Search for a dileptonic edge with CMS

We present a search for a kinematic edge in the invariant mass distribution of two opposite-sign same-flavor leptons, in final states with jets and missing transverse energy. The analysis makes use of $19.4$ fb$^{-1}$ proton-proton collision data at $\sqrt{s} = 8$ TeV. The data have been recorded with the CMS experiment. Complementary methods have been used for the background estimation, which when combined achieve a total uncertainty of $5\%$ ($10\%$) for leptons in the central (forward) rapidity of the detector. We do not observe a statistically significant signal and the results are consistent with the background-only hypothesis.Comment: ICNFP2014 conference proceedings, presented in August 2014, prepared for submission in EP

Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms: support vector regression forecast combinations

The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999–2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm

Forecasting US unemployment with radial basis neural networks, kalman filters and support vector regressions

This study investigates the efficiency of the radial basis function neural networks in forecasting the US unemployment and explores the utility of Kalman filter and support vector regression as forecast combination techniques. On one hand, an autoregressive moving average model, a smooth transition autoregressive model and three different neural networks architectures, namely a multi-layer perceptron, recurrent neural network and a psi sigma network are used as benchmarks for our radial basis function neural network. On the other hand, our forecast combination methods are benchmarked with a simple average and a least absolute shrinkage and selection operator. The statistical performance of our models is estimated throughout the period of 1972–2012, using the last 7 years for out-of-sample testing. The results show that the radial basis function neural network statistically outperforms all models’ individual performances. The forecast combinations are successful, since both Kalman filter and support vector regression techniques improve the statistical accuracy. Finally, support vector regression is found to be the superior model of the forecasting competition. The empirical evidence of this application are further validated by the use of the modified Diebold–Mariano test

Inflation and unemployment forecasting with genetic support vector regression

In this paper a hybrid genetic algorithm–support vector regression (GA-SVR) model in economic forecasting and macroeconomic variable selection is introduced. The proposed algorithm is applied to the task of forecasting US inflation and unemployment. GA-SVR genetically optimizes the SVR parameters and adapts to the optimal feature subset from a feature space of potential inputs. The feature space includes a wide pool of macroeconomic variables that might affect the two series under study. The forecasting performance of GA-SVR is benchmarked with a random walk model, an autoregressive moving average model, a moving average convergence/divergence model, a multi-layer perceptron, a recurrent neural network and a genetic programming algorithm. In terms of our results, GA-SVR outperforms all benchmark models and provides evidence on which macroeconomic variables can be relevant predictors of US inflation and unemployment in the specific period under study

Optimisation Models for Pathway Activity Inference in Cancer

BACKGROUND: With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models. METHODOLOGY: A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation. RESULTS: The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction

Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures

Named Entity Recognition and Intent Classification are among the most important subfields of the field of Natural Language Processing. Recent research has lead to the development of faster, more sophisticated and efficient models to tackle the problems posed by those two tasks. In this work we explore the effectiveness of two separate families of Deep Learning networks for those tasks: Bidirectional Long Short-Term networks and Transformer-based networks. The models were trained and tested on the ATIS benchmark dataset for both English and Greek languages. The purpose of this paper is to present a comparative study of the two groups of networks for both languages and showcase the results of our experiments. The models, being the current state-of-the-art, yielded impressive results and achieved high performance.Comment: 24 pages, 5 figures, 11 tables, dataset availabl

Apolipoprotein Proteomics for Residual Lipid-Related Risk in Coronary Heart Disease

BACKGROUND: Recognition of the importance of conventional lipid measures and the advent of novel lipid-lowering medications have prompted the need for more comprehensive lipid panels to guide use of emerging treatments for the prevention of coronary heart disease (CHD). This report assessed the relevance of 13 apolipoproteins measured using a single mass-spectrometry assay for risk of CHD in the PROCARDIS case-control study of CHD (941 cases/975 controls). METHODS: The associations of apolipoproteins with CHD were assessed after adjustment for established risk factors and correction for statin use. Apolipoproteins were grouped into 4 lipid-related classes [lipoprotein(a), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides] and their associations with CHD were adjusted for established CHD risk factors and conventional lipids. Analyses of these apolipoproteins in a subset of the ASCOT trial (Anglo-Scandinavian Cardiac Outcomes Trial) were used to assess their within-person variability and to estimate a correction for statin use. The findings in the PROCARDIS study were compared with those for incident cardiovascular disease in the Bruneck prospective study (n=688), including new measurements of Apo(a). RESULTS: Triglyceride-carrying ApoC1, ApoC3, and ApoE (apolipoproteins) were most strongly associated with the risk of CHD (2- to 3-fold higher odds ratios for top versus bottom quintile) independent of conventional lipid measures. Likewise, ApoB was independently associated with a 2-fold higher odds ratios of CHD. Lipoprotein(a) was measured using peptides from the Apo(a)-kringle repeat and Apo(a)-constant regions, but neither of these associations differed from the association with conventionally measured lipoprotein(a). Among HDL-related apolipoproteins, ApoA4 and ApoM were inversely related to CHD, independent of conventional lipid measures. The disease associations with all apolipoproteins were directionally consistent in the PROCARDIS and Bruneck studies, with the exception of ApoM. CONCLUSIONS: Apolipoproteins were associated with CHD independent of conventional risk factors and lipids, suggesting apolipoproteins could help to identify patients with residual lipid-related risk and guide personalized approaches to CHD risk reduction

