Antidepressant-induced mania in panic disorder: a single-case study of clinical and functional connectivity characteristics

BackgroundMental health issues, including panic disorder (PD), are prevalent and often co-occur with anxiety and bipolar disorders. While panic disorder is characterized by unexpected panic attacks, and its treatment often involves antidepressants, there is a 20–40% risk of inducing mania (antidepressant-induced mania) during treatment, making it crucial to understand mania risk factors. However, research on clinical and neurological characteristics of patients with anxiety disorders who develop mania is limited.MethodsIn this single case study, we conducted a larger prospective study on panic disorder, comparing baseline data between one patient who developed mania (PD-manic) and others who did not (PD-NM group). We enrolled 27 patients with panic disorder and 30 healthy controls (HCs) and examined alterations in amygdala-based brain connectivity using a seed-based whole-brain approach. We also performed exploratory comparisons with healthy controls using ROI-to-ROI analyses and conducted statistical inferences at a threshold of cluster-level family-wise error-corrected p < 0.05, with the cluster-forming threshold at the voxel level of uncorrected p < 0.001.ResultsThe patient with PD-mania showed lower connectivity in brain regions related to the default mode network (left precuneous cortex, maximum z-value within the cluster = −6.99) and frontoparietal network (right middle frontal gyrus, maximum z-value within the cluster = −7.38; two regions in left supramarginal gyrus, maximum z-value within the cluster = −5.02 and −5.86), and higher in brain regions associated with visual processing network (right lingual gyrus, maximum z-value within the cluster = 7.86; right lateral occipital cortex, maximum z-value within the cluster = 8.09; right medial temporal gyrus, maximum z-value within the cluster = 8.16) in the patient with PD-mania compared to the PD-NM group. One significantly identified cluster, the left medial temporal gyrus (maximum z-value within the cluster = 5.82), presented higher resting-state functional connectivity with the right amygdala. Additionally, ROI-to-ROI analysis revealed that significant clusters between PD-manic and PD-NM groups differed from HCs in the PD-manic group but not in the PD-NM group.ConclusionHere, we demonstrate altered amygdala-DMN and amygdala-FPN connectivity in the PD-manic patient, as reported in bipolar disorder (hypo) manic episodes. Our study suggests that amygdala-based resting-state functional connectivity could serve as a potential biomarker for antidepressant-induced mania in panic disorder patients. Our findings provide an advance in understanding the neurological basis of antidepressant-induced mania, but further research with larger cohorts and more cases is necessary for a broader perspective on this issue

Integrated visual security management for optimal condition explorations in resource-constrained systems

Abstract As more and more mobile video applications contain important and private video data, various video encryption techniques have been proposed to protect them. Many of those applications typically utilize the standard video compression while running on mobile platforms which have limited amount of resources. Therefore, we need to consider not only the protection but also the compression and the energy in the mobile video applications. Accordingly, the selective video encryption which protects partial data within the standard video compression has been challenging thanks to format compliance and low computational complexity. However, various parameters of the video compression and the video encryption result in different amount of visual security, compression efficiency, and energy efficiency. Further, it is difficult to find the one solution to maximize all those performance indices at once since there exist tradeoff relationships among them. Therefore, based on the tradeoff relationships, design space exploration should be required to find the interesting parameter set under the given requirements. For the efficient design space exploration, we propose the BEVS (bitrate and energy-bound visual security) to examine each Joint Video Compression and Encryption scheme in terms of the visual security, the compression efficiency, and the energy efficiency. Utilizing the proposed BEVS in our experimental design space, we achieve up to 36.7% visual security improvement under the empirical budgets

POP-ON: Prediction of Process Using One-Way Language Model Based on NLP Approach

In business process management, the monitoring service is an important element that can prevent various problems in advance from before they occur in companies and industries. Execution log is created in an information system that is aware of the enterprise process, which helps predict the process. The ultimate goal of the proposed method is to predict the process following the running process instance and predict events based on previously completed event log data. Companies can flexibly respond to unwanted deviations in their workflow. When solving the next event prediction problem, we use a fully attention-based transformer, which has performed well in recent natural language processing approaches. After recognizing the name attribute of the event in the natural language and predicting the next event, several necessary elements were applied. It is trained using the proposed deep learning model according to specific pre-processing steps. Experiments using various business process log datasets demonstrate the superior performance of the proposed method. The name of the process prediction model we propose is “POP-ON”

