Signal-to-Noise Ratio Analysis for the Voltage-Mode Read-Out of Quartz Tuning Forks in QEPAS Applications.

Quartz tuning forks (QTFs) are employed as sensitive elements for gas sensing applications implementing quartz-enhanced photoacoustic spectroscopy. Therefore, proper design of the QTF read-out electronics is required to optimize the signal-to-noise ratio (SNR), and in turn, the minimum detection limit of the gas concentration. In this work, we present a theoretical study of the SNR trend in a voltage-mode read-out of QTFs, mainly focusing on the effects of (i) the noise contributions of both the QTF-equivalent resistor and the input bias resistor RL of the preamplifier, (ii) the operating frequency, and (iii) the bandwidth (BW) of the lock-in amplifier low-pass filter. A MATLAB model for the main noise contributions was retrieved and then validated by means of SPICE simulations. When the bandwidth of the lock-in filter is sufficiently narrow (BW = 0.5 Hz), the SNR values do not strongly depend on both the operating frequency and RL values. On the other hand, when a wider low-pass filter bandwidth is employed (BW = 5 Hz), a sharp SNR peak close to the QTF parallel-resonant frequency is found for large values of RL (RL > 2 MΩ), whereas for small values of RL (RL < 2 MΩ), the SNR exhibits a peak around the QTF series-resonant frequency.

A rare case of quadrileaflet mitral valve and ostium primum atrial septal defect.

A 60-year-old woman was referred to our clinic for evaluation of her rapidly progressive dyspnea, and she had no previous history of heart disease. A murmur was noted on her examination, and transthoracic echocardiography was so difficult to be performed due to poor acoustic windows so she was referred to do a transesophageal echocardiography that showed an ostium primum atrial septal defect (ASD) with left-to-right shunt and a quadrileaflet mitral valve with severe regurgitation. Later on, she underwent surgery with ostium primum ASD closure by a patch and double cleft repair by suture after right heart catheterization.

The mediational effect of social support between organizational identification and employees' health: a three-wave study on the social cure model.

BACKGROUND: Recent research postulated that organizational identification plays an important role in employees' health and well-being. Building on the Social Identity Approach as a framework, we test the so-called social cure hypothesis, according to which group-based processes of social support should reduce employees' psychological distress. DESIGN AND METHODS: While there is a considerable amount of cross-sectional evidence concerning the positive role played by organizational identification in this dynamic, there is a lack of full panel studies. This study tries to fill this gap by using data from a sample of technical and administrative staff of a University in Italy at three time points (N = 96). Data were analyzed using Autoregressive Cross-Lagged Panel models. RESULTS: We found support for the hypothesized longitudinal mediational model. Specifically, strongly identified employees tend to receive more social support, and this in turn reduces psychological distress over time. CONCLUSIONS: This study is the first test of the social cure hypothesis in an organizational context that uses a panel study design. We discuss the theoretical and practical implications for management.

A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task.

Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden regularity or an abstract rule has to be learned dynamically. Although performance in such tasks is considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing observed response patterns by simple heuristic scoring measures. With this work, we propose and validate a new computational Bayesian model accounting for individual performance in the Wisconsin Card Sorting Test (WCST), a renowned clinical tool to measure set-shifting and deficient inhibitory processes on the basis of environmental feedback. We formalize the interaction between the task's structure, the received feedback, and the agent's behavior by building a model of the information processing mechanisms used to infer the hidden rules of the task environment. Furthermore, we embed the new model within the mathematical framework of the Bayesian Brain Theory (BBT), according to which beliefs about hidden environmental states are dynamically updated following the logic of Bayesian inference. Our computational model maps distinct cognitive processes into separable, neurobiologically plausible, information-theoretic constructs underlying observed response patterns. We assess model identification and expressiveness in accounting for meaningful human performance through extensive simulation studies. We then validate the model on real behavioral data in order to highlight the utility of the proposed model in recovering cognitive dynamics at an individual level. We highlight the potentials of our model in decomposing adaptive behavior in the WCST into several information-theoretic metrics revealing the trial-by-trial unfolding of information processing by focusing on two exemplary individuals whose behavior is examined in depth. Finally, we focus on the theoretical implications of our computational model by discussing the mapping between BBT constructs and functional neuroanatomical correlates of task performance. We further discuss the empirical benefit of recovering the assumed dynamics of information processing for both clinical and research practices, such as neurological assessment and model-based neuroscience.

Reaching to sounds in virtual reality: A multisensory-motor approach to promote adaptation to altered auditory cues.

When localising sounds in space the brain relies on internal models that specify the correspondence between the auditory input reaching the ears, initial head-position and coordinates in external space. These models can be updated throughout life, setting the basis for re-learning spatial hearing abilities in adulthood. In addition, strategic behavioural adjustments allow people to quickly adapt to atypical listening situations. Until recently, the potential role of dynamic listening, involving head-movements or reaching to sounds, have remained largely overlooked. Here, we exploited visual virtual reality (VR) and real-time kinematic tracking, to study the role of active multisensory-motor interactions when hearing individuals adapt to altered binaural cues (one ear plugged and muffed). Participants were immersed in a VR scenario showing 17 virtual speakers at ear-level. In each trial, they heard a sound delivered from a real speaker aligned with one of the virtual ones and were instructed to either reach-to-touch the perceived sound source (Reaching group), or read the label associated with the speaker (Naming group). Participants were free to move their heads during the task and received audio-visual feedback on their performance. Most importantly, they performed the task under binaural or monaural listening. Results show that both groups adapted rapidly to monaural listening, improving sound localisation performance across trials and changing their head-movement behaviour. Reaching the sounds induced faster and larger sound localisation improvements, compared to just naming its position. This benefit was linked to progressively wider head-movements to explore auditory space, selectively in the Reaching group. In conclusion, reaching to sounds in an immersive visual VR context proved most effective for adapting to altered binaural listening. Head-movements played an important role in adaptation, pointing to the importance of dynamic listening when implementing training protocols for improving spatial hearing.

Individuation of object parts in aging.

Research on enumeration with isolated objects has indicated that young and older adults can report up to three elements with similar efficiency (subitizing effect). Recent studies on subitizing in young adults have shown that individuation occurs over parts of an object as efficiently as over physically disconnected objects, suggesting that spatial separation is a sufficient requirement for efficient individuation. Do young and older adults share this sufficient requirement? In two experiments, we tested for the presence of subitizing in an enumeration task with a varying number of distinct objects and object parts. In Experiment 1, results indicated the presence of a bilinear function (with an inflection point between 3 and 4 elements, a proxy for subitizing) in the response speed of young and older adults, and in both stimulus conditions. In addition, the enumeration slope in older participants was steeper for object parts than for objects in the subitizing range, possibly due to perceptual degradation (e.g., in contour detection). The pattern found generalizes to other stimuli (Experiment 2), thus highlighting the robustness of the present findings. Overall, the results indicate that while some perceptual factors (such as contour detection or curvature polarity) may hamper subitizing speed of older individuals relative to young adults, the subitizing span remains at approximately three to four elements for multiple objects and object parts in both young and older adults. Thus, individuation of multiple objects and object parts is a mechanism relatively resistant to aging.

A Joint Modelling Approach to Analyze Risky Decisions by Means of Diffusion Tensor Imaging and Behavioural Data.

Understanding dependencies between brain functioning and cognition is a challenging task which might require more than applying standard statistical models to neural and behavioural measures to be accomplished. Recent developments in computational modelling have demonstrated the advantage to formally account for reciprocal relations between mathematical models of cognition and brain functional, or structural, characteristics to relate neural and cognitive parameters on a model-based perspective. This would allow to account for both neural and behavioural data simultaneously by providing a joint probabilistic model for the two sources of information. In the present work we proposed an architecture for jointly modelling the reciprocal relation between behavioural and neural information in the context of risky decision-making. More precisely, we offered a way to relate Diffusion Tensor Imaging data to cognitive parameters of a computational model accounting for behavioural outcomes in the popular Balloon Analogue Risk Task (BART). Results show that the proposed architecture has the potential to account for individual differences in task performances and brain structural features by letting individual-level parameters to be modelled by a joint distribution connecting both sources of information. Such a joint modelling framework can offer interesting insights in the development of computational models able to investigate correspondence between decision-making and brain structural connectivity.

