Markbeier2069

A large number of experiments and deep analysis on real DEAP datasets and SEED datasets was carried out. The results show that the MA-PCA method has superior or comparable robustness and generalization performance to EEG-based emotion recognition.Software is intangible, invisible, and at the same time pervasive in everyday devices, activities, and services accompanying our life. Therefore, citizens hardly realize its complexity, power, and impact in many aspects of their daily life. In this study, we report on one experiment that aims at letting citizens make sense of software presence and activity in their everyday lives, through sound the invisible complexity of the processes involved in the shutdown of a personal computer. SR10221 datasheet We used sonification to map information embedded in software events into the sound domain. The software events involved in a shutdown have names related to the physical world and its actions write events (information is saved into digital memories), kill events (running processes are terminated), and exit events (running programs are exited). The research study presented in this article has a "double character." It is an artistic realization that develops specific aesthetic choices, and it has also pedagogical purposes informing the causal listener about the complexity of software behavior. Two different sound design strategies have been applied one strategy is influenced by the sonic characteristics of the Glitch music scene, which makes deliberate use of glitch-based sound materials, distortions, aliasing, quantization noise, and all the "failures" of digital technologies; and a second strategy based on the sound samples of a subcontrabass Paetzold recorder, an unusual and special acoustic instrument which unique sound has been investigated in the contemporary art music scene. Analysis of quantitative ratings and qualitative comments of 37 participants revealed that the sound design strategies succeeded in communicating the nature of the computer processes. Participants also showed in general an appreciation of the aesthetics of the peculiar sound models used in this study.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional brain network (FBN) is deemed important for extracting data on brain imaging biomarkers. Unfortunately, most existing studies have reported the utilization of the information from the connection to train the classifier; such an approach ignores the topological information and, in turn, limits its performance. Thus, effective utilization of the FBN provides insights for improving the diagnostic performance.

We propose the combination of the information derived from both FBN and its corresponding graph theory measurements to identify and distinguish ASD from normal controls (NCs). Specifically, a multi-kernel support vector machine (MK-SVM) was used to combine multiple types of information.

The experimental results illustrate that the combination of information from multiple connectome features (i.e., functional connections and graph measurements) can provide a superior identification performance with an area under the receiver operating characteristic curve (ROC) of 0.9191 and an accuracy of 82.60%. Furthermore, the graph theoretical analysis illustrates that the significant nodal graph measurements and consensus connections exists mostly in the salience network (SN), default mode network (DMN), attention network, frontoparietal network, and social network.

This work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning.

This work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning.Parkinson's disease (PD) is one of the most common neurodegenerative disorders, affecting nearly 7-10 million people worldwide. Over the last decade, there has been considerable progress in our understanding of the genetic basis of PD, in the development of stem cell-based and animal models of PD, and in management of some clinical features. However, there remains little ability to change the trajectory of PD and limited knowledge of the underlying etiology of PD. The role of genetics versus environment and the underlying physiology that determines the trajectory of the disease are still debated. Moreover, even though protein aggregates such as Lewy bodies and Lewy neurites may provide diagnostic value, their physiological role remains to be fully elucidated. Finally, limitations to the model systems for probing the genetics, etiology and biology of Parkinson's disease have historically been a challenge. Here, we review highlights of the genetics of PD, advances in understanding molecular pathways and physiology, especially transcriptional factor (TF) regulators, and the development of model systems to probe etiology and potential therapeutic applications.

Crohn's disease (CD) is characterized by repetitive phases of remission and exacerbation, the quality of life of patients with CD is strongly influenced by disease activity, as patients in the active phase experience significantly worse symptoms. To investigate the underlying mechanism of how the course of CD is exacerbated based on the bi-directionality of the brain-gut axis (BGA), we conducted a multi-modality neuroimaging study that combined resting-state functional magnetic resonance imaging (rs-fMRI) with proton magnetic resonance spectroscopy (MRS) to detect abnormalities in the anterior cingulate cortex (ACC).

Clinical scales including Visual Analog Scale (VAS) and Hospital Anxiety and Depression Scale (HADS) were used to evaluate the degree of abdominal pain and mood state of participants. We made a comparison between CD patients in the active phase, the remission phase and healthy controls (HCs), not only employed the innovative wavelet-transform to analyze the amplitude of low frequency fluctuats, concentrations of Glu positively correlated with mWavelet-ALFF in the ACC in all participants (

= 0.367,

= 0.006).

