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IntroductionCerebrospinal fluid (CSF) flow is involved in brain waste clearance and may be impaired in neurodegenerative diseases such as Parkinson’s disease. This study aims to investigate the relationship between the CSF pulsation and the development of dementia in Parkinson’s disease (PD) patients using EPI-based fMRI.MethodsWe measured CSF pulsation in the 4th ventricle of 17 healthy controls and 35 PD patients using a novel CSF pulsation index termed “CSFpulse” based on echo-planar imaging (EPI)-based fMRI. The PD patients were classified into a PD with dementia high-risk group (PDD-H, n = 19) and a low risk group (PDD-L, n = 16), depending on their development of dementia within 5 years after initial brain imaging. The size of the 4th ventricle was measured using intensity-based thresholding.ResultsWe found that CSF pulsation was significantly higher in PD patients than in healthy controls, and that PD patients with high risk of dementia (PDD-H) had the highest CSF pulsation. We also observed an enlargement of the 4th ventricle in PD patients compared to healthy controls.ConclusionOur results suggest that CSF pulsation may be a potential biomarker for PD progression and cognitive decline, and that EPI-based fMRI can be a useful tool for studying CSF flow and brain function in PD.

The present study aimed to understand how Chinese international students perceived and experience healthcare utilization in Korea and to broaden Andersen’s framework in the age of globalization, allowing easier maintenance of transnational relationships with one’s native country. We analyzed the in-depth personal interviews with 31 Chinese international students. The grounded theory was employed to analyze the interview data. Among factors suggested by Andersen, predisposing (perceptions about the Korean healthcare system) and enabling (financial barriers, language barriers, and lack of social support) factors contributed to healthcare utilization among Chinese international students in Korea. Self-medication via broad medical transnational ties with the home country (bringing medications from home) impeded their healthcare utilization. We highlighted the roles of their campus health center to promote optimal healthcare utilization for Chinese international students. It is important to educate international students about the risks or negative effects of self-medication.

Abstract With the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention; the lack of such datasets limits research advances in affective computing and human-computer interaction. This study presents K-EmoPhone, a real-world multimodal dataset collected from 77 students over seven days. This dataset contains (1) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices, (2) context and interaction data collected from individuals’ smartphones, and (3) 5,582 self-reported affect states, including emotions, stress, attention, and task disturbance, acquired by the experience sampling method. We anticipate the dataset will contribute to advancements in affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.

Objective To investigate whether there is a link between cognitive function and motor reserve (i.e., individual capacity to cope with nigrostriatal dopamine depletion) in patients with newly diagnosed Parkinson’s disease (PD). Methods A total of 163 patients with drug-naïve PD who underwent 18F-FP-CIT PET, brain MRI, and a detailed neuropsychological test were enrolled. We estimated individual motor reserve based on initial motor deficits and striatal dopamine depletion using a residual model. We performed correlation analyses between motor reserve estimates and cognitive composite scores. Diffusion connectometry analysis was performed to map the white matter fiber tracts, of which fractional anisotropy (FA) values were well correlated with motor reserve estimates. Additionally, Cox regression analysis was used to assess the effect of initial motor reserve on the risk of dementia conversion. Results The motor reserve estimate was positively correlated with the composite score of the verbal memory function domain (γ = 0.246) and with the years of education (γ = 0.251). Connectometry analysis showed that FA values in the left fornix were positively correlated with the motor reserve estimate, while no fiber tracts were negatively correlated with the motor reserve estimate. Cox regression analysis demonstrated that higher motor reserve estimates tended to be associated with a lower risk of dementia conversion (hazard ratio, 0.781; 95% confidence interval, 0.576–1.058). Conclusion The present study demonstrated that the motor reserve estimate was well correlated with verbal memory function and with white matter integrity in the left fornix, suggesting a possible link between cognition and motor reserve in patients with PD.

