Direct entropy determination and application to artificial spin ice

From thermodynamic origins, the concept of entropy has expanded to a range of statistical measures of uncertainty, which may still be thermodynamically significant. However, laboratory measurements of entropy continue to rely on direct measurements of heat. New technologies that can map out myriads of microscopic degrees of freedom suggest direct determination of configurational entropy by counting in systems where it is thermodynamically inaccessible, such as granular and colloidal materials, proteins and lithographically fabricated nanometre-scale arrays. Here, we demonstrate a conditional-probability technique to calculate entropy densities of translation-invariant states on lattices using limited configuration data on small clusters, and apply it to arrays of interacting nanometre-scale magnetic islands (artificial spin ice). Models for statistically disordered systems can be assessed by applying the method to relative entropy densities. For artificial spin ice, this analysis shows that nearest-neighbour correlations drive longer-range ones.Comment: 10 page

Molecular Biomechanics: The Molecular Basis of How Forces Regulate Cellular Function

Recent advances have led to the emergence of molecular biomechanics as an essential element of modern biology. These efforts focus on theoretical and experimental studies of the mechanics of proteins and nucleic acids, and the understanding of the molecular mechanisms of stress transmission, mechanosensing and mechanotransduction in living cells. In particular, single-molecule biomechanics studies of proteins and DNA, and mechanochemical coupling in biomolecular motors have demonstrated the critical importance of molecular mechanics as a new frontier in bioengineering and life sciences. To stimulate a more systematic study of the basic issues in molecular biomechanics, and attract a broader range of researchers to enter this emerging field, here we discuss its significance and relevance, describe the important issues to be addressed and the most critical questions to be answered, summarize both experimental and theoretical/computational challenges, and identify some short-term and long-term goals for the field. The needs to train young researchers in molecular biomechanics with a broader knowledge base, and to bridge and integrate molecular, subcellular and cellular level studies of biomechanics are articulated.National Institutes of Health (U.S.) (grant UO1HL80711-05 to GB)National Institutes of Health (U.S.) (grant R01GM076689-01)National Institutes of Health (U.S.) (grant R01AR033236-26)National Institutes of Health (U.S.) (grant R01GM087677-01A1)National Institutes of Health (U.S.) (grant R01AI44902)National Institutes of Health (U.S.) (grant R01AI38282)National Science Foundation (U.S.) (grant CMMI-0645054)National Science Foundation (U.S.) (grant CBET-0829205)National Science Foundation (U.S.) (grant CAREER-0955291

Prognostic value of nuclear morphometry in patients with TNM stage T1 ovarian clear cell adenocarcinoma

In 40 patients with TNM stage T1 ovarian clear cell adenocarcinoma, we used nuclear morphometry to study the relations among morphometric variables, clinical prognostic factors and outcome. The presence of one or more giant nuclear cells was positively associated with death (OR = 10.6, P = 0.02) and tended to be associated with disease recurrence (OR = 5.1, P = 0.07). Nuclear irregularity (expressed in terms of the nuclear roundness factor) was positively associated with both death (OR = 8.6, P = 0.02) and disease recurrence (OR = 8.2, P = 0.02). A combination of giant nuclear cell presence or nuclear irregularity proved to be a useful prognostic indicator, with a sensitivity and specificity of 83% and 71% in the prediction of death, and 75% and 71% in the prediction of disease recurrence. Patients' age and substage were of no prognostic value. We conclude that the nuclear morphometric characteristics, especially the presence of giant nuclear cells and nuclear irregularity, may be useful in predicting outcome in patients with early stage ovarian clear cell adenocarcinoma. © 1999 Cancer Research Campaig

Detection of distant evolutionary relationships between protein families using theory of sequence profile-profile comparison

<p>Abstract</p> <p>Background</p> <p>Detection of common evolutionary origin (homology) is a primary means of inferring protein structure and function. At present, comparison of protein families represented as sequence profiles is arguably the most effective homology detection strategy. However, finding the best way to represent evolutionary information of a protein sequence family in the profile, to compare profiles and to estimate the biological significance of such comparisons, remains an active area of research.</p> <p>Results</p> <p>Here, we present a new homology detection method based on sequence profile-profile comparison. The method has a number of new features including position-dependent gap penalties and a global score system. Position-dependent gap penalties provide a more biologically relevant way to represent and align protein families as sequence profiles. The global score system enables an analytical solution of the statistical parameters needed to estimate the statistical significance of profile-profile similarities. The new method, together with other state-of-the-art profile-based methods (HHsearch, COMPASS and PSI-BLAST), is benchmarked in all-against-all comparison of a challenging set of SCOP domains that share at most 20% sequence identity. For benchmarking, we use a reference ("gold standard") free model-based evaluation framework. Evaluation results show that at the level of protein domains our method compares favorably to all other tested methods. We also provide examples of the new method outperforming structure-based similarity detection and alignment. The implementation of the new method both as a standalone software package and as a web server is available at <url>http://www.ibt.lt/bioinformatics/coma</url>.</p> <p>Conclusion</p> <p>Due to a number of developments, the new profile-profile comparison method shows an improved ability to match distantly related protein domains. Therefore, the method should be useful for annotation and homology modeling of uncharacterized proteins.</p