Protein Aggregation Is an Early Manifestation of Phospholamban p.(Arg14del)-Related Cardiomyopathy:Development of PLN-R14del-Related Cardiomyopathy

BACKGROUND: The p.(Arg14del) pathogenic variant (R14del) of the PLN (phospholamban) gene is a prevalent cause of cardiomyopathy with heart failure. The exact underlying pathophysiology is unknown, and a suitable therapy is unavailable. We aim to identify molecular perturbations underlying this cardiomyopathy in a clinically relevant PLN-R14del mouse model. METHODS: We investigated the progression of cardiomyopathy in PLN-R14Δ/Δ mice using echocardiography, ECG, and histological tissue analysis. RNA sequencing and mass spectrometry were performed on cardiac tissues at 3 (before the onset of disease), 5 (mild cardiomyopathy), and 8 (end stage) weeks of age. Data were compared with cardiac expression levels of mice that underwent myocardial ischemia-reperfusion or myocardial infarction surgery, in an effort to identify alterations that are specific to PLN-R14del-related cardiomyopathy. RESULTS: At 3 weeks of age, PLN-R14Δ/Δ mice had normal cardiac function, but from the age of 4 weeks, we observed increased myocardial fibrosis and impaired global longitudinal strain. From 5 weeks onward, ventricular dilatation, decreased contractility, and diminished ECG voltages were observed. PLN protein aggregation was present before onset of functional deficits. Transcriptomics and proteomics revealed differential regulation of processes involved in remodeling, inflammation, and metabolic dysfunction, in part, similar to ischemic heart disease. Altered protein homeostasis pathways were identified exclusively in PLN-R14Δ/Δ mice, even before disease onset, in concert with aggregate formation. CONCLUSIONS: We mapped the development of PLN-R14del-related cardiomyopathy and identified alterations in proteostasis and PLN protein aggregation among the first manifestations of this disease, which could possibly be a novel target for therapy

Metabolic recovery after weight loss surgery is reflected in serum microRNAs.

Funder: Ministerio de Educación, Cultura y Deporte - SpainFunder: Fondation Leducq; FundRef: http://dx.doi.org/10.13039/501100001674Funder: Marie Skłodowska-Curie Innovative Training Network TRAIN-HEARTFunder: National Institute of Health ResearchINTRODUCTION: Bariatric surgery offers the most effective treatment for obesity, ameliorating or even reverting associated metabolic disorders, such as type 2 diabetes. We sought to determine the effects of bariatric surgery on circulating microRNAs (miRNAs) that have been implicated in the metabolic cross talk between the liver and adipose tissue. RESEARCH DESIGN AND METHODS: We measured 30 miRNAs in 155 morbidly obese patients and 47 controls and defined associations between miRNAs and metabolic parameters. Patients were followed up for 12 months after bariatric surgery. Key findings were replicated in a separate cohort of bariatric surgery patients with up to 18 months of follow-up. RESULTS: Higher circulating levels of liver-related miRNAs, such as miR-122, miR-885-5 p or miR-192 were observed in morbidly obese patients. The levels of these miRNAs were positively correlated with body mass index, percentage fat mass, blood glucose levels and liver transaminases. Elevated levels of circulating liver-derived miRNAs were reversed to levels of non-obese controls within 3 months after bariatric surgery. In contrast, putative adipose tissue-derived miRNAs remained unchanged (miR-99b) or increased (miR-221, miR-222) after bariatric surgery, suggesting a minor contribution of white adipose tissue to circulating miRNA levels. Circulating levels of liver-derived miRNAs normalized along with the endocrine and metabolic recovery of bariatric surgery, independent of the fat percentage reduction. CONCLUSIONS: Since liver miRNAs play a crucial role in the regulation of hepatic biochemical processes, future studies are warranted to assess whether they may serve as determinants or mediators of metabolic risk in morbidly obese patients