Attention Scores Research Articles

Associations between neuromelanin depletion and cortical rhythmic activity in Parkinson's disease.

Parkinson's disease (PD) is marked by the death of neuromelanin-rich dopaminergic and noradrenergic cells in the substantia nigra (SN) and the locus coeruleus (LC), respectively, resulting in motor and cognitive impairments. While SN dopamine dysfunction has clear neurophysiological effects, the association of reduced LC norepinephrine signaling with brain activity in PD remains to be established. We used neuromelanin-sensitive T1-weighted MRI (NPD = 58; NHC = 27) and task-free magnetoencephalography (NPD = 58; NHC = 65) to identify neuropathophysiological factors related to the degeneration of the LC and SN in patients with PD. We found pathological increases in rhythmic alpha (8-12 Hz) activity in patients with decreased LC neuromelanin, with a stronger association in patients with worse attentional impairments. This negative alpha-LC neuromelanin relationship is strongest in fronto-motor cortices, where alpha activity is inversely related to attention scores. Using neurochemical colocalization analyses with normative atlases of neurotransmitter transporters, we also show that this effect is more pronounced in regions with high densities of norepinephrine transporters. These observations support a noradrenergic association between LC integrity and alpha band activity. Our data also show that rhythmic beta (15-29 Hz) activity in the left somato-motor cortex decreases with lower levels of SN neuromelanin; the same regions where beta activity reflects axial motor symptoms. Together, our findings clarify the association of well-documented alterations of rhythmic neurophysiology in PD with cortical and subcortical neurochemical systems. Specifically, attention-related alpha activity is related to dysfunction of the noradrenergic system, and beta activity with relevance to motor impairments reflects dopaminergic dysfunction.

B - 122 A Preliminary Analysis of the Neuropsychological Evaluation of the UPSA (NEUPSA)

Abstract Objective The UCSD Performance Skills Assessment (UPSA) is a well-established performance instrument created to evaluate functional outcomes. We applied a neuropsychological framework to the UPSA and created the Neuropsychological Evaluation of the UPSA (NEUPSA), assessing the cognitive processes that underlie the functional tasks in the UPSA. The study aimed to compare the executive function and attention components of the NEUPSA with a validated computerized cognitive assessment. Methods Participants were 86 adults drawn from the San Diego community. Participants completed the UPSA, 5 Choice CPT (5CCPT) which evaluates attention by calculating a d-prime score that assesses sustained attention, and the Iowa Gambling Task (IGT) which assesses risky decision making. We employed a Spearman’s correlation analysis with a 95% confidence interval to assess the strength of the relationship between NEUPSA attention scores and 5CCPT and IGT performance. Results NEUPSA subscores were significantly correlated with performance on computerized cognitive assessments. 5CCPT scores were positively correlated with NEUPSA attention scores (r = 0.24, p = 0.03). IGT scores were positively correlated with NEUPSA executive functioning scores (r = 0.23, p = 0.04). Conclusion These findings provide preliminary evidence that the NEUPSA assesses aspects of attention (i.e., sustained attention) and executive function (i.e., risky decision making). Further research, however, needs to be conducted on the validity of this measure and the utility in assessing neuropsychological function in clinical populations. Next steps include assessing NEUPSA performance in various non-healthy comparison populations. In this study, we emphasize the fundamental cognitive principles necessary for performance, offering a promising avenue for comprehending impaired functioning.

Enhancing SNN-based spatio-temporal learning: A benchmark dataset and Cross-Modality Attention model

Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable attention in recent years. Similar to Artificial Neural Networks (ANNs), high-quality benchmark datasets are of great importance to the advances of SNNs. However, our analysis indicates that many prevalent neuromorphic datasets lack strong temporal correlation, preventing SNNs from fully exploiting their spatio-temporal representation capabilities. Meanwhile, the integration of event and frame modalities offers more comprehensive visual spatio-temporal information. Yet, the SNN-based cross-modality fusion remains underexplored.In this work, we present a neuromorphic dataset called DVS-SLR that can better exploit the inherent spatio-temporal properties of SNNs. Compared to existing datasets, it offers advantages in terms of higher temporal correlation, larger scale, and more varied scenarios. In addition, our neuromorphic dataset contains corresponding frame data, which can be used for developing SNN-based fusion methods. By virtue of the dual-modal feature of the dataset, we propose a Cross-Modality Attention (CMA) based fusion method. The CMA model efficiently utilizes the unique advantages of each modality, allowing for SNNs to learn both temporal and spatial attention scores from the spatio-temporal features of event and frame modalities, subsequently allocating these scores across modalities to enhance their synergy. Experimental results demonstrate that our method not only improves recognition accuracy but also ensures robustness across diverse scenarios.

A new hybrid deep neural network for multiple sites PM2.5 forecasting

Many studies have confirmed that fine particulate matter (PM2.5) poses significant hazards to both human health and the ecological environment. Predicting future trends in PM2.5 concentrations is crucial for preventing air pollution hazards to human health. However, current machine learning-based forecasting models often focus on specific regions or sites, resulting in poor spatial generalization performance. To address this issue, this study utilizes ERA5 meteorological reanalysis data, MERRA2 assimilated aerosol optical depth (AOD) data, and ground pollutant monitoring site data to construct a three-dimensional dataset. A novel hybrid deep neural network model (D_GAT) is proposed to simultaneously forecast the future 72h PM2.5 concentration trends for all sites in China. The D_GAT model combines the advantages of a graph attention network and a long short-term memory neural network, which can effectively aggregate spatial information between sites and simulate the spatial transport process and time series change characteristics of pollutant particles. Moreover, the proposed calculation method for attention scores quantified based on the actual pollutant propagation characteristics, combined with the upper atmosphere pollution transport process represented by AOD, can better express the pollution contributions of different neighboring sites to the predicted central site. Experimental results show that, compared to four other baseline models, the novel model achieves an average R2 of 0.732 for all sites in 72-hour prediction, which is the highest among the five models, and the prediction accuracy of 72h is also in the forefront compared with the current similar research. The RMSE and MAE are 14.63μg/m3 and 9.49μg/m3, respectively, which are 2-4μg/m3 lower than the other four baseline models, indicating that it has higher reliability and can achieve the goal of timely prediction and early warning. Overall, the new model is an effective tool for multiple sites generalization forecasting.

Resting-State Brain Function Alteration in Colorectal Cancer Patients.

To investigate the abnormal pattern of altered functional activity in the brain and the neuroimaging mechanisms underlying the cognitive impairment of patients with colorectal cancer (CRC) via resting-state functional magnetic resonance imaging (rs-fMRI). CRC patients (n = 56) and healthy controls (HCs) (n = 50) were studied. The participants underwent rs-fMRI scans and the Montreal Cognitive Assessment (MoCA). The amplitude of low-frequency fluctuations (ALFF), degree centrality (DC), regional homogeneity (ReHo), and MoCA scores, were calculated for participants. The scores of executives, visuospatial, memory, language and attention were lower in CRC patients. ReHo and ALFF values in the left postcentral gyrus, ReHo values in the right postcentral gyrus, ALFF and DC values in the left middle occipital gyrus, ReHo and DC values in the right lingual gyrus, DC values in the right angular gyrus and precuneus, and ALFF values in the left middle temporal gyrus decreased conspicuously in the CRC patients. CRC patients have abnormal resting state function, mainly in the brain areas involved in cognitive function. The overlapping brain regions with abnormal functional indicators are in the middle occipital gyrus, postcentral gyrus, and lingual gyrus. This study reveals the potential biological pathways involved in brain impairment and neurocognitive decline in patients with CRC.

TDCS-enhanced cognitive training improves attention and alters connectivity in control and somatomotor networks: A triple blind study

BackgroundExecutive dysfunction such as inattention or forgetfulness can lead to disruptions in a person's daily functioning and quality of life. Objective/HypothesisThis triple-blinded randomized clinical trial assessed the efficacy of bifrontal (over the forehead) transcranial direct current stimulation (tDCS) concurrent with cognitive training to improve cognitive performance in a healthy sample. MethodsFifty-eight participants were randomly assigned to one of three stimulation conditions (2 mA left anode–right cathode, 2 mA right anode–left cathode, or sham), which was administered with cognitive training tasks 3x/week over 12 weeks with assessments at baseline, midpoint (6 weeks), and post-training (12 weeks). We assessed cognitive performance, functional connectivity, and the influence of individual differences in training advancement. ResultsForty participants completed training. We found that at midpoint and post, all groups improved significantly on overall cognitive performance. The left anode group's attention & vigilance score improved significantly at post, but the other two groups did not. Greater attention training advancement predicted attention improvement by post, most notably in the left anode group. Finally, within-network connectivity decreased in the control network and increased in the somatomotor network across all groups. ConclusionsThese results suggest that, given cognitive training, the left anode montage is more effective at improving attention than the right anode montage and sham. Future research may focus on the application of the left anode montage during cognitive training to assess its effectiveness in improving cognition in neuropsychiatric disorders.

Bibliometric top ten healthcare-related ChatGPT publications in the first ChatGPT anniversary.

Since its public release on November 30, 2022, ChatGPT has shown promising potential in diverse healthcare applications despite ethical challenges, privacy issues, and possible biases. The aim of this study was to identify and assess the most influential publications in the field of ChatGPT utility in healthcare using bibliometric analysis. The study employed an advanced search on three databases, Scopus, Web of Science, and Google Scholar, to identify ChatGPT-related records in healthcare education, research, and practice between November 27 and 30, 2023. The ranking was based on the retrieved citation count in each database. The additional alternative metrics that were evaluated included (1) Semantic Scholar highly influential citations, (2) PlumX captures, (3) PlumX mentions, (4) PlumX social media and (5) Altmetric Attention Scores (AASs). A total of 22 unique records published in 17 different scientific journals from 14 different publishers were identified in the three databases. Only two publications were in the top 10 list across the three databases. Variable publication types were identified, with the most common being editorial/commentary publications (n=8/22, 36.4%). Nine of the 22 records had corresponding authors affiliated with institutions in the United States (40.9%). The range of citation count varied per database, with the highest range identified in Google Scholar (1019-121), followed by Scopus (242-88), and Web of Science (171-23). Google Scholar citations were correlated significantly with the following metrics: Semantic Scholar highly influential citations (Spearman's correlation coefficient ρ=0.840, p<0.001), PlumX captures (ρ=0.831, p<0.001), PlumX mentions (ρ=0.609, p=0.004), and AASs (ρ=0.542, p=0.009). In conclusion, despite several acknowledged limitations, this study showed the evolving landscape of ChatGPT utility in healthcare. There is an urgent need for collaborative initiatives by all stakeholders involved to establish guidelines for ethical, transparent, and responsible use of ChatGPT in healthcare. The study revealed the correlation between citations and alternative metrics, highlighting its usefulness as a supplement to gauge the impact of publications, even in a rapidly growing research field.

Is Open Access Criminology Influential?

Open access is a hot topic throughout the sciences, including in criminology and criminal justice (CCJ), with increasing calls for accessibility of high-quality research for the public and policymakers. Yet, the question remains: How influential are open access publications? We use Altmetric data from 95 CCJ journals to examine whether the presence and level of open access corresponds with variations in three influence indicators: Altmetric Attention Scores, Twitter/X mentions, and news mentions. Results indicate that open access is positively associated with Altmetric influence, even at lower tiers of access.

RSVP-based BCI for inconspicuous targets: detection, localization, and modulation of attention

Objective.While brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) is widely used in target detection, patterns of event-related potential (ERP), as well as the performance on detecting inconspicuous targets remain unknown. Moreover, participant-screening methods to excluded 'BCI-blind' users are still lacking.Approach.A RSVP paradigm was designed with targets of varied concealment, size, and location. ERPs (e.g. P300 and N2pc) and target detection accuracy were compared among these conditions. The relationship between participants' attention scores and target detection accuracy was also analyzed to test attention level as a criterion for participant screening.Main results.Statistical analysis showed that the conditions of target concealment and size significantly influenced ERP. In particular, ERP for inconspicuous targets, such as concealed and small targets, exhibited lower amplitudes and longer latencies. In consistent, the accuracy of detection in inconspicuous condition was significantly lower than that of conspicuous condition. In addition, a significant association was found between attention scores and target detection accuracy for camouflaged targets.Significance.The study was the first to address ERP features among multiple dimensions of concealment, size, and location. The conclusion provided insights into the relationship between ERP decoding and properties of targets. In addition, the association between attention scores and detection accuracy implied a promising method in screening well-behaved participants for camouflaged target detection.

A Genetic Causal Explainer for Deep Knowledge Tracing

Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services. Deep learning-based knowledge tracing (DLKT) achieves superb performance in terms of prediction accuracy, but it lacks of explainability, which makes us hard to trust or understand models. The previous work on explaining DLKT was mainly based on gradients or attention scores, which is susceptible to spurious correlations, reducing the credibility of the explanation. To address this limitation, in this paper, we propose a causal explanation method based on the genetic algorithm (GA), named Genetic Causal Explainer(GCE), which constructs a causal framework to estimate the attribution of subsequence to the predictions of DLKT models, and a genetic coding system is designed. Further, A multi-strategy initialization method inspired by domain prior knowledge is proposed, and a global empirical matrix is introduced to capture the causal correlation knowledge during the search process across instances, and guiding the mutation operators. The GCE as a post hoc explanation method can generate explanation results without affecting model training, and can be applied to analyze different DLKT models. Experimental results demonstrate the GCE perform better than other explanation methods in terms of accuracy and readability in quantitative assessments. Meanwhile, the GCE also shows good application prospects in mining educational laws and comparing KT models.

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data

BackgroundWide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. New methodA hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. ResultsSleep states were classified with an accuracy of 84 % and Cohen’s κ of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. Comparison with existing methodOn a held out, repeated 3-hour WFCI recording, the CNN-BiLSTM achieved a κ of 0.67, comparable to a κ of 0.65 corresponding to the human EEG/EMG-based scoring. ConclusionsThe CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep research.

MRSAPose: Multi-level routing sparse attention for multi-person pose estimation

Multi-person Pose Estimation (MPPE) aims to reconstruct human poses by locating and connecting keypoints of individuals in input images. The variability of human poses and the complexity of scenes make MPPE reliant on both local details and global structures, and the absence of either can lead to the generation of deformed poses. With the emergence of Transformer, the performance of MPPE has been significantly improved. However, due to self-attention computing attention scores between each pair of positions, the current Transformer-based MPPE exhibits high quadratic complexity. To address these issues, this paper proposes a novel pose estimation model, MRSAPose. MRSAPose utilizes Multi-level Routing Sparse Attention (MRSA) to dynamically select relevant regions for attention, reducing computational complexity and mitigating the impact of irrelevant regions. Furthermore, MRSAPose constructs a Transformer-CNN Parallel Interaction Block (T-CP block) through MRSA and Recursive Residual Gated Convolution (Res-gnConv), facilitating parallel learning of global and local information. By relying on multi-level routing algorithms and high-order spatial interactions conducted by recursive processing of adjacent features, T-CP block helps MRSAPose effectively alleviates the issues of occlusion and misalignment in pose estimation. On multiple challenging keypoint datasets, MRSAPose outperforms current state-of-the-art algorithms, particularly excelling in crowded and occluded scenes.

