Performance evaluation of Crystal

Crystal tries to combine the simplicity to write software of Ruby with the performance of C. This presentation aims to show performance comparisons of Crystal with the programming languages Ruby, C and Go. This is done by using different example programs that use specific parts used in real world applications. Those include iterative and recursive implementations of the Fibonacci sequence, reading and writing files, listening to sockets, as well as calling a method written in C. The results show that Crystal can be considered a fast programming language. While C with all optimisations of gcc is still faster, the performance of Crystal is comparable with Go. As expected is Ruby, with just-in-time (JIT) compilation or without, by a factor of 8 respectively 9 slower than Crystal

Cost-effective Simulation-based Test Selection in Self-driving Cars Software

Simulation environments are essential for the continuous development of complex cyber-physical systems such as self-driving cars (SDCs). Previous results on simulation-based testing for SDCs have shown that many automatically generated tests do not strongly contribute to identification of SDC faults, hence do not contribute towards increasing the quality of SDCs. Because running such "uninformative" tests generally leads to a waste of computational resources and a drastic increase in the testing cost of SDCs, testers should avoid them. However, identifying "uninformative" tests before running them remains an open challenge. Hence, this paper proposes SDCScissor, a framework that leverages Machine Learning (ML) to identify SDC tests that are unlikely to detect faults in the SDC software under test, thus enabling testers to skip their execution and drastically increase the cost-effectiveness of simulation-based testing of SDCs software. Our evaluation concerning the usage of six ML models on two large datasets characterized by 22'652 tests showed that SDC-Scissor achieved a classification F1-score up to 96%. Moreover, our results show that SDC-Scissor outperformed a randomized baseline in identifying more failing tests per time unit. Webpage & Video: https://github.com/ChristianBirchler/sdc-scisso

Identification of an iron–hepcidin complex

Following its identification as a liver-expressed antimicrobial peptide, the hepcidin peptide was later shown to be a key player in iron homoeostasis. It is now proposed to be the 'iron hormone' which, by interacting with the iron transporter ferroportin, prevents further iron import into the circulatory system. This conclusion was reached using the corresponding synthetic peptide, emphasizing the functional importance of the mature 25-mer peptide, but omitting the possible functionality of its maturation. From urine-purified native hepcidin, we recently demonstrated that a proportion of the purified hepcidin had formed iron-hepcidin complexes. This interaction was investigated further by computer modelling and, based on the sequence similarity of hepcidin with metallothionein, a three-dimensional model of hepcidin, containing one atom of iron, was constructed. To characterize these complexes further, the interaction with iron was analysed using different spectroscopic methods. Monoferric hepcidin was identified by MS, as were possibly other complexes containing two and three atoms of iron respectively, although these were present only in minor amounts. UV/visible absorbance and CD studies identified the iron-binding events which were facilitated at a physiological pH. EPR spectroscopy identified the ferric state of the bound metal, and indicated that the iron-hepcidin complex shares some similarities with the rubredoxin iron-sulfur complex, suggesting the presence of Fe(3+) in a tetrahedral sulfur co-ordination. The potential roles of iron binding for hepcidin are discussed, and we propose either a regulatory function in the maturation of pro-hepcidin into active hepcidin or as the necessary link in the interaction between hepcidin and ferroportin

Cost-effective simulation-based test selection in self-driving cars software

Simulation environments are essential for the continuous development of complex cyber-physical systems such as self-driving cars (SDCs). Previous results on simulation-based testing for SDCs have shown that many automatically generated tests do not strongly contribute to identification of SDC faults, hence do not contribute towards increasing the quality of SDCs. Because running such "uninformative" tests generally leads to a waste of computational resources and a drastic increase in the testing cost of SDCs, testers should avoid them. However, identifying "uninformative" tests before running them remains an open challenge. Hence, this paper proposes SDCScissor, a framework that leverages Machine Learning (ML) to identify SDC tests that are unlikely to detect faults in the SDC software under test, thus enabling testers to skip their execution and drastically increase the cost-effectiveness of simulation-based testing of SDCs software. Our evaluation concerning the usage of six ML models on two large datasets characterized by 22'652 tests showed that SDC-Scissor achieved a classification F1-score up to 96%. Moreover, our results show that SDC-Scissor outperformed a randomized baseline in identifying more failing tests per time unit. Webpage & Video: https://github.com/ChristianBirchler/sdc-scisso

Cost-effective simulation-based test selection in self-driving cars software with SDC-Scissor

