248 research outputs found

    Direct determination of the solar neutrino fluxes from solar neutrino data

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    We determine the solar neutrino fluxes from a global analysis of the solar and terrestrial neutrino data in the framework of three-neutrino mixing. Using a Bayesian approach we reconstruct the posterior probability distribution function for the eight normalization parameters of the solar neutrino fluxes plus the relevant masses and mixing, with and without imposing the luminosity constraint. This is done by means of a Markov Chain Monte Carlo employing the Metropolis-Hastings algorithm. We also describe how these results can be applied to test the predictions of the Standard Solar Models. Our results show that, at present, both models with low and high metallicity can describe the data with good statistical agreement.Comment: 24 pages, 1 table, 7 figures. Acknowledgments correcte

    WoMMBAT: A user interface for hierarchical Bayesian estimation of working memory capacity

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    The change detection paradigm has become an important tool for researchers studying working memory. Change detection is especially useful for studying visual working memory, because recall paradigms are difficult to employ in the visual modality. Pashler (Perception & Psychophysics, 44, 369–378, 1988) and Cowan (Behavioral and Brain Sciences, 24, 87–114, 2001) suggested formulas for estimating working memory capacity from change detection data. Although these formulas have become widely used, Morey (Journal of Mathematical Psychology, 55, 8–24, 2011) showed that the formulas suffer from a number of issues, including inefficient use of information, bias, volatility, uninterpretable parameter estimates, and violation of ANOVA assumptions. Morey presented a hierarchical Bayesian extension of Pashler’s and Cowan’s basic models that mitigates these issues. Here, we present WoMMBAT (Working Memory Modeling using Bayesian Analysis Techniques) software for fitting Morey’s model to data. WoMMBAT has a graphical user interface, is freely available, and is cross-platform, running on Windows, Linux, and Mac operating systems

    Constraints from muon g-2 and LFV processes in the Higgs Triplet Model

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    Constraints from the muon anomalous magnetic dipole moment and lepton flavor violating processes are translated into lower bounds on v_Delta*m_H++ in the Higgs Triplet Model by considering correlations through the neutrino mass matrix. The discrepancy of the sign of the contribution to the muon anomalous magnetic dipole moment between the measurement and the prediction in the model is clarified. It is shown that mu to e gamma, tau decays (especially, tau to mu e e), and the muonium conversion can give a more stringent bound on v_Delta*m_H++ than the bound from mu to eee which is expected naively to give the most stringent one.Comment: 18 pages, 16 figure

    Spontaneous R-Parity Violation, A4A_4 Flavor Symmetry and Tribimaximal Mixing

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    We explore the possibility of spontaneous R parity violation in the context of A4A_4 flavor symmetry. Our model contains SU(3)c×SU(2)L×U(1)YSU(3)_c \times SU(2)_L \times U(1)_Y singlet matter chiral superfields which are arranged as triplet of A4A_4 and as well as few additional Higgs chiral superfields which are singlet under MSSM gauge group and belong to triplet and singlet representation under the A4A_4 flavor symmetry. R parity is broken spontaneously by the vacuum expectation values of the different sneutrino fields and hence we have neutrino-neutralino as well as neutrino-MSSM gauge singlet higgsino mixings in our model, in addition to the standard model neutrino- gauge singlet neutrino, gaugino-higgsino and higgsino-higgsino mixings. Because all of these mixings we have an extended neutral fermion mass matrix. We explore the low energy neutrino mass matrix for our model and point out that with some specific constraints between the sneutrino vacuum expectation values as well as the MSSM gauge singlet Higgs vacuum expectation values, the low energy neutrino mass matrix will lead to a tribimaximal mixing matrix. We also analyze the potential minimization for our model and show that one can realize a higher vacuum expectation value of the SU(3)c×SU(2)L×U(1)YSU(3)_c \times SU(2)_L \times U(1)_Y singlet sneutrino fields even when the other sneutrino vacuum expectation values are extremely small or even zero.Comment: 18 page

    Beyond the standard seesaw: neutrino masses from Kahler operators and broken supersymmetry

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    We investigate supersymmetric scenarios in which neutrino masses are generated by effective d=6 operators in the Kahler potential, rather than by the standard d=5 superpotential operator. First, we discuss some general features of such effective operators, also including SUSY-breaking insertions, and compute the relevant renormalization group equations. Contributions to neutrino masses arise at low energy both at the tree level and through finite threshold corrections. In the second part we present simple explicit realizations in which those Kahler operators arise by integrating out heavy SU(2)_W triplets, as in the type II seesaw. Distinct scenarios emerge, depending on the mechanism and the scale of SUSY-breaking mediation. In particular, we propose an appealing and economical picture in which the heavy seesaw mediators are also messengers of SUSY breaking. In this case, strong correlations exist among neutrino parameters, sparticle and Higgs masses, as well as lepton flavour violating processes. Hence, this scenario can be tested at high-energy colliders, such as the LHC, and at lower energy experiments that measure neutrino parameters or search for rare lepton decays.Comment: LaTeX, 34 pages; some corrections in Section

    Analysis of the Trajectory of Drosophila melanogaster in a Circular Open Field Arena

