Performance and optimization of support vector machines in high-energy physics classification problems

In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new- physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery- significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.Comment: 20 pages, 6 figure

Reconstruction of electromagnetic showers in calorimeters using Deep Learning

The precise reconstruction of properties of photons and electrons in modern high energy physics detectors, such as the CMS or Atlas experiments, plays a crucial role in numerous physics results. Conventional geometrical algorithms are used to reconstruct the energy and position of these particles from the showers they induce in the electromagnetic calorimeter. Despite their accuracy and efficiency, these methods still suffer from several limitations, such as low-energy background and limited capacity to reconstruct close-by particles. This paper introduces an innovative machine-learning technique to measure the energy and position of photons and electrons based on convolutional and graph neural networks, taking the geometry of the CMS electromagnetic calorimeter as an example. The developed network demonstrates a significant improvement in resolution both for photon energy and position predictions compared to the algorithm used in CMS. Notably, one of the main advantages of this new approach is its ability to better distinguish between multiple close-by electromagnetic showers

RNAi-based validation of antibodies for reverse phase protein arrays

<p>Abstract</p> <p>Background</p> <p>Reverse phase protein arrays (RPPA) have been demonstrated to be a useful experimental platform for quantitative protein profiling in a high-throughput format. Target protein detection relies on the readout obtained from a single detection antibody. For this reason, antibody specificity is a key factor for RPPA. RNAi allows the specific knockdown of a target protein in complex samples and was therefore examined for its utility to assess antibody performance for RPPA applications.</p> <p>Results</p> <p>To proof the feasibility of our strategy, two different anti-EGFR antibodies were compared by RPPA. Both detected the knockdown of EGFR but at a different rate. Western blot data were used to identify the most reliable antibody. The RNAi approach was also used to characterize commercial anti-STAT3 antibodies. Out of ten tested anti-STAT3 antibodies, four antibodies detected the STAT3-knockdown at 80-85%, and the most sensitive anti-STAT3 antibody was identified by comparing detection limits. Thus, the use of RNAi for RPPA antibody validation was demonstrated to be a stringent approach to identify highly specific and highly sensitive antibodies. Furthermore, the RNAi/RPPA strategy is also useful for the validation of isoform-specific antibodies as shown for the identification of AKT1/AKT2 and CCND1/CCND3-specific antibodies.</p> <p>Conclusions</p> <p>RNAi is a valuable tool for the identification of very specific and highly sensitive antibodies, and is therefore especially useful for the validation of RPPA-suitable detection antibodies. On the other hand, when a set of well-characterized RPPA-antibodies is available, large-scale RNAi experiments analyzed by RPPA might deliver useful information for network reconstruction.</p

A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

A problem-structuring model for analyzing transportation–environment relationships

This is the post-print version of the final paper published in European Journal of Operational Research. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V.This study discusses a decision support framework that guides policy makers in their strategic transportation related decisions by using multi-methodology. For this purpose, a methodology for analyzing the effects of transportation policies on environment, society, economy, and energy is proposed. In the proposed methodology, a three-stage problem structuring model is developed. Initially, experts’ opinions are structured by using a cognitive map to determine the relationships between transportation and environmental concepts. Then a structural equation model (SEM) is constructed, based on the cognitive map, to quantify the relations among external transportation and environmental factors. Finally the results of the SEM model are used to evaluate the consequences of possible policies via scenario analysis. In this paper a pilot study that covers only one module of the whole framework, namely transportation–environment interaction module, is conducted to present the applicability and usefulness of the methodology. This pilot study also reveals the impacts of transportation policies on the environment. To achieve a sustainable transportation system, the extent of the relationships between transportation and the environment must be considered. The World Development Indicators developed by the World Bank are used for this purpose

Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions

<p>Abstract</p> <p>Background</p> <p>Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems.</p> <p>Results</p> <p>In this paper, we propose Deterministic Effects Propagation Networks (DEPNs) as a special Bayesian Network approach to reverse engineer signaling networks from a combination of protein expression and perturbation data. DEPNs allow to reconstruct protein networks based on combinatorial intervention effects, which are monitored via changes of the protein expression or activation over one or a few time points. Our implementation of DEPNs allows for latent network nodes (i.e. proteins without measurements) and has a built in mechanism to impute missing data. The robustness of our approach was tested on simulated data. We applied DEPNs to reconstruct the <it>ERBB </it>signaling network in <it>de novo </it>trastuzumab resistant human breast cancer cells, where protein expression was monitored on Reverse Phase Protein Arrays (RPPAs) after knockdown of network proteins using RNAi.</p> <p>Conclusion</p> <p>DEPNs offer a robust, efficient and simple approach to infer protein signaling networks from multiple interventions. The method as well as the data have been made part of the latest version of the R package "nem" available as a supplement to this paper and via the Bioconductor repository.</p

Treatment of head lice with dimeticone 4% lotion: comparison of two formulations in a randomised controlled trial in rural Turkey

<p>Abstract</p> <p>Background</p> <p>Dimeticone 4% lotion was shown to be an effective treatment for head louse infestation in two randomised controlled trials in England. It is not affected by insecticide resistance but efficacy obtained (70-75%) was lower than expected. This study was designed to evaluate efficacy of dimeticone 4% lotion in a geographically, socially, and culturally different setting, in rural Turkey and, in order to achieve blinding, it was compared with a potential alternative formulation.</p> <p>Methods</p> <p>Children from two village schools were screened for head lice by detection combing. All infested students and family members could participate, giving access to treatment for the whole community. Two investigator applied treatments were given 7 days apart. Outcome was assessed by detection combing three times between treatments and twice the week following second treatment.</p> <p>Results</p> <p>In the intention to treat group 35/36 treated using dimeticone 4% had no lice after the second treatment but there were two protocol violators giving 91.7% treatment success. The alternative product gave 30/36 (83.3%) treatment success, a difference of 8.4% (95% CI -9.8% to 26.2%). The cure rates per-protocol were 33/34 (97.1%) and 30/35 (85.7%) respectively. We were unable to find any newly emerged louse nymphs on 77.8% of dimeticone 4% treated participants or on 66.7% of those treated with the alternative formulation. No adverse events were identified.</p> <p>Conclusion</p> <p>Our results confirm the efficacy of dimeticone 4% lotion against lice and eggs and we found no detectable difference between this product and dimeticone 4% lotion with nerolidol 2% added. We believe that the high cure rate was related to the lower intensity of infestation in Turkey, together with the level of community engagement, compared with previous studies in the UK.</p> <p>Trial Registration</p> <p>Current Controlled Trials ISRCTN10431107</p

The impact of Ki-67 index, squamous differentiation, and several clinicopathologic parameters on the recurrence of low and intermediate-risk endometrial cancer

Endometrial endometrioid carcinoma (EEC) represents approximately 75-80% of endometrial carcinoma cases. Three hundred and thirty-six patients with EEC followed-up in the authors’ medical center between 2010 and 2018 were included in our study. Two hundred and seventy-two low and intermediate EEC patients were identified using the European Society for Medical Oncology criteria and confirmed by histopathological examination. Recurrence was reported in 17 of these patients. The study group consisted of patients with relapse. A control group of 51 patients was formed at a ratio of 3:1 according to age, stage, and grade, similar to that in the study group. Of the 17 patients with recurrent disease, 13 patients (76.5%) were Stage 1A, and 4 patients (23.5%) were Stage 1B. No significant difference was found in age, stage, and grade between the case and control groups (p > 0.05). Body mass index, parity, tumor size, lower uterine segment involvement, SqD, and Ki-67 index with p<0.25 in the univariate logistic regression analysis were included in the multivariate analysis. Ki-67 was statistically significant in multivariate analysis (p = 0.018); however, there was no statistical significance in SqD and other parameters. Our data suggest that the Ki-67 index rather than SqD needs to be assessed for recurrence in patients with low- and intermediate-risk EEC