Simulation platforms facilitate the continuous development of complex systems such as self-driving cars (SDCs). However, previous results on testing SDCs using simulations have shown that most of the automatically generated tests do not strongly contribute to establishing confidence in the quality and reliability of the SDC. Therefore, those tests can be characterized as “uninformative”, and running them generally means wasting precious computational resources. We address this issue with SDC-Scissor, a framework that leverages Machine Learning to identify simulation-based tests that are unlikely to detect faults in the SDC software under test and skip them before their execution. Consequently, by filtering out those tests, SDC-Scissor reduces the number of long-running simulations to execute and drastically increases the cost-effectiveness of simulation-based testing of SDCs software. Our evaluation concerning two large datasets and around 12’000 tests showed that SDC-Scissor achieved a higher classification F1-score (between 47% and 90%) than a randomized baseline in identifying tests that lead to a fault and reduced the time spent running uninformative tests (speedup between 107% and 170%). Webpage & Video: https://github.com/ChristianBirchler/sdc-scisso

Expression of hepcidin mRNA is uniformly suppressed in hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>The present study evaluated the expression of hepcidin mRNA in hepatocellular carcinoma (HCC).</p> <p>Methods</p> <p>Samples of cancerous and non-cancerous liver tissue were taken from 40 patients with HCC who underwent hepatectomy. Expression of hepcidin mRNA was evaluated by real-time PCR, and compared in tumors differing in their degree of differentiation, number of tumors, and vessel invasion. Correlations between hepcidin expression and the interval until HCC recurrence, and the serum concentration of hepcidin were evaluated, together with the expression of mRNAs for other iron metabolism molecules, ferroportin and transferrin receptor 2 (Trf2).</p> <p>Results</p> <p>Hepcidin mRNA expression in non-cancerous and cancerous tissues was 1891.8 (32.3–23187.4) and 53.4 (1.9–3185.8), respectively (<it>P </it>< 0.0001). There were no significant differences in hepcidin expression among tumors differing in their degree of differentiation, number of tumors, or vessel invasion. There was no significant correlation between hepcidin expression and the interval until HCC recurrence. The serum concentration of hepcidin-25 was not correlated with hepcidin-mRNA expression. Finally, there were no significant differences in the expression of mRNA for ferroportin and Trf2 between cancerous and non-cancerous tissues.</p> <p>Conclusion</p> <p>Expression of hepcidin mRNA is strikingly suppressed in cancerous, but not in non-cancerous tissues, in patients with HCC, irrespective of ferroportin or Trf2 expression. Uniform suppression of hepcidin may be linked to the development of HCC.</p

Is the analysis of flow at the CERN SPS reliable?

Several heavy ion experiments at SPS have measured azimuthal distributions of particles with respect to the reaction plane. These distributions are deduced from two-particle azimuthal correlations under the assumption that they result solely from correlations with the reaction plane. In this paper, we investigate other sources of azimuthal correlations: transverse momentum conservation, which produces back-to-back correlations, resonance decays, HBT correlations and final state interactions. These correlations increase with impact parameter: most of them vary with the multiplicity N like 1/N. When they are taken into account, the experimental results of the NA49 collaboration at SPS are significantly modified. These correlations might also explain an important fraction of the pion directed flow observed by WA98. Data should be reanalyzed taking into account carefully these non--flow correlations.Comment: Revised version (minor corrections), 13 pages, LaTeX, 6 Postscript figures included. Submitted to Physical Review

A Novel Immunological Assay for Hepcidin Quantification in Human Serum

Contains fulltext : 81054.pdf (publisher's version ) (Open Access)BACKGROUND: Hepcidin is a 25-aminoacid cysteine-rich iron regulating peptide. Increased hepcidin concentrations lead to iron sequestration in macrophages, contributing to the pathogenesis of anaemia of chronic disease whereas decreased hepcidin is observed in iron deficiency and primary iron overload diseases such as hereditary hemochromatosis. Hepcidin quantification in human blood or urine may provide further insights for the pathogenesis of disorders of iron homeostasis and might prove a valuable tool for clinicians for the differential diagnosis of anaemia. This study describes a specific and non-operator demanding immunoassay for hepcidin quantification in human sera. METHODS AND FINDINGS: An ELISA assay was developed for measuring hepcidin serum concentration using a recombinant hepcidin25-His peptide and a polyclonal antibody against this peptide, which was able to identify native hepcidin. The ELISA assay had a detection range of 10-1500 microg/L and a detection limit of 5.4 microg/L. The intra- and interassay coefficients of variance ranged from 8-15% and 5-16%, respectively. Mean linearity and recovery were 101% and 107%, respectively. Mean hepcidin levels were significantly lower in 7 patients with juvenile hemochromatosis (12.8 microg/L) and 10 patients with iron deficiency anemia (15.7 microg/L) and higher in 7 patients with Hodgkin lymphoma (116.7 microg/L) compared to 32 age-matched healthy controls (42.7 microg/L). CONCLUSIONS: We describe a new simple ELISA assay for measuring hepcidin in human serum with sufficient accuracy and reproducibility