A Compromise between Neutrino Masses and Collider Signatures in the Type-II Seesaw Model

A natural extension of the standard $SU(2)_{\rm L} \times U(1)_{\rm Y}$ gauge model to accommodate massive neutrinos is to introduce one Higgs triplet and three right-handed Majorana neutrinos, leading to a $6\times 6$ neutrino mass matrix which contains three $3\times 3$ sub-matrices $M_{\rm L}$, $M_{\rm D}$ and $M_{\rm R}$. We show that three light Majorana neutrinos (i.e., the mass eigenstates of $\nu_e$, $\nu_\mu$ and $\nu_\tau$) are exactly massless in this model, if and only if $M_{\rm L} = M_{\rm D} M_{\rm R}^{-1} M_{\rm D}^T$ exactly holds. This no-go theorem implies that small but non-vanishing neutrino masses may result from a significant but incomplete cancellation between $M_{\rm L}$ and $M_{\rm D} M_{\rm R}^{-1} M_{\rm D}^T$ terms in the Type-II seesaw formula, provided three right-handed Majorana neutrinos are of ${\cal O}(1)$ TeV and experimentally detectable at the LHC. We propose three simple Type-II seesaw scenarios with the $A_4 \times U(1)_{\rm X}$ flavor symmetry to interpret the observed neutrino mass spectrum and neutrino mixing pattern. Such a TeV-scale neutrino model can be tested in two complementary ways: (1) searching for possible collider signatures of lepton number violation induced by the right-handed Majorana neutrinos and doubly-charged Higgs particles; and (2) searching for possible consequences of unitarity violation of the $3\times 3$ neutrino mixing matrix in the future long-baseline neutrino oscillation experiments.Comment: RevTeX 19 pages, no figure

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

ANMM4CBR: a case-based reasoning method for gene expression data classification

<p>Abstract</p> <p>Background</p> <p>Accurate classification of microarray data is critical for successful clinical diagnosis and treatment. The "curse of dimensionality" problem and noise in the data, however, undermines the performance of many algorithms.</p> <p>Method</p> <p>In order to obtain a robust classifier, a novel Additive Nonparametric Margin Maximum for Case-Based Reasoning (ANMM4CBR) method is proposed in this article. ANMM4CBR employs a case-based reasoning (CBR) method for classification. CBR is a suitable paradigm for microarray analysis, where the rules that define the domain knowledge are difficult to obtain because usually only a small number of training samples are available. Moreover, in order to select the most informative genes, we propose to perform feature selection via additively optimizing a nonparametric margin maximum criterion, which is defined based on gene pre-selection and sample clustering. Our feature selection method is very robust to noise in the data.</p> <p>Results</p> <p>The effectiveness of our method is demonstrated on both simulated and real data sets. We show that the ANMM4CBR method performs better than some state-of-the-art methods such as support vector machine (SVM) and <it>k </it>nearest neighbor (<it>k</it>NN), especially when the data contains a high level of noise.</p> <p>Availability</p> <p>The source code is attached as an additional file of this paper.</p

Insula-specific responses induced by dental pain: a proton magnetic resonance spectroscopy study

OBJECTIVES: To evaluate whether induced dental pain leads to quantitative changes in brain metabolites within the left insular cortex after stimulation of the right maxillary canine and to examine whether these metabolic changes and the subjective pain intensity perception correlate. METHODS: Ten male volunteers were included in the pain group and compared with a control group of 10 other healthy volunteers. The pain group received a total of 87-92 electrically induced pain stimuli over 15 min to the right maxillary canine tooth. Contemporaneously, they evaluated the subjective pain intensity of every stimulus using an analogue scale. Neurotransmitter changes within the left insular cortex were evaluated by MR spectroscopy. RESULTS: Significant metabolic changes in glutamine (+55.1%), glutamine/glutamate (+16.4%) and myo-inositol (-9.7%) were documented during pain stimulation. Furthermore, there was a significant negative correlation between the subjective pain intensity perception and the metabolic levels of Glx, Gln, glutamate and N-acetyl aspartate. CONCLUSION: The insular cortex is a metabolically active region in the processing of acute dental pain. Induced dental pain leads to quantitative changes in brain metabolites within the left insular cortex resulting in significant alterations in metabolites. Negative correlation between subjective pain intensity rating and specific metabolites could be observed

A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

<p>Abstract</p> <p>Background</p> <p>This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.</p> <p>Methods</p> <p>Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.</p> <p>Results</p> <p>For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.</p> <p>Conclusion</p> <p>This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.</p

Agreement of Self-Reported and Genital Measures of Sexual Arousal in Men and Women: A Meta-Analysis

The assessment of sexual arousal in men and women informs theoretical studies of human sexuality and provides a method to assess and evaluate the treatment of sexual dysfunctions and paraphilias. Understanding measures of arousal is, therefore, paramount to further theoretical and practical advances in the study of human sexuality. In this meta-analysis, we review research to quantify the extent of agreement between self-reported and genital measures of sexual arousal, to determine if there is a gender difference in this agreement, and to identify theoretical and methodological moderators of subjective-genital agreement. We identified 132 peer- or academically-reviewed laboratory studies published between 1969 and 2007 reporting a correlation between self-reported and genital measures of sexual arousal, with total sample sizes of 2,505 women and 1,918 men. There was a statistically significant gender difference in the agreement between self-reported and genital measures, with men (r = .66) showing a greater degree of agreement than women (r = .26). Two methodological moderators of the gender difference in subjective-genital agreement were identified: stimulus variability and timing of the assessment of self-reported sexual arousal. The results have implications for assessment of sexual arousal, the nature of gender differences in sexual arousal, and models of sexual response

The Role of Growth Retardation in Lasting Effects of Neonatal Dexamethasone Treatment on Hippocampal Synaptic Function

BACKGROUND: Dexamethasone (DEX), a synthetic glucocorticoid, is commonly used to prevent or lessen the morbidity of chronic lung disease in preterm infants. However, evidence is now increasing that this clinical practice negatively affects somatic growth and may result in long-lasting neurodevelopmental deficits. We therefore hypothesized that supporting normal somatic growth may overcome the lasting adverse effects of neonatal DEX treatment on hippocampal function. METHODOLOGY/PRINCIPAL FINDINGS: To test this hypothesis, we developed a rat model using a schedule of tapering doses of DEX similar to that used in premature infants and examined whether the lasting influence of neonatal DEX treatment on hippocampal synaptic plasticity and memory performance are correlated with the deficits in somatic growth. We confirmed that neonatal DEX treatment switched the direction of synaptic plasticity in hippocampal CA1 region, favoring low-frequency stimulation- and group I metabotropic glutamate receptor agonist (S)-3,5,-dihydroxyphenylglycine-induced long-term depression (LTD), and opposing the induction of long-term potentiation (LTP) by high-frequency stimulation in the adolescent period. The effects of DEX on LTP and LTD were correlated with an increase in the autophosphorylation of Ca(2+)/calmodulin-dependent protein kinase II at threonine-286 and a decrease in the protein phosphatase 1 expression. Neonatal DEX treatment resulted in a disruption of memory retention subjected to object recognition task and passive avoidance learning. The adverse effects of neonatal DEX treatment on hippocampal synaptic plasticity and memory performance of the animals from litters culled to 4 pups were significantly less than those for the 8-pup litters. However, there was no significant difference in maternal care between groups. CONCLUSION/SIGNIFICANCE: Our results demonstrate that growth retardation plays a crucial role in DEX-induced long-lasting influence of hippocampal function. Our findings suggest that therapeutic strategies designed to support normal development and somatic growth may exert beneficial effects to reduce lasting adverse effects following neonatal DEX treatment

The Spin Structure of the Nucleon

We present an overview of recent experimental and theoretical advances in our understanding of the spin structure of protons and neutrons.Comment: 84 pages, 29 figure

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente