Identifying potential gene biomarkers for Parkinson's disease through an information entropy based approach.

Parkinson's disease (PD) is a chronic, progressive neurodegenerative disease and represents the most common disease of this type, after Alzheimer's dementia. It is characterized by motor and nonmotor features and by a long prodromal stage that lasts many years. Genetic research has shown that PD is a complex and multisystem disorder. To capture the molecular complexity of this disease we used a complex network approach. We maximized the information entropy of the gene co-expression matrix betweenness to obtain a gene adjacency matrix; then we used a fast greedy algorithm to detect communities. Finally we applied principal component analysis on the detected gene communities, with the ultimate purpose of discriminating between PD patients and healthy controls by means of a random forests classifier. We used a publicly available substantia nigra microarray dataset, GSE20163, from NCBI GEO database, containing gene expression profiles for 10 PD patients and 18 normal controls. With this methodology we identified two gene communities that discriminated between the two groups with mean accuracy of 0.88 ± 0.03 and 0.84 ± 0.03, respectively, and validated our results on an independent microarray experiment. The two gene communities presented a considerable reduction in size, over 100 times, compared to the initial network and were stable within a range of tested parameters. Further research focusing on the restricted number of genes belonging to the selected communities may reveal essential mechanisms responsible for PD at a network level and could contribute to the discovery of new biomarkers for PD.

Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system.

BACKGROUND: Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians' work with an automatic tool. METHODS: In this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules. RESULTS: The detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78% with 2.87 false positives per image. CONCLUSIONS: Experimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues.

A Gradient-Based Approach for Breast DCE-MRI Analysis.

Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis.

Trial latencies estimation of event-related potentials in EEG by means of genetic algorithms.

OBJECTIVE: Event-related potentials (ERPs) are usually obtained by averaging thus neglecting the trial-to-trial latency variability in cognitive electroencephalography (EEG) responses. As a consequence the shape and the peak amplitude of the averaged ERP are smeared and reduced, respectively, when the single-trial latencies show a relevant variability. To date, the majority of the methodologies for single-trial latencies inference are iterative schemes providing suboptimal solutions, the most commonly used being the Woody's algorithm. APPROACH: In this study, a global approach is developed by introducing a fitness function whose global maximum corresponds to the set of latencies which renders the trial signals most aligned as possible. A suitable genetic algorithm has been implemented to solve the optimization problem, characterized by new genetic operators tailored to the present problem. MAIN RESULTS: The results, on simulated trials, showed that the proposed algorithm performs better than Woody's algorithm in all conditions, at the cost of an increased computational complexity (justified by the improved quality of the solution). Application of the proposed approach on real data trials, resulted in an increased correlation between latencies and reaction times w.r.t. the output from RIDE method. SIGNIFICANCE: The above mentioned results on simulated and real data indicate that the proposed method, providing a better estimate of single-trial latencies, will open the way to more accurate study of neural responses as well as to the issue of relating the variability of latencies to the proper cognitive and behavioural correlates.

Time Dependence of the Electron and Positron Components of the Cosmic Radiation Measured by the PAMELA Experiment between July 2006 and December 2015.

Cosmic-ray electrons and positrons are a unique probe of the propagation of cosmic rays as well as of the nature and distribution of particle sources in our Galaxy. Recent measurements of these particles are challenging our basic understanding of the mechanisms of production, acceleration, and propagation of cosmic rays. Particularly striking are the differences between the low energy results collected by the space-borne PAMELA and AMS-02 experiments and older measurements pointing to sign-charge dependence of the solar modulation of cosmic-ray spectra. The PAMELA experiment has been measuring the time variation of the positron and electron intensity at Earth from July 2006 to December 2015 covering the period for the minimum of solar cycle 23 (2006-2009) until the middle of the maximum of solar cycle 24, through the polarity reversal of the heliospheric magnetic field which took place between 2013 and 2014. The positron to electron ratio measured in this time period clearly shows a sign-charge dependence of the solar modulation introduced by particle drifts. These results provide the first clear and continuous observation of how drift effects on solar modulation have unfolded with time from solar minimum to solar maximum and their dependence on the particle rigidity and the cyclic polarity of the solar magnetic field.

Hybrid x-space: a new approach for MPI reconstruction.

