A systematic review of childhood maltreatment and resting state functional connectivity

Resting-state functional connectivity (rsFC) has the potential to shed light on how childhood abuse and neglect relates to negative psychiatric outcomes. However, a comprehensive review of the impact of childhood maltreatment on the brain's resting state functional organization has not yet been undertaken. We systematically searched rsFC studies in children and youth exposed to maltreatment. Nineteen studies (total n = 3079) met our inclusion criteria. Two consistent findings were observed. Childhood maltreatment was linked to reduced connectivity between the anterior insula and dorsal anterior cingulate cortex, and with widespread heightened amygdala connectivity with key structures in the salience, default mode, and prefrontal regulatory networks. Other brain regions showing altered connectivity included the ventral anterior cingulate cortex, dorsolateral prefrontal cortex, and hippocampus. These patterns of altered functional connectivity associated with maltreatment exposure were independent of symptoms, yet comparable to those seen in individuals with overt clinical disorder. Summative findings indicate that rsFC alterations associated with maltreatment experience are related to poor cognitive and social functioning and are prognostic of future symptoms. In conclusion, maltreatment is associated with altered rsFC in emotional reactivity, regulation, learning, and salience detection brain circuits. This indicates patterns of recalibration of putative mechanisms implicated in maladaptive developmental outcomes

Effect of the polymer structure on the viscoelastic and interfacial healing behaviour of poly(urea-urethane) networks containing aromatic disulphides

The macroscopic interfacial healing behaviour in a series of urea-urethane networks as function of the hydrogen bonds and disulphides content is presented. The polymers were prepared with different crosslinking densities but with the same amount of dynamic covalent bonds (disulphide linkages). Tensile and fracture measurements were adopted to evaluate the degree of recovery of the mechanical properties after damage. Healing kinetics and healing efficiencies were quantitatively determined as a function of network composition, healing temperature and contact time. Finally, the recovery of mechanical properties was correlated with the viscoelastic response of the networks through rheological measurements and time-temperature superposition principle (TTS). The application of the TTS approach on both fracture healing and DMTA and subsequent mathematical descriptive model led to a better understanding of the influence of polymer architecture and that of the amount of reversible groups on the healing process

Matrix biorthogonal polynomials on the unit circle and non-Abelian Ablowitz-Ladik hierarchy

Adler and van Moerbeke \cite{AVM} described a reduction of 2D-Toda hierarchy called Toeplitz lattice. This hierarchy turns out to be equivalent to the one originally described by Ablowitz and Ladik \cite{AL} using semidiscrete zero-curvature equations. In this paper we obtain the original semidiscrete zero-curvature equations starting directly from the Toeplitz lattice and we generalize these computations to the matrix case. This generalization lead us to the semidiscrete zero-curvature equations for the non-abelian (or multicomponent) version of Ablowitz-Ladik equations \cite{GI}. In this way we extend the link between biorthogonal polynomials on the unit circle and Ablowitz-Ladik hierarchy to the matrix case.Comment: 23 pages, accepted on publication on J. Phys. A., electronic link: http://stacks.iop.org/1751-8121/42/36521

Distinctive physiological muscle synergy patterns define the Box and Block Task execution as revealed by electromyographic features

Stroke survivors experience muscular pattern alterations of the upper limb that decrease their ability to perform daily-living activities. The Box and Block test (BBT) is widely used to assess the unilateral manual dexterity. Although BBT provides insights into functional performance, it returns limited information about the mechanisms contributing to the impaired movement. This study aims at exploring the BBT by means of muscle synergies analysis during the execution of BBT in a sample of 12 healthy participants with their dominant and non-dominant upper limb. Results revealed that: (i) the BBT can be described by 1 or 2 synergies; the number of synergies (ii) does not differ between dominant and non-dominant sides and (iii) varies considering each phase of the task; (iv) the transfer phase requires more synergies. Clinical Relevance— This preliminary study characterizes muscular synergies during the BBT task in order to establish normative patterns that could assist in understanding the neuromuscular demands and support future evaluations of stroke deficit