Baseline brain volume predicts home-based transcranial direct current stimulation effects on inattention in adults with attention-deficit/hyperactivity disorder

BackgroundHome-based transcranial direct current stimulation (Hb-tDCS) is a non-invasive brain stimulation technique that utilizes low-intensity electric currents delivered via scalp electrodes to modulate brain activity. It holds significant promise for addressing inattention in adults with attention-deficit/hyperactivity disorder (ADHD). However, its effectiveness varies among individuals, and predicting outcomes remains uncertain, partially due to the influence of individual differences in ADHD-related brain anatomy. MethodsWe analyzed data from a subsample, composed by twenty-nine adult patients with ADHD, of the Treatment of Inattention Symptoms in Adult Patients with ADHD (TUNED) trial. Fourteen patients underwent active anodal right cathodal left dorsolateral prefrontal cortex (DLPFC) Hb-tDCS for 4 weeks and fifteen received sham-related tDCS intervention. Inattention outcome was evaluated at both baseline and endpoint (4th week). Baseline structural measures of the DLPFC, anterior cingulate cortex (ACC) and subcortical structures, previously associated with ADHD, were quantified. Several linear mixed models, with a three-way interaction between the fixed predictors brain volume or thickness, time, and treatment were calculated. Multiple comparison corrections were applied using the Benjamini-Hochberg method. ResultsBaseline volume of the left DLPFC regions middle frontal gyrus (t (25) = 3.33, p-adjusted = 0.045, Cohen's d = 1.33, 95% CI = [0.45, 2.19]), inferior frontal gyrus (orbital part) (t (25) = 3.10, p-adjusted = 0.045, Cohen's d = 1.24, 95% CI = [0.37, 2.08]), and of the left ACC supragenual (t (25) = 3.15, p-adjusted = 0.045, Cohen's d = 1.26, 95% CI = [0.39, 2.11]) presented significant association with the inattentive score improvement only in the active tDCS group. More specifically, the smaller these regions were, the more the symptoms improved following anodal right cathodal left DLPFC Hb-tDCS. ConclusionHb-tDCS was associated with greater improvement in brain areas related to attention regulation. Brain MRI can be potentially used to predict clinical response to tDCS in ADHD adults.

Public Interest in Research on Aging: Analysis of Altmetric Attention Scores Over 5 Years.

This study pinpoints the areas in gerontological research that have commanded the most public attention as scored by Altmetric. We collected 12,740 outputs published in gerontological journals over 5 years from 2017 to 2022. Next, we identified the top 5% of outputs with the highest Altmetric Attention Scores. After removing duplicate publications and those without an abstract, 588 outputs remained. Our content analysis of the abstracts was conducted deductively and inductively. Theme 1 (50%; N = 293) revolved around "Health and Well-Being" (Theme 1). Theme 2 (28%; N = 164) was about "Health Services, Interventions and Innovations." Theme 3 was about "Informal and Long-Term Care" (13%; N = 80). Theme 4 focused on "Structural Inequalities" affecting older adults (9%; N = 51). Gerontologists should strengthen collaboration with other stakeholders to better engage the public across different age-related issues. These partnerships will enable the creation of initiatives that better serve the needs of aging populations.

Social anxiety and attentional bias to negative emotional information: the relationship and intervention

BackgroundAccording to the cognitive behavioral model of social anxiety, attentional bias to negative emotional information causes and maintains anxiety. The goal of attentional bias modification (ABM) is to reduce anxiety by reducing attention bias to negative emotional information.MethodWe used questionnaires and experiments to explore the improvement effect of ABM training on social anxiety in college students. In Study 1, we used dot-probe tasks to investigate the attentional bias to negative emotional information and the relationship with social anxiety severity in college students. In Study 2, college students with high social anxiety were divided into two groups: attentional bias modification training task group (ABM) and attention control condition task group (ACC). The ABM group received a continuous intervention for 10 days to observe changes in social anxiety levels and attentional bias scores in the pretest and posttest stages.ResultsThe results showed that the correlation of attentional bias to negative emotional information and social anxiety severity was significant. Meanwhile, the high social anxiety participants responded more quickly to negative emotional information. After the intervention, social anxiety levels and attentional bias scores of the training group were significantly reduced.ConclusionsThe results showed that attentional bias modification training can reduce attentional bias to negative emotional information in college students with social anxiety and effectively improve their social anxiety.

Features of Importance in Nasal Endoscopy: Deriving a Meaningful Framework.