Island method for estimating the statistical significance of profile-profile alignment scores

<p>Abstract</p> <p>Background</p> <p>In the last decade, a significant improvement in detecting remote similarity between protein sequences has been made by utilizing alignment profiles in place of amino-acid strings. Unfortunately, no analytical theory is available for estimating the significance of a gapped alignment of two profiles. Many experiments suggest that the distribution of local profile-profile alignment scores is of the Gumbel form. However, estimating distribution parameters by random simulations turns out to be computationally very expensive.</p> <p>Results</p> <p>We demonstrate that the background distribution of profile-profile alignment scores heavily depends on profiles' composition and thus the distribution parameters must be estimated independently, for each pair of profiles of interest. We also show that accurate estimates of statistical parameters can be obtained using the "island statistics" for profile-profile alignments.</p> <p>Conclusion</p> <p>The island statistics can be generalized to profile-profile alignments to provide an efficient method for the alignment score normalization. Since multiple island scores can be extracted from a single comparison of two profiles, the island method has a clear speed advantage over the direct shuffling method for comparable accuracy in parameter estimates.</p

Dynamic tracking error with shortfall control using stochastic programming

In this contribution we tackle the issue of portfolio management combining benchmarking and risk control. We propose a dynamic tracking error problem and we consider the problem of monitoring at discrete points the shortfalls of the portfolio below a set of given reference levels of wealth.We formulate and solve the resulting dynamic optimization problem using stochastic programming. The proposed model allows for a great flexibility in the combination of the tracking goal and the downside risk protection. We provide the results of out-of-sample simulation experiments, on real data, for different portfolio configurations and different market conditions

Abstract P3-04-19: Inhibition of ER-positive breast cancer progression with duavee, a new form of hormone replacement therapy

Abstract Menopause occurs in all women between the ages of 45 and 55 and often results in undesirable vasomotor symptoms. Hormone replacement therapy (HRT) can alleviate these symptoms, including hot flashes, difficulty sleeping, fatigue, and vaginal atrophy, and also prevents osteoporosis. PremPro, a HRT formulation that combines conjugated equine estrogens (CE) with medroxyprogesterone acetate, was found to increase the risk of breast cancer in the Women's Health Initiative (WHI) trial. Due to the perceived risk based largely on the results of the WHI trial, the number of women taking HRT has dramatically decreased. Studies suggest that breast cancer cases from PremPro treatment were primarily due to the outgrowth of occult tumors, not the formation of new disease. Duavee, a new form of HRT that combines CE and bazedoxifene (BZA), a selective estrogen receptor modulator (SERM) and degrader (SERD), has been approved by the FDA for treatment of moderate to severe hot flashes and to reduce the risk of osteoporosis. Importantly, this CE+BZA mixture not only relieves symptoms associated with menopause, but it also does not stimulate the breast or uterus. Several preclinical studies suggest that CE+BZA might be protective in the breast, however the mechanism of action of this new combination therapy is not known. Our goal, therefore, is to elucidate the underlying molecular mechanisms by which CE+BZA differentially affects estrogen receptor alpha (ERα) action in the mammary gland, using transcriptome and whole genome occupancy analysis in breast cancer cell lines. We are also studying the effects of CE+BZA on early mammary cancer progression in the polyoma middle T antigen (PyMT) transgenic mouse model, which is sensitive to estrogens. In addition, we are studying the effects of CE+BZA in an ERa-positive patient-derived xenograft (PDX) mouse model. We have determined that CE modulates gene expression in MCF7 cells similar to 17β-estradiol (E2), and that BZA is able to inhibit these effects. We have also observed that CE increases ERα occupancy, similar to E2, at response elements associated with some estrogen target genes, whereas CE+BZA decreases this occupancy. In the PyMT mouse model, CE+BZA delays the onset of mammary tumors in ovariectomized mice and prolongs their survival when compared to E2 and CE treatment alone. In the PDX model, the CE+BZA tumor growth curve is below vehicle, although it does not quite reach statistical significance. An improved understanding of the molecular mechanisms of CE+BZA action in hormone sensitive breast cancer cell and animal models should have important implications for women considering HRT. Citation Format: Dembo AG, Aledort E, Greene GL. Inhibition of ER-positive breast cancer progression with duavee, a new form of hormone replacement therapy [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-04-19.</jats:p