•We present a machine learning approach to discriminate schizophrenic and normal subjects.•Schizophrenia and other mental disorders can be characterized by changes in brain connectivity.•We extract ...graph-theoretic features from resting-state functional magnetic resonance images.•We compare several feature selection techniques and evaluate the performance on a large dataset.•We show the best reported performance among graph-theoretic methods for schizophrenia detection.
Resting-state functional magnetic resonance imaging (Rs-fMRI) is a promising imaging modality to study the changes of functional brain networks in schizophrenic patients. Several representations have been proposed to capture the essential features of these networks. In particular, graph-theoretic representations can be effectively used to discriminate healthy subjects from schizophrenic patients. In this paper, we propose a machine-learning system based on a graph-theoretic approach to investigate and differentiate the brain network alterations. The fMRI data samples are first preprocessed to reduce noise and normalize the images. The automated anatomical labeling (AAL) atlas is then used to parcellate the brain into 90 regions and construct a region connectivity matrix. A weighted undirected graph is hence constructed and graph measures are computed for each subject. These graph measures include betweenness centrality, characteristic path length, degree, clustering coefficient, local efficiency, global efficiency, participation coefficient and small-worldness. After that, feature selection algorithms are used to choose the most discriminant features. Finally, a SVM classifier is trained and tested on discriminant graph features. Experiments were performed on a large Rs-fMRI dataset formed of 70 schizophrenic patients and 70 healthy subjects. The performance was evaluated using nested-loop 10-fold cross-validation. The best detection results were found using the feature selection methods of Welch's t-test (82.85%), l0-norm (91.43%), and feature selection via concave minimization (FSV) (95.00%). Our results outperform those of recent state-of-the-art graph-theoretic methods.
A novel approach for designing the next generation of vertex detectors foresees to employ wafer-scale sensors that can be bent to truly cylindrical geometries after thinning them to thicknesses of ...20–40 μm. To solidify this concept, the feasibility of operating bent MAPS was demonstrated using 1.5cm×3cm ALPIDE chips. Already with their thickness of 50µm, they can be successfully bent to radii of about 2cm without any signs of mechanical or electrical damage. During a subsequent characterisation using a 5.4GeV electron beam, it was further confirmed that they preserve their full electrical functionality as well as particle detection performance.
In this article, the bending procedure and the setup used for characterisation are detailed. Furthermore, the analysis of the beam test, including the measurement of the detection efficiency as a function of beam position and local inclination angle, is discussed. The results show that the sensors maintain their excellent performance after bending to radii of 2cm, with detection efficiencies above 99.9% at typical operating conditions, paving the way towards a new class of detectors with unprecedented low material budget and ideal geometrical properties.
We review the main results obtained by the BRAHMS Collaboration on the properties of hot and dense hadronic and partonic matter produced in ultrarelativistic heavy ion collisions at RHIC. A ...particular focus of this paper is to discuss to what extent the results collected so far by BRAHMS, and by the other three experiments at RHIC, can be taken as evidence for the formation of a state of deconfined partonic matter, the so-called quark–gluon plasma (QGP). We also discuss evidence for a possible precursor state to the QGP, i.e., the proposed color glass condensate.
Understanding the role of parton mass and Casimir color factors in the quantum chromodynamics parton shower represents an important step in characterizing the emission properties of heavy quarks. ...Recent experimental advances in jet substructure techniques have provided the opportunity to isolate and characterize gluon emissions from heavy quarks. In this Letter, the first direct experimental constraint on the charm-quark splitting function is presented, obtained via the measurement of the groomed shared momentum fraction of the first splitting in charm jets, tagged by a reconstructed D^{0} meson. The measurement is made in proton-proton collisions at sqrts=13 TeV, in the low jet transverse-momentum interval of 15≤p_{T}^{jet ch}<30 GeV/c where the emission properties are sensitive to parton mass effects. In addition, the opening angle of the first perturbative emission of the charm quark, as well as the number of perturbative emissions it undergoes, is reported. Comparisons to measurements of an inclusive-jet sample show a steeper splitting function for charm quarks compared with gluons and light quarks. Charm quarks also undergo fewer perturbative emissions in the parton shower, with a reduced probability of large-angle emissions.
Using the large acceptance apparatus FOPI, we study central collisions in the reactions (energies in A GeV are given in parentheses): 40Ca + 40Ca (0.4, 0.6, 0.8, 1.0, 1.5, 1.93), 58Ni + 58Ni (0.15, ...0.25, 0.4), 96Ru + 96Ru (0.4, 1.0, 1.5), 96Zr + 96Zr (0.4, 1.0, 1.5), 129Xe + CsI (0.15, 0.25, 0.4), 197Au + 197Au (0.09, 0.12, 0.15, 0.25, 0.4, 0.6, 0.8, 1.0, 1.2, 1.5). The observables include cluster multiplicities, longitudinal and transverse rapidity distributions and stopping, and radial flow. The data are compared to earlier data where possible and to transport model simulations.
Genetic control of flowering time in sorghum was investigated using a recombinant inbred lines population derived from a cross between IS 2807, a slightly photoperiod sensitive tropical caudatum ...landrace, and IS 7680,a highly photoperiod sensitive tropical guinea landrace. Progenies were sown with their parents at six different dates between 1995 and 1997 in Burkina Faso. Direct field measures and synthetic measures derived from the implementation of a model were used to characterize the photoperiod response. Emphasis was put to identify the most relevant traits to account for Basic Vegetative Phase (BVP) and photoperiod sensitivity sensus stricto. One QTL was detected on Linkage Group (LG) F for the traits related to BVP. Two QTLs were detected on LGs C and H for the traits related to the photoperiod sensitivity sensus stricto. This gives credit to at least partially independent genetic determinisms for those two components of photoperiod response. Evidences for possible orthology of the QTLs detected here with other QTLs and major genes involved in flowering time of sorghum and rice are discussed.
Transverse momentum spectra of protons and anti-protons measured in the rapidity range 0<y<3.1 from 0–10% central Au+Au collisions at sNN=62.4 GeV are presented. The rapidity densities, dN/dy, of ...protons, anti-protons and net-protons (Np–Np¯) have been deduced from the spectra over a rapidity range wide enough to observe the expected maximum net-baryon density. From mid-rapidity to y=1 the net-proton yield is roughly constant (dN/dy∼10), but rises to dN/dy∼25 at 2.3<y<3.1. The mean rapidity loss is 2.01±0.14±0.12 units from beam rapidity. The measured rapidity distributions are compared to model predictions. Systematics of net-baryon distributions and rapidity loss vs. collision energy are discussed.
Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic ...resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of 70 schizophrenic subjects and 70 healthy subjects. The decision-level fusion method outperformed the single-feature-type approaches and achieved a 97.85% accuracy, a 98.33% sensitivity, a 96.83% specificity. Moreover, feature-fusion scheme resulted in a 98.57% accuracy, a 99.71% sensitivity, a 97.66% specificity, and an area under the ROC curve of 0.9984. In general, decision-level and feature-level fusion schemes boosted the performance of schizophrenia detectors based on fMRI features.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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