Modelling state dependence and feedback effects between poverty, employment and parental home emancipation among European youth

Youth is one of the phases in the life-cycle when some of the most decisivelife transitions take place. Entering the labour market or leaving parentalhome are events with important consequences for the economic well-beingof young adults. In this paper, the interrelationship between employment,residential emancipation and poverty dynamics is studied for eight Europeancountries by means of an econometric model with feedback effects. Resultsshow that youth poverty genuine state dependence is positive and highly significant.Evidence proves there is a strong causal effect between poverty andleaving home in Scandinavian countries, however, time in economic hardshipdoes not last long. In Southern Europe, instead, youth tend to leave theirparental home much later in order to avoid falling into a poverty state that ismore persistent. Past poverty has negative consequences on the likelihood ofemployment.

Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical {\sc vc} dimension, empirical {\sc vc} entropy, andmargin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Random measurement error and regression dilution bias.

Summary points: - The bias introduced by random measurement error will be different depending on whether the error is in an exposure variable (risk factor) or outcome variable (disease) - Random measurement error in an exposure variable will bias the estimates of regression slope coefficients towards the null - Random measurement error in an outcome variable will instead increase the standard error of the estimates and widen the corresponding confidence intervals, making results less likely to be statistically significant - Increasing sample size will help minimise the impact of measurement error in an outcome variable but will only make estimates more precisely wrong when the error is in an exposure variable

Macrophage migration inhibitory factor is involved in a positive feedback loop increasing aromatase expression in endometriosis.

Immune-endocrine interplay may play a major role in the pathogenesis of endometriosis. In the present study, we have investigated the interaction between macrophage migration inhibitory factor (MIF), a major pro-inflammatory and growth-promoting factor markedly expressed in active endometriotic lesions, and estradiol (E(2)) in ectopic endometrial cells. Our data showed a significant increase of MIF protein secretion and mRNA expression in endometriotic cells in response to E(2). MIF production was blocked by Fulvestrant, an estrogen receptor (ER) antagonist, and induced by ERα and ERβ selective agonists propyl-pyrazole-triol (PPT) and diarylpropionrile (DPN), respectively, thus demonstrating a specific receptor-mediated effect. Cell transfection with MIF promoter construct showed that E(2) significantly stimulates MIF promoter activity. Interestingly, our data further revealed that MIF reciprocally stimulates aromatase protein and mRNA expression via a posttranscriptional mRNA stabilization mechanism, that E(2) itself can upregulate aromatase expression, and that inhibition of endogenous MIF, using MIF specific siRNA, significantly inhibits E(2)-induced aromatase. Thus, the present study revealed the existence of a local positive feedback loop by which estrogen acts directly on ectopic endometrial cells to upregulate the expression of MIF, which, in turn, displays the capability of inducing the expression of aromatase, the key and rate-limiting enzyme for estrogen synthesis. Such interplay may have a considerable impact on the development of endometriosis.

Effect of conditioned media, nutritive elements, and mitotic synchronization on the accuracy of the cytogenetic analysis in acute nonlymphocytic leukemia patients presenting with inv(16)/t(16;16) or t(15;17).

To improve the yield of the cytogenetic analysis in patients with acute nonlymphocytic leukemia (ANLL), six culture conditions for bone marrow or peripheral blood cells were tested in parallel. Two conditioned media (CM), phytohemagglutinin leukocyte PHA-LCM and 5637 CM, nutritive elements (NE), and methotrexate (MTX) cell synchronization were investigated in 14 patients presenting with either inv(16)/ t(16;16) (group 1, n = 9 patients) or t(15;17) (group 2, n = 5). The criteria used to identify the most favorable culture conditions were the mitotic index (MI), the morphological index (MorI), and the percentage of abnormal metaphases. In the presence of PHA-LCM and 5637 CM, the MI were significantly increased in group 2, whereas in the MTX conditions, MI remained very low in both groups. The values of the MorI did not reveal any significant changes in chromosome resolution between the conditions in either group. The addition of NE did not have a positive effect in quantity or quality of metaphases. Because of the variability of the response of leukemic cells to different stimulations in vitro, several culture conditions in parallel are required to ensure a satisfactory yield of the chromosome analysis in ANLL.

A new estimator to minimize the error due to breathing in the measurement of respiratory impedance

This paper presents a new respiratory impedance estimator to minimize the error due to breathing. Its practical reliability was evaluated in a simulation using realistic signals. These signals were generated by superposing pressure and flow records obtained in two conditions: 1) when applying forced oscillation to a resistance- inertance- elastance (RIE) mechanical model; 2) when healthy subjects breathed through the unexcited forced oscillation generator. Impedances computed (4-32 Hz) from the simulated signals with the new estimator resulted in a mean value which was scarcely biased by the added breathing (errors less than 1 percent in the mean R, I , and E ) and had a small variability (coefficients of variation of R, I, and E of 1.3, 3.5, and 9.6 percent, respectively). Our results suggest that the proposed estimator reduces the error in measurement of respiratory impedance without appreciable extracomputational cost.

Cardiac re-synchronization therapy in a patient with isolated ventricular non-compaction: a case report.

Isolated ventricular non-compaction (IVNC) is a rare, congenital, unclassified cardiomyopathy characterized by prominent trabecular meshwork and deep recesses. Major clinical manifestations of IVNC are heart failure, atrial and ventricular arrhythmias, and thrombo-embolic events. We describe a case of a 69-year-old woman in whom the diagnosis of IVNC was discovered late, whereas former echocardiographic examinations were considered normal. She was known for systolic left ventricular dysfunction for 3 years and then became symptomatic (NYHA III). In the past, she suffered from multiple episodes of deep vein thrombosis and pulmonary embolism. Electrocardiogram revealed a wide QRS complex, and transthoracic echocardiography showed typical apical thickening of the left and right ventricular myocardial wall with two distinct layers. The ratio of non-compacted to compacted myocardium was >2:1. Cardiac MRI confirmed the echocardiographic images. Cerebral MRI revealed multiple ischaemic sequellae. In view of the persistent refractory, heart failure in medical treatment of patients with classical criteria for cardiac re-synchronization therapy, as well as the ventricular arrhythmias, a biventricular automatic intracardiac defibrillator (biventricular ICD) was implanted. The 2-year follow-up period was characterized by improvement of NYHA functional class from III to I and increasing in left ventricular function. We hereby present a case of IVNC with favourable outcome after biventricular ICD implantation. Cardiac re-synchronization therapy could be considered in the management of this pathology.

Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity.

Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, the visual network, the auditory network, the cognitive control networks, the self-referential network, and combinations of these and other RSNs. We studied the mechanism originating the observed spatiotemporal synchronization dynamics by using a network model of phase oscillators connected through the brain's anatomical connectivity estimated using diffusion imaging human data. The model consistently approximates the temporal and spatial synchronization patterns of the empirical data, and reveals that multiple clusters that transiently synchronize and desynchronize emerge from the complex topology of anatomical connections, provided that oscillators are heterogeneous.

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

Evolution of source EEG synchronization in early Alzheimer's disease.

Alzheimer's disease (AD) disrupts functional connectivity in distributed cortical networks. We analyzed changes in the S-estimator, a measure of multivariate intraregional synchronization, in electroencephalogram (EEG) source space in 15 mild AD patients versus 15 age-matched controls to evaluate its potential as a marker of AD progression. All participants underwent 2 clinical evaluations and 2 EEG recording sessions on diagnosis and after a year. The main effect of AD was hyposynchronization in the medial temporal and frontal regions and relative hypersynchronization in posterior cingulate, precuneus, cuneus, and parietotemporal cortices. However, the S-estimator did not change over time in either group. This result motivated an analysis of rapidly progressing AD versus slow-progressing patients. Rapidly progressing AD patients showed a significant reduction in synchronization with time, manifest in left frontotemporal cortex. Thus, the evolution of source EEG synchronization over time is correlated with the rate of disease progression and should be considered as a cost-effective AD biomarker.

Turn-on-time statistics of modulated lasers subjected to resonant weak optical feedback

We present analytical calculations of the turn-on-time probability distribution of intensity-modulated lasers under resonant weak optical feedback. Under resonant conditions, the external cavity round-trip time is taken to be equal to the modulation period. The probability distribution of the solitary laser results are modified to give reduced values of the mean turn-on-time and its variance. Numerical simulations have been carried out showing good agreement with the analytical results.

Accurate and efficient simulation of multiphase flow in a heterogeneous reservoir by using error estimate and control in the multiscale finite-volume framework

The multiscale finite-volume (MSFV) method is designed to reduce the computational cost of elliptic and parabolic problems with highly heterogeneous anisotropic coefficients. The reduction is achieved by splitting the original global problem into a set of local problems (with approximate local boundary conditions) coupled by a coarse global problem. It has been shown recently that the numerical errors in MSFV results can be reduced systematically with an iterative procedure that provides a conservative velocity field after any iteration step. The iterative MSFV (i-MSFV) method can be obtained with an improved (smoothed) multiscale solution to enhance the localization conditions, with a Krylov subspace method [e.g., the generalized-minimal-residual (GMRES) algorithm] preconditioned by the MSFV system, or with a combination of both. In a multiphase-flow system, a balance between accuracy and computational efficiency should be achieved by finding a minimum number of i-MSFV iterations (on pressure), which is necessary to achieve the desired accuracy in the saturation solution. In this work, we extend the i-MSFV method to sequential implicit simulation of time-dependent problems. To control the error of the coupled saturation/pressure system, we analyze the transport error caused by an approximate velocity field. We then propose an error-control strategy on the basis of the residual of the pressure equation. At the beginning of simulation, the pressure solution is iterated until a specified accuracy is achieved. To minimize the number of iterations in a multiphase-flow problem, the solution at the previous timestep is used to improve the localization assumption at the current timestep. Additional iterations are used only when the residual becomes larger than a specified threshold value. Numerical results show that only a few iterations on average are necessary to improve the MSFV results significantly, even for very challenging problems. Therefore, the proposed adaptive strategy yields efficient and accurate simulation of multiphase flow in heterogeneous porous media.