SUPPORT FOR COLLABORATIVE AUTHORING VIA EMAIL - THE MESSIE ENVIRONMENT

MESSIE is a collaborative authoring environment to support the production of large-scale documents by teams of geographically distributed groups of authors working with hetereogenous systems. The environment allows authors to submit text at various stages of gestation (e.g. list of topics, first draft) to a shared filestore via email. All authors collaborating on a document can read each others’ contributions, and add suggestions, comments and additional material directly to the document. The system integrates automatically answered electronic mail, shared file store administration, and a version control tool in a UNIX environment. The paper describes design and implementation strategy, and reports observations and a number of changes which were made during a 4-month trial period with three collaborative authoring teams

Quantum enhanced positioning and clock synchronization

A wide variety of positioning and ranging procedures are based on repeatedly sending electromagnetic pulses through space and measuring their time of arrival. This paper shows that quantum entanglement and squeezing can be employed to overcome the classical power/bandwidth limits on these procedures, enhancing their accuracy. Frequency entangled pulses could be used to construct quantum positioning systems (QPS), to perform clock synchronization, or to do ranging (quantum radar): all of these techniques exhibit a similar enhancement compared with analogous protocols that use classical light. Quantum entanglement and squeezing have been exploited in the context of interferometry, frequency measurements, lithography, and algorithms. Here, the problem of positioning a party (say Alice) with respect to a fixed array of reference points will be analyzed.Comment: 4 pages, 2 figures. Accepted for publication by Natur

Big bang simulation in superfluid 3He-B -- Vortex nucleation in neutron-irradiated superflow

We report the observation of vortex formation upon the absorption of a thermal neutron in a rotating container of superfluid $^3$He-B. The nuclear reaction n + $^3$He = p + $^3$H + 0.76MeV heats a cigar shaped region of the superfluid into the normal phase. The subsequent cooling of this region back through the superfluid transition results in the nucleation of quantized vortices. Depending on the superflow velocity, sufficiently large vortex rings grow under the influence of the Magnus force and escape into the container volume where they are detected individually with nuclear magnetic resonance. The larger the superflow velocity the smaller the rings which can expand. Thus it is possible to obtain information about the morphology of the initial defect network. We suggest that the nucleation of vortices during the rapid cool-down into the superfluid phase is similar to the formation of defects during cosmological phase transitions in the early universe.Comment: 4 pages, LaTeX file, 4 figures are available at ftp://boojum.hut.fi/pub/publications/lowtemp/LTL-95009.p

Counting defects with the two-point correlator

We study how topological defects manifest themselves in the equal-time two-point field correlator. We consider a scalar field with Z_2 symmetry in 1, 2 and 3 spatial dimensions, allowing for kinks, domain lines and domain walls, respectively. Using numerical lattice simulations, we find that in any number of dimensions, the correlator in momentum space is to a very good approximation the product of two factors, one describing the spatial distribution of the defects and the other describing the defect shape. When the defects are produced by the Kibble mechanism, the former has a universal form as a function of k/n, which we determine numerically. This signature makes it possible to determine the kink density from the field correlator without having to resort to the Gaussian approximation. This is essential when studying field dynamics with methods relying only on correlators (Schwinger-Dyson, 2PI).Comment: 11 pages, 7 figures

Intraprostatic Botulinum Toxin Type A injection in patients with benign prostatic enlargement: duration of the effect of a single treatment

<p>Abstract</p> <p>Background</p> <p>Botulinum Toxin Type-A (BoNT/A) intraprostatic injection can induce prostatic involution and improve LUTS and urinary flow in patients with Benign Prostatic Enlargement (BPE). However, the duration of these effects is unknown. The objective of this work was to determine the duration of prostate volume reduction after one single intraprostatic injection of 200U of Botulinum Toxin Type-A.</p> <p>Methods</p> <p>This is an extension of a 6 month study in which 21 frail elderly patients with refractory urinary retention and unfit for surgery were submitted to intraprostatic injection of BoNT/A-200U, by ultrasound guided transrectal approach. In spite of frail conditions, eleven patients could be followed during 18 months. Prostate volume, total serum PSA, maximal flow rate (Qmax), residual volume (PVR) and IPSS-QoL scores were determined at 1, 3, 6, 12 and 18 months post-treatment.</p> <p>Results</p> <p>Mean prostate volume at baseline, 82 ± 16 ml progressively decreased from month one coming to 49 ± 9,5 ml (p = 0,003) at month six. From this moment on, prostate volume slowly recovered, becoming identical to baseline at 18 months (73 ± 16 ml, p = 0.03). Albeit non significant, serum PSA showed a 25% decrease from baseline to month 6. The 11 patients resumed spontaneous voiding at month one. Mean Qmax was 11,3 ± 1,7 ml/sec and remained unchanged during the follow-up period. PVR ranged from 55 ± 17 to 82 ± 20 ml and IPSS score from10 to 12 points.</p> <p>Conclusion</p> <p>Intraprostatic BoNT/A injection is safe and can reduce prostate volume for a period of 18 months. During this time a marked symptomatic improvement can be maintained.</p

Parameters of Pseudo-Random Quantum Circuits

Pseudorandom circuits generate quantum states and unitary operators which are approximately distributed according to the unitarily invariant Haar measure. We explore how several design parameters affect the efficiency of pseudo-random circuits, with the goal of identifying relevant trade-offs and optimizing convergence. The parameters we explore include the choice of single- and two-qubit gates, the topology of the underlying physical qubit architecture, the probabilistic application of two-qubit gates, as well as circuit size, initialization, and the effect of control constraints. Building on the equivalence between pseudo-random circuits and approximate $t$-designs, a Markov matrix approach is employed to analyze asymptotic convergence properties of pseudo-random second-order moments to a 2-design. Quantitative results on the convergence rate as a function of the circuit size are presented for qubit topologies with a sufficient degree of symmetry. Our results may be theoretically and practically useful to optimize the efficiency of random state and operator generation.Comment: 17 pages, 14 figures, 2 Appendice

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

Consistent model of magnetism in ferropnictides

The discovery of superconductivity in LaFeAsO introduced the ferropnictides as a major new class of superconducting compounds with critical temperatures second only to cuprates. The presence of magnetic iron makes ferropnictides radically different from cuprates. Antiferromagnetism of the parent compounds strongly suggests that superconductivity and magnetism are closely related. However, the character of magnetic interactions and spin fluctuations in ferropnictides, in spite of vigorous efforts, has until now resisted understanding within any conventional model of magnetism. Here we show that the most puzzling features can be naturally reconciled within a rather simple effective spin model with biquadratic interactions, which is consistent with electronic structure calculations. By going beyond the Heisenberg model, this description explains numerous experimentally observed properties, including the peculiarities of the spin wave spectrum, thin domain walls, crossover from first to second order phase transition under doping in some compounds, and offers new insight in the occurrence of the nematic phase above the antiferromagnetic phase transition.Comment: 5 pages, 3 figures, revtex

Neuroendocrine (Merkel cell) carcinoma of the retroperitoneum with no identifiable primary site

<p>Abstract</p> <p>Background</p> <p>Neuroendocrine carcinoma is an aggressive neoplasm that mainly affects elderly Caucasians and typically arises in sun-exposed areas of the skin. The disease is rather rare and only a relatively few cases present with no apparent primary lesion.</p> <p>Case presentation</p> <p>We report a case of an 81-year-old Caucasian male with neuroendocrine carcinoma, which initially presented as a large retroperitoneal mass. Pathological and immunohistochemical analysis of the transabdominal CT-guided biopsy specimen revealed tissue consistent with neuroendocrine carcinoma. The patient underwent exploratory laparotomy and the mass was successfully excised along with an associated mesenteric lymph node.</p> <p>Discussion</p> <p>There are currently two possible explanations for what occurred in our patient. First, the retroperitoneal mass could be a massively enlarged lymph node where precursor cells became neoplastic. This would be consistent with a presumptive diagnosis of primary nodal disease. Alternatively, an initial skin lesion could have spontaneously regressed and the retroperitoneal mass represents a single site of metastasis. Since Merkel cell precursors have never been identified within lymph nodes, the latter theory seems more befitting. Moreover, metastasis to the retroperitoneal lymph nodes has been reported as relatively common when compared to other sites such as liver, bone, brain and skin.</p> <p>Conclusion</p> <p>Wide local excision of the primary tumor is the surgical treatment of choice for localized disease. We propose that further studies are needed to elucidate the true efficacy of chemotherapy in conventional as well as unconventional patients with neuroendocrine carcinoma.</p

On dynamic network entropy in cancer

The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network to induce a stochastic dynamics on the network, we here demonstrate that cancer cells are characterised by an increase in the dynamic network entropy, compared to cells of normal physiology. Using a fundamental relation between the macroscopic resilience of a dynamical system and the uncertainty (entropy) in the underlying microscopic processes, we argue that cancer cells will be more robust to random gene perturbations. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local dynamic entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local network dynamics. In particular, we also find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in the dynamic network entropy. In summary, our results support the view that the observed increased robustness of cancer cells to perturbation and therapy may be due to an increase in the dynamic network entropy that allows cells to adapt to the new cellular stresses. Conversely, genes that exhibit local flux entropy decreases in cancer may render cancer cells more susceptible to targeted intervention and may therefore represent promising drug targets.Comment: 10 pages, 3 figures, 4 tables. Submitte