The Asiago-ESO/RASS QSO Survey. III. Clustering analysis and its theoretical interpretation

This is the third paper of a series describing the Asiago-ESO/RASS QSO survey (AERQS), a project aimed at the construction of an all-sky statistically well-defined sample of relatively bright QSOs (B<15) at z<0.3. We present here the clustering analysis of the full spectroscopically identified database (392 AGN). The clustering signal at 0.02<z<0.22 is detected at a 3-4 sigma level and its amplitude is measured to be r_0=8.6\pm 2.0 h^{-1} Mpc (in a LambdaCDM model). The comparison with other classes of objects shows that low-redshift QSOs are clustered in a similar way to Radio Galaxies, EROs and early-type galaxies in general, although with a marginally smaller amplitude. The comparison with recent results from the 2QZ shows that the correlation function of QSOs is constant in redshift or marginally increasing toward low redshift. We discuss this behavior with physically motivated models, deriving interesting constraints on the typical mass of the dark matter halos hosting QSOs, M_DMH= 10^{12.7} h^{-1} M_sun (10^{12.0}-10^{13.5}h^{-1} M_sun at 1 sigma confidence level). Finally, we use the clustering data to infer the physical properties of local AGN, obtaining M_BH=2 10^8 h^{-1} M_sun (10^7-3 10^9 h^{-1} M_sun) for the mass of the active black holes, tau_{AGN}= 8 10^6 yr (2 10^{6}-5 10^{7} yr) for their life-time and eta = 0.14 for their efficiency (always for a LambdaCDM model).Comment: 37 pages, Astronomical Journal in press. Changes to match the referee comment

Intraoperative Radiation Therapy for Breast Cancer: Technical Notes

Abstract: Interest in intraoperative radiation therapy (IORT) for breast cancer is increasing as the possible benefits of this technique for the patient become apparent. The rationale for the use of this segmental radiation therapy in place of whole-breast irradiation is based on the finding that approximately 85% of breast relapses are confined to the same quadrant of the breast as the primary tumor. Phase I and II trials have demonstrated no increase in postsurgical complication rates following the use of single-dose IORT in localized breast cancers. Longer follow-up is needed to assess the cosmetic outcome. Clinical trials to evaluate the effectiveness of IORT in the treatment of breast cancer are currently under way at the European Institute of Oncology (EIO) at the University of Milan, Italy, and at Memorial Sloan-Kettering Cancer Center (MSKCC) in New York. Here we report the two different techniques in use in these trials

Finite-size and correlation-induced effects in Mean-field Dynamics

The brain's activity is characterized by the interaction of a very large number of neurons that are strongly affected by noise. However, signals often arise at macroscopic scales integrating the effect of many neurons into a reliable pattern of activity. In order to study such large neuronal assemblies, one is often led to derive mean-field limits summarizing the effect of the interaction of a large number of neurons into an effective signal. Classical mean-field approaches consider the evolution of a deterministic variable, the mean activity, thus neglecting the stochastic nature of neural behavior. In this article, we build upon two recent approaches that include correlations and higher order moments in mean-field equations, and study how these stochastic effects influence the solutions of the mean-field equations, both in the limit of an infinite number of neurons and for large yet finite networks. We introduce a new model, the infinite model, which arises from both equations by a rescaling of the variables and, which is invertible for finite-size networks, and hence, provides equivalent equations to those previously derived models. The study of this model allows us to understand qualitative behavior of such large-scale networks. We show that, though the solutions of the deterministic mean-field equation constitute uncorrelated solutions of the new mean-field equations, the stability properties of limit cycles are modified by the presence of correlations, and additional non-trivial behaviors including periodic orbits appear when there were none in the mean field. The origin of all these behaviors is then explored in finite-size networks where interesting mesoscopic scale effects appear. This study leads us to show that the infinite-size system appears as a singular limit of the network equations, and for any finite network, the system will differ from the infinite system

Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis

Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation