Operations and Performance of the PACS Instrument 3He Sorption Cooler on board of the Herschel Space Observatory

A 3He sorption cooler produced the operational temperature of 285mK for the bolometer arrays of the Photodetector Array Camera and Spectrometer (PACS) instrument of the Herschel Space Observatory. This cooler provided a stable hold time between 60 and 73h, depending on the operational conditions of the instrument. The respective hold time could be determined by a simple functional relation established early on in the mission and reliably applied by the scientific mission planning for the entire mission. After exhaustion of the liquid 3He due to the heat input by the detector arrays, the cooler was recycled for the next operational period following a well established automatic procedure. We give an overview of the cooler operations and performance over the entire mission and distinguishing in-between the start conditions for the cooler recycling and the two main modes of PACS photometer operations. As a spin-off, the cooler recycling temperature effects on the Herschel cryostat 4He bath were utilized as an alternative method to dedicated Direct Liquid Helium Content Measurements in determining the lifetime of the liquid Helium coolant.Comment: 34 pages, 13 figures, accepted in Experimental Astronom

ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules

Background: The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected. Results: We present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools. Conclusion: We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input

TIRA: Toolbox for Interval Reachability Analysis

This paper presents TIRA, a Matlab library gathering several methods for the computation of interval over-approximations of the reachable sets for both continuous- and discrete-time nonlinear systems. Unlike other existing tools, the main strength of interval-based reachability analysis is its simplicity and scalability, rather than the accuracy of the over-approximations. The current implementation of TIRA contains four reachability methods covering wide classes of nonlinear systems, handled with recent results relying on contraction/growth bounds and monotonicity concepts. TIRA's architecture features a central function working as a hub between the user-defined reachability problem and the library of available reachability methods. This design choice offers increased extensibility of the library, where users can define their own method in a separate function and add the function call in the hub function

Predicting breast cancer using an expression values weighted clinical classifier

Background: Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. Results: We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies. Conclusions: Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems

pi(-)p atom in ChPT: strong energy-level shift

The general formula of the pi(-)p atom strong energy-level shift in the 1s state is derived in the next-to-leading order in the isospin breaking, and in all orders in chiral expansion. Isospin-breaking corrections to the level shift are explicitly evaluated at order p^2 in ChPT. The results clearly demonstrate the necessity to critically reaccess the values of the piN scattering lengths, extracted from the energy-level shift measurement by means of the potential model-based theoretical analysis.Comment: 16 pages, LaTeX-file, 1 Figur

Inferring transcriptional modules from ChIP-chip, motif and microarray data

'ReMoDiscovery' is an intuitive algorithm to correlate regulatory programs with regulators and corresponding motifs to a set of co-expressed genes. It exploits in a concurrent way three independent data sources: ChIP-chip data, motif information and gene expression profiles. When compared to published module discovery algorithms, ReMoDiscovery is fast and easily tunable. We evaluated our method on yeast data, where it was shown to generate biologically meaningful findings and allowed the prediction of potential novel roles of transcriptional regulators