Neonatal Cortical Auditory Evoked Potentials Are Affected by Clinical Conditions Occurring in Early Prematurity

Purpose: Cortical auditory evoked potentials may serve as an early indicator of developmental problems in the auditory cortex. The aim of the study was to determine the effect on neonatal cortical auditory processing of clinical conditions occurring in early prematurity. Methods: Sixty-seven preterm infants born at 29 weeks mean gestational age (range, 23\u201334 weeks) were recorded at a mean postconception age of 35 weeks, before discharge from the third level neonatal intensive care unit. The average of 330 responses to standard 1000 Hz pure tones delivered in an oddball paradigm was recorded at frontal location. Data of 45 of 67 recruited premature infants were available for analysis. Mean amplitudes calculated from the data points of 30 milliseconds centered on P1 and N2 peaks in the waveforms of each subject were measured. The effect of perinatal clinical factors on cortical auditory evoked responses was evaluated. Results: The amplitude of P1 component was significantly lower in infants with bronco-pulmonary dysplasia (P \ubc 0.004) and retinopathy of prematurity (P \ubc 0.03). The multivariate analysis, done to evaluate the relative weight of gestational age and bronco-pulmonary dysplasia and/or retinopathy of prematurity on cortical auditory evoked potentials components, showed an effect of clinical factors on P1 (P \ubc 0.005) and of gestational age on N2 (P \ubc 0.02). Conclusions: Cortical auditory processing seems to be influenced by clinical conditions complicating extremely preterm birth

Il project financing e la segregazione patrimoniale: profili economico-aziendali.

Il project financing: definizioni e differenze con il corporate finance. I fondamenti teorici della finanza di progetto. Il quadro giuridico del project financing e i modelli di segregazione. Il patrimonio destinato e il project financing. Il finanziamento destinato e il project financing. Il trust e il project financing.Il project financing: definizioni e differenze con il corporate finance. I fondamenti teorici della finanza di progetto. Il quadro giuridico del project financing e i modelli di segregazione. Il patrimonio destinato e il project financing. Il finanziamento destinato e il project financing. Il trust e il project financing.LUISS PhD Thesi

Parametric connectivity analysis in time and frequency domain from in silico and EEG data

In this decade, establishing structure-function relationships in human brain has become one of the most influential concepts in modern cognitive neuroscience since interactions among cerebral components are fundamental to explain cortical activities ([1]; [2]; [3]). In literature such relationships have been defined in terms of structural, functional and effective connectivity. This distinction, mainly focused on the theoretic concept, is also related to the different measurement instruments and analytical tools used for acquiring and processing the data. The structural connectivity refers to a pattern of anatomical links among brain regions. Its analysis aims to characterize the architecture of complex networks underlying the cerebral functional organization. Magnetic Resonance Imaging and especially Diffusion Tensor Imaging can be used to convey information concerning the physical connection between neuronal populations. Functional/effective connectivity aims at identifying the presence and the strength of connections in terms of statistically significant dependency. The former is defined as the temporal correlation between neurophysiological events occurring in distributed neuronal groups and areas. The latter describes the causal influence that one neural system exerts over another either directly or indirectly in terms of temporal precedence and physical control ([4];[5]). Functional and effective connectivity can be estimated exploiting both Functional Magnetic Resonance Imaging (fMRI) and electrophysiological signals, such as Electroencephalography (EEG) and Magnetoencephalography (MEG), with different advantages and drawbacks, respectively. fMRI provides high spatial resolution (mm) but poor temporal precision (s) while EEG/MEG has more limited spatial resolution (cm) and higher temporal precision (ms). Because functional and effective connectivity are largely estimated over time, EEG and MEG are more suitable for calculating such connectivity. In literature several methods have been developed to characterize brain connectivity in terms of network topology, connections strength and causality, following two main approaches: the data-driven, where topology, causality and strength are all inferred from data, and the neural model-based, where the model topology is postulated from a priori knowledge and only the connections strength is estimated from the data. - Data driven approach. The data driven approach includes linear, non-linear and information-based techniques. The linear ones provide a battery of indices derived by multivariate autoregressive models (MVAR) based on Granger causality principles ([6]) or MVAR frequency response ([7]). Such are Ordinary Coherence, Partial Coherence, Directed Transfer Function (DTF) and Partial Directed Coherence (PDC). These indexes measure the strength of the linear coupling between two signals; in addition DTF and PDC provide information about causal influence ([8]). o Among the non-linear techniques, phase synchronization has been shown to be very effcient in detecting interactions between oscillators. The phase locking values approach assumes that two dynamic systems may have their phases synchronized even if their amplitude are zero correlates ([9]). o The most representative information-based technique is the cross mutual information that measures the mutual dependence between two signals by quantifying the amount of information gained about one signal from measuring the other, as a function of delay between these two signals ([10]). - Neural model based approach. Representative methods are the Structural Equations Modelling (SEM) and the Dynamic Causal Modelling (DCM) ([11]; [12]). They are multivariate technique used to test hypothesis regarding the influences among interacting variables, but different concepts underlies these two methods. SEM approach assumes that neuronal dynamics are very fast in relation to signals uctuations and, hence, is based on a static neuronal model. This case, the neuronal activity has reached steady-state and changes in connectivity are led directly by changes in the covariance structure of the observed time series ([13]). On the other hand, in DCM the observed time series are modelled as a deterministic dynamical system in which external inputs causes changes in neural activity and therefore in connectivity values ([14]). Most approaches, like those based on Granger causality principles, have been examined in literature to quantify their ability in revealing cerebral connections ([15];[16]; [11]) but their simulation studies do not provide a comprehensive analysis because they use in silico data generated by self-referential linear methods which do not reproduce the complexity of brain. To overcome this issue, an innovative simulation approach has been developed in this work, based on a nonlinear neural mass model ([17]) totally independent of SEM and MVAR linear equation and able to address the complexity of neural networks. This no-self referential approach was exploited to generate in silico network data to be used as a benchmark, to quantitatively compare obtained results with true connections. The main objective of this work was to understand limits and advantages of MVAR indexes and SEM by exploiting the simulation study. Thus, it mainly serves as a proof-of-concept for connectivity measures under ideal conditions. Our purpose was to derive from simulation results some practical procedures in order to classify different brain states to support both cognitive research and clinical activity. First, research activity was focused to address connectivity on simulated data obtained on three regions networks characterized by different strength connections and based on different levels of non linearity. Second, a dataset, made available by Department of Medicine, University of Padova was used to explore application of these methods to real data by applying the simulation study suggestions. This thesis consists of three main section. The ffrst one includes Chapter 1-2-3 describing in detailed the considered connectivity measures, such are those based on Multivariate Autoregressive models and the Structural Equation Modelling, and the simulation study. The second part depicts in silico results and the application to EEG data. Finally, comments are reported in Discussion and Conclusions. Chapter 1 explains how the connecting parameters of MVAR and SEM models are identified on EEG data and describes procedures commonly exploited to analyse connectivity. Chapter 2 reports an overview about the principal models used to generate in silico data, namely the neural mass models, and described the neural mass model exploited in this work. Finally, it characterizes network models adopted to simulate data and lists the procedure followed to generate in silico datasets. Chapter 3 summarizes the computations implemented to have more insights on our data by analysing the output of each methods. It describes the procedure used to evaluate the statistical signiffcance of each index results, such are the F-test for Granger causality index and the null distribution threshold using surrogate data for MVAR frequency indexes. Chapter 4 illustrates the results obtained with the simulation study. First, we reported the complete analysis for a representative subset of experiments, then for all datasets we showed topology and strength estimates. Chapter 6 delineates the procedure followed to study the connectivity in case of hepatic encephalopathy. Chapter 7 covers the Discussion and Conclusions. The Appendix is a parallel work aimed to understand the meaning of connectivity indexes computed via Structural Equation Modelling. By exploiting the neural mass model used to simulate cortical data, the objective is to quantify which measure its estimates represent. We demonstrated that Granger causality is a good estimator with high values both of sensitivity and specificity, while frequency indexes, DTF and PDC, are too much affected by the threshold choice and their interpretation in terms of absolute strength connection is not clear. As regard SEM, we proved the difficulty of its approach to describe just simple situations. Even if SEM is based on linear regression as well as MVAR models, it differently assumes there is no connection with past information, as if brain connectivity could describe time series relationships by the instant we observe it. Hence, it is not sufficiently robust to characterize neuronal dynamic activity

Impairment of Goodwill: Level of Compliance and Quality of Disclosure during the Crisis-An Analysis of Italian Listed Companies

This paper investigates the level of disclosure on impairment test of goodwill in the Italian context. The research is based on the analysis of the consolidated financial statements 2007\u20132011 of companies listed on FTSE MIB of Milan Stock Exchange at 31st December 2012. The main objective of the research is to verify if financial crisis has impacted on the level of compliance with IAS 36 and Guidelines issued by Italian Authorities. In addition, it tests if there are any relations between the level of disclosure and factors such as market capitalization, the ratio Goodwill on Equity and Impairment loss/Goodwill. Our results show that the quality of disclosure is still incomplete, even if it is clear that there is a significant improvement in the period covered by the analysis. In addition, we observe that, at least in relation to our data, there is no relation between the quality of mandatory disclosure on goodwill and the mentioned factors

Il project financing e la segregazione patrimoniale: profili economico-aziendali.

Il project financing: definizioni e differenze con il corporate finance. I fondamenti teorici della finanza di progetto. Il quadro giuridico del project financing e i modelli di segregazione. Il patrimonio destinato e il project financing. Il finanziamento destinato e il project financing. Il trust e il project financing

The Shape of Water: How Tai Chi and Mental Imagery Effect the Kinematics of a Reach-to-Grasp Movement

The aim of the present study was to investigate the effect of Tai Chi (TC) and mental imagery (MI) on motor performance. MI is the ability of representing different types of images and it can be improved through constant practice (e.g., of TC). The majority of previous literature has mainly investigated the impact of this mental factor by means of qualitative indexes, whereas studies considering more rigorous measures such as kinematic parameters are rare. In this vein, little is known about how MI can affect reach-to-grasp, one of the most studied models in kinematic research. The present study attempts to fill that gap by investigating the relationship between MI and motor performance in TC, a practice that largely promotes the adoption of mental training. One TC master, four instructors, ten apprentices and fifteen untrained participants were requested to reach toward and grasp an object while mentally representing one out of five different images related to water with an increasing degree of dynamicity and expansion (i.e., still water, flowing water, wave, whirlpool, and opening water flower). Kinematic profiles of movements were recorded by means of six infra-red cameras using a 3-D motion analysis system. We tested whether: (i) focusing on MI during the task would help in optimizing motor efficiency, and (ii) expertise in TC would be reflected in higher flexibility during the task. The results indicate that kinematics is highly sensitive to MI and TC practice. In particular, our main finding suggests a statistically significant general improvement in motor efficiency for the TC group and a beneficial effect for all the participants when focusing on the most expansive image (i.e., opening water flower). Moreover, regression analysis indicates that MI and TC practice make online control more flexible in an experience-based way. These results have important implications for the use of mental imagery and TC in the retraining of motor function in people with physical disabilities

What is missing in the study of emotion expression?

While approaching celebrations for the 150 years of “The Expression of the Emotions in Man and Animals”, scientists’ conclusions on emotion expression are still debated. Emotion expression has been traditionally anchored to prototypical and mutually exclusive facial expressions (e.g., anger, disgust, fear, happiness, sadness, and surprise). However, people express emotions in nuanced patterns and – crucially – not everything is in the face. In recent decades considerable work has critiqued this classical view, calling for a more fluid and flexible approach that considers how humans dynamically perform genuine expressions with their bodies in context. A growing body of evidence suggests that each emotional display is a complex, multi-component, motoric event. The human face is never static, but continuously acts and reacts to internal and environmental stimuli, with the coordinated action of muscles throughout the body. Moreover, two anatomically and functionally different neural pathways sub-serve voluntary and involuntary expressions. An interesting implication is that we have distinct and independent pathways for genuine and posed facial expressions, and different combinations may occur across the vertical facial axis. Investigating the time course of these facial blends, which can be controlled consciously only in part, is recently providing a useful operational test for comparing the different predictions of various models on the lateralization of emotions. This concise review will identify shortcomings and new challenges regarding the study of emotion expressions at face, body, and contextual levels, eventually resulting in a theoretical and methodological shift in the study of emotions. We contend that the most feasible solution to address the complex world of emotion expression is defining a completely new and more complete approach to emotional investigation. This approach can potentially lead us to the roots of emotional display, and to the individual mechanisms underlying their expression (i.e., individual emotional signatures)

