Cellular Diversity in Human Subgenual Anterior Cingulate and Dorsolateral Prefrontal Cortex by Single-Nucleus RNA-Sequencing.

Regional cellular heterogeneity is a fundamental feature of the human neocortex; however, details of this heterogeneity are still undefined. We used single-nucleus RNA-sequencing to examine cell-specific transcriptional features in the dorsolateral PFC (DLPFC) and the subgenual anterior cingulate cortex (sgACC), regions implicated in major psychiatric disorders. Droplet-based nuclei-capture and library preparation were performed on replicate samples from 8 male donors without history of psychiatric or neurologic disorder. Unsupervised clustering identified major neural cell classes. Subsequent iterative clustering of neurons further revealed 20 excitatory and 22 inhibitory subclasses. Inhibitory cells were consistently more abundant in the sgACC and excitatory neuron subclusters exhibited considerable variability across brain regions. Excitatory cell subclasses also exhibited greater within-class transcriptional differences between the two regions. We used these molecular definitions to determine which cell classes might be enriched in loci carrying a genetic signal in genome-wide association studies or for differentially expressed genes in mental illness. We found that the heritable signals of psychiatric disorders were enriched in neurons and that, while the gene expression changes detected in bulk-RNA-sequencing studies were dominated by glial cells, some alterations could be identified in specific classes of excitatory and inhibitory neurons. Intriguingly, only two excitatory cell classes exhibited concomitant region-specific enrichment for both genome-wide association study loci and transcriptional dysregulation. In sum, by detailing the molecular and cellular diversity of the DLPFC and sgACC, we were able to generate hypotheses on regional and cell-specific dysfunctions that may contribute to the development of mental illness.SIGNIFICANCE STATEMENT Dysfunction of the subgenual anterior cingulate cortex has been implicated in mood disorders, particularly major depressive disorder, and the dorsolateral PFC, a subsection of the PFC involved in executive functioning, has been implicated in schizophrenia. Understanding the cellular composition of these regions is critical to elucidating the neurobiology underlying psychiatric and neurologic disorders. We studied cell type diversity of the subgenual anterior cingulate cortex and dorsolateral PFC of humans with no neuropsychiatric illness using a clustering analysis of single-nuclei RNA-sequencing data. Defining the transcriptomic profile of cellular subpopulations in these cortical regions is a first step to demystifying the cellular and molecular pathways involved in psychiatric disorders.

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study.

BACKGROUND: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. METHODS: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO2/FIO2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine). The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley's additive explanations (SHAP). RESULTS: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756-0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626-0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. CONCLUSION: Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features.

Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time-series data related to power load can be captured for STLF. Recent research shows that deep neural networks (DNNs) are capable of achieving accurate STLP since they are effective in predicting nonlinear and complicated time-series data. To perform STLP, existing DNNs use time-varying dynamics of either past load consumption or past power correlated features such as weather, meteorology or date. However, the existing DNN approaches do not use the time-invariant features of users, such as building spaces, ages, isolation material, number of building floors or building purposes, to enhance STLF. In fact, those time-invariant features are correlated to user load consumption. Integrating time-invariant features enhances STLF. In this paper, a fuzzy clustering-based DNN is proposed by using both time-varying and time-invariant features to perform STLF. The fuzzy clustering first groups users with similar time-invariant behaviours. DNN models are then developed using past time-varying features. Since the time-invariant features have already been learned by the fuzzy clustering, the DNN model does not need to learn the time-invariant features; therefore, a simpler DNN model can be generated. In addition, the DNN model only learns the time-varying features of users in the same cluster; a more effective learning can be performed by the DNN and more accurate predictions can be achieved. The performance of the proposed fuzzy clustering-based DNN is evaluated by performing STLF, where both time-varying features and time-invariant features are included. Experimental results show that the proposed fuzzy clustering-based DNN outperforms the commonly used long short-term memory networks and convolution neural networks.

Altered prefrontal beta oscillatory activity during removal of information from working memory in obsessive-compulsive disorder.

BACKGROUND: Obsessive-compulsive disorder (OCD) is related to working memory impairment. Since patients with OCD have difficulty controlling their obsessive thoughts, removal of irrelevant information might be important in the pathophysiology of OCD. However, little is known about brain activity during the removal of information from working memory in patients with OCD. Our goal was to explore potential deficits in inhibitory function related to working memory processes in patients with OCD. METHODS: Sixteen OCD patients and 20 healthy controls (HCs) were recruited. We compared in prefrontal alpha and beta band activity derived from magnetoencephalography (MEG) between patients with OCD and HCs during multiple phases of information processing associated with working memory, especially in post-trial period of the visuospatial working memory task (the delayed matching-to-sample task), which is presumed to be related to the information removal process of working memory. RESULTS: Prefrontal post-trial beta power change (presumed to occur at high levels during the post-trial period) exhibited significant reductions in patients with OCD compared to HCs. In addition, the post-trial beta power change was negatively correlated with Obsessive-Compulsive Inventory-Revised total scores in patients with OCD. CONCLUSIONS: These findings suggest that impairment in the removal of information from working memory might be a key mechanism underlying the inability of OCD patients to rid themselves of their obsessions.

Dynamic multimodal holograms of conjugated organogels via dithering mask lithography.

Polymeric materials have been used to realize optical systems that, through periodic variations of their structural or optical properties, interact with light-generating holographic signals. Complex holographic systems can also be dynamically controlled through exposure to external stimuli, yet they usually contain only a single type of holographic mode. Here, we report a conjugated organogel that reversibly displays three modes of holograms in a single architecture. Using dithering mask lithography, we realized two-dimensional patterns with varying cross-linking densities on a conjugated polydiacetylene. In protic solvents, the organogel contracts anisotropically to develop optical and structural heterogeneities along the third dimension, displaying holograms in the form of three-dimensional full parallax signals, both in fluorescence and bright-field microscopy imaging. In aprotic solvents, these heterogeneities diminish as organogels expand, recovering the two-dimensional periodicity to display a third hologram mode based on iridescent structural colours. Our study presents a next-generation hologram manufacturing method for multilevel encryption technologies.

5G Security Threat Assessment in Real Networks.

Advances in mobile communication networks from 2G to 5G have brought unprecedented traffic growth, and 5G mobile communication networks are expected to be used in a variety of industries based on innovative technologies, fast not only in terms of extremely low latency but massive access devices. Various types of services, such as enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low latency communication (uRLLC), represent an increase in the number of attacks on users' personal information, confidential information, and privacy information. Therefore, security assessments are essential to verify and cope with these various attacks. In this research, we (1) looked at 5G mobile communication network backgrounds and problems to investigate existing vulnerabilities and (2) assessed the current situation through evaluation of 5G security threats in real-world mobile networks in service.

The Inhibitory Effects of Plant Derivate Polyphenols on the Main Protease of SARS Coronavirus 2 and Their Structure-Activity Relationship.

The main protease (Mpro) is a major protease having an important role in viral replication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the novel coronavirus that caused the pandemic of 2020. Here, active Mpro was obtained as a 34.5 kDa protein by overexpression in E. coli BL21 (DE3). The optimal pH and temperature of Mpro were 7.5 and 37 °C, respectively. Mpro displayed a Km value of 16 µM with Dabcyl-KTSAVLQ↓SGFRKME-Edans. Black garlic extract and 49 polyphenols were studied for their inhibitory effects on purified Mpro. The IC50 values were 137 µg/mL for black garlic extract and 9-197 µM for 15 polyphenols. The mixtures of tannic acid with puerarin, daidzein, and/or myricetin enhanced the inhibitory effects on Mpro. The structure-activity relationship of these polyphenols revealed that the hydroxyl group in C3', C4', C5' in the B-ring, C3 in the C-ring, C7 in A-ring, the double bond between C2 and C3 in the C-ring, and glycosylation at C8 in the A-ring contributed to inhibitory effects of flavonoids on Mpro.

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses.

Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline; BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 ± 7.1/85.5 ± 8.1% and 85.8 ± 8.6/79.5 ± 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.

Brain Areas Responsible for Vigilance: An EEG Source Imaging Study.

Vigilance, sometimes referred to as sustained attention, is an important type of human attention as it is closely associated with cognitive activities required in various daily-life situations. Although many researchers have investigated which brain areas control the maintenance of vigilance, findings have been inconsistent. We hypothesized that this inconsistency might be due to the use of different experimental paradigms in the various studies. We found that most of the previous studies used paradigms that included specific cognitive tasks requiring a high cognitive load, which could complicate identification of brain areas associated only with vigilance. To minimize the influence of cognitive processes other than vigilance on the analysis results, we adopted the d2-test of attention, which is a well-known neuropsychological test of attention that does not require high cognitive load, and searched for brain areas at which EEG source activities were temporally correlated with fluctuation of vigilance over a prolonged period of time. EEG experiments conducted with 31 young adults showed that left prefrontal cortex activity was significantly correlated with vigilance variation in the delta, beta1, beta2, and gamma frequency bands, but not the theta and alpha frequency bands. Our study results suggest that the left prefrontal cortex plays a key role in vigilance modulation, and can therefore be used to monitor individual vigilance changes over time or serve as a potential target of noninvasive brain stimulation.

Source activation of P300 correlates with negative symptom severity in patients with schizophrenia.

It is well known that the P300 amplitude is reduced in schizophrenia patients, which may reflect the pathophysiology and symptom severity of schizophrenia, particularly related to negative symptoms. However, the relationship between the underlying neural generator of the P300 and symptomatic outcomes are not yet fully understood. This study aimed to verify the abnormal P300 of schizophrenia in terms of its source activation to and further examine the relationship between reduced source activation and symptom severity of patients. For this purpose, the P300 was recorded from 34 patients with schizophrenia and matched healthy controls using an auditory oddball paradigm. We found that the P300 amplitude of schizophrenia patients was significantly decreased along the midline electrodes and both bilateral temporal areas compared with healthy controls. In comparing the source activation between the two groups, schizophrenia patients showed decreased source activation predominantly over the left hemisphere, including the cingulate, inferior occipital gyrus, middle occipital gyrus, middle temporal gyrus, posterior cingulate, precuneus, and superior occipital gyrus. Furthermore, we found that the decreased activation of the contrasted areas showed significant negative correlation with PANSS negative symptom scores in the middle temporal gyrus, posterior cingulate, precuneus, and superior occipital gyrus. Our findings suggest that the reduced P300 source activation in schizophrenia might reflect deficits in fronto-temporal-parietal circuit.