Design and Verification of Process Discovery Based on NLP Approach and Visualization for Manufacturing Industry

When a consultant of a company that provides a smart factory solution consults with a customer, it is difficult to define the outline of the manufacturing process and create all activities within the process by case. It requires a large amount of resources from the company to perform a task. In this study, we propose a process discovery automation system that helps consultants define manufacturing processes. In addition, for process discovery, a fully attention-based transformer model, which has recently shown a strong performance, was applied. To be useful to consultants, we solved the black box characteristics of the deep learning model applied to process discovery and proposed a visualization method that can be used in the monitoring system when explaining the discovery process. In this study, we used the event log of the metal fabrication process to perform the modeling, visualization, and evaluation

Design and Verification of Process Discovery Based on NLP Approach and Visualization for Manufacturing Industry

When a consultant of a company that provides a smart factory solution consults with a customer, it is difficult to define the outline of the manufacturing process and create all activities within the process by case. It requires a large amount of resources from the company to perform a task. In this study, we propose a process discovery automation system that helps consultants define manufacturing processes. In addition, for process discovery, a fully attention-based transformer model, which has recently shown a strong performance, was applied. To be useful to consultants, we solved the black box characteristics of the deep learning model applied to process discovery and proposed a visualization method that can be used in the monitoring system when explaining the discovery process. In this study, we used the event log of the metal fabrication process to perform the modeling, visualization, and evaluation

Structural and mutational analyses of psychrophilic and mesophilic adenylate kinases highlight the role of hydrophobic interactions in protein thermal stability

Protein thermal stability is an important field since thermally stable proteins are desirable in many academic and industrial settings. Information on protein thermal stabilization can be obtained by comparing homologous proteins from organisms living at distinct temperatures. Here, we report structural and mutational analyses of adenylate kinases (AKs) from psychrophilic Bacillus globisporus (AKp) and mesophilic Bacillus subtilis (AKm). Sequence and structural comparison showed suboptimal hydrophobic packing around Thr26 in the CORE domain of AKp, which was replaced with an Ile residue in AKm. Mutations that improved hydrophobicity of the Thr residue increased the thermal stability of the psychrophilic AKp, and the largest stabilization was observed for a Thr-to-Ile substitution. Furthermore, a reverse Ile-to-Thr mutation in the mesophilic AKm significantly decreased thermal stability. We determined the crystal structures of mutant AKs to confirm the impact of the residue substitutions on the overall stability. Taken together, our results provide a structural basis for the stability difference between psychrophilic and mesophilic AK homologues and highlight the role of hydrophobic interactions in protein thermal stability

Pathological phenotypes of astrocytes in Alzheimer’s disease

Abstract Astrocytes are involved in various processes in the central nervous system (CNS). As the most abundant cell type in the CNS, astrocytes play an essential role in neuronal maintenance and support, synaptic activity, neuronal metabolism, and amyloid-beta (Aβ) clearance. Alzheimer’s disease (AD) is a neurodegenerative disorder associated with cognitive and behavioral impairment. The transformation of astrocytes is involved in various neurodegenerative diseases, such as AD. Since astrocytes have functional diversity and morphological and physiological heterogeneity in the CNS, AD-related astrocytes might show various pathological phenotypes during AD. Astrocytes developing pathological phenotypes could contribute to AD progression. In this review, we provide an overview of the pathological phenotypes of astrocytes in the context of AD, highlighting recent findings in human and mouse AD

A Novel Embedding Model Based on a Transition System for Building Industry-Collaborative Digital Twin

Recently, the production environment has been rapidly changing, and accordingly, correct mid term and short term decision-making for production is considered more important. Reliable indicators are required for correct decision-making, and the manufacturing cycle time plays an important role in manufacturing. A method using digital twin technology is being studied to implement accurate prediction, and an approach utilizing process discovery was recently proposed. This paper proposes a digital twin discovery framework using process transition technology. The generated digital twin will unearth its characteristics in the event log. The proposed method was applied to actual manufacturing data, and the experimental results demonstrate that the proposed method is effective at discovering digital twins