM and MSSM couplings and conventions are also given.Comment: 43 pages LaTeX. PS figures included with epsf.sty. Labels `N' and `T' in Tables 2,3 exchange

Sympatric and Allopatric Divergence of MHC Genes in Threespine Stickleback

Parasites can strongly affect the evolution of their hosts, but their effects on host diversification are less clear. In theory, contrasting parasite communities in different foraging habitats could generate divergent selection on hosts and promote ecological speciation. Immune systems are costly to maintain, adaptable, and an important component of individual fitness. As a result, immune system genes, such as those of the Major Histocompatability Complex (MHC), can change rapidly in response to parasite-mediated selection. In threespine stickleback (Gasterosteus aculeatus), as well as in other vertebrates, MHC genes have been linked with female mating preference, suggesting that divergent selection acting on MHC genes might influence speciation. Here, we examined genetic variation at MHC Class II loci of sticklebacks from two lakes with a limnetic and benthic species pair, and two lakes with a single species. In both lakes with species pairs, limnetics and benthics differed in their composition of MHC alleles, and limnetics had fewer MHC alleles per individual than benthics. Similar to the limnetics, the allopatric population with a pelagic phenotype had few MHC alleles per individual, suggesting a correlation between MHC genotype and foraging habitat. Using a simulation model we show that the diversity and composition of MHC alleles in a sympatric species pair depends on the amount of assortative mating and on the strength of parasite-mediated selection in adjacent foraging habitats. Our results indicate parallel divergence in the number of MHC alleles between sympatric stickleback species, possibly resulting from the contrasting parasite communities in littoral and pelagic habitats of lakes