Races of the wheat stem rust fungus in Brazil, from 1982 to 1985

Nos anos de 1982 a 1985, foram colhidas e analisadas 652 amostras do fungo da ferrugem-do-colmo-do-trigo (Puccinia graminis f. sp. tritici), originárias de várias regiões tritícolas do Brasil e de alguns países da América do Sul. Em 1982 e 1983, a raça G17 predominou em mais de 50% do total de isolados identificados, seguida de G19 e G20 em 1982 (aproximadamente 17% cada), e da G20 em 1983 (21,8%). Em 1984, prevaleceram as raças G17, G19 e G20, com uma frequência ao redor de 25% cada uma. No ano de 1985, as raças G15, G17 e G19 apresentaram, cada uma, frequência de ocorrência de, aproximadamente, 20%. Neste período, foram identificadas quatro raças novas: em 1983, a raça G21, isolada em amostras de Patos de Minas, MG e de Londrina, PR; em 1984, as raças G22 em amostras de Palotina, PR, Dourados, MS, e de São Borja, RS, e G23, em amostras de Piratini, RS; e em 1985, a G24 de Cruz Alta, RS, e de Londrina, PR. Considerando-se as raças ocorrentes nas áreas amostradas, os genes Sr 22, Sr 24, Sr 25, Sr 26, Sr 27, Sr 31 e Sr 32 conferem, isoladamente, resistência a todas as raças de P. graminis tritici.From 1982 to 1985, 652 samples of the wheat stem rust fungus (Puccinia graminis E. sp. tritici) were collected in the Brazilian wheat regions and in some other countries of South America. The race G17 was predominant in 1982 and 1983, constituting more than 50% at the isolates identified. Race G19 and G20 composed about 17% in 1982 and race G20 about 22% in 1983. In 1984, races G17, G19 and G20 were prevalent in about 25% each. In 1985, races G15, G17 and G19 presented a frequency of about 20% each. During 1982-1983, four new races were identified: G21, isolated from Patos de Minas, MG, and Londrina. PR, in 1983; G22 from Palotina, PR, Dourados, MS and São Borja, RS; G23 from Piratini, RS in 1984, and G24 from Cruz Alta, RS and Londrina, PR in 1985. Considering the occurring races in the sampled areas, the genes Sr 22, Sr 24, Sr 25, Sr 26, Sr 27, Sr 31 and Sr 32, confer, isolately, resistance to all races of Puccinia graminis tritici

The Spatiotemporal Dynamics of Facial Movements Reveals the Left Side of a Posed Smile

Humans can recombine thousands of different facial expressions. This variability is due to the ability to voluntarily or involuntarily modulate emotional expressions, which, in turn, depends on the existence of two anatomically separate pathways. The Voluntary (VP) and Involuntary (IP) pathways mediate the production of posed and spontaneous facial expressions, respectively, and might also affect the left and right sides of the face differently. This is a neglected aspect in the literature on emotion, where posed expressions instead of genuine expressions are often used as stimuli. Two experiments with different induction methods were specifically designed to investigate the unfolding of spontaneous and posed facial expressions of happiness along the facial vertical axis (left, right) with a high-definition 3-D optoelectronic system. The results showed that spontaneous expressions were distinguished from posed facial movements as revealed by reliable spatial and speed key kinematic patterns in both experiments. Moreover, VP activation produced a lateralization effect: compared with the felt smile, the posed smile involved an initial acceleration of the left corner of the mouth, while an early deceleration of the right corner occurred in the second phase of the movement, after the velocity peak