Robust Inference of Trees

This paper is concerned with the reliable inference of optimal tree-approximations to the dependency structure of an unknown distribution generating data. The traditional approach to the problem measures the dependency strength between random variables by the index called mutual information. In this paper reliability is achieved by Walley's imprecise Dirichlet model, which generalizes Bayesian learning with Dirichlet priors. Adopting the imprecise Dirichlet model results in posterior interval expectation for mutual information, and in a set of plausible trees consistent with the data. Reliable inference about the actual tree is achieved by focusing on the substructure common to all the plausible trees. We develop an exact algorithm that infers the substructure in time O(m^4), m being the number of random variables. The new algorithm is applied to a set of data sampled from a known distribution. The method is shown to reliably infer edges of the actual tree even when the data are very scarce, unlike the traditional approach. Finally, we provide lower and upper credibility limits for mutual information under the imprecise Dirichlet model. These enable the previous developments to be extended to a full inferential method for trees.Comment: 26 pages, 7 figure

Kaon effective mass and energy from a novel chiral SU(3)-symmetric Lagrangian

A new chiral SU(3) Lagrangian is proposed to describe the properties of kaons and antikaons in the nuclear medium, the ground state of dense matter and the kaon-nuclear interactions consistently. The saturation properties of nuclear matter are reproduced as well as the results of the Dirac-Br\"{u}ckner theory. Our numerical results show that the kaon effective mass might be changed only moderately in the nuclear medium due to the highly non-linear density effects. After taking into account the coupling between the omega meson and the kaon, we obtain similar results for the effective kaon and antikaon energies as calculated in the one-boson-exchange model while in our model the parameters of the kaon-nuclear interactions are constrained by the SU(3) chiral symmetry.Comment: 13 pages, Latex, 3 PostScript figures included; replaced by the revised version, to appear in Phys. Rev.

Searching for periodic sources with LIGO

We investigate the computational requirements for all-sky, all-frequency searches for gravitational waves from spinning neutron stars, using archived data from interferometric gravitational wave detectors such as LIGO. These sources are expected to be weak, so the optimal strategy involves coherent accumulaton of signal-to-noise using Fourier transforms of long stretches of data (months to years). Earth-motion-induced Doppler shifts, and intrinsic pulsar spindown, will reduce the narrow-band signal-to-noise by spreading power across many frequency bins; therefore, it is necessary to correct for these effects before performing the Fourier transform. The corrections can be implemented by a parametrized model, in which one does a search over a discrete set of parameter values. We define a metric on this parameter space, which can be used to determine the optimal spacing between points in a search; the metric is used to compute the number of independent parameter-space points Np that must be searched, as a function of observation time T. The number Np(T) depends on the maximum gravitational wave frequency and the minimum spindown age tau=f/(df/dt) that the search can detect. The signal-to-noise ratio required, in order to have 99% confidence of a detection, also depends on Np(T). We find that for an all-sky, all-frequency search lasting T=10^7 s, this detection threshhold is at a level of 4 to 5 times h(3/yr), where h(3/yr) is the corresponding 99% confidence threshhold if one knows in advance the pulsar position and spin period.Comment: 18 pages, LaTeX, 12 PostScript figures included using psfig. Submitted to Phys. Rev.

Biallelic mutations in valyl-tRNA synthetase gene VARS are associated with a progressive neurodevelopmental epileptic encephalopathy.

Aminoacyl-tRNA synthetases (ARSs) function to transfer amino acids to cognate tRNA molecules, which are required for protein translation. To date, biallelic mutations in 31 ARS genes are known to cause recessive, early-onset severe multi-organ diseases. VARS encodes the only known valine cytoplasmic-localized aminoacyl-tRNA synthetase. Here, we report seven patients from five unrelated families with five different biallelic missense variants in VARS. Subjects present with a range of global developmental delay, epileptic encephalopathy and primary or progressive microcephaly. Longitudinal assessment demonstrates progressive cortical atrophy and white matter volume loss. Variants map to the VARS tRNA binding domain and adjacent to the anticodon domain, and disrupt highly conserved residues. Patient primary cells show intact VARS protein but reduced enzymatic activity, suggesting partial loss of function. The implication of VARS in pediatric neurodegeneration broadens the spectrum of human diseases due to mutations in tRNA synthetase genes

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks

BACKGROUND. Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models. RESULTS. We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time. CONCLUSION. The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.Italian Ministry of University and Scientific Research; National Institutes of Health & National Human Genome Research Institute (HG003354-01A2); Collegio Ghislieri, Pavia Italy fellowshi

The Quantity Theory of Money is Valid. The New Keynesians are Wrong!