Magnetic particle imaging (MPI) is a new medical imaging technique capable of recovering the distribution of superparamagnetic particles from their measured induced signals. In literature there are two main MPI reconstruction techniques: measurement-based (MB) and x-space (XS). The MB method is expensive because it requires a long calibration procedure as well as a reconstruction phase that can be numerically costly. On the other side, the XS method is simpler than MB but the exact knowledge of the field free point (FFP) motion is essential for its implementation. Our simulation work focuses on the implementation of a new approach for MPI reconstruction: it is called hybrid x-space (HXS), representing a combination of the previous methods. Specifically, our approach is based on XS reconstruction because it requires the knowledge of the FFP position and velocity at each time instant. The difference with respect to the original XS formulation is how the FFP velocity is computed: we estimate it from the experimental measurements of the calibration scans, typical of the MB approach. Moreover, a compressive sensing technique is applied in order to reduce the calibration time, setting a fewer number of sampling positions. Simulations highlight that HXS and XS methods give similar results. Furthermore, an appropriate use of compressive sensing is crucial for obtaining a good balance between time reduction and reconstructed image quality. Our proposal is suitable for open geometry configurations of human size devices, where incidental factors could make the currents, the fields and the FFP trajectory irregular.

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool.

In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer's Disease Neuroimaging Initiative and the Open Access Series of Imaging Studies. HUMAN is accurate and achieves state-of-the-art performance (Dice[Formula: see text] and Dice[Formula: see text]). It is also a robust method that remains stable when applied to the whole hippocampus or to sub-regions (patches). HUMAN also compares favorably with a basic multi-atlas approach and a benchmark segmentation tool such as FreeSurfer.

Multiple RF classifier for the hippocampus segmentation: Method and validation on EADC-ADNI Harmonized Hippocampal Protocol.

The hippocampus has a key role in a number of neurodegenerative diseases, such as Alzheimer's Disease. Here we present a novel method for the automated segmentation of the hippocampus from structural magnetic resonance images (MRI), based on a combination of multiple classifiers. The method is validated on a cohort of 50 T1 MRI scans, comprehending healthy control, mild cognitive impairment, and Alzheimer's Disease subjects. The preliminary release of the EADC-ADNI Harmonized Protocol training labels is used as gold standard. The fully automated pipeline consists of a registration using an affine transformation, the extraction of a local bounding box, and the classification of each voxel in two classes (background and hippocampus). The classification is performed slice-by-slice along each of the three orthogonal directions of the 3D-MRI using a Random Forest (RF) classifier, followed by a fusion of the three full segmentations. Dice coefficients obtained by multiple RF (0.87 ± 0.03) are larger than those obtained by a single monolithic RF applied to the entire bounding box, and are comparable to state-of-the-art. A test on an external cohort of 50 T1 MRI scans shows that the presented method is robust and reliable. Additionally, a comparison of local changes in the morphology of the hippocampi between the three subject groups is performed. Our work showed that a multiple classification approach can be implemented for the segmentation for the measurement of volume and shape changes of the hippocampus with diagnostic purposes.

New upper limit on strange quark matter abundance in cosmic rays with the PAMELA space experiment.

In this work we present results of a direct search for strange quark matter (SQM) in cosmic rays with the PAMELA space spectrometer. If this state of matter exists it may be present in cosmic rays as particles, called strangelets, having a high density and an anomalously high mass-to-charge (A/Z) ratio. A direct search in space is complementary to those from ground-based spectrometers. Furthermore, it has the advantage of being potentially capable of directly identifying these particles, without any assumption on their interaction model with Earth's atmosphere and the long-term stability in terrestrial and lunar rocks. In the rigidity range from 1.0 to ∼1.0×10^{3} GV, no such particles were found in the data collected by PAMELA between 2006 and 2009. An upper limit on the strangelet flux in cosmic rays was therefore set for particles with charge 1≤Z≤8 and mass 4≤A≤1.2×10^{5}. This limit as a function of mass and as a function of magnetic rigidity allows us to constrain models of SQM production and propagation in the Galaxy.

Automated voxel-by-voxel tissue classification for hippocampal segmentation: methods and validation.

The hippocampus is an important structural biomarker for Alzheimer's disease (AD) and has a primary role in the pathogenesis of other neurological and psychiatric diseases. This study presents a fully automated pattern recognition system for an accurate and reproducible segmentation of the hippocampus in structural Magnetic Resonance Imaging (MRI). The method was validated on a mixed cohort of 56 T1-weighted structural brain images, and consists of three processing levels: (a) Linear registration: all brain images were registered to a standard template and an automated method was applied to capture the global shape of the hippocampus. (b) Feature extraction: all voxels included in the previously selected volume were characterized by 315 features computed from local information. (c) Voxel classification: a Random Forest algorithm was used to classify voxels as belonging or not belonging to the hippocampus. In order to improve the classification performance, an adaptive learning method based on the use of the Pearson's correlation coefficient was developed. The segmentation results (Dice similarity index = 0.81 ± 0.03) compare well with other state-of-the art approaches. A validation study was conducted on an independent dataset of 100 T1-weighted brain images, achieving significantly better results than those obtained with FreeSurfer.

Cosmic-ray positron energy spectrum measured by PAMELA.

Precision measurements of the positron component in the cosmic radiation provide important information about the propagation of cosmic rays and the nature of particle sources in our Galaxy. The satellite-borne experiment PAMELA has been used to make a new measurement of the cosmic-ray positron flux and fraction that extends previously published measurements up to 300 GeV in kinetic energy. The combined measurements of the cosmic-ray positron energy spectrum and fraction provide a unique tool to constrain interpretation models. During the recent solar minimum activity period from July 2006 to December 2009, approximately 24,500 positrons were observed. The results cannot be easily reconciled with purely secondary production, and additional sources of either astrophysical or exotic origin may be required.

Post-detection analysis for grating-based ultra-small angle X-ray scattering.

Until recently, the hard X-ray, phase-sensitive imaging technique called grating interferometry was thought to provide information only in real space. However, by utilizing an alternative approach to data analysis we demonstrated that the angular resolved ultra-small angle X-ray scattering distribution can be retrieved from experimental data. Thus, reciprocal space information is accessible by grating interferometry in addition to real space. Naturally, the quality of the retrieved data strongly depends on the performance of the employed analysis procedure, which involves deconvolution of periodic and noisy data in this context. The aim of this article is to compare several deconvolution algorithms to retrieve the ultra-small angle X-ray scattering distribution in grating interferometry. We quantitatively compare the performance of three deconvolution procedures (i.e., Wiener, iterative Wiener and Lucy-Richardson) in case of realistically modeled, noisy and periodic input data. The simulations showed that the algorithm of Lucy-Richardson is the more reliable and more efficient as a function of the characteristics of the signals in the given context. The availability of a reliable data analysis procedure is essential for future developments in grating interferometry.

Imaging the ultrasmall-angle x-ray scattering distribution with grating interferometry.

X-ray imaging with grating interferometry has previously been regarded as a technique providing information only in direct space. It delivers absorption, phase, and dark-field contrast, which can be viewed as parameters of the underlying but unresolved scattering distribution. Here, we present a method that provides the ultrasmall-angle x-ray scattering distribution and, thus, allows simultaneous access to direct and reciprocal space information.

Cosmic-ray electron flux measured by the PAMELA experiment between 1 and 625 GeV.

Precision measurements of the electron component in the cosmic radiation provide important information about the origin and propagation of cosmic rays in the Galaxy. Here we present new results regarding negatively charged electrons between 1 and 625 GeV performed by the satellite-borne experiment PAMELA. This is the first time that cosmic-ray e⁻ have been identified above 50 GeV. The electron spectrum can be described with a single power-law energy dependence with spectral index -3.18 ± 0.05 above the energy region influenced by the solar wind (> 30 GeV). No significant spectral features are observed and the data can be interpreted in terms of conventional diffusive propagation models. However, the data are also consistent with models including new cosmic-ray sources that could explain the rise in the positron fraction.

Management of corrosive injuries of the upper gastrointestinal tract. Our experience in 58 patients.

BACKGROUND: The ingestion of caustic substances is one of the most difficult conditions to be treated in Emergency Department. PATIENTS AND METHODS: The medical records of patients with caustic ingestion and hospitalized from 2003 to 2008 at the Division of General Emergency Surgery with Polyspecialistic Observation of AORN "A. Cardarelli "in Naples, have been revalued. RESULTS: From 2003 to 2008, 58 patients with caustic ingestion were admitted to our Division. Ten of these patients (17.24%) underwent surgery. Six patients underwent oesophageal and gastric resection with cervical esophagostomy and alimentary digiunostomy in emergency; two underwent exploratory laparotomy, two had gastroenteroanastomosis for antropyloric stenosis. One patient underwent new operation for a complication. In total, three reconstructions of oesophagus with colon were performed . Of the six patients undergoing esofagogastrectomy, two died in the first postoperative day, but four have passed the acute phase. CONCLUSIONS: There is no universally accepted diagnostic and therapeutic procedure for the management of these patients, who are often left - as it appears in literature - to the personal experience of the surgeon who is dealing with this situation.

PAMELA measurements of cosmic-ray proton and helium spectra.

Protons and helium nuclei are the most abundant components of the cosmic radiation. Precise measurements of their fluxes are needed to understand the acceleration and subsequent propagation of cosmic rays in our Galaxy. We report precision measurements of the proton and helium spectra in the rigidity range 1 gigavolt to 1.2 teravolts performed by the satellite-borne experiment PAMELA (payload for antimatter matter exploration and light-nuclei astrophysics). We find that the spectral shapes of these two species are different and cannot be described well by a single power law. These data challenge the current paradigm of cosmic-ray acceleration in supernova remnants followed by diffusive propagation in the Galaxy. More complex processes of acceleration and propagation of cosmic rays are required to explain the spectral structures observed in our data.

PAMELA results on the cosmic-ray antiproton flux from 60 MeV to 180 GeV in kinetic energy.

The satellite-borne experiment PAMELA has been used to make a new measurement of the cosmic-ray antiproton flux and the antiproton-to-proton flux ratio which extends previously published measurements down to 60 MeV and up to 180 GeV in kinetic energy. During 850 days of data acquisition approximately 1500 antiprotons were observed. The measurements are consistent with purely secondary production of antiprotons in the Galaxy. More precise secondary production models are required for a complete interpretation of the results.

An anomalous positron abundance in cosmic rays with energies 1.5-100 GeV.

Antiparticles account for a small fraction of cosmic rays and are known to be produced in interactions between cosmic-ray nuclei and atoms in the interstellar medium, which is referred to as a 'secondary source'. Positrons might also originate in objects such as pulsars and microquasars or through dark matter annihilation, which would be 'primary sources'. Previous statistically limited measurements of the ratio of positron and electron fluxes have been interpreted as evidence for a primary source for the positrons, as has an increase in the total electron+positron flux at energies between 300 and 600 GeV (ref. 8). Here we report a measurement of the positron fraction in the energy range 1.5-100 GeV. We find that the positron fraction increases sharply over much of that range, in a way that appears to be completely inconsistent with secondary sources. We therefore conclude that a primary source, be it an astrophysical object or dark matter annihilation, is necessary.

New measurement of the antiproton-to-proton flux ratio up to 100 GeV in the cosmic radiation.

A new measurement of the cosmic-ray antiproton-to-proton flux ratio between 1 and 100 GeV is presented. The results were obtained with the PAMELA experiment, which was launched into low-Earth orbit on-board the Resurs-DK1 satellite on June 15th 2006. During 500 days of data collection a total of about 1000 antiprotons have been identified, including 100 above an energy of 20 GeV. The high-energy results are a tenfold improvement in statistics with respect to all previously published data. The data follow the trend expected from secondary production calculations and significantly constrain contributions from exotic sources, e.g., dark matter particle annihilations.

MAGIC-5: an Italian mammographic database of digitised images for research.

The implementation of a database of digitised mammograms is discussed. The digitised images were collected beginning in 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals as a first step in developing and implementing a computer-aided detection (CAD) system. All 3,369 mammograms were collected from 967 patients and classified according to lesion type and morphology, breast tissue and pathology type. A dedicated graphical user interface was developed to visualise and process mammograms to support the medical diagnosis directly on a high-resolution screen. The database has been the starting point for developing other medical imaging applications, such as a breast CAD, currently being upgraded and optimised for use in a distributed environment with grid services, in the framework of the Instituto Nazionale di Fisicia Nucleare (INFN)-funded Medical Applications on a Grid Infrastructure Connection (MAGIC)-5 project.