A Dynamic Framework for Modelling Set-Shifting Performances.

Higher-order cognitive functions can be seen as a class of cognitive processes which are crucial in situations requiring a flexible adjustment of behaviour in response to changing demands of the environment. The cognitive assessment of these functions often relies on tasks which admit a dynamic, or longitudinal, component requiring participants to flexibly adapt their behaviour during the unfolding of the task. An intriguing feature of such experimental protocols is that they allow the performance of an individual to change as the task unfolds. In this work, we propose a Latent Markov Model approach to capture some dynamic aspects of observed response patterns of both healthy and substance dependent individuals in a set-shifting task. In particular, data from a Wisconsin Card Sorting Test were analysed in order to represent performance trends in terms of latent cognitive states dynamics. The results highlighted how a dynamic modelling approach can considerably improve the amount of information a researcher, or a clinician, can obtain from the analysis of a set-shifting task.

A State Space Approach to Dynamic Modeling of Mouse-Tracking Data.

Mouse-tracking recording techniques are becoming very attractive in experimental psychology. They provide an effective means of enhancing the measurement of some real-time cognitive processes involved in categorization, decision-making, and lexical decision tasks. Mouse-tracking data are commonly analyzed using a two-step procedure which first summarizes individuals' hand trajectories with independent measures, and then applies standard statistical models on them. However, this approach can be problematic in many cases. In particular, it does not provide a direct way to capitalize the richness of hand movement variability within a consistent and unified representation. In this article we present a novel, unified framework for mouse-tracking data. Unlike standard approaches to mouse-tracking, our proposal uses stochastic state-space modeling to represent the observed trajectories in terms of both individual movement dynamics and experimental variables. The model is estimated via a Metropolis-Hastings algorithm coupled with a non-linear recursive filter. The characteristics and potentials of the proposed approach are illustrated using a lexical decision case study. The results highlighted how dynamic modeling of mouse-tracking data can considerably improve the analysis of mouse-tracking tasks and the conclusions researchers can draw from them.

A new nonparametric test for the race model inequality.

The race model inequality (RMI), as first introduced by Miller (Cognitive Psychology, 14, 247-279, 1982), entails an upper bound on the amount of statistical facilitation for reaction times (RTs) attainable by a race model within the redundant-signals paradigm. A violation of RMI may be considered as empirical evidence for a coactivation model rather than a race model. Here, we introduce a novel nonparametric procedure for evaluating the RMI for single participant analysis. The statistical procedure is based on a new probabilistic representation that highlights some neglected, but important distributional features of the RMI. In particular, we show how the reconstructed distribution function under maximal statistical facilitation for a race model is characterized by a specific truncated-type property. The results of two Monte Carlo simulation studies suggest that our procedure efficiently controls for type I error with reasonable power. Finally, unlike previous proposals for single participant analysis (e.g., Maris and Maris (Journal of Mathematical Psychology 47, 507-514, 2003)), our approach is also more consistent with the typical way to collect RT data in experimental works. R script functions for running the statistical analysis on single participant data are made freely available to the readers on a dedicated web server.

The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo Results.

Correlational measures are probably the most spread statistical tools in psychological research. They are used by researchers to investigate, for example, relations between self-report measures usually collected using paper-pencil or online questionnaires. Like many other statistical analysis, also correlational measures can be seriously affected by specific sources of bias which constitute serious threats to the final observed results. In this contribution, we will focus on the impact of the fake data threat on the interpretation of statistical results for two well-know correlational measures (the Pearson product-moment correlation and the Spearman rank-order correlation). By using the Sample Generation by Replacement (SGR) approach, we analyze uncertainty in inferences based on possible fake data and evaluate the implications of fake data for correlational results. A population-level analysis and a Monte Carlo simulation are performed to study different modulations of faking on bivariate discrete variables with finite supports and varying sample sizes. We show that by using our paradigm it is always possible, under specific faking conditions, to increase (resp. decrease) the original correlation between two discrete variables in a predictable and systematic manner.

Empirical Scenarios of Fake Data Analysis: The Sample Generation by Replacement (SGR) Approach.

Many self-report measures of attitudes, beliefs, personality, and pathology include items whose responses can be easily manipulated or distorted, as an example in order to give a positive impression to others, to obtain financial compensation, to avoid being charged with a crime, to get a job, or else. This fact confronts both researchers and practitioners with the crucial problem of biases yielded by the usage of standard statistical models. The current paper presents three empirical applications to the issue of faking of a recent probabilistic perturbation procedure called Sample Generation by Replacement (SGR; Lombardi and Pastore, 2012). With the intent to study the behavior of some statistics under fake perturbation and data reconstruction processes, ad-hoc faking scenarios were implemented and tested. Overall, results proved that SGR could be successfully applied both in the case of research designs traditionally proposed in order to deal with faking (e.g., use of fake-detecting scales, experimentally induced faking, or contrasting applicants vs. incumbents), and in the case of ecological research settings, where no information as regards faking could be collected by the researcher or the practitioner. Implications and limitations are presented and discussed.

Analyzing spatial data from mouse tracker methodology: An entropic approach.

Mouse tracker methodology has recently been advocated to explore the motor components of the cognitive dynamics involved in experimental tasks like categorization, decision-making, and language comprehension. This methodology relies on the analysis of computer-mouse trajectories, by evaluating whether they significantly differ in terms of direction, amplitude, and location when a given experimental factor is manipulated. In this kind of study, a descriptive geometric approach is usually adopted in the analysis of raw trajectories, where they are summarized with several measures, such as maximum-deviation and area under the curve. However, using raw trajectories to extract spatial descriptors of the movements is problematic due to the noisy and irregular nature of empirical movement paths. Moreover, other significant components of the movement, such as motor pauses, are disregarded. To overcome these drawbacks, we present a novel approach (EMOT) to analyze computer-mouse trajectories that quantifies movement features in terms of entropy while modeling trajectories as composed by fast movements and motor pauses. A dedicated entropy decomposition analysis is additionally developed for the model parameters estimation. Two real case studies from categorization tasks are finally used to test and evaluate the characteristics of the new approach.

Exercise in Patients on Dialysis: A Multicenter, Randomized Clinical Trial.

Previous studies have suggested the benefits of physical exercise for patients on dialysis. We conducted the Exercise Introduction to Enhance Performance in Dialysis trial, a 6-month randomized, multicenter trial to test whether a simple, personalized walking exercise program at home, managed by dialysis staff, improves functional status in adult patients on dialysis. The main study outcomes included change in physical performance at 6 months, assessed by the 6-minute walking test and the five times sit-to-stand test, and in quality of life, assessed by the Kidney Disease Quality of Life Short Form (KDQOL-SF) questionnaire. We randomized 296 patients to normal physical activity (control; n=145) or walking exercise (n=151); 227 patients (exercise n=104; control n=123) repeated the 6-month evaluations. The distance covered during the 6-minute walking test improved in the exercise group (mean distance±SD: baseline, 328±96 m; 6 months, 367±113 m) but not in the control group (baseline, 321±107 m; 6 months, 324±116 m; P<0.001 between groups). Similarly, the five times sit-to-stand test time improved in the exercise group (mean time±SD: baseline, 20.5±6.0 seconds; 6 months, 18.2±5.7 seconds) but not in the control group (baseline, 20.9±5.8 seconds; 6 months, 20.2±6.4 seconds; P=0.001 between groups). The cognitive function score (P=0.04) and quality of social interaction score (P=0.01) in the kidney disease component of the KDQOL-SF improved significantly in the exercise arm compared with the control arm. Hence, a simple, personalized, home-based, low-intensity exercise program managed by dialysis staff may improve physical performance and quality of life in patients on dialysis.

Fitness for entering a simple exercise program and mortality: a study corollary to the exercise introduction to enhance performance in dialysis (EXCITE) trial.

BACKGROUND/AIMS: In this corollary analysis of the EXCITE study, we looked at possible differences in baseline risk factors and mortality between subjects excluded from the trial because non-eligible (n=216) or because eligible but refusing to participate (n=116). METHODS: Baseline characteristics and mortality data were recorded. Survival and independent predictors of mortality were assessed by Kaplan-Meier and Cox regression analyses. RESULTS: The incidence rate of mortality was higher in non-eligible vs. eligible non-randomized patients (21.0 vs. 10.9 deaths/100 persons-year; P<0.001). The crude excess risk of death in non-eligible patients (HR 1.96; 95% CI 1.36 to 2.77; P<0.001) was reduced after adjustment for risk factors which differed in the two cohorts including age, blood pressure, phosphate, CRP, smoking, diabetes, triglycerides, cardiovascular comorbidities and history of neoplasia (HR 1.60; 95% CI 1.10 to 2.35; P=0.017) and almost nullified after including in the same model also information on deambulation impairment (HR 1.16; 95% CI 0.75 to 1.80; P=0.513). CONCLUSIONS: Deambulation ability mostly explains the difference in survival rate in non-eligible and eligible non-randomized patients in the EXCITE trial. Extending data analyses and outcome reporting also to subjects not taking part in a trial may be helpful to assess the representability of the study population.

Physical performance and clinical outcomes in dialysis patients: a secondary analysis of the EXCITE trial.

BACKGROUND/AIMS: Scarce physical activity predicts shorter survival in dialysis patients. However, the relationship between physical (motor) fitness and clinical outcomes has never been tested in these patients. METHODS: We tested the predictive power of an established metric of motor fitness, the Six-Minute Walking Test (6MWT), for death, cardiovascular events and hospitalization in 296 dialysis patients who took part in the trial EXCITE (ClinicalTrials.gov Identifier: NCT01255969). RESULTS: During follow up 69 patients died, 90 had fatal and non-fatal cardiovascular events, 159 were hospitalized and 182 patients had the composite outcome. In multivariate Cox models - including the study allocation arm and classical and non-classical risk factors - an increase of 20 walked metres during the 6MWT was associated to a 6% reduction of the risk for the composite end-point (P=0.001) and a similar relationship existed between the 6MWT, mortality (P<0.001) and hospitalizations (P=0.03). A similar trend was observed for cardiovascular events but this relationship did not reach statistical significance (P=0.09). CONCLUSIONS: Poor physical performance predicts a high risk of mortality, cardiovascular events and hospitalizations in dialysis patients. Future studies, including phase-2 EXCITE, will assess whether improving motor fitness may translate into better clinical outcomes in this high risk population.

Operand-order effect in multiplication and addition: the long-term effects of reorganization processand acquisition sequence.

Butterworth, Marchesini, and Girelli (2003) showed that children solved multiplications faster when the larger operand was first (e.g., 5 · 2) than when the smaller operand was first (e.g., 2 · 5). This result was interpreted according to the reorganization hypothesis, which states that, as children begin to switch from counting-based strategies (e.g., repeated additions) to direct retrieval, non-retrieval strategies generate an advantage for the larger-operand-first order. In two experiments we showed that order preferences also persist into adulthood. With additions, the larger-operand-first order was solved faster than the inverse order. With multiplications we obtained a novel result: Largeroperand-first problems were solved faster when at least one operand was smaller than 5, whereas smaller-operand-first problems were solved faster when both operands were larger than 5. Since the reorganization process alone cannot explain our results, we propose that order preferences are also influenced by the sequence in which the members of a commuted pair are acquired.

Brain dynamics of attention and working memory engagement in subitizing.

This study addressed the engagement of attention and working memory, as inferred from electrophysiological measurements, in the processing of small sets of objects. We recorded N2pc and CDA, two lateralized components of the EEG signal associated respectively with individuation and visual working memory, while participants enumerated a variable number (1-9) of uniquely colored targets among distractors. Behavioral results showed a clear subitizing effect, with lower error rates for smaller (1-3 targets) than larger sets. ERP results showed that both N2pc and CDA amplitudes increased as a function of target numerosity up to approximately three targets. However, individual differences in the enumeration efficiency were correlated only with the individual variation in the N2pc modulations. The results suggest that the constraints of the attentional individuation system play a significant role in the occurrence of the subitizing phenomenon.