We report the emergence of a giant Mpemba effect in the uniformly heated gas of inelastic rough hard spheres: The initially hotter sample may cool sooner than the colder one, even when the initial temperatures differ by more than one order of magnitude. In order to understand this behavior, it suffices to consider the simplest Maxwellian approximation for the velocity distribution in a kinetic approach. The largeness of the effect stems from the fact that the rotational and translational temperatures, which obey two coupled evolution equations, are comparable. Our theoretical predictions agree very well with molecular dynamics and direct simulation Monte Carlo data.Let us consider two beakers of water at different temperatures. Mpemba and Osborne showed that the initially hotter sample cools sooner under certain conditions [1], i.e., the curve giving the time evolution of its temperature crosses that of the initially cooler sample and stays below it for longer times. This is called the Mpemba memory effect, which is known since antiquity in cultures for which water in the form of ice and snow is common [2]. Later, the Mpemba effect has been clearly identified in different physical systems [3][4][5][6][7], although there is still some debate about its existence in water [8].From a physical point of view, one would like to answer how different the initial preparation of two samples of the system under study must be so that the Mpemba effect arises. This is the main-currently unresolved in general-question, although there has been some recent progress in this respect [5,6]. Lu and Raz [5] analyzed the Mpemba effect in a generic Markovian system by monitoring the relaxation of an entropy-like variable that measures the distance to the steady state. This makes it possible to define and investigate Mpemba-like effects in systems for which there is not an obvious definition of a nonequilibrium temperature, but makes the comparison with the usual experimental setup described above difficult.A different approach was carried out by some of us in the study of the Mpemba effect for a granular fluid of smooth hard spheres [6]. Therein, the granular temperature-basically the average kinetic energy per particle-is the physical quantity monitored to investigate the Mpemba effect. In the smooth-sphere case, the angular velocities play no role since there is no energy transfer between the translational and the rotational degrees of freedom, and the kinetic energy is thus purely * prados@us.es translational. We showed that the Mpemba effect stems from the coupling of the granular temperature and the kurtosis, which measures the deviation of the velocity distribution function from the Maxwellian shape at the lowest order. More specifically, it is the difference between the initial values of the kurtosis of the two samples that controls the appearance of the Mpemba effect.In the granular fluid of smooth hard spheres, the kurtosis is typically small. On the one hand, this facilitates the theoretical analysis, because it makes it possi...
Expression Profiler (EP, http://www.ebi.ac.uk/expressionprofiler) is a web-based platform for microarray gene expression and other functional genomics-related data analysis. The new architecture, Expression Profiler: next generation (EP:NG), modularizes the original design and allows individual analysis-task-related components to be developed by different groups and yet still seamlessly to work together and share the same user interface look and feel. Data analysis components for gene expression data preprocessing, missing value imputation, filtering, clustering methods, visualization, significant gene finding, between group analysis and other statistical components are available from the EBI (European Bioinformatics Institute) web site. The web-based design of Expression Profiler supports data sharing and collaborative analysis in a secure environment. Developed tools are integrated with the microarray gene expression database ArrayExpress and form the exploratory analytical front-end to those data. EP:NG is an open-source project, encouraging broad distribution and further extensions from the scientific community.
Rapid accumulation and availability of gene expression datasets in public repositories have enabled large-scale meta-analyses of combined data. The richness of cross-experiment data has provided new biological insights, including identification of new cancer genes. In this study, we compiled a human gene expression dataset from ∼40,000 publicly available Affymetrix HG-U133Plus2 arrays. After strict quality control and data normalisation the data was quantified in an expression matrix of ∼20,000 genes and ∼28,000 samples. To enable different ways of sample grouping, existing annotations where subjected to systematic ontology assisted categorisation and manual curation. Groups like normal tissues, neoplasmic tissues, cell lines, homoeotic cells and incompletely differentiated cells were created. Unsupervised analysis of the data confirmed global structure of expression consistent with earlier analysis but with more details revealed due to increased resolution. A suitable mixed-effects linear model was used to further investigate gene expression in solid tissue tumours, and to compare these with the respective healthy solid tissues. The analysis identified 1,285 genes with systematic expression change in cancer. The list is significantly enriched with known cancer genes from large, public, peer-reviewed databases, whereas the remaining ones are proposed as new cancer gene candidates. The compiled dataset is publicly available in the ArrayExpress Archive. It contains the most diverse collection of biological samples, making it the largest systematically annotated gene expression dataset of its kind in the public domain.
Rapid accumulation of large and standardized microarray data collections is opening up novel opportunities for holistic characterization of genome function. The limited scalability of current preprocessing techniques has, however, formed a bottleneck for full utilization of these data resources. Although short oligonucleotide arrays constitute a major source of genome-wide profiling data, scalable probe-level techniques have been available only for few platforms based on pre-calculated probe effects from restricted reference training sets. To overcome these key limitations, we introduce a fully scalable online-learning algorithm for probe-level analysis and pre-processing of large microarray atlases involving tens of thousands of arrays. In contrast to the alternatives, our algorithm scales up linearly with respect to sample size and is applicable to all short oligonucleotide platforms. The model can use the most comprehensive data collections available to date to pinpoint individual probes affected by noise and biases, providing tools to guide array design and quality control. This is the only available algorithm that can learn probe-level parameters based on sequential hyperparameter updates at small consecutive batches of data, thus circumventing the extensive memory requirements of the standard approaches and opening up novel opportunities to take full advantage of contemporary microarray collections.
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