Large Panels with Common Factors and Spatial Correlations

This paper considers the statistical analysis of large panel data sets where even after condi-tioning on common observed effects the cross section units might remain dependently distrib-uted. This could arise when the cross section units are subject to unobserved common effects and/or if there are spill over effects due to spatial or other forms of local dependencies. The paper provides an overview of the literature on cross section dependence, introduces the con-cepts of time-specific weak and strong cross section dependence and shows that the commonly used spatial models are examples of weak cross section dependence. It is then established that the Common Correlated Effects (CCE) estimator of panel data model with a multifactor error structure, recently advanced by Pesaran (2006), continues to provide consistent estimates of the slope coefficient, even in the presence of spatial error processes. Small sample properties of the CCE estimator under various patterns of cross section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross sectionally correlated errors. We also explore the role of certain characteristics of spatial processes in determining the performance of CCE estimators, such as the form and intensity of spatial dependence, and the sparseness of the spatial weight matrix.panels, Common Correlated Effects, strong and weak cross section dependence

Weak and Strong Cross Section Dependence and Estimation of Large Panels

This paper introduces the concepts of time-specific weak and strong cross section dependence. A double-indexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic mean, as N is increased without bounds for all weights that satisfy certain ‘granularity’ conditions. Relationship with the notions of weak and strong common factors is investigated and an application to the estimation of panel data models with an infinite number of weak factors and a finite number of strong factors is also considered. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects.panels, strong and weak cross section dependence, weak and strong factors

Musculoskeletal MRI at 7 T: do we need more or is it more than enough?

Ultra-high field magnetic resonance imaging (UHF-MRI) provides important diagnostic improvements in musculoskeletal imaging. The higher signal-to-noise ratio leads to higher spatial and temporal resolution which results in improved anatomic detail and higher diagnostic confidence. Several methods, such as T2, T2*, T1rho mapping, delayed gadolinium-enhanced, diffusion, chemical exchange saturation transfer, and magnetisation transfer techniques, permit a better tissue characterisation. Furthermore, UHF-MRI enables in vivo measurements by low-γ nuclei (23Na, 31P, 13C, and 39K) and the evaluation of different tissue metabolic pathways. European Union and Food and Drug Administration approvals for clinical imaging at UHF have been the first step towards a more routinely use of this technology, but some drawbacks are still present limiting its widespread clinical application. This review aims to provide a clinically oriented overview about the application of UHF-MRI in the different anatomical districts and tissues of musculoskeletal system and its pros and cons. Further studies are needed to consolidate the added value of the use of UHF-MRI in the routine clinical practice and promising efforts in technology development are already in progress

Modeling brain connectivity dynamics in functional Magnetic Resonance Imaging via Particle Filtering

Interest in the studying of functional connections in the brain has grown considerably in the last decades, as many studies have pointed out that alterations in the interaction among brain areas can play a role as markers of neurological diseases. Most studies in this field treat the brain network as a system of connections stationary in time, but dynamic features of brain connectivity can provide useful information, both on physiology and pathological conditions of the brain. In this paper, we propose the application of a computational methodology, named Particle Filter (PF), to study non-stationarities in brain connectivity in functional Magnetic Resonance Imaging (fMRI). The PF algorithm estimates time-varying hidden parameters of a first-order linear time-varying Vector Autoregressive model (VAR) through a Sequential Monte Carlo strategy. On simulated time series, the PF approach effectively detected and enabled to follow time-varying hidden parameters and it captured causal relationships among signals. The method was also applied to real fMRI data, acquired in presence of periodic tactile or visual stimulations, in different sessions. On these data, the PF estimates were consistent with current knowledge on brain functioning. Most importantly, the approach enabled to detect statistically significant modulations in the cause-effect relationship between brain areas, which correlated with the underlying visual stimulation pattern presented during the acquisition

Assuring potato tuber quality during storage: A future perspective

Potatoes represent an important staple food crop across the planet. Yet, to maintain tuber quality and extend availability, there is a necessity to store tubers for long periods often using industrial-scale facilities. In this context, preserving potato quality is pivotal for the seed, fresh and processing sectors. The industry has always innovated and invested in improved post-harvest storage. However, the pace of technological change has and will continue to increase. For instance, more stringent legislation and changing consumer attitudes have driven renewed interest in creating alternative or complementary post-harvest treatments to traditional chemically reliant sprout suppression and disease control. Herein, the current knowledge on biochemical factors governing dormancy, the use of chlorpropham (CIPC) as well as existing and chemical alternatives, and the effects of pre- and post-harvest factors to assure potato tuber quality is reviewed. Additionally, the role of genomics as a future approach to potato quality improvement is discussed. Critically, and through a more industry targeted research, a better mechanistic understanding of how the pre-harvest environment influences tuber quality and the factors which govern dormancy transition should lead to a paradigm shift in how sustainable storage can be achieved

Double-Tuned Birdcage Radio Frequency Coil for 7 T MRI: Optimization, Construction and Workbench Validation

The aim of the present study is the optimization, construction, and workbench validation of a double-tuned 1H- 23Na volume radio frequency (RF) coil suitable for human head imaging at 7 T, based on the birdcage geometry. The birdcage-like design which is considered is the four-ring model, in which two standard birdcage-like structures with the same diameters are nested along the longitudinal axis. Simulations based on Maxwell’s equations are performed to evaluate the RF magnetic field homogeneity and the RF coil efficiency varying the coil geometrical parameters. The RF magnetic field homogeneity is evaluated both on the transverse (z = 0) and longitudinal (y = 0) planes without performing the impedance matching procedure, so that the RF coil symmetry is not perturbed by the matching network. The RF coil efficiency is instead dependent on the effective coil input RF power, and it is evaluated after matching the coil, so that the reflected power is minimized, assuming that the stimulation power is totally delivered to the RF coil. Considering the simulation results and the target application, the useful RF coil geometrical parameters are fixed. The four-ring model, which showed the best performances, has been built and tested on a workbench, using a cylindrical phantom filled with a 0.05 M saline solution as load. This provides the first example of a four-ring realization intended 1H- 23Na for human head imaging at 7 T

Effects of the load size on the maximum local SAR at 7T

In this study we investigated the effects of the load size on the maximum local SAR at 7T. Specifically, we resorted to: i) 3D full wave numerical electromagnetic simulations for analyzing a surface loop loaded with anatomic human calves models; ii) 2D analytical approach for analyzing a volume resonator loaded with homogeneous cylindrical phantoms having average tissue dielectric properties. In both cases we noticed that the maximum local SAR decreases with decreasing load size: this holds true if the RF magnetic fields (B1+) for the different load sizes are scaled so to achieve the same slice average value of 1ìT