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Charge density waves (CDWs) involved with both electronic and phononic subsystems simultaneously are a common quantum state in solid-state physics, especially in low-dimensional materials. However, there is no complete CDW phase diagram in various dimensions and the phase transition mechanism is currently moot. Using the distinct temperature evolution of orientation-dependent ultrafast electron and phonon dynamics, variously dimensional CDW phases are verified in CuTe and the electronic subsystem in CuTe is also demonstrated to drive the formation of one-dimensional (1D) CDW chain phase at Tc of 335 K. At T=280 K, electron-phonon coupling creates collective modes along the a-axis, which synchronize via an interchain interaction to establish a two-dimensional (2D) CDW phase on the ab-plane while T

Dementia due to Alzheimer’s disease has become a global health burden. Early detection of one of its potential prodromal state, mild cognitive impairment (MCI), appears to be critical for early interventions. The present study, therefore, addressed the challenging issue of classifying individuals with MCI vs. healthy controls (HC) based on resting-state electroencephalography (EEG). Specifically, we design a feature extraction and feature fusion framework to extract discriminative features. In the first step, we extracted the sample entropy (SampEn) and Katz’s fractal dimension (KFD) features from their corresponding best electrodes selected by a wrapper-based algorithm. Next, the two best feature sets were fused by a principal component analysis (PCA) method. The eigen-complexity pattern (ECP) feature extracted by this framework was subsequently fed into the proposed MCI detector, the conformal kernel-based fuzzy support vector machine (CKF-SVM). The CKF-SVM not only adopts a fuzzified penalty strategy to avoid the overfitting often observed in conventional SVM due to outliers, but also applies a conformally transformed kernel to further increase the class separability. The results carried out on 51 participants (24 MCI, 27 HC) and leave-one-participant-out cross-validation (LOPO-CV) show that the ECP feature combined with a simple linear discriminant analysis classifier achieved an accuracy of 84.31%, higher than the one by either of the two complexity measures (SampEn and KFD), as well as the ones by spectral powers of different scalp regions. The results also show that CKF-SVM outperformed SVM and other classifiers commonly used in the MCI-HC classification studies. A high LOPO-CV accuracy 90.19% (sensitivity = 87.50%, specificity = 92.60%) was achieved by using the ECP feature and the CKF-SVM classifier. These results suggested that the proposed approach has a potential for developing a sensitive EEG-based computer-aided diagnosis (CAD) system that may, in the future, provide an objective measure to assist physicians’ diagnose of MCI and even a neurofeedback brain–computer interface (BCI) system for monitoring the intervention response of individuals with MCI.

Trust and cooperation within and between political identity groups are important issues for building a healthy civil society and democratic development. However, this research problem has seldom been analyzed under different political identity conditions by means of experimental design in Taiwan. Presidential elections have reproduced the polarization between two groups of voters supporting different presidential candidates. Therefore, in this article the authors are interested in whether political identity matters in trust exchanges among strangers. This study applies a three-stage trust game experiment to examine how trust is developed within pairs of subjects with either the same or different political identity. In the first stage subjects were randomly matched in pairs as trustor and trustee, and their political identities were not disclosed. In the second stage the pairs were still randomly matched, but each subject was informed of their partner’s political identity. In the final stage each subject could choose the preferred political identity of his/her partner. There were two mechanisms of trust-behavior formation under different identity conditions. The first mechanism was political identity. Supporters of presidential candidate Ma Ying-Jeou were more trustful than supporters of candidate Tsai Ing-Wen. Under the condition of subjects knowing their partner’s political identity, the identity effect became strongly significant in stages 2 and 3 of the experiment, especially for that of the Ma–Ma group. The second mechanism was mutuality. The mutuality effect was very significant in all three stages of the trust experiment, and that effect was stronger for those who voted for Tsai.

A proximity effect facilitates the penetration of Cooper pairs that permits superconductivity in a normal metal, offering a promising approach to turn heterogeneous materials into superconductors and develop exceptional quantum phenomena. Here, we have systematically investigated proximity-induced anisotropic superconductivity in a monolayer Ni-Pb binary alloy by combining scanning tunneling microscopy/spectroscopy (STM/STS) with theoretical calculations. By means of high-temperature growth, the

Structural balance theory is a foundational theory of social network research. Despite enduring interest in seeking network evidence for the theory, the fundamental question of why people care about structural (im)balance has received relatively little attention. The original answer to the question, dating back to Heider’s work six decades ago, is that structural imbalance causes a person to experience cognitive dissonance and sentimental disturbance. In this paper, we used a state-of-the-art neuroimaging technique to test the argument. Our study shows that individuals’ psychological states, evidenced by the activation of brain areas, are different when they are situated in unbalanced rather than balanced triads. More specifically, the differences in the brain activation between triadic imbalance and balance were found in brain regions known for processing cognitive dissonance, as discovered by previous research. Our study provides novel brain evidence in support of Heider’s original account for the psychological and biological foundations of structural balance theory in the formation of social networks.

A hyperactive default mode network (DMN) has been observed in people with major depressive disorder (MDD), and weak DMN suppression has been linked to depressive symptoms. However, whether dysregulation of the DMN contributes to blunted positive emotional experience in people with MDD is unclear.We recorded 128-channel electroencephalograms (EEGs) from 24 participants with MDD and 31 healthy controls in a resting state (RS) and an emotion-induction state (ES), in which participants engaged with emotionally positive pictures. We combined Granger causality analysis and data-driven decomposition to extract latent brain networks shared among states and groups, and we further evaluated their interactions across individuals.We extracted 2 subnetworks. Subnetwork 1 represented a delta (δ)-band (1~4 Hz) frontal network that was activated more in the ES than the RS (i.e., task-positive). Subnetwork 2 represented an alpha (α)-band (8~13 Hz) parietal network that was suppressed more in the ES than the RS (i.e., task-negative). These subnetworks were anticorrelated in both the healthy control and MDD groups, but with different sensitivities: for participants with MDD to achieve the same level of task-positive (subnetwork 1) activation as healthy controls, more suppression of task-negative (subnetwork 2) activation was necessary. Furthermore, the anticorrelation strength in participants with MDD correlated with the severity of 2 core MDD symptoms: anhedonia and rumination.The sample size was small.Our findings revealed altered coordination between 2 functional networks in MDD and suggest that weak suppression of the task-negative α-band parietal network contributes to blunted positive emotional responses in adults with depression. The subnetworks identified here could be used for diagnosis or targeted for treatment in the future.

In this work, we focus on the emergence of Majorana zero modes in heterostructures composed of superconducting and ferromagnetic materials. We numerically determine self-consistent solutions to the Bogoliubov-de Gennes equations suitable for our system. For the first part, we consider a conventional superconductor sandwiched by two conical ferromagnets. We vary the direction of the conical axes and the strength of Zeeman field for both ferromagnetic regions to study the stability of the topological properties of our system. For the second part, we turn our attention to a trilayer consisting of two superconductors and a conical ferromagnet in the geometry of a conventional Josephson junction. By choosing appropriate parameters, the Josephson junction can also host Majorana zero modes and exhibit topological signatures.

Classification between individuals with mild cognitive impairment (MCI) and healthy controls (HC) based on electroencephalography (EEG) has been considered a challenging task to be addressed for the purpose of its early detection. In this study, we proposed a novel EEG feature, the kernel eigen-relative-power (KERP) feature, for achieving high classification accuracy of MCI versus HC. First, we introduced the relative powers (RPs) between pairs of electrodes across 21 different subbands of 2-Hz width as the features, which have not yet been used in previous MCI-HC classification studies. Next, the Fisher’s class separability criterion was applied to determine the best electrode pairs (five electrodes) as well as the frequency subbands for extracting the most sensitive RP features. The kernel principal component analysis (kernel PCA) algorithm was further performed to extract a few more discriminating nonlinear principal components from the optimal RPs, and these components form a KERP feature vector. Results carried out on 51 participants (24 MCI and 27 HC) show that the newly introduced subband RP feature showed superior classification performance to commonly used spectral power features, including the band power, single-electrode relative power, and also the RP based on the conventional frequency bands. A high leave-one-participant-out cross-validation (LOPO-CV) classification accuracy 86.27% was achieved by the RP feature, using a simple linear discriminant analysis (LDA) classifier. Moreover, with the same classifier, the proposed KERP further improved the accuracy to 88.24%. Finally, cascading the KERP feature to a nonlinear classifier, the support vector machine (SVM), yields a high MCI-HC classification accuracy of 90.20% (sensitivity = 87.50% and specificity = 92.59%). The proposed method demonstrated a high accuracy and a high usability (only five electrodes are required), and therefore, has great potential to further develop an EEG-based computer-aided diagnosis system that can be applied for the early detection of MCI.

We theoretically study the superconducting proximity effect in a quantum dot coupled to two superconducting leads when the intradot interaction between electrons is made attractive. Because of the superconducting proximity effect, the electronic states for the embedded quantum dot are either spin-polarized states with an odd occupation number or BCS-like states with an even occupation number. We show that in the presence of an external magnetic field, the system can exhibit quantum phase transitions of fermion parity associated with the occupation number. In this paper, we adopt a self-consistent theoretical method to extend our considerations beyond the so-called superconducting atomic limit in which the superconducting gap for the leads is assumed to be the largest energy scale. This method enables us to numerically investigate the electronic structure of the dot as a result of the attractive interaction. For energy phase diagrams in the regime away from the atomic limit, we find a reentrant behavior where a BCS-like phase of the dot exists in an intermediate range of the hybridization strength between the quantum dot and the leads. We also consider Josephson current-phase relations and identify a number of examples showing 0-$\ensuremath{\pi}$ phase transitions that may offer important switching effects.

Objective The purposes of this study were to examine the test-retest reliability, concurrent validity, and ecological validity of the Computerized Digit Vigilance Test (C-DVT) in patients with schizophrenia. Method Each participant was assessed four times, with 1-week intervals. In each assessment, the participants completed both the C-DVT and the original DVT. The participants were also assessed using the Lawton Instrumental Activities of Daily Living Scale (LIADL) and the Personal and Social Performance Scale (PSP). Results Forty-nine participants were recruited in this study. The results showed that the test-retest agreement of the C-DVT was good-to-excellent (intraclass correlation coefficient = 0.71-0.89). The random measurement errors of the C-DVT were acceptable (percentages of minimal detectable change = 12.9%-24.1%). The practice effect of the C-DVT reached a plateau after three assessments (effect size

Experiments on planar Josephson junction architectures have recently been shown to provide an alternative way of creating topological superconductors hosting accessible Majorana modes. These zero-energy modes can be found at the ends of a one-dimensional channel in the junction of a two-dimensional electron gas (2DEG) proximitized by two spatially separated superconductors. The channel, which is below the break between the superconductors, is not in direct contact with the superconducting leads, so that proximity coupling is expected to be weaker and less well-controlled than in the simple nanowire configuration widely discussed in the literature. This provides a strong incentive for this paper which investigates the nature of proximitization in these Josephson architectures. At a microscopic level we demonstrate how and when it can lead to topological phases. We do so by going beyond simple tunneling models through solving self-consistently the Bogoliubov-de Gennes equations of a heterostructure multicomponent system involving two spatially separated $s$-wave superconductors in contact with a normal Rashba spin-orbit-coupled 2DEG. Importantly, within our self-consistent theory we present ways of maximizing the proximity-induced superconducting gap by studying the effect of the Rashba spin-orbit coupling, chemical potential mismatch between the superconductor and 2DEG, and sample geometry on the gap. Finally, we note (as in experiment) a Fulde-Ferrell-Larkin-Ovchinnikov phase is also found to appear in the 2DEG channel, albeit under circumstances which are not ideal for topological superconducting phase., 19 pages, 14 figures

BackgroundImpaired executive function (EF) is suggested to be one of the core features in individuals with autism spectrum disorders (ASD); however, little is known about whether the extent of worse EF in ASD than typically developing (TD) controls is age-dependent. We used age-stratified analysis to reveal this issue.MethodWe assessed 111 youths with ASD (aged 12.5 ± 2.8 years, male 94.6%) and 114 age-, and sex-matched TD controls with Digit Span and four EF tasks of the Cambridge Neuropsychological Test Automated Battery (CANTAB): Spatial Span (SSP), Spatial Working Memory (SWM), Stockings of Cambridge (SOC), and Intradimensional/Extradimensional Shift Test (I/ED).ResultsCompared to TD controls, youths with ASD performed poorer on the Digit Span, SWM, SOC, and I/ED tasks. The performance of all the tasks improved with age for both groups. Age-stratified analyses were conducted due to significant age × group interactions in visuospatial planning (SOC) and set-shifting (I/ED) and showed that poorer performance on these two tasks in ASD than TD controls was found only in the child (aged 8–12 years) rather than the adolescent (aged 13–18 years) group. By contrast, youths with ASD had impaired working memory, regardless of age. The increased magnitude of group difference in visuospatial planning (SOC) with increased task demands differed between the two age groups but no age moderating effect on spatial working memory.ConclusionsOur findings support deficits in visuospatial working memory and planning in youths with ASD; however, worse performance in set-shifting may only be demonstrated in children with ASD.

To develop a Tablet-based Symbol Digit Modalities Test (T-SDMT) and to examine the test-retest reliability and concurrent validity of the T-SDMT in patients with stroke.The study had two phases. In the first phase, six experts, nine college students and five outpatients participated in the development and testing of the T-SDMT. In the second phase, 52 outpatients were evaluated twice (2 weeks apart) with the T-SDMT and SDMT to examine the test-retest reliability and concurrent validity of the T-SDMT.The T-SDMT was developed via expert input and college student/patient feedback. Regarding test-retest reliability, the practise effects of the T-SDMT and SDMT were both trivial (d=0.12) but significant (p≦0.015). The improvement in the T-SDMT (4.7%) was smaller than that in the SDMT (5.6%). The minimal detectable changes (MDC%) of the T-SDMT and SDMT were 6.7 (22.8%) and 10.3 (32.8%), respectively. The T-SDMT and SDMT were highly correlated with each other at the two time points (Pearson's r=0.90-0.91).The T-SDMT demonstrated good concurrent validity with the SDMT. Because the T-SDMT had a smaller practise effect and less random measurement error (superior test-retest reliability), it is recommended over the SDMT for assessing information processing speed in patients with stroke. Implications for Rehabilitation The Symbol Digit Modalities Test (SDMT), a common measure of information processing speed, showed a substantial practise effect and considerable random measurement error in patients with stroke. The Tablet-based SDMT (T-SDMT) has been developed to reduce the practise effect and random measurement error of the SDMT in patients with stroke. The T-SDMT had smaller practise effect and random measurement error than the SDMT, which can provide more reliable assessments of information processing speed.

Date Presented 03/26/20 The Traditional Chinese version of the Revised Self-Efficacy Scale showed adequate goodness of fit when applied to schizophrenic patients in Taiwan. That means this scale can measure the subjective feelings of self-efficacy in schizophrenic people, specifically relating to their positive and negative symptoms and interpersonal deficits. Future studies would be suggested to apply the same validation method for this disease-specific self-efficacy scale in other cultures. Primary Author and Speaker: Lin-Jye Huang Additional Authors and Speakers: Fu-Chang Hu, Chien-Te Wu, Yi-Hong Yang, Shu-Chun Lee, Powen Lin, Shih-Jui Hu

Importance: Children with hemiplegic cerebral palsy (CP) demonstrate spatial attention disregard, but the rehabilitation approach to CP is traditionally motor oriented. Objective: To explore spatial attention disregard in children with hemiplegic CP and its relationship to their motor performance in daily activities. Design: Cross-sectional study. Setting: Community. Participants: Twenty-five children with hemiplegic CP and 25 age-matched typically developing children. Outcomes and Measures: For spatial attention performance, the Random Visual Stimuli Detection Task; for developmental disregard, the Observatory Test of Capacity, Performance, and Developmental Disregard; and for motor performance, the Melbourne Assessment 2. Results: Children with hemiplegic CP evidenced spatial attention disregard on their more affected sides, and this phenomenon was correlated with developmental disregard. Conclusions and Relevance: Children with hemiplegic CP demonstrate developmental disregard in both the motor and the visual–spatial attention domains. Including evaluation of and intervention for visual–spatial attention for children with hemiplegic CP in the traditionally motor-oriented rehabilitation approach is recommended. What This Article Adds: This research provides evidence that children with hemiplegic CP demonstrate disregard in the domain of visual–spatial attention. The findings suggest that evaluation of and intervention for visual–spatial attention should be included in CP rehabilitation in addition to the traditionally motor-oriented approach.

Electroencephalography (EEG) can assist with the detection of major depressive disorder (MDD). However, the ability to distinguish adults with MDD from healthy individuals using resting-state EEG features has reached a bottleneck. To address this limitation, we collected EEG data as participants engaged with positive pictures from the International Affective Picture System. Because MDD is associated with blunted positive emotions, we reasoned that this approach would yield highly dissimilar EEG features in healthy versus depressed adults. We extracted three types of relative EEG power features from different frequency bands (delta, theta, alpha, beta, and gamma) during the emotion task and resting state. We also applied a novel classifier, called a conformal kernel support vector machine (CK-SVM), to try to improve the generalization performance of conventional SVMs. We then compared CK-SVM performance with three machine learning classifiers: linear discriminant analysis (LDA), conventional SVM, and quadratic discriminant analysis. The results from the initial analyses using the LDA classifier on 55 participants (24 MDD, 31 healthy controls) showed that the participant-independent classification accuracy obtained by leave-one-participant-out cross-validation (LOPO-CV) was higher for the EEG recorded during the positive emotion induction versus the resting state for all types of relative EEG power. Furthermore, the CK-SVM classifier achieved higher LOPO-CV accuracy than the other classifiers. The best accuracy (83.64%, sensitivity = 87.50%, specificity = 80.65%) was achieved by the CK-SVM, using seven relative power features extracted from seven electrodes. Overall, combining positive emotion induction with the CK-SVM classifier proved useful for detecting MDD on the basis of EEG signals. In the future, this approach might be used to develop a brain&ndash, computer interface system to assist with the detection of MDD in the clinic. Importantly, such a system could be implemented with a low-density electrode montage (seven electrodes), highlighting its practical utility.

Purpose: To investigate the responsiveness and predictive validity of the computerized digit vigilance test (C-DVT) in inpatients receiving rehabilitation following stroke.Methods: Forty-ni...

We investigate the charge and spin transport in half-metallic ferromagnet ($F$) and superconductor ($S$) nanojunctions. We utilize a self-consistent microscopic method that can accommodate the broad range of energy scales present, and ensures that proximity effects that account for the interactions at the interfaces are accurately determined. Two experimentally relevant half-metallic junction types are considered: The first is an ${F}_{1}{F}_{2}S$ structure, where a half-metallic ferromagnet ${F}_{1}$ adjoins a weaker conventional ferromagnet ${F}_{2}$. The current is injected through the ${F}_{1}$ layer by means of an applied bias voltage. The second configuration involves an $S{F}_{1}{F}_{2}{F}_{3}S$ Josephson junction whereby a phase difference $\mathrm{\ensuremath{\Delta}}\ensuremath{\varphi}$ between the two superconducting electrodes generates the supercurrent flow. In this case, the central half-metallic ${F}_{2}$ layer is surrounded by two weak ferromagnets ${F}_{1}$ and ${F}_{3}$. By placing a ferromagnet with a weak exchange field adjacent to an $S$ layer, we are able to optimize the conversion process in which opposite-spin triplet pairs are converted into equal-spin triplet pairs that propagate deep into the half-metallic regions in both junction types. For the tunnel junctions, we study the bias-induced local magnetization, spin currents, and spin-transfer torques for various orientations of the relative magnetization angle $\ensuremath{\theta}$ in the $F$ layers. We find that the bias-induced equal-spin triplet pairs are maximized in the half metal for $\ensuremath{\theta}\ensuremath{\approx}{90}^{\ensuremath{\circ}}$ and, as part of the conversion process, are anticorrelated with the opposite-spin pairs. We show that the charge current density is maximized, corresponding to the occurrence of a large amplitude of equal-spin triplet pairs, when the exchange interaction of the weak ferromagnet is about $0.1{E}_{F}.$ For the half-metallic Josephson junctions we often find that the spin current flowing in the half metal is equivalent to the charge supercurrent flowing throughout the junction. This is indicative that the current consists of spin-polarized triplet pairs. The conversion process of the opposite-spin triplet pairs to the equal-spin triplet pairs in the weaker magnets is clearly demonstrated. This is exemplified by the fact that the supercurrent in the half metal was found to be relatively insensitive to its thickness.