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    BACKGROUND: Obtaining a complete phenotypic characterization of a freely moving organism is a difficult task, yet such a description is desired in many neuroethological studies. Many metrics currently used in the literature to describe locomotor and exploratory behavior are typically based on average quantities or subjectively chosen spatial and temporal thresholds. All of these measures are relatively coarse-grained in the time domain. It is advantageous, however, to employ metrics based on the entire trajectory that an organism takes while exploring its environment. METHODOLOGY/PRINCIPAL FINDINGS: To characterize the locomotor behavior of Drosophila melanogaster, we used a video tracking system to record the trajectory of a single fly walking in a circular open field arena. The fly was tracked for two hours. Here, we present techniques with which to analyze the motion of the fly in this paradigm, and we discuss the methods of calculation. The measures we introduce are based on spatial and temporal probability distributions and utilize the entire time-series trajectory of the fly, thus emphasizing the dynamic nature of locomotor behavior. Marginal and joint probability distributions of speed, position, segment duration, path curvature, and reorientation angle are examined and related to the observed behavior. CONCLUSIONS/SIGNIFICANCE: The measures discussed in this paper provide a detailed profile of the behavior of a single fly and highlight the interaction of the fly with the environment. Such measures may serve as useful tools in any behavioral study in which the movement of a fly is an important variable and can be incorporated easily into many setups, facilitating high-throughput phenotypic characterization

    Using indirect methods to constrain symbiotic nitrogen fixation rates : a case study from an Amazonian rain forest

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    © The Authors 2009. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License. The definitive version was published in Biogeochemistry 99 (2010): 1-13, doi:10.1007/s10533-009-9392-y.Human activities have profoundly altered the global nitrogen (N) cycle. Increases in anthropogenic N have had multiple effects on the atmosphere, on terrestrial, freshwater and marine ecosystems, and even on human health. Unfortunately, methodological limitations challenge our ability to directly measure natural N inputs via biological N fixation (BNF)—the largest natural source of new N to ecosystems. This confounds efforts to quantify the extent of anthropogenic perturbation to the N cycle. To address this gap, we used a pair of indirect methods—analytical modeling and N balance—to generate independent estimates of BNF in a presumed hotspot of N fixation, a tropical rain forest site in central Rondônia in the Brazilian Amazon Basin. Our objectives were to attempt to constrain symbiotic N fixation rates in this site using indirect methods, and to assess strengths and weaknesses of this approach by looking for areas of convergence and disagreement between the estimates. This approach yielded two remarkably similar estimates of N fixation. However, when compared to a previously published bottom-up estimate, our analysis indicated much lower N inputs via symbiotic BNF in the Rondônia site than has been suggested for the tropics as a whole. This discrepancy may reflect errors associated with extrapolating bottom-up fluxes from plot-scale measures, those resulting from the indirect analyses, and/or the relatively low abundance of legumes at the Rondônia site. While indirect methods have some limitations, we suggest that until the technological challenges of directly measuring N fixation are overcome, integrated approaches that employ a combination of model-generated and empirically-derived data offer a promising way of constraining N inputs via BNF in natural ecosystems.We acknowledge and are grateful for financial support from the Andrew W. Mellon Foundation (C.C. and B.H.), the National Science Foundation (NSF DEB-0515744 to C.C. and A.T. and DEB-0315656 to C.N.), and the NASA LBA Program (NCC5-285 to C.N.)

    A functional perspective on machine learning via programmable induction and abduction

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    We present a programming language for machine learning based on the concepts of ‘induction’ and ‘abduction’ as encountered in Peirce’s logic of science. We consider the desirable features such a language must have, and we identify the ‘abductive decoupling’ of parameters as a key general enabler of these features. Both an idealised abductive calculus and its implementation as a PPX extension of OCaml are presented, along with several simple examples

    A chronic fatigue syndrome – related proteome in human cerebrospinal fluid

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    BACKGROUND: Chronic Fatigue Syndrome (CFS), Persian Gulf War Illness (PGI), and fibromyalgia are overlapping symptom complexes without objective markers or known pathophysiology. Neurological dysfunction is common. We assessed cerebrospinal fluid to find proteins that were differentially expressed in this CFS-spectrum of illnesses compared to control subjects. METHODS: Cerebrospinal fluid specimens from 10 CFS, 10 PGI, and 10 control subjects (50 μl/subject) were pooled into one sample per group (cohort 1). Cohort 2 of 12 control and 9 CFS subjects had their fluids (200 μl/subject) assessed individually. After trypsin digestion, peptides were analyzed by capillary chromatography, quadrupole-time-of-flight mass spectrometry, peptide sequencing, bioinformatic protein identification, and statistical analysis. RESULTS: Pooled CFS and PGI samples shared 20 proteins that were not detectable in the pooled control sample (cohort 1 CFS-related proteome). Multilogistic regression analysis (GLM) of cohort 2 detected 10 proteins that were shared by CFS individuals and the cohort 1 CFS-related proteome, but were not detected in control samples. Detection of ≥1 of a select set of 5 CFS-related proteins predicted CFS status with 80% concordance (logistic model). The proteins were α-1-macroglobulin, amyloid precursor-like protein 1, keratin 16, orosomucoid 2 and pigment epithelium-derived factor. Overall, 62 of 115 proteins were newly described. CONCLUSION: This pilot study detected an identical set of central nervous system, innate immune and amyloidogenic proteins in cerebrospinal fluids from two independent cohorts of subjects with overlapping CFS, PGI and fibromyalgia. Although syndrome names and definitions were different, the proteome and presumed pathological mechanism(s) may be shared
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