Correlation Analysis Between Cortical Structural Features and Acoustic Features in Patients With Parkinson's Disease.

Purpose: Parkinson disease (PD) is a progressive neurodegenerative disease. The aim of this study is to investigate the association between acoustic and cortical brain features in Parkinson's disease patients.

Methods: We recruited 19 (eight females, 11 males) Parkinson's disease patients and 19 (eight females, 11 males) healthy subjects to participate in the experiment.

Lie Detection Based on Acoustic Analysis.

Purpose: Acoustic lie detection, prized for its covert nature and capability for remote processing, has spurred growing interest in acoustic features that can reliably aid in lie detection. In this study, the aim was to construct an acoustic polygraph based on a variety of phonetic and acoustic features rather than on electrodermal, cardiovascular, and respiratory values.

Methods: Sixty-two participants from the University of Science and Technology of China, aged 18-30 years old, were involved in the mock crime experiment and were randomly assigned to the innocent and guilty groups.

A Study on Voice Measures in Patients with Parkinson's Disease.

Purpose: This research aims to identify acoustic features which can distinguish patients with Parkinson's disease (PD patients) and healthy speakers.

Methods: Thirty PD patients and 30 healthy speakers were recruited in the experiment, and their speech was collected, including three vowels (/i/, /a/, and /u/) and nine consonants (/p/, /pʰ/, /t/, /tʰ/, /k/, /kʰ/, /l/, /m/, and /n/). Acoustic features like fundamental frequency (F0), Jitter, Shimmer, harmonics-to-noise ratio (HNR), first formant (F1), second formant (F2), third formant (F3), first bandwidth (B1), second bandwidth (B2), third bandwidth (B3), voice onset, voice onset time were analyzed in our experiment.

Carotid Ultrasound Combined with Plasma Atherosclerosis Index (AIP) in the Severe Analysis of Coronary Heart Disease Treated with Atorvastatin.

Background: Atherosclerotic coronary heart disease (CHD) stands as a paramount cardiovascular concern and the primary cause of mortality. To underscore the significance of our study, it is crucial to highlight the existing gaps in current diagnostic methods and prognostic assessments of CHD. By addressing these gaps, our research aims to contribute valuable insights and advancements in the understanding and management of this prevalent cardiovascular condition.

Deep learning-based speech analysis for Alzheimer's disease detection: a literature review.

Background: Alzheimer's disease has become one of the most common neurodegenerative diseases worldwide, which seriously affects the health of the elderly. Early detection and intervention are the most effective prevention methods currently. Compared with traditional detection methods such as traditional scale tests, electroencephalograms, and magnetic resonance imaging, speech analysis is more convenient for automatic large-scale Alzheimer's disease detection and has attracted extensive attention from researchers.

A Study on Voice Measures in Patients With Alzheimer's Disease.

Objective: As Alzheimer's disease (AD) might provoke certain nerve disorders, patients with AD can acquire sensorimotor adaptation problems, and thus the acoustic characteristics of the speech they produce may differ from those of healthy subjects. This study aimed to (1) extract acoustic characteristics (relating to articulatory gestures) potentially useful for detecting AD and (2) examine whether these characteristics could help identify AD patients.

Methods: A total of 50 individuals participated in the study, including the AD group (17 cases), the Neurologically Healthy (NH) group (13 cases), the Mild Cognitive Impairment (MCI) group (11 cases), and the Vascular Cognitive Impairment (VCI) group (9 cases).

Fully automated discrimination of Alzheimer's disease using resting-state electroencephalography signals.

Background: The Alzheimer's disease (AD) population increases worldwide, placing a heavy burden on the economy and society. Presently, there is no cure for AD. Developing a convenient method of screening for AD and mild cognitive impairment (MCI) could enable early intervention, thus slowing down the progress of the disease and enabling better overall disease management.

Selecting and Analyzing Speech Features for the Screening of Mild Cognitive Impairment.

The total number of patients with Alzheimer's Disease (AD) has exceeded 10 million in China, while the consultation rate is only 14%. Large-scale early screening of cognitive impairment is necessary, however, the methods of traditional screening are expensive and time-consuming. This study explores a speech-based method for the early screening of cognitive impairment by selecting and analyzing speech features to reduce cost and increase efficiency.

Bidirectional Attention for Text-Dependent Speaker Verification.

Automatic speaker verification provides a flexible and effective way for biometric authentication. Previous deep learning-based methods have demonstrated promising results, whereas a few problems still require better solutions. In prior works examining speaker discriminative neural networks, the speaker representation of the target speaker is regarded as a fixed one when comparing with utterances from different speakers, and the joint information between enrollment and evaluation utterances is ignored.

Detecting Alzheimer's Disease from Continuous Speech Using Language Models.

Background: Recently, many studies have been carried out to detect Alzheimer's disease (AD) from continuous speech by linguistic analysis and modeling. However, few of them utilize language models (LMs) to extract linguistic features and to investigate the lexical-level differences between AD and healthy speech.

Objective: Our goals include obtaining state-of-art performance of automatic AD detection, emphasizing N-gram LMs as powerful tools for distinguishing AD patients' narratives from those of healthy controls, and discovering the differences of lexical usages between AD patients and healthy people.

Automatic detection and classification of marmoset vocalizations using deep and recurrent neural networks.

This paper investigates the methods to detect and classify marmoset vocalizations automatically using a large data set of marmoset vocalizations and deep learning techniques. For vocalization detection, neural networks-based methods, including deep neural network (DNN) and recurrent neural network with long short-term memory units, are designed and compared against a conventional rule-based detection method. For vocalization classification, three different classification algorithms are compared, including a support vector machine (SVM), DNN, and long short-term memory recurrent neural networks (LSTM-RNNs).

Representational similarity analysis reveals task-dependent semantic influence of the visual word form area.

Access to semantic information of visual word forms is a key component of reading comprehension. In this study, we examined the involvement of the visual word form area (VWFA) in this process by investigating whether and how the activity patterns of the VWFA are influenced by semantic information during semantic tasks. We asked participants to perform two semantic tasks - taxonomic or thematic categorization - on visual words while obtaining the blood-oxygen-level-dependent (BOLD) fMRI responses to each word.

Organizational Principles of Abstract Words in the Human Brain.

words constitute nearly half of the human lexicon and are critically associated with human abstract thoughts, yet little is known about how they are represented in the brain. We tested the neural basis of 2 classical cognitive notions of abstract meaning representation: by linguistic contexts and by semantic features. We collected fMRI BOLD responses for 360 abstract words and built theoretical representational models from state-of-the-art corpus-based natural language processing models and behavioral ratings of semantic features.

Human respiratory syncytial virus nonstructural protein NS2 antagonizes the activation of beta interferon transcription by interacting with RIG-I.

A wide variety of RNA viruses have been shown to produce proteins that inhibit interferon (IFN) production and signaling. For human respiratory syncytial virus (RSV), the nonstructural NS1 and NS2 proteins have been shown to block IFN signaling by causing the proteasomal degradation of STAT2. In addition, recombinant RSVs lacking either NS1 or NS2 induce more IFN production than wild-type (wt) RSV in infected cells.

Purification and characterization of recombinant human respiratory syncytial virus nonstructural protein NS1.

We report here the first biochemical and structural characterization of the respiratory syncytial virus (RSV) NS1 protein. We have used a pET-ubiquitin expression system to produce respiratory syncytial virus (RSV) NS1 protein in E. coli that contains a hexahistidine-tag on either the amino- or carboxyl-terminus (His(6)-NS1 and NS1-His(6), respectively).