Critical components of the nasal endoscopic examination have not been definitively established for either the normal examination or for clinical disorders. This study aimed to identify concordance among rhinologists regarding the importance of examination findings for various nasal pathologies. A consortium of 19 expert rhinologists across the United States was asked to rank the importance of findings on nasal endoscopy for 5 different sinonasal symptom presentations. An online questionnaire was distributed in July 2023. The questionnaire utilized JotForm® software and featured 5 cases with a set of 4 identical questions per case, each covering a common indication for nasal endoscopy. Rankings were synthesized into Normalized Attention Scores (NASs) and Weighted Normalized Attention Scores (W-NASs) to represent the perceived importance of each feature, scaled from 0 to 1. General concordance was found for examination findings on nasal endoscopy within each case. The perceived features of importance differed between cases based on clinical presentation. For instance, in evaluating postnasal drip, the middle meatus was selected as the most important structure to examine (NAS, 0.73), with mucus selected as the most important abnormal finding (W-NAS, 0.66). The primary feature of interest for mucus was whether it was purulent or not (W-NAS, 0.67). Similar analyses were performed for features in each case. The implicit framework existing among rhinologists may help standardize examinations and improve diagnostic accuracy, augment the instruction of trainees, and inform the development of artificially intelligent algorithms to enhance clinical decision-making during nasal endoscopy.

Prevalence of Dehydration in Egyptian Primary School Children and its Impact on their Cognitive performance

Abstract Background More than 75% of children do not meet the water intake recommendations by the Institute of medicine. In addition, more than 80% of children in many European countries do not achieve the water intake recommended by the guidelines of the European food safety authority. Studies have revealed a correlation between hydration status and cognitive achievements in visual attention, immediate memory span, semantic flexibility, and automatic application of arithmetic operations in school children living in hot climates. Aim of the Work Assessment of the hydration status in Egyptian primary school children and determining the relationship between hydration status and cognitive function. Patients and Methods A cross-sectional questionnaire-based study was conducted on 200 children, the age group was from 9 to 13 years old. Data collection included the governmental, private and international schools in Cairo. The questionnaire was fulfilled during school time. A child-friendly “fluids and liquid food” diary was designed to prospectively record the volume of total fluid intake including water, sugar sweetened beverages and fruit juices in the previous day and during school day. Hydration status was assessed by self-reported urine colour using eight-point colour scale. The students were asked to answer four cognitive function tests that were designed to test the cognitive skills which were known to be affected by dehydration (Visual attention, Visual memory, Visual search, Visuomotor performance). Results 54 children (27%) had “Satisfactory knowledge” i.e. the children who met the criteria in the self- answered questionnaire and 146 children (73%) of them had “Unsatisfactory knowledge of proper total fluid intake. 166 children (83%) reported low total fluid intake and 34 children (17%) had adequate total fluid intake (according to the guidelines of the European food safety authority), 118 children (59%) were dehydrated, and 34 children (17%) were severely dehydrated. There was a statistically significant higher total fluid intake in international school regarding the amount of total fluid intake during school time and the amount of total fluid intake per day. In our study, the most consumed fluids after water were sugar sweetened beverages and fruit juices. There was a highly statistically significant positive correlation between total fluid intake and cognitive function regarding visual attention, visual memory, visual search, visuomotor performance and total score of cognitive functions, with p-value (p &amp;lt; 0.001). Conclusion We concluded that there was a significant correlation between school students’ hydration status and total fluid intake and their cognitive functions regarding their Visual attention, Visual memory, Visual search, Visuomotor performance. That calls for the importance of corroboration of our study results with studies that further explore students’ awareness and perception about their daily need of water and benefits of proper hydration.

Effects of transcranial direct current stimulation along with cognitive training on cognitive functions and activities of daily living in patients with Parkinson's disease: A pilot randomized controlled trial

AbstractBackgroundCognitive impairment is one of the most common symptoms of Parkinson's disease (PD). In recent years, the effect of transcranial direct current stimulation (tDCS) have been investigated on various disabilities related to PD, including sensory and motor deficits and fatigue.AimTo investigate effects of transcranial direct current stimulation (tDCS) along with cognitive training on attention, working memory, reaction time (RTI), response inhibition, and activities of daily living performance.MethodsThis pilot randomized, single‐blinded, and sham‐controlled trial was performed on 32 people with PD who were blocked randomized equally into intervention and control groups. Intervention group received ten 20‐min sessions of tDCS during ten 45‐min sessions of cognitive training, while control group received ten 20‐min sessions of sham‐tDCS during ten 45‐min sessions of cognitive training. Anodal electrode of tDCS (2 mA) was placed over left dorsolateral prefrontal cortex (DLPFC), while cathode was over right supraorbital region. A blinded assessor performed pretest, posttest, and follow‐up assessments using the Unified Parkinson's Disease Rating Scale and four subtests of the CANTAB computer test including Match to Sample Visual Search, spatial span task, stop signal task, and RTI.ResultsThe results showed significant differences within groups over time in the mean scores of attention (F = 24.73, p = 0.00), working memory (F = 19.06, p = 0.000), RTI (F = 8.63, p = 0.005), response inhibition (F = 15.75, p = 0.000), and activity of daily living performance (F = 6.73, p = 0.013).ConclusiontDCS (anodal electrode over the left DLPFC and cathodal electrode over the right supraorbital region) along with cognitive training could facilitate improvement of cognitive functions and activities of daily living performance compared to routine cognitive exercises in people with PD.

Attention Similarity Outlier Suppression with Hierarchical Feature Integration for Defect Segmentation

Defect segmentation holds significant importance in industrial applications. However, most existing algorithms are tailored to specific scenarios, lacking a unified defect segmentation algorithm for defect segmentation. Based on the analysis of the characteristics of defects, we aim to propose a unified transformer‐based defect segmentation algorithm. On the one hand, the defect foreground exhibits significant differences from the normal background. Nevertheless, the attention mechanism in the transformer involves all feature nodes in the feature map to the similarity score of one patch with all other patches. This approach may compromise the features of potential defect areas where the defect foreground significantly contrasts with the background. We introduce the Attention Similarity Outlier Suppression (ASOS) strategy to address this challenge. Our method is designed to select valid attention scores and filter out outlier scores for appropriate attention fusion, in which the feature of potential defect areas is not easily affected by background. On the other hand, the defect regions often have relatively small areas. We incorporate a Hierarchical Feature Integration (HFI) module in our framework to facilitate defect recognition at different scales. By progressively up‐sampling, features can achieve better integration to generate the final segmentation result. Extensive experiments have verified the effectiveness of our proposed method in defect segmentation tasks. Moreover, thorough ablation studies have demonstrated the efficacy of our algorithm, reinforcing its robust performance. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Novel behavior-enhanced long- and short-term interest model for sequential recommendation

In the realm of modern recommender systems, user-item interaction data often exhibit sequential patterns in relation to various behaviors, such as clicks and purchases on e-commerce platforms. The objective of heterogeneous sequential recommendation (HSR) is to predict each user's next item of interest under a specific behavior based on these interactions. However, existing approaches to HSR struggle to fully capture item transition relationships, as they consider these relationships from a single dimension and at a coarse-grained level. To bridge this gap, we propose the novel Behavior-enhanced Long- and Short-term Interest (BLSI) model, which explores fine-grained item transition relationships in both local and global dimensionalities. At its core, BLSI incorporates a behavior-enhanced self-attention network (BSAN) to capture short-term user preferences. BSAN distinguishes the effects of different behaviors and considers cross-type behavior influences during the linear projection and attention score calculation stages. Additionally, BLSI employs a heterogeneous graph neural network (HGNN) to model long-term user interests by discriminatively aggregating the information of neighboring nodes according to their behavior transition relationships. Furthermore, a gating mechanism is implemented to adaptively fuse short- and long-term preferences for personalized recommendations. Extensive experimental results on three datasets demonstrate that BLSI significantly outperforms state-of-the-art recommendation methods, highlighting the advantages of leveraging sequentiality and behavioral heterogeneity.