6,642 research outputs found
Rosenfeld functional for non-additive hard spheres
The fundamental measure density functional theory for hard spheres is
generalized to binary mixtures of arbitrary positive and moderate negative
non-additivity between unlike components. In bulk the theory predicts
fluid-fluid phase separation into phases with different chemical compositions.
The location of the accompanying critical point agrees well with previous
results from simulations over a broad range of non-additivities and both for
symmetric and highly asymmetric size ratios. Results for partial pair
correlation functions show good agreement with simulation data.Comment: 8 pages with 4 figure
Heterofusion:Fusing genomics data of different measurement scales
In systems biology, it is becoming increasingly common to measure biochemical entities at different levels of the same biological system. Hence, data fusion problems are abundant in the life sciences. With the availability of a multitude of measuring techniques, one of the central problems is the heterogeneity of the data. In this paper, we discuss a specific form of heterogeneity, namely, that of measurements obtained at different measurement scales, such as binary, ordinal, interval, and ratio‐scaled variables. Three generic fusion approaches are presented of which two are new to the systems biology community. The methods are presented, put in context, and illustrated with a real‐life genomics example
Numerical study of the glass-glass transition in short-ranged attractive colloids
We report extensive numerical simulations in the {\it glass} region for a
simple model of short-ranged attractive colloids, the square well model. We
investigate the behavior of the density autocorrelation function and of the
static structure factor in the region of temperatures and packing fractions
where a glass-glass transition is expected according to theoretical
predictions. We strengthen our observations by studying both waiting time and
history dependence of the numerical results. We provide evidence supporting the
possibility that activated bond-breaking processes destabilize the attractive
glass, preventing the full observation of a sharp glass-glass kinetic
transition.Comment: 15 pages, 9 figures; Proceedings of "Structural Arrest Transitions in
Colloidal Systems with Short-Range Attractions", Messina, Italy, December
2003 (submitted to J. Phys.: Condens. Matt.
A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer
Recently, several classifiers that combine primary tumor data, like gene
expression data, and secondary data sources, such as protein-protein
interaction networks, have been proposed for predicting outcome in breast
cancer. In these approaches, new composite features are typically constructed
by aggregating the expression levels of several genes. The secondary data
sources are employed to guide this aggregation. Although many studies claim
that these approaches improve classification performance over single gene
classifiers, the gain in performance is difficult to assess. This stems mainly
from the fact that different breast cancer data sets and validation procedures
are employed to assess the performance. Here we address these issues by
employing a large cohort of six breast cancer data sets as benchmark set and by
performing an unbiased evaluation of the classification accuracies of the
different approaches. Contrary to previous claims, we find that composite
feature classifiers do not outperform simple single gene classifiers. We
investigate the effect of (1) the number of selected features; (2) the specific
gene set from which features are selected; (3) the size of the training set and
(4) the heterogeneity of the data set on the performance of composite feature
and single gene classifiers. Strikingly, we find that randomization of
secondary data sources, which destroys all biological information in these
sources, does not result in a deterioration in performance of composite feature
classifiers. Finally, we show that when a proper correction for gene set size
is performed, the stability of single gene sets is similar to the stability of
composite feature sets. Based on these results there is currently no reason to
prefer prognostic classifiers based on composite features over single gene
classifiers for predicting outcome in breast cancer
Not Just Efficiency: Insolvency Law in the EU and Its Political Dimension
Certain insolvency law rules, like creditors’ priorities and set-off rights, have a distributive impact on creditors. Distributional rules reflect the hierarchies of values and interests in each jurisdiction and, as a result, have high political relevance and pose an obstacle to reforming the EU Insolvency Regulation. This paper will show the difficulty of reform by addressing two alternative options to regulate cross-border insolvencies in the European Union. The first one is the ‘choice model’, under which companies can select the insolvency law they prefer. Although such a model would allow distressed firms to select the most efficient insolvency law, it would also displace Member States’ power to protect local constituencies. The choice model therefore produces negative externalities and raises legitimacy concerns. The opposite solution is full harmonisation of insolvency law at EU level, including distributional rules. Full harmonisation would have the advantage of internalising all externalities produced by cross-border insolvencies. However, the EU legislative process, which is still based on negotiations between states, is not apt to decide on distributive insolvency rules; additionally, if harmonisation includes such rules, it will indirectly modify national social security strategies and equilibria. This debate shows that the choice regarding power allocation over bankruptcies in the EU depends on the progress of European integration and is mainly a matter of political legitimacy, not only of efficiency
Artificial intelligence for renal cancer: From imaging to histology and beyond
Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation
Longitudinal Flow of Protons from 2-8 AGeV Central Au+Au Collisions
Rapidity distributions of protons from central Au + Au
collisions measured by the E895 Collaboration in the energy range from 2 to 8
AGeV at the Brookhaven AGS are presented. Longitudinal flow parameters derived
using a thermal model including collective longitudinal expansion are extracted
from these distributions. The results show an approximately linear increase in
the longitudinal flow velocity, , as a function of the
logarithm of beam energy.Comment: 5 Pages, including 3 figures, 1 tabl
The PKC, HOG and Ca2+ signalling pathways co-ordinately regulate chitin synthesis in Candida albicans
Open Access via PMC2649417Peer reviewedPublisher PD
Fragmented in space: the oral history narrative of an Arab Christian from Antioch, Turkey
This study uses the case of Can Kılçıksız, an Arab Christian refugee youth from Antioch, Turkey, to argue that globalization may result in fragmented families and subjectivities and can also accelerate processes initiated by modernity and the construction of national identities. Can Kılçıksız and his siblings now live in Turkey, Germany, France and Finland. His life story suggests that males of Arab Christian origin from Antioch who had access to schooling are more likely to be involved in politics whereas females tend to be drawn to evangelical Christian organizations. The case also suggests that sibling ties might prove more durable in the course of transnational migration than conjugal ties. The case of Can Kılçıksız shows that the time/space linked to childhood through memory can play an important role in identity construction of subjects circulating in transnational space
Flow and non-flow event anisotropies at the SPS
A study of differential elliptic event anisotropies (v_2) of charged
particles and high-pt pions in 158 AGeV/c Pb+Au collisions is presented.
Results from correlations with respect to the event plane and from two-particle
azimuthal correlations are compared. The latter give systematically higher v_2
values at pt>1.2GeV/c providing possibly an evidence of a non-flow semihard
component.Comment: 4 pages, 6 figures, Quark Matter 2002, Nantes, to appear in Nucl.
Phys.
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