MUMAL: multivariate analysis in shotgun proteomics using machine learning techniques.

BACKGROUND: The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry) is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs) needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity. RESULTS: Here, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches. CONCLUSION: Our approach not only enhances the computational performance, and thus the turn around time of MS-based experiments in proteomics, but also improves the information content with benefits of a higher proteome coverage. This improvement, for instance, increases the chance to identify important drug targets or biomarkers for drug development or molecular diagnostics

Jet Evolution in the Quark-Gluon Plasma from RHIC to the LHC

The observed suppression of high-$p_\perp$ hadrons allows different explanations. We discuss two possible scenarios: In scenario 1, parton energy loss from scattering in the hot medium is complemented by final state interactions in the resonance matter. Scenario 2 has an enhanced transport parameter $\hat q$ which is fitted to RHIC data. For LHC, the two scenarios lead to very different predictions for the nuclear modification factor of hadrons. In addition, jet reconstruction allows more specific tests of the mechanisms responsible for jet quenching. We calculate the distribution of partons inside a jet and find different results for the two scenarios.Comment: 25 pages, 6 figure

Expansion for the solutions of the Bogomolny equations on the torus

We show that the solutions of the Bogomolny equations for the Abelian Higgs model on a two-dimensional torus, can be expanded in powers of a quantity epsilon measuring the departure of the area from the critical area. This allows a precise determination of the shape of the solutions for all magnetic fluxes and arbitrary position of the Higgs field zeroes. The expansion is carried out to 51 orders for a couple of representative cases, including the unit flux case. We analyse the behaviour of the expansion in the limit of large areas, in which case the solutions approach those on the plane. Our results suggest convergence all the way up to infinite area.Comment: 26 pages, 8 figures, slightly revised version as published in JHE

Potentiation rather than distraction in a trace fear conditioning procedure

Trace conditioning procedures are defined by the introduction of a trace interval between conditioned stimulus (CS, e.g. noise or light) offset and unconditioned stimulus (US, e.g. footshock). The introduction of an additional stimulus as a distractor has been suggested to increase the attentional demands of the task and to extend the usefulness of the behavioural model. In Experiment 1, the CS was noise and the distractor was provided by an intermittent light. In Experiment 2, the CS was light and the distractor was provided by an intermittent noise. In both experiments, the introduction of a 10s trace interval weakened associative learning compared with that seen in a 0s delay conditioned group. However, there was no consistent evidence of distraction. On the contrary, in Experiment 1, associative learning was stronger (in both trace and delay conditioned groups) for rats conditioned also in the presence of the intermittent light. In Experiment 2, there was no such effect when the roles of the stimuli were reversed. The results of Experiment 2 did however confirm the particular salience of the noise stimulus. The finding of increased associative learning dependent on salience is consistent with arousal-mediated effects on associative learning

Effects of Short Range Correlations on Ca Isotopes

The effect of Short Range Correlations (SRC) on Ca isotopes is studied using a simple phenomenological model. Theoretical expressions for the charge (proton) form factors, densities and moments of Ca nuclei are derived. The role of SRC in reproducing the empirical data for the charge density differences is examined. Their influence on the depletion of the nuclear Fermi surface is studied and the fractional occupation probabilities of the shell model orbits of Ca nuclei are calculated. The variation of SRC as function of the mass number is also discussed.Comment: 11 pages (RevTex), 6 Postscript figures available upon request at [email protected] Physical Review C in prin

Strangeness nuclear physics: a critical review on selected topics

Selected topics in strangeness nuclear physics are critically reviewed. This includes production, structure and weak decay of $\Lambda$--Hypernuclei, the $\bar K$ nuclear interaction and the possible existence of $\bar K$ bound states in nuclei. Perspectives for future studies on these issues are also outlined.Comment: 63 pages, 51 figures, accepted for publication on European Physical Journal

A geometric approach to time evolution operators of Lie quantum systems

Lie systems in Quantum Mechanics are studied from a geometric point of view. In particular, we develop methods to obtain time evolution operators of time-dependent Schrodinger equations of Lie type and we show how these methods explain certain ad hoc methods used in previous papers in order to obtain exact solutions. Finally, several instances of time-dependent quadratic Hamiltonian are solved.Comment: Accepted for publication in the International Journal of Theoretical Physic

Effects of dopamine D1 modulation of the anterior cingulate cortex in a fear conditioning procedure

The anterior cingulate cortex (AC) component of the medial prefrontal cortex (mPFC) has been implicated in attention and working memory as measured by trace conditioning. Since dopamine (DA) is a key modulator of mPFC function, the present study evaluated the role of DA receptor agents in rat AC, using trace fear conditioning. A conditioned stimulus (CS, noise) was followed by an unconditioned stimulus (US, shock) with or without a 10s trace interval interposed between these events in a between-subjects design. Conditioned suppression of drinking was assessed in response to presentation of the CS or an experimental background stimulus (flashing lights, previously presented for the duration of the conditioning session). The selective D1 agonist SKF81297 (0.05 µg/side) or D1 antagonist SCH23390 (0.5 µg/side) was administered by intra-cerebral microinfusion directly into AC. It was predicted that either of these manipulations should be sufficient to impair trace (but not delay) conditioning. Counter to expectation, there was no effect of DA D1 modulation on trace conditioning as measured by suppression to the noise CS. However, rats infused with SKF81297 acquired stronger conditioned suppression to the experimental background stimulus than those infused with SCH23390 or saline. Thus, the DA D1 agonist SKF81297 increased conditioned suppression to the contextual background light stimulus but was otherwise without effect on fear conditioning

An altered microbiota pattern precedes Type 2 diabetes mellitus development: From the CORDIOPREV study

Introduction. A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results. A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusión. These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease