Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

A gene expression inflammatory signature specifically predicts multiple myeloma evolution and patients survival

Multiple myeloma (MM) is closely dependent on cross-talk between malignant plasma cells and cellular components of the inflammatory/immunosuppressive bone marrow milieu, which promotes disease progression, drug resistance, neo-angiogenesis, bone destruction and immune-impairment. We investigated the relevance of inflammatory genes in predicting disease evolution and patient survival. A bioinformatics study by Ingenuity Pathway Analysis on gene expression profiling dataset of monoclonal gammopathy of undetermined significance, smoldering and symptomatic-MM, identified inflammatory and cytokine/chemokine pathways as the most progressively affected during disease evolution. We then selected 20 candidate genes involved in B-cell inflammation and we investigated their role in predicting clinical outcome, through univariate and multivariate analyses (log-rank test, logistic regression and Cox-regression model). We defined an 8-genes signature (IL8, IL10, IL17A, CCL3, CCL5, VEGFA, EBI3 and NOS2) identifying each condition (MGUS/smoldering/symptomatic-MM) with 84% accuracy. Moreover, six genes (IFNG, IL2, LTA, CCL2, VEGFA, CCL3) were found independently correlated with patients' survival. Patients whose MM cells expressed high levels of Th1 cytokines (IFNG/LTA/IL2/CCL2) and low levels of CCL3 and VEGFA, experienced the longest survival. On these six genes, we built a prognostic risk score that was validated in three additional independent datasets. In this study, we provide proof-of-concept that inflammation has a critical role in MM patient progression and survival. The inflammatory-gene prognostic signature validated in different datasets clearly indicates novel opportunities for personalized anti-MM treatment

Monitoring soil wetness variations by means of satellite passive microwave observations: the HYDROPTIMET study cases

International audienceSoil moisture is an important component of the hydrological cycle. In the framework of modern flood warning systems, the knowledge of soil moisture is crucial, due to the influence on the soil response in terms of infiltration-runoff. Precipitation-runoff processes, in fact, are related to catchment's hydrological conditions before the precipitation. Thus, an estimation of these conditions is of significant importance to improve the reliability of flood warning systems. Combining such information with other weather-related satellite products (i.e. rain rate estimation) might represent a useful exercise in order to improve our capability to handle (and possibly mitigate or prevent) hydro-geological hazards. Remote sensing, in the last few years, has supported several techniques for soil moisture/wetness monitoring. Most of the satellite-based techniques use microwave data, thanks to the all-weather and all-time capability of these data, as well as to their high sensitivity to water content in the soil. On the other hand, microwave data are unfortunately highly affected by the presence of surface roughness or vegetation coverage within the instantaneous satellite field of view (IFOV). Those problems, consequently, strongly limit the efficiency and the reliability of traditional satellite techniques. Recently, using data coming from AMSU (Advanced Microwave Sounding Unit), flying aboard NOAA (National Oceanic and Atmospheric Administration) satellites, a new methodology for soil wetness estimation has been proposed. The proposed index, called Soil Wetness Variation Index (SWVI), developed by a multi-temporal analysis of AMSU records, seems able to reduce the problems related to vegetation and/or roughness effects. Such an approach has been tested, with promising results, on the analysis of some flooding events which occurred in Europe in the past. In this study, results achieved for the HYDROPTIMET test cases will be analysed and discussed in detail. This analysis allows us to evaluate the reliability and the efficiency of the proposed technique in identifying different amounts of soil wetness variations in different observational conditions. In particular, the proposed indicator was able to document the actual effects of meteorological events, in terms of space-time evolution of soil wetness changes, for all the analysed HYDROPTIMET test cases. Moreover, in some circumstances, the SWVI was able to identify the presence of a sort of "early" signal in terms of soil wetness variations, which may be regarded as a timely indication of an anomalous value of soil water content. This evidence suggests the opportunity to use such an index in the pre-operational phases of the modern flood warning systems, in order to improve their forecast capabilities and their reliability

Early Blockade of CB1 Receptors Ameliorates Schizophrenia-like Alterations in the Neurodevelopmental MAM Model of Schizophrenia

In agreement with the neurodevelopmental hypothesis of schizophrenia, prenatal exposure of Sprague-Dawley rats to the antimitotic agent methylazoxymethanol acetate (MAM) at gestational day 17 produces long-lasting behavioral alterations such as social withdrawal and cognitive impairment in adulthood, mimicking a schizophrenia-like phenotype. These abnormalities were preceded at neonatal age both by the delayed appearance of neonatal reflexes, an index of impaired brain maturation, and by higher 2-arachidonoylglycerol (2-AG) brain levels. Schizophrenia-like deficits were reversed by early treatment [from postnatal day (PND) 2 to PND 8] with the CB1 antagonist/inverse agonist AM251 (0.5 mg/kg/day). By contrast, early CB1 blockade affected the behavioral performance of control rats which was paralleled by enhanced 2-AG content in the prefrontal cortex (PFC). These results suggest that prenatal MAM insult leads to premorbid anomalies at neonatal age via altered tone of the endocannabinoid system, which may be considered as an early marker preceding the development of schizophrenia-like alterations in adulthood

Simultaneous biochemical and physiological responses of the roots and leaves of pancratium maritimum (Amaryllidaceae) to mild salt stress

Pancratium maritimum (Amaryllidaceae) is a bulbous geophyte growing on coastal sands. In this study, we investigated changes in concentrations of metabolites in the root and leaf tissue of P. maritimum in response to mild salt stress. Changes in concentrations of osmolytes, glutathione, sodium, mineral nutrients, enzymes, and other compounds in the leaves and roots were measured at 0, 3, and 10 days during a 10‐day exposure to two levels of mild salt stress, 50 mM NaCl or 100 mM NaCl in sandy soil from where the plants were collected in dunes near Cuma, Italy. Sodium accumulated in the roots, and relatively little was translocated to the leaves. At both concentrations of NaCl, higher values of the concentrations of oxidized glutathione disulfide (GSSG), compared to reduced glutathione (GSH), in roots and leaves were associated with salt tolerance. The concentration of proline increased more in the leaves than in the roots, and glycine betaine increased in both roots and leaves. Differences in the accumulation of organic osmolytes and electron donors synthesized in both leaves and roots demonstrate that osmoregulatory and electrical responses occur in these organs of P. maritimum under mild salt stress

Assessing the potential of <i>SWVI</i> (Soil Wetness Variation Index) for hydrological risk monitoring by means of satellite microwave observations

International audienceIn the last years satellite remote sensing applications in hydrology have considerably progressed. A new multi-temporal satellite data-analysis approach has been recently suggested in order to estimate space-time changes of geophysical parameters possibly related to the increase of environmental and hydro-geological hazards. Such an approach has been already used both for flooded area mapping (using AVHRR data) and for soil wetness index estimation (using AMSU data). In this work, a preliminary sensitivity analysis of the proposed Soil Wetness Variation Index (SWVI) is made in the case of low intensity meteorological events by the comparison with hydrological (precipitation) data. This analysis, as a first step of a more complex work in progress, is targeted to a first evaluation of the reliability of the SWVI in describing soil response to precipitations of different duration and intensity

An XMM-Newton and INTEGRAL view on the hard state of EXO 1745-248 during its 2015 outburst