Whether action representation in individuals with autism spectrum disorder (ASD) is deficient remains controversial, as previous studies of action observation or imitation report conflicting results. Here we investigated the characteristics of action representation in adolescents with ASD through motor imagery (MI) using a hand rotation and an object rotation task. Comparable with the typically-developing group, the individuals with ASD were able to spontaneously use kinesthetic MI to perform the hand rotation task, as manifested by the significant biomechanical effects. However, the ASD group performed significantly slower only in the hand rotation task, but not in the object rotation task. The findings suggest that the adolescents with ASD showed inefficient but functional kinesthetic MI, implicating that their action representation might be preserved.

To examine the ecological validity, predictive validity, and responsiveness of the Five Digit Test (FDT) in patients with stroke.We included inpatients with stroke (n = 144, 114 and 105 in the ecological validity, predictive validity, and responsiveness analysis, respectively) in the study. At admission, the FDT and Barthel Index (BI) were assessed; at discharge, the FDT, BI, Postural Assessment Scale for Stroke Patients (PASS), and Mobility Subscale of the Stroke Rehabilitation Assessment of Movement (MO-STREAM) were assessed.In the ecological validity analysis, the scores of the selective and alternating attention indices of the FDT were moderately correlated with those of the BI at admission and discharge (Spearman ρ = -0.38 to -0.45). In the predictive validity analysis, the scores of the two attention indices of the FDT at admission were moderately correlated with the scores of the BI, PASS, and MO-STREAM at discharge (ρ = -0.33 to -0.45). In the responsiveness analysis, the two attention indices of the FDT between admission and discharge had large differences (success rate difference = 0.56-0.67, Wilcoxon Z = -5.90 to -6.60).Our results indicate that the selective and alternating attention indices of the FDT have acceptable ecological validity, predictive validity, and good responsiveness in patients with stroke.The Five Digit Test (FDT), an efficient and culture-free assessment tool, has been used to assess selective attention and alternating attention The selective index and alternating attention index of the FDT showed acceptable ecological validity, predictive validity, and good responsiveness in patients with stroke.

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e.

We aimed to compare the test-retest agreement, random measurement error, practice effect, and ecological validity of the original and Tablet-based Symbol Digit Modalities Test (T-SDMT) over five serial assessments, and to examine the concurrent validity of the T-SDMT in patients with schizophrenia. Sixty patients with chronic schizophrenia completed five serial assessments (one week apart) of the SDMT and T-SDMT and one assessment of the Activities of Daily Living Rating Scale III at the first time point. Both measures showed high test-retest agreement, similar levels of random measurement error over five serial assessments. Moreover, the practice effects of the two measures did not reach a plateau phase after five serial assessments in young and middle-aged participants. Nevertheless, only the practice effect of the T-SDMT became trivial after the first assessment. Like the SDMT, the T-SDMT had good ecological validity. The T-SDMT also had good concurrent validity with the SDMT. In addition, only the T-SDMT had discriminative validity to discriminate processing speed in young and middle-aged participants. Compared to the SDMT, the T-SDMT had overall slightly better psychometric properties, so it can be an alternative measure to the SDMT for assessing processing speed in patients with schizophrenia.

In this paper we demonstrate the necessity of including the generally omitted collective mode contributions in calculations of the Meissner effect for non-uniform superconductors. We consider superconducting pairing with non-zero center of mass momentum, as is relevant to high transition temperature cuprates, cold atoms, and quantum chromodynamic superconductors. For the concrete example of the Fulde-Ferrell phase we present a quantitative calculation of the superfluid density, showing the collective mode contributions are not only appreciable but that they derive from the amplitude mode of the order parameter. This latter mode (related to the Higgs mode in a charged system) is generally viewed as being invisible in conventional superconductors. However, our analysis shows that it is extremely important in pair-density wave type superconductors, where it destroys superfluidity well before the mean-field order parameter vanishes.