In the last few decades, adverse reactions to pharmaceuticals have been evaluated using 2D in vitro models and animal models. However, with increasing computational power, and as the key drivers of cellular behavior have been identified, in silico models have emerged. These models are time-efficient and cost-effective, but the prediction of adverse reactions to unknown drugs using these models requires relevant experimental input. Accordingly, the physiome concept has emerged to bridge experimental datasets with in silico models. The brain physiome describes the systemic interactions of its components, which are organized into a multilevel hierarchy. Because of the limitations in obtaining experimental data corresponding to each physiome component from 2D in vitro models and animal models, 3D in vitro brain models, including brain organoids and brain-on-a-chip, have been developed. In this review, we present the concept of the brain physiome and its hierarchical organization, including cell- and tissue-level organizations. We also summarize recently developed 3D in vitro brain models and link them with the elements of the brain physiome as a guideline for dataset collection. The connection between in vitro 3D brain models and in silico modeling will lead to the establishment of cost-effective and time-efficient in silico models for the prediction of the safety of unknown drugs.

Measurement(s) electroencephalography (EEG) Technology Type(s) electroencephalography (EEG) Factor Type(s) Chronic sleep restriction Sample Characteristic - Organism Mus musculus

BackgroundAs a form of the Internet of Things (IoT)–gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety. ObjectiveThis paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors? MethodsWe selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86%; EBSCO: 149/1268, 11.75%; ScienceDirect: 248/1268, 19.55%; and Google Scholar: 784/1268, 61.82%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility. ResultsSmart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32%), and the 2 most frequent studies including simulation were industry (23/57, 40%) and sports (23/57, 40%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting. ConclusionsA summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality.

Abstract Background Considering the limited accessibility of amyloid position emission tomography (PET) in patients with dementia, we proposed a deep learning (DL)-based amyloid PET positivity classification model from PET images with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (2-[18F]FDG). Methods We used 2-[18F]FDG PET datasets from the Alzheimer's Disease Neuroimaging Initiative and Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer’s disease for model development. Moreover, we used an independent dataset from another hospital. A 2.5-D deep learning architecture was constructed using 291 submodules and three axes images as the input. We conducted the voxel-wise analysis to assess the regions with substantial differences in glucose metabolism between the amyloid PET-positive and PET-negative participants. This facilitated an understanding of the deep model classification. In addition, we compared these regions with the classification probability from the submodules. Results There were 686 out of 1433 (47.9%) and 50 out of 100 (50%) amyloid PET-positive participants in the training and internal validation datasets and the external validation datasets, respectively. With 50 times iterations of model training and validation, the model achieved an AUC of 0.811 (95% confidence interval (CI) of 0.803–0.819) and 0.798 (95% CI, 0.789–0.807) on the internal and external validation datasets, respectively. The area under the curve (AUC) was 0.860 when tested with the model with the highest value (0.864) on the external validation dataset. Moreover, it had 75.0% accuracy, 76.0% sensitivity, 74.0% specificity, and 75.0% F1-score. We found an overlap between the regions within the default mode network, thus generating high classification values. Conclusion The proposed model based on the 2-[18F]FDG PET imaging data and a DL framework might successfully classify amyloid PET positivity in clinical practice, without performing amyloid PET, which have limited accessibility.

Face masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various “smart” mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of individual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables.

“Reserve” refers to the individual clinical differences in response to a neuropathological burden. We explored the behavioral reserve (BR) and associated neural substrates in 40 participants with behavioral variant frontotemporal dementia (bvFTD) who were assessed with the frontal behavioral inventory (FBI) and magnetic resonance imaging. Because neuroimaging abnormality showed a high negative correlation with the FBI negative (but not positive) symptom scores, we developed a linear model only to calculate the nBR (BR for negative symptoms) marker using neuroimaging abnormalities and the FBI score. Participants were divided into high nBR and low nBR groups based on the nBR marker. The FBI negative symptom score was lower in the high nBR group than in the low nBR group having the same neuroimaging abnormalities. However, the high nBR group noted a steeper decline in cortical atrophy and showed less atrophy in the left frontotemporal cortices than the low nBR group. In addition, the fractional anisotropy (FA) values were greater in the high nBR than in the low nBR group, except in the sensory-motor and occipital areas. We identified an nBR-related functional network composed of bilateral frontotemporal areas and the left occipital pole. We propose the concept of BR in bvFTD, and these findings can help predict the disease progression.

Identification of neurotransmitters remains challenging for surface enhanced Raman spectroscopy (SERS) due to presence of noise. Here, the authors present spread spectrum SERS, which by encoding excited light and decoding SERS signals enables detection of unlabelled neurotransmitters at attomolar concentrations.