Abnormal spontaneous activity and metabolic levels in the ACC were detected in CD patients in the active phase along with severer abdominal pain and worse mood state, these may contribute to the exacerbation of CD. Therefore, the ACC might be a potential neural alternative for managing the exacerbation of CD.

Abnormal spontaneous activity and metabolic levels in the ACC were detected in CD patients in the active phase along with severer abdominal pain and worse mood state, these may contribute to the exacerbation of CD. Therefore, the ACC might be a potential neural alternative for managing the exacerbation of CD.In this article, a novel method for continuous blood pressure (BP) estimation based on multi-scale feature extraction by the neural network with multi-task learning (MST-net) has been proposed and evaluated. First, we preprocess the target (Electrocardiograph; Photoplethysmography) and label signals (arterial blood pressure), especially using peak-to-peak time limits of signals to eliminate the interference of the false peak. Then, we design a MST-net to extract multi-scale features related to BP, fully excavate and learn the relationship between multi-scale features and BP, and then estimate three BP values simultaneously. Finally, the performance of the developed neural network is verified by using a public multi-parameter intelligent monitoring waveform database. The results show that the mean absolute error ± standard deviation for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) with the proposed method against reference are 4.04 ± 5.81, 2.29 ± 3.55, and 2.46 ± 3.58 mmHg, respectively; the correlation coefficients of SBP, DBP, and MAP are 0.96, 0.92, and 0.94, respectively, which meet the Association for the Advancement of Medical Instrumentation standard and reach A level of the British Hypertension Society standard. This study provides insights into the improvement of accuracy and efficiency of a continuous BP estimation method with a simple structure and without calibration. The proposed algorithm for BP estimation could potentially enable continuous BP monitoring by mobile health devices.

Early-stage glaucoma diagnosis has been a challenging problem in ophthalmology. The current state-of-the-art glaucoma diagnosis techniques do not completely leverage the functional measures' such as electroretinogram's immense potential; instead, focus is on structural measures like optical coherence tomography. The current study aims to take a foundational step toward the development of a novel and reliable predictive framework for early detection of glaucoma using machine-learning-based algorithm capable of leveraging medically relevant information that ERG signals contain.

ERG signals from 60 eyes of DBA/2 mice were grouped for binary classification based on age. The signals were also grouped based on intraocular pressure (IOP) for multiclass classification. Statistical and wavelet-based features were engineered and extracted. Important predictors (ERG tests and features) were determined, and the performance of five machine learning-based methods were evaluated.

Random forest (bagged trees) ensemble ing the proposed machine-learning-based framework leveraging an established ERG data set, we conclude that the novel framework allows for detection of functional deficits of early/various stages of glaucoma in mice.Meningiomas are a common pathology in the central nervous system requiring complete surgical resection. However, in cases of recurrence and post-irradiation, accurate identification of tumor remnants and a dural tail under bright light remains challenging. We aimed to perform real-time intraoperative visualization of the meningioma and dural tail using a delayed-window indocyanine green (ICG) technique with microscopy. Fifteen patients with intracranial meningioma received 0.5 mg/kg ICG a few hours before observation during the surgery. We used near-infrared (NIR) fluorescence to identify the tumor location. NIR fluorescence could visualize meningiomas in 12 out of 15 cases. Near-infrared visualization during the surgery ranged from 1 to 4 h after the administration of ICG. The mean signal-to-background ratio (SBR) of the intracranial meningioma in delayed-window ICG (DWIG) was 3.3 ± 2.6. The ratio of gadolinium-enhanced T1 tumor signal to the brain (T1BR) (2.5 ± 0.9) was significantly correlated with the tumor SBR (p = 0.016). K trans , indicating blood-brain barrier permeability, was significantly correlated with tumor SBR (p less then 0.0001) and T1BR (p = 0.013) on dynamic contrast-enhanced magnetic resonance imaging (MRI). DWIG demonstrated a sensitivity of 94%, specificity of 38%, positive predictive value (PPV) of 76%, and negative predictive value (NPV) of 75% for meningiomas. This is the first pilot study in which DWIG fluorescence-guided surgery was used to visualize meningioma and dural tail intraoperatively with microscopy. DWIG is comparable with second-window ICG in terms of mean SBR. Gadolinium-enhanced T1 tumor signal may predict NIR fluorescence of the intracranial meningioma. Blood-brain barrier permeability as shown by K trans on dynamic contrast-enhanced MRI can contribute to gadolinium enhancement on MRI and to ICG retention and tumor fluorescence by NIR.