We test the quantity theory of money (QTM) using a novel approach and a large new sample. We do not follow the usual approach of first differentiating the logarithm of the Cambridge equation to obtain an equation relating the growth rate of real GDP, the growth rate of money and inflation. These variables must then again be ‘integrated’ by averaging in order to obtain stable relationships. Instead we suggest a much simpler procedure for testing directly the stability of the coefficient of the Cambridge equation. For 125 countries and post-war data we find the coefficient to be surprisingly stable. We do not select for high inflation episodes as was done in most empirical studies; inflation rates do not even appear in our data set. Much work supporting the QTM has been done by economic historians and at the University of Chicago by Milton Friedman and his associates. The QTM was a foundation stone of the monetarist revolution. Subsequently belief in it waned. The currently dominant New Keynesian School, implicitly or explicitly denies the validity of the QTM. We survey this history and argue that the QTM is valid and New Keynesians are wrong

STING expression and response to treatment with STING ligands in premalignant and malignant disease.

Human papilloma virus positive (HPV+) tumors represent a large proportion of anal, vulvar, vaginal, cervical and head and neck squamous carcinomas (HNSCC) and late stage invasive disease is thought to originate from a premalignant state. Cyclic dinucleotides that activate STimulator of INterferon Genes (STING) have been shown to cause rapid regression of a range of advanced tumors. We aimed to investigate STING ligands as a novel treatment for papilloma. We tested therapies in a spontaneous mouse model of papilloma of the face and anogenital region that histologically resembles human HPV-associated papilloma. We demonstrate that STING ligands cause rapid regression of papilloma, associated with T cell infiltration, and are significantly more effective than Imiquimod, a current immunotherapy for papilloma. In humans, we show that STING is expressed in the basal layer of normal skin and lost during keratinocyte differentiation. We found STING was expressed in all HPV-associated cervical and anal dysplasia and was strongly expressed in the cancer cells of HPV+ HNSCC but not in HPV-unrelated HNSCC. We found no strong association between STING expression and progressive disease in non-HPV oral dysplasia and oral pre-malignancies that are not HPV-related. These data demonstrate that STING is expressed in basal cells of the skin and is retained in HPV+ pre-malignancies and advanced cancers, but not in HPV-unrelated HNSCC. However, using a murine HNSCC model that does not express STING, we demonstrate that STING ligands are an effective therapy regardless of expression of STING by the cancer cells

Predicting mental imagery based BCI performance from personality, cognitive profile and neurophysiological patterns

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy— EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants’ BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants’ performance with a mean error of less than 3 points. This study determined how users’ profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user

Can the Universe Create Itself?

The question of first-cause has troubled philosophers and cosmologists alike. Now that it is apparent that our universe began in a Big Bang explosion, the question of what happened before the Big Bang arises. Inflation seems like a very promising answer, but as Borde and Vilenkin have shown, the inflationary state preceding the Big Bang must have had a beginning also. Ultimately, the difficult question seems to be how to make something out of nothing. This paper explores the idea that this is the wrong question --- that that is not how the Universe got here. Instead, we explore the idea of whether there is anything in the laws of physics that would prevent the Universe from creating itself. Because spacetimes can be curved and multiply connected, general relativity allows for the possibility of closed timelike curves (CTCs). Thus, tracing backwards in time through the original inflationary state we may eventually encounter a region of CTCs giving no first-cause. This region of CTCs, may well be over by now (being bounded toward the future by a Cauchy horizon). We illustrate that such models --- with CTCs --- are not necessarily inconsistent by demonstrating self-consistent vacuums for Misner space and a multiply connected de Sitter space in which the renormalized energy-momentum tensor does not diverge as one approaches the Cauchy horizon and solves Einstein's equations. We show such a Universe can be classically stable and self-consistent if and only if the potentials are retarded, giving a natural explanation of the arrow of time. Some specific scenarios (out of many possible ones) for this type of model are described. For example: an inflationary universe gives rise to baby universes, one of which turns out to be itself. Interestingly, the laws of physics may allow the Universe to be its own mother.Comment: 48 pages, 8 figure