Abstract Cerebral amyloid angiopathy (CAA), defined as the accumulation of amyloid-beta (Aβ) on the vascular wall, is a major pathology of Alzheimer’s disease (AD) and has been thought to be caused by the failure of Aβ clearance. Although two types of perivascular clearance mechanisms, intramural periarterial drainage (IPAD) and the perivascular cerebrospinal fluid (CSF) influx, have been identified, the exact contribution of CAA on perivascular clearance is still not well understood. In this study, we investigated the effect of CAA on the structure and function of perivascular clearance systems in the APP/PS1 transgenic mouse model. To investigate the pathological changes accompanied by CAA progression, the key elements of perivascular clearance such as the perivascular basement membrane, vascular smooth muscle cells (vSMCs), and vascular pulsation were evaluated in middle-aged (7–9 months) and old-aged (19–21 months) mice using in vivo imaging and immunofluorescence staining. Changes in IPAD and perivascular CSF influx were identified by ex vivo fluorescence imaging after dextran injection into the parenchyma or cisterna magna. Amyloid deposition on the vascular wall disrupted the integrity and morphology of the arterial basement membrane. With CAA progression, vascular pulsation was augmented, and conversely, vSMC coverage was decreased. These pathological changes were more pronounced in the surface arteries with earlier amyloid accumulation than in penetrating arteries. IPAD and perivascular CSF influx were impaired in the middle-aged APP/PS1 mice and further aggravated in old age, showing severely impaired tracer influx and efflux patterns. Reduced clearance was also observed in old wild-type mice without changing the tracer distribution pattern in the influx and efflux pathway. These findings suggest that CAA is not merely a consequence of perivascular clearance impairment, but rather a contributor to this process, causing changes in arterial function and structure and increasing AD severity.

Abstract Background This study aimed to investigate feasible gray matter microstructural biomarkers with high sensitivity for early Alzheimer’s disease (AD) detection. We propose a diffusion tensor imaging (DTI) measure, “radiality”, as an early AD biomarker. It is the dot product of the normal vector of the cortical surface and primary diffusion direction, which reflects the fiber orientation within the cortical column. Methods We analyzed neuroimages from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, including images from 78 cognitively normal (CN), 50 early mild cognitive impairment (EMCI), 34 late mild cognitive impairment (LMCI), and 39 AD patients. We then evaluated the cortical thickness (CTh), mean diffusivity (MD), which are conventional AD magnetic resonance imaging (MRI) biomarkers, and the amount of accumulated amyloid and tau using positron emission tomography (PET). Radiality was projected on the gray matter surface to compare and validate the changes with different stages alongside other neuroimage biomarkers. Results The results revealed decreased radiality primarily in the entorhinal, insula, frontal, and temporal cortex with further progression of disease. In particular, radiality could delineate the difference between the CN and EMCI groups, while the other biomarkers could not. We examined the relationship between radiality and other biomarkers to validate its pathological evidence in AD. Overall, radiality showed a high association with conventional biomarkers. Additional ROI analysis revealed the dynamics of AD-related changes as stages onward. Conclusion Radiality in cortical gray matter showed AD-specific changes and relevance with other conventional AD biomarkers with high sensitivity. Moreover, radiality could identify the group differences seen in EMCI, representative of changes in early AD, which supports its superiority in early diagnosis compared to that possible with conventional biomarkers. We provide evidence of structural changes with cognitive impairment and suggest radiality as a sensitive biomarker for identifying early AD.

The neurovascular unit is a functional unit composed of neurons, glial cells, pericytes, and endothelial cells which sustain brain activity. While pericyte is a key component of the neurovascular unit, its role in cerebral blood flow regulation remains elusive. Recently, capillary stalling, which means the transient interruption of microcirculation in capillaries, has been shown to have an outsized impact on microcirculatory changes in several neurological diseases. In this study, we investigated capillary stalling and its possible causes, such as the cerebral endothelial glycocalyx and leukocyte adhesion molecules after depleting pericytes postnatally in mice. Moreover, we investigated hypoxia and gliosis as consequences of capillary stalling. Although there were no differences in the capillary structure and RBC flow, longitudinal optical coherence tomography angiography showed an increased number of stalled segments in capillaries after pericyte loss. Furthermore, the extent of the cerebral endothelial glycocalyx was decreased with increased expression of leukocyte adhesion molecules, suggesting enhanced interaction between leukocytes and endothelial cells. Finally, pericyte loss induced cerebral hypoxia and gliosis. Cumulatively, the results suggest that pericyte loss induces capillary stalling through increased interaction between leukocytes and endothelial cells in the brain.

The concept of cognitive reserve (CR) has been proposed as a protective factor that modifies the effect of brain pathology on cognitive performance. It has been characterized through CR proxies; however, they have intrinsic limitations. In this study, we utilized two different datasets containing tau, amyloid PET, and T1 magnetic resonance imaging. First, 91 Alzheimer’s disease (AD) continuum subjects were included from Alzheimer’s Disease Neuroimaging Initiative 3. CR was conceptualized as the residual between actual cognition and estimated cognition based on amyloid, tau, and neurodegeneration. The proposed marker was tested by the correlation with CR proxy and modulation of brain pathology effects on cognitive function. Second, longitudinal data of baseline 53 AD spectrum and 34 cognitively unimpaired (CU) participants in the MEMORI dataset were analyzed. CR marker was evaluated for the association with disease conversion rate and clinical progression. Applying our multimodal CR model, this study demonstrates the differential effect of CR on clinical progression according to the disease status and the modulating effect on the relationship between brain pathology and cognition. The proposed marker was associated with years of education and modulated the effect of pathological burden on cognitive performance in the AD spectrum. Longitudinally, higher CR marker was associated with lower disease conversion rate among prodromal AD and CU individuals. Higher CR marker was related to exacerbated cognitive decline in the AD spectrum; however, it was associated with a mitigated decline in CU individuals. These results provide evidence that CR may affect the clinical progression differentially depending on the disease status.

Background: Social isolation (SI) leads to various mental health disorders. Despite abundant studies on behavioral and neurobiological changes induced by post-weaning SI, the characterization of its imaging correlates, such as resting-state functional connectivity (RSFC), is critically lacking. In addition, the effects of resocialization after isolation remain unclear. Therefore, this study aimed to explore the effects of 1) SI on cortical functional connectivity and 2) subsequent resocialization on behavior and functional connectivity. Methods: Behavioral tests were conducted to validate the post-weaning SI mouse model, which is isolated during the juvenile period. Wide-field optical mapping was performed to observe both neuronal and hemodynamic signals in the cortex under anesthesia. Using seed-based and graph theoretical analyses, RSFC was analyzed. SI mice were then resocialized and the array of behavior and imaging tests was conducted. Results: Behaviorally, SI mice showed elevated anxiety, social preference, and aggression. RSFC analyses using the seed-based approach revealed decreased cortical functional connectivity in SI mice, especially in the frontal region. Graph network analyses demonstrated significant reduction in network segregation measures. After resocialization, mice exhibited recovered anxiogenic and aggressive behavior, but RSFC data did not show significant changes. Conclusions: We observed an overall decrease in functional connectivity in SI mice. Moreover, resocialization restored the disruptions in behavioral patterns but functional connectivity was not recovered. To our knowledge, this is the first study to report that, despite the recovering tendencies of behavior in resocialized mice, similar changes in RSFC were not observed. This suggests that disruptions in functional connectivity caused by social isolation remain as long-term sequelae.

Objective Parkinson’s disease (PD) is a neurodegenerative disorder that mainly leads to the impairment of patients’ motor function, as well as of cognition, as it progresses. This study tried to investigate the impact of PD on the resting state functional connectivity of the default mode network (DMN), as well as of the entire brain. Methods Sixty patients with PD were included and compared to 60 matched normal control (NC) subjects. For the local connectivity analysis, the resting state fMRI data were analyzed by seed-based correlation analyses, and then a novel persistent homology analysis was implemented to examine the connectivity from a global perspective. Results The functional connectivity of the DMN was decreased in the PD group compared to the NC, with a stronger difference in the medial prefrontal cortex. Moreover, the results of the persistent homology analysis indicated that the PD group had a more locally connected and less globally connected network compared to the NC. Conclusion Our findings suggest that the DMN is altered in PD, and persistent homology analysis, as a useful measure of the topological characteristics of the networks from a broader perspective, was able to identify changes in the large-scale functional organization of the patients’ brain.

Brain function is often characterized by the connections and interactions between highly interconnected brain regions. Pathological disruptions in these networks often result in brain dysfunction, which manifests as brain disease. Typical analysis investigates disruptions in network connectivity based correlations between large brain regions. To obtain a more detailed description of disruptions in network connectivity, we propose a new method where functional nodes are identified in each region based on their maximum connectivity to another brain region in a given network. Since this method provides a unique approach to identifying functionally relevant nodes in a given network, we can provide a more detailed map of brain connectivity and determine new measures of network connectivity. We applied this method to resting state fMRI of Alzheimer's disease patients to validate our method and found decreased connectivity within the default mode network. In addition, new measure of network connectivity revealed a more detailed description of how the network connections deteriorate with disease progression. This suggests that analysis using key relative network hub regions based on regional correlation can be used to detect detailed changes in resting state network connectivity.

Current therapies for depression consist primarily of pharmacological agents, including antidepressants, and/or psychiatric counseling, such as psychotherapy. However, light therapy has recently begun to be considered as an effective tool for the treatment of the neuropsychiatric behaviors and symptoms of a variety of brain disorders or diseases, including depression. One methodology employed in light therapy involves flickering photic stimulation within a specific frequency range. The present study investigated whether flickering and flashing photic stimulation with light emitting diodes (LEDs) could improve depression-like behaviors in a corticosterone (CORT)-induced mouse model of depression. Additionally, the effects of the flickering and flashing lights on depressive behavior were compared with those of fluoxetine. Rhythmical flickering photic stimulation at alpha frequencies from 9-11 Hz clearly improved performance on behavioral tasks assessing anxiety, locomotor activity, social interaction, and despair. In contrast, fluoxetine treatment did not strongly improve behavioral performance during the same period compared with flickering photic stimulation. The present findings demonstrated that LED-derived flickering photic stimulation more rapidly improved behavioral outcomes in a CORT-induced mouse model of depression compared with fluoxetine. Thus, the present study suggests that rhythmical photic stimulation at alpha frequencies may aid in the improvement of the quality of life of patients with depression.

We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.

BackgroundRecently, non-motor symptoms of Parkinson's disease (PD) have been considered crucial factors in determining a patient's quality of life and have been proposed as the predominant features of the premotor phase. Researchers have investigated the relationship between non-motor symptoms and the motor laterality; however, this relationship remains disputed. This study investigated the neural connectivity correlates of non-motor and motor symptoms of PD with respect to motor laterality.MethodsEight-seven patients with PD were recruited and classified into left-more-affected PD (n = 44) and right-more affected PD (n = 37) based on their MDS-UPDRS (Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale) motor examination scores. The patients underwent MRI scanning, which included resting fMRI. Brain regions were labeled as ipsilateral and contralateral to the more-affected body side. Correlation analysis between the functional connectivity across brain regions and the scores of various symptoms was performed to identify the neural connectivity correlates of each symptom.ResultsThe resting functional connectivity centered on the ipsilateral inferior orbito-frontal area was negatively correlated with the severity of non-motor symptoms, and the connectivity of the contralateral inferior parietal area was positively correlated with the severity of motor symptoms (p < 0.001, |r| > 0.3).ConclusionsThese results suggest that the inferior orbito-frontal area may play a crucial role in non-motor dysfunctions, and that the connectivity information may be utilized as a neuroimaging biomarker for the early diagnosis of PD.

Background: Legumes, such as lentils, field peas, Faba beans and chickpeas, are high in vitamins, fiber, important minerals and protein and can help avoid obesity and cardiovascular illnesses. They also contribute to ecosystem services, such as nitrogen fixation and resilience to environmental stresses. Despite a 60% increase in global pulse production from 2000 to 2021, a demand-supply gap, especially in South Asia, raises concerns about nutritional access. Since illnesses are currently an issue to the food security of faba beans, machine learning is required for efficient disease identification. Methods: This research employs Convolutional Neural Networks (CNNs) for robust Faba bean leaf disease identification. The CNN model is trained with diverse images representing specific diseases. The study focuses on diseases like Chocolate Spot, Faba Bean Gall, Rust and Healthy leaves. Image processing involves resizing, grayscale conversion and labeling. The CNN architecture includes eight convolutional layers, four max-pooling layers and three dropout layers. The model is trained using 80% of the dataset, validated using 20% and tested for accuracy. Result: The CNN model achieves an accuracy of 99.37% during training and 89.69% during validation after 75 epochs. Confusion matrix and classification report illustrate the model’s performance. It shows high precision, recall and F1 scores for each class, indicating balanced performance. Chocolate Spot and Rust exhibit the highest precision and F1 scores. The overall accuracy is 91%, comparable to other studies on Faba bean disease detection. The study presents a CNN-based disease identification system for Faba beans, demonstrating high accuracy and balanced performance across different diseases. The model’s effectiveness is comparable to other advanced techniques. The research highlights the potential of machine learning in optimizing disease management for Faba beans. Future work could explore a broader range of diseases and incorporate hybrid machine learning algorithms for further improvement. [ABSTRACT FROM AUTHOR]

The present study aims to investigate the effects of smartphone use by college students on their perceived academic performance. Using five hypotheses derived from the literature related to smartphone use, the initial model was set up for path analysis to reveal the relationships among variables regarding college students' smartphone use in the academic setting. Moreover, multiple group analyses were additionally conducted to verify whether students exhibited different relationships in the hypothesized model depending on their majors. The results from path analyses showed that all path coefficients were positive and statistically significant, which indicated that all five hypothesized paths were supported. Above all, the results from multiple group analysis showed that one path (from "Behavioural Intention to Use Smartphone to Academic Performance") differed significantly across groups. The findings from the current study provide educational policymakers and educators with information on how smartphone utilization in learning activities influences students' academic performance.

In the contemporary university environment, there is a growing trend towards the use of innovative pedagogical methods aimed at increasing student engagement and learner-centeredness. Despite this shift, traditional lecture formats continue to be used, particularly in large classes or language courses. This is largely due to the perceived efficiency and convenience of the traditional lecture format for both teachers and students. However, the limited interaction and communication inherent in traditional lectures can hinder student satisfaction and participation. To address this, the integration of an instant response system (IRS) into the classroom environment offers a promising solution. These systems, which leverage technology and anonymity, facilitate real-time feedback and active student participation without placing a significant burden on the instructor. This study examines the implementation of IRSs in Hindi language learning courses at Hankuk University of Foreign Studies, Seoul, focusing on their impact on student satisfaction, engagement, and self-efficacy at different levels of academic achievement. The findings reveal nuanced differences in perceptions and outcomes between high, average, and low achievers, highlighting the potential of IRSs to foster engagement and communication in diverse learner cohorts. Contrary to expectations, satisfaction levels did not consistently correlate with academic performance. In fact, middle achievers showed significant benefits. Qualitative findings further elucidate students' experiences and highlight the importance of tailored approaches to maximize the effectiveness of IRSs. Overall, this research highlights the adaptability and effectiveness of IRSs in promoting active learning environments, and offers valuable implications for instructional design and pedagogical practice. [ABSTRACT FROM AUTHOR]

Background/Aims: Papillary adenocarcinoma is classified to differentiated-type gastric cancer and is indicated for endoscopic submucosal dissection. However, due to its rare nature, there are limited studies on it. The purpose of this study was to determine the outcome of endoscopic submucosal dissection in patients with papillary-type early gastric cancer and to find the risk factors of lymph node metastasis. Methods: Patients diagnosed with papillary-type early gastric cancer at eight medical centers, who underwent endoscopic submucosal dissection or surgical treatment, were retrospectively reviewed. The clinical results and long-term outcomes of post-endoscopic submucosal dissection were evaluated, and the risk factors of lymph node metastasis in the surgery group were analyzed. Results: One-hundred and seventy-six patients with papillary-type early gastric cancer were enrolled: 44.9% (n=79) in the surgery group and 55.1% (n=97) in the endoscopic submucosal dissection group. As a result of endoscopic submucosal dissection, the en bloc resection and curative resection rates were 91.8% and 86.6%, respectively. The procedure-related complication rate was 4.1%, and local recurrence occurred in 3.1% of patients. Submucosal invasion (odds ratio, 3.735; 95% confidence interval, 1.026 to 12.177; p=0.047) and lymphovascular invasion (odds ratio, 7.636; 95% confidence interval, 1.730 to 22.857; p=0.004) were the risk factors of lymph node metastasis in papillary-type early gastric cancer patients. Conclusions: The clinical results of endoscopic submucosal dissection in papillary-type early gastric cancer were relatively favorable, and endoscopic submucosal dissection is considered safe if appropriate indications are confirmed by considering the risk of lymph node metastasis. [ABSTRACT FROM AUTHOR]