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Perovskite solar cells have been proven to enhance cell characteristics by introducing passivation materials that suppress defect formation. Defect states between the electron transport layer and the absorption layer reduce electron extraction and carrier transport capabilities, leading to a significant decline in device performance and stability, as well as an increased probability of non-radiative recombination. This study proposes the use of an amino acid (L-Histidine) self-assembled monolayer material between the transport layer and the perovskite absorption layer. Surface analysis revealed that the introduction of L-Histidine improved both the uniformity and roughness of the perovskite film surface. X-ray photoelectron spectroscopic analysis showed a reduction in oxygen vacancies in the lattice and an increase in Ti4+, indicating that L-Histidine successfully passivated trap states at the perovskite and TiO2 electron transport layer interface. In terms of device performance, the introduction of L-Histidine significantly improved the fill factor (FF) because the reduction in interface defects could suppress charge accumulation and reduce device hysteresis. The FF of large-area solar modules (25 cm2) with L-Histidine increased from 55% to 73%, and the power conversion efficiency (PCE) reached 16.5%. After 500 h of aging tests, the PCE still maintained 91% of its original efficiency. This study demonstrates the significant impact of L-Histidine on transport properties and showcases its potential for application in the development of large-area perovskite module processes.

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Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge.

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Creativity is defined by three key factors: novelty, feasibility and value. While many creativity tests focus primarily on novelty, they often neglect feasibility and value, thereby limiting their reflection of real-world creativity. In this study, we employ GPT-4, a large language model, to assess these three dimensions in a Japanese-language Alternative Uses Test (AUT). Using a crowdsourced evaluation method, we acquire ground truth data for 30 question items and test various GPT prompt designs. Our findings show that asking for multiple responses in a single prompt, using an 'explain first, rate later' design, is both cost-effective and accurate (r = .62, .59 and .33 for novelty, feasibility and value, respectively). Moreover, our method offers comparable accuracy to existing methods in assessing novelty, without the need for training data. We also evaluate additional models such as GPT-4 Turbo, GPT-4 Omni and Claude 3.5 Sonnet. Comparable performance across these models demonstrates the universal applicability of our prompt design. Our results contribute a straightforward platform for instant AUT evaluation and provide valuable ground truth data for future methodological research.

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Mismatch negativity (MMN) is commonly recognized as a neural signal of prediction error evoked by deviants from the expected patterns of sensory input. Studies show that MMN diminishes when sequence patterns become more predictable over a longer timescale. This implies that MMN is composed of multiple subcomponents, each responding to different levels of temporal regularities. To probe the hypothesized subcomponents in MMN, we record human electroencephalography during an auditory local-global oddball paradigm where the tone-to-tone transition probability (local regularity) and the overall sequence probability (global regularity) are manipulated to control temporal predictabilities at two hierarchical levels. We find that the size of MMN is correlated with both probabilities and the spatiotemporal structure of MMN can be decomposed into two distinct subcomponents. Both subcomponents appear as negative waveforms, with one peaking early in the central-frontal area and the other late in a more frontal area. With a quantitative predictive coding model, we map the early and late subcomponents to the prediction errors that are tied to local and global regularities, respectively. Our study highlights the hierarchical complexity of MMN and offers an experimental and analytical platform for developing a multitiered neural marker applicable in clinical settings.

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Charge density waves (CDWs) involved with electronic and phononic subsystems simultaneously are a common quantum state in solid-state physics, especially in low-dimensional materials. However, CDW phase dynamics in various dimensions are yet to be studied, and their phase transition mechanism is currently moot. Here we show that using the distinct temperature evolution of orientation-dependent ultrafast electron and phonon dynamics, different dimensional CDW phases are verified in CuTe. When the temperature decreases, the shrinking of c-axis length accompanied with the appearance of interchain and interlayer interactions causes the quantum fluctuations (QF) of the CDW phase until 220 K. At T < 220 K, the CDWs on the different ab-planes are finally locked with each other in anti-phase to form a CDW phase along the c-axis. This study shows the dimension evolution of CDW phases in one CDW system and their stabilized mechanisms in different temperature regimes.

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AIM: Despite the emphasis on sensory dysfunction phenotypes in the revised diagnostic criteria for autism spectrum disorder (ASD), there has been limited research, particularly in the field of neurobiology, investigating the concordance in sensory features between individuals with ASD and their genetic relatives. Therefore, our objective was to examine whether neurobehavioral sensory patterns could serve as endophenotypic markers for ASD. METHODS: We combined questionnaire- and lab-based sensory evaluations with sensory fMRI measures to examine the patterns of sensory responsivity in 30 clinically diagnosed with ASD, 26 matched controls (CON), and 48 biological parents for both groups (27 parents of individuals with ASD [P-ASD] and 21 for individuals with CON [P-CON]). RESULTS: The ASD and P-ASD groups had higher sensory responsivity and rated sensory stimuli as more unpleasant than the CON and P-CON groups, respectively. They also exhibited greater hemodynamic responses within the sensory cortices. Overlapping activations were observed within these sensory cortices in the ASD and P-ASD groups. Using a machine learning approach with robust prediction models across cohorts, we demonstrated that the sensory profile of biological parents accurately predicted the likelihood of their offspring having ASD, achieving a prediction accuracy of 71.4%. CONCLUSIONS: These findings provide support for the hereditary basis of sensory alterations in ASD and suggest a potential avenue to improve ASD diagnosis by utilizing the sensory signature of biological parents, especially in families with a high risk of ASD. This approach holds promising prospects for early detection, even before the birth of the offspring.

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The high prevalence rate of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has been a serious public health threat to the modern society. Recently, many studies have demonstrated the potential of using non-invasive electroencephalography (EEG) and machine learning to assist the diagnosis of AD/MCI. However, the majority of these research recorded EEG signals from a single center, leading to significant concerns regarding the generalizability of the findings in clinical settings. The current study aims to reevaluate the effectiveness of EEG-based machine learning model for the detection of AD/MCI in the case of a relatively large and diverse data set. We collected resting-state EEG data from 150 participants across six hospitals and examined the classification performances of Linear Discriminative Analysis (LDA) classifiers on the phase lag index (PLI) feature. We also compared the performance of PLI over the other commonly-used EEG features and other classifiers. The model was first tested on a training set to select the feature subset and then further validated with an independent test set. The results demonstrate that PLI performs the best compared to other features. The LDA classifier trained with the optimal PLI features can provide 82.50% leave-one-participant-out cross-validation (LOPO-CV) accuracy on the training set and maintain a good enough performance with 75.00% accuracy on the test set. Our results suggest that PLI-based functional connectivity could be considered as a reliable bio-maker to detect AD/MCI in the real-world clinical settings.

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IMPORTANCE: Limited evidence exists to support cognitive intervention improving the daily function of adults with subjective cognitive decline (SCD). OBJECTIVE: To examine the preliminary efficacy of a group-based multicomponent cognitive intervention that integrates Lifestyle Redesign® (LR) techniques. DESIGN: Single-arm two-period crossover trial; 16-wk waiting period, 16-wk intervention, and 16-wk follow-up. SETTING: Memory clinic in a medical center, Taiwan. PARTICIPANTS: Purposive sample of adults ages >55 yr with SCD. INTERVENTION: Sixteen 1.5-hr weekly multicomponent sessions of cognitive training, cognitive rehabilitation, psychological intervention, and lifestyle intervention. OUTCOMES AND MEASURES: Primary outcomes were (1) self-reported daily function, measured with the Activities of Daily Living Questionnaire (ADLQ) and Cognitive Failure Questionnaire; (2) performance-based daily function, measured with the Brief University of California San Diego Performance-Based Skills Assessment-Traditional Chinese Version; and (3) functional cognition, measured with the Contextual Memory Test (CMT) and Miami Prospective Memory Test. Secondary outcomes included cognitive functions, anxiety, and depression. RESULTS: Seventeen participants completed the intervention; 4 missed the follow-up. The generalized estimating equations model showed significant changes from baseline to pretest (control) and pretest to posttest (intervention) on the ADLQ (p = .014) and CMT-delayed (p = .003). Effects remained at the 16-wk follow-up. After adjusting for the effects of covariates, the self-reported daily function of participants ages ≤ 63 yr improved more than that of other participants (p = .003). CONCLUSIONS AND RELEVANCE: Multicomponent cognitive interventions integrating LR techniques may improve self-reported daily function and context-dependent memory function of adults with SCD, with efficacy sustained at follow-up. What This Article Adds: A group-based multicomponent cognitive intervention consisting of cognitive training, cognitive rehabilitation, psychoeducation, and lifestyle intervention may provide benefits for the daily function and cognitive function of adults with SCD.

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Plumbene, with a structure similar to graphene, is expected to possess a strong spin-orbit coupling and thus enhances its superconducting critical temperature (Tc ). In this work, a buckled plumbene-Au Kagome superstructure grown by depositing Au on Pb(111) is investigated. The superconducting gap monitored by temperature-dependent scanning tunneling microscopy/spectroscopy shows that the buckled plumbene-Au Kagome superstructure not only has an enhanced Tc with respect to that of a monolayer Pb but also possesses a higher value than what owned by a bulk Pb substrate. By combining angle-resolved photoemission spectroscopy with density functional theory, the monolayer Au-intercalated low-buckled plumbene sandwiched between the top Au Kagome layer and the bottom Pb(111) substrate is confirmed and the electron-phonon coupling-enhanced superconductivity is revealed. This work demonstrates that a buckled plumbene-Au Kagome superstructure can enhance superconducting Tc and Rashba effect, effectively triggering the novel properties of a plumbene.

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BACKGROUND: 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. METHODS: 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. RESULTS: 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. LIMITATIONS: The sample size was small. CONCLUSION: 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.

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The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. In support of this theory, feedforward signals for prediction error have been reported. However, the identification of feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. Here, we use a quantitative model to decompose these signals in electroencephalography during an auditory task, and identify their spatio-spectral-temporal signatures across two functional hierarchies. Two prediction signals are identified in the period prior to the sensory input: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for the multi-tone sequence structure in the low beta band. Subsequently, prediction-error signals dependent on the prior predictions are found in the gamma band. Our findings reveal a frequency ordering of prediction signals and their hierarchical interactions with prediction-error signals supporting predictive coding theory.

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Electroencephalography (EEG) signals measured under fixed conditions have been exploited as biometric identifiers. However, what contributes to the uniqueness of one's brain signals remains unclear. In the present research, we conducted a multi-task and multi-week EEG study with ten pairs of monozygotic (MZ) twins to examine the nature and components of person-identifiable brain signals. Through machine-learning analyses, we uncovered a person-identifying EEG component that served as "base signals" shared across tasks and weeks. Such task invariance and temporal stability suggest that these person-identifying EEG characteristics are more of structural brainprints than functional mindprints. Moreover, while these base signals were more similar within than between MZ twins, it was still possible to distinguish twin siblings, particularly using EEG signals coming primarily from late rather than early developed areas in the brain. Besides theoretical clarifications, the discovery of the EEG base signals has practical implications for privacy protection and the application of brain-computer interfaces.

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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 (33×33)R30o Ni-Pb surface alloy has been fabricated on Pb(111) and the appearance of a domain boundary as well as a structural phase transition can be deduced from a half-unit-cell lattice displacement. Given the high spatial and energy resolution, tunneling conductance (dI/dU) spectra have resolved the reduced but anisotropic superconducting gap ΔNiPb ≈ 1.0 meV, in stark contrast to the isotropic ΔPb ≈ 1.3 meV. In addition, the higher density of states at the Fermi energy (D(EF)) of the Ni-Pb surface alloy results in an enhancement of coherence peak height. According to the same Tc ≈ 7.1 K with Pb(111) from the temperature-dependent ΔNiPb and the short decay length Ld ≈ 3.55 nm from the spatially monotonic decrease of ΔNiPb, both results are supportive of a proximity-induced superconductivity. Despite a lack of a bulk counterpart, the atomically thick Ni-Pb bimetallic compound opens a pathway to engineer superconducting properties down to the two-dimensional limit, giving rise to the emergence of anisotropic superconductivity via a proximity effect.

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Objectives: Rumination, a response style characterized by self-reflection loops of negative thoughts, tends to exacerbate depressive symptoms and may impair daily functional behaviors of individuals with depression. However, the specific impacts of rumination on activity participation remain unclear. The current study was aimed at examining the differences in daily activity participation profiles between clinically depressed people with higher versus lower rumination tendencies, with the hope to provide insightful suggestions for improving the quality of life of ruminative individuals with major depression. Methods: We recruited 143 participants with a depression-related diagnosis from psychiatric daycare centers or clinics and analyzed the differences in activity participation profiles between individuals with higher versus lower rumination tendencies. Results: Although compared to those with lower rumination tendencies, participants with higher rumination tendencies spent a longer time in activity participation; they experienced lower participation quality during these activities. Furthermore, their activity participation was primarily motivated by meeting others' expectations rather than self-interest. They also misattributed participation restriction to "lack of family support," indicating that the unhealthy rumination pattern might be the cause of their lack of positive feelings from engaging in meaningful daily activities. Conclusions: The current results suggest that the unhealthy motivation behind activity participation seems to be an important factor that decreases the quality of participation in individuals with higher rumination tendency. Establishing a healthy motivation for activity participation is therefore critical for improving their quality of participation. As an initial step, OT interventions could put a focus on helping them clarify and escape from the source of negative rumination cycles that impede their positive feeling of activity participation.

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The study of the magnonic thermal Hall effect in magnets with Dzyaloshinskii-Moriya interaction (DMI) has recently drawn attention because of the underlying topology. Topological phase transitions may arise when there exist two or more distinct topological phases, and they are often revealed by a gap-closing phenomenon. In this work, we consider the magnons in honeycomb ferromagnets described by a Heisenberg Hamiltonian containing both an out-of-plane DMI and a Zeeman interaction. We demonstrate that the magnonic system exhibits temperature (or magnetic field) driven topological phase transitions due to magnon-magnon interactions. Specifically, when the temperature increases, the magnonic energy gap at Dirac points closes and reopens at a critical temperature, T_{c}. By showing that the Chern numbers of the magnonic bands are distinct above and below T_{c}, we confirm that the gap-closing phenomenon is indeed a signature for the topological phase transitions. Furthermore, our analysis indicates that the thermal Hall conductivity in the magnonic system exhibits a sign reversal at T_{c}, which can serve as an experimental probe of its topological nature. Our theory predicts that in CrI_{3} such a phenomenon exists and is experimentally accessible.

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Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

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Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.

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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.

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Purpose: To investigate the responsiveness and predictive validity of the computerized digit vigilance test (C-DVT) in inpatients receiving rehabilitation following stroke. Methods: Forty-nine patients completed the C-DVT and the Barthel Index (BI) after admission to and before discharge from the rehabilitation ward. The standardized response mean (SRM) was used to examine the responsiveness of the C-DVT. We used a paired t-test to determine the statistical significance of the changes in scores on the C-DVT. We estimated the predictive validity of the C-DVT with the Pearson correlation coefficient (r) to investigate the association between the scores of the C-DVT at admission and the scores of the BI at discharge. Results: Our data showed a small SRM (-0.31) and a significant difference (paired t-test, p = 0.034) between the C-DVT scores at admission and discharge. These findings indicate that the C-DVT can appropriately detect changes in sustained attention. In addition, we found a moderate association (r = 0.48) between the scores of the C-DVT at admission and the scores of the BI at discharge, suggesting the sufficient predictive validity of the C-DVT. Conclusions: Our results showed that the C-DVT had adequate responsiveness and sufficient predictive validity in inpatients receiving rehabilitation following stroke. Implications for rehabilitation The computerized digit vigilance test (C-DVT) had adequate responsiveness to be an outcome measure for assessing the sustained attention in inpatients receiving rehabilitation after stroke. The C-DVT had sufficient predictive validity to predict daily function in inpatients receiving rehabilitation after stroke.

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