CONTEXT - Transient low-mass X-ray binaries (LMXBs) often show outbursts lasting typically a few-weeks and characterized by a high X-ray luminosity ($L_{x} \approx 10^{36}-10^{38}$ erg/sec), while for most of the time they are found in X-ray quiescence ($L_X\approx10^{31} -10^{33}$ erg/sec). EXO 1745-248 is one of them. AIMS - The broad-band coverage, and the sensitivity of instrument on board of {\xmm} and {\igr}, offers the opportunity to characterize the hard X-ray spectrum during {\exo} outburst. METHODS - In this paper we report on quasi-simultaneous {\xmm} and {\igr} observations of the X-ray transient {\exo} located in the globular cluster Terzan 5, performed ten days after the beginning of the outburst (on 2015 March 16th) shown by the source between March and June 2015. The source was caught in a hard state, emitting a 0.8-100 keV luminosity of $\simeq10^{37}$~{\lumcgs}. RESULTS - The spectral continuum was dominated by thermal Comptonization of seed photons with temperature $kT_{in}\simeq1.3$ keV, by a cloud with moderate optical depth $\tau\simeq2$ and electron temperature $kT_e\simeq 40$ keV. A weaker soft thermal component at temperature $kT_{th}\simeq0.6$--0.7 keV and compatible with a fraction of the neutron star radius was also detected. A rich emission line spectrum was observed by the EPIC-pn on-board {\xmm}; features at energies compatible with K-$\alpha$ transitions of ionized sulfur, argon, calcium and iron were detected, with a broadness compatible with either thermal Compton broadening or Doppler broadening in the inner parts of an accretion disk truncated at $20\pm6$ gravitational radii from the neutron star. Strikingly, at least one narrow emission line ascribed to neutral or mildly ionized iron is needed to model the prominent emission complex detected between 5.5 and 7.5 keV. (Abridged)Comment: 14 pages, 6 figure, 2 tables. Accepted for publication on A&A (21/03/2017

Monitoring soil wetness variations by means of satellite passive microwave observations: the HYDROPTIMET study cases

Soil moisture is an important component of the hydrological cycle. In the framework of modern flood warning systems, the knowledge of soil moisture is crucial, due to the influence on the soil response in terms of infiltration-runoff. Precipitation-runoff processes, in fact, are related to catchment's hydrological conditions before the precipitation. Thus, an estimation of these conditions is of significant importance to improve the reliability of flood warning systems. Combining such information with other weather-related satellite products (i.e. rain rate estimation) might represent a useful exercise in order to improve our capability to handle (and possibly mitigate or prevent) hydro-geological hazards. <P style='line-height: 20px;'> Remote sensing, in the last few years, has supported several techniques for soil moisture/wetness monitoring. Most of the satellite-based techniques use microwave data, thanks to the all-weather and all-time capability of these data, as well as to their high sensitivity to water content in the soil. On the other hand, microwave data are unfortunately highly affected by the presence of surface roughness or vegetation coverage within the instantaneous satellite field of view (IFOV). Those problems, consequently, strongly limit the efficiency and the reliability of traditional satellite techniques. <P style='line-height: 20px;'> Recently, using data coming from AMSU (Advanced Microwave Sounding Unit), flying aboard NOAA (National Oceanic and Atmospheric Administration) satellites, a new methodology for soil wetness estimation has been proposed. The proposed index, called Soil Wetness Variation Index (<I>SWVI</I>), developed by a multi-temporal analysis of AMSU records, seems able to reduce the problems related to vegetation and/or roughness effects. Such an approach has been tested, with promising results, on the analysis of some flooding events which occurred in Europe in the past. <P style='line-height: 20px;'> In this study, results achieved for the HYDROPTIMET test cases will be analysed and discussed in detail. This analysis allows us to evaluate the reliability and the efficiency of the proposed technique in identifying different amounts of soil wetness variations in different observational conditions. In particular, the proposed indicator was able to document the actual effects of meteorological events, in terms of space-time evolution of soil wetness changes, for all the analysed HYDROPTIMET test cases. Moreover, in some circumstances, the <I>SWVI</I> was able to identify the presence of a sort of 'early' signal in terms of soil wetness variations, which may be regarded as a timely indication of an anomalous value of soil water content. This evidence suggests the opportunity to use such an index in the pre-operational phases of the modern flood warning systems, in order to improve their forecast capabilities and their reliability