Enhancing space-time video super-resolution via spatial-temporal feature interaction.

The target of space-time video super-resolution (STVSR) is to increase both the frame rate (also referred to as the temporal resolution) and the spatial resolution of a given video. Recent approaches solve STVSR using end-to-end deep neural networks. A popular solution is to first increase the frame rate of the video; then perform feature refinement among different frame features; and at last, increase the spatial resolutions of these features.

Facial Video-Based Non-Contact Stress Recognition Utilizing Multi-Task Learning With Peak Attention.

Negative emotional states, such as anxiety and depression, pose significant challenges in contemporary society, often stemming from the stress encountered in daily activities. Stress (state or level) recognition is a crucial prerequisite for effective stress management and intervention. Presently, wearable devices have been employed to capture physiological signals and analyze stress states.

Facial Video-Based Remote Physiological Measurement via Self-Supervised Learning.

Facial video-based remote physiological measurement aims to estimate remote photoplethysmography (rPPG) signals from human facial videos and then measure multiple vital signs (e.g., heart rate, respiration frequency) from rPPG signals.

Non-Contact Negative Mood State Detection Using Reliability-Focused Multi-Modal Fusion Model.

Negative mood states include tension, depression, anger, fatigue, and confusion, which represent the weak internal emotions of a human. Negative mood states exert adverse impact on individuals' ability to make rational decisions, which entails the practicable method of negative mood state detection. The most commonly used negative mood state detection methods are based on the psychological scale, which requires additional work and brings inconvenience to the subject in the application scenarios.

Automatic Acetowhite Lesion Segmentation via Specular Reflection Removal and Deep Attention Network.

Automatic acetowhite lesion segmentation in colposcopy images (cervigrams) is essential in assisting gynecologists for the diagnosis of cervical intraepithelial neoplasia grades and cervical cancer. It can also help gynecologists determine the correct lesion areas for further pathological examination. Existing computer-aided diagnosis algorithms show poor segmentation performance because of specular reflections, insufficient training data and the inability to focus on semantically meaningful lesion parts.

Automatic CIN Grades Prediction of Sequential Cervigram Image Using LSTM With Multistate CNN Features.

Cervical cancer ranks as the second most common cancer in women worldwide. In clinical practice, colposcopy is an indispensable part of screening for cervical intraepithelial neoplasia (CIN) grades and cervical cancer but exhibits high misdiagnosis rate. Existing computer-assisted algorithms for analyzing cervigram images have neglected that colposcopy is a sequential and multistate process, which is unsuitable for clinical applications.

[Effect of tooth extraction and antibiotics on carotid artery wall and serum interleukin 6 in chronic periodontitis rats with or without atherosclerosis].

Objective: To investigate the effect of tooth extraction with antibiotics on atherosclerosis, and to examine the expression of serum interleukin 6(IL-6) and the pathological changes of the carotid artery in chronic periodontitis(CP) rats with or without atherosclerosis(As).

Methods: A total of 44 SD rats were randomly divided into four groups, group A(normal control), group B(As), group C(CP), group D(CP+As). After model establishment, group C and group D were randomly divided into group C1/D1 (tooth extraction) and group C2/D2(tooth extraction with antibiotics) according to random number table and received the corresponding oral intervention treatment respectively.

[Effect of oral intervention on matrix metalloproteinase-2, 9 expression in carotid arteries and serum interleukin-6 in rats with chronic periodontitis].

Objective: To establish chronic periodontitis model in SD rats, and to investigate the effect of oral intervention on atherosclerosis.

Methods: Fifty male SD rats were randomly divided into three groups, group A (normal control), group B (atherosclerosis,As) and group C (chronic periodontitis, CP). Group C was further divided into group C1 (natural process), group C2 (simple mechanical treatment), group C3 (systemic antibiotics), group C4-1 (teeth extraction) and group C4-2 (teeth extraction+systemic antibiotics), each group consisted of 7 rats.

[Influence of periodontal therapy on serum interleukin-6 and carotid metalloproteinases in animals with chronic periodontitis associated with atherosclerosis].

Objective: To investigate the effect of periodontal mechanical treatment on serum interleukin-6 (IL-6) and carotid artery matrix metalloproteinase (MMP)-2 and MMP-9 expression in chronic periodontitis (CP) SD rats with atherosclerosis (As).

Methods: Forty-four six-week-old male SD rats were randomly divided into three groups: control group (group A), As group (group B), As+CP group(group C). According to different periodontal interventions, group C was randomly subdivided into four groups: natural process group (C1), the periodontal mechanical treatment group (C2), the periodontal mechanical treatment+ local drugs group (C3), and the periodontal mechanical treatment+local and system drugs group (C4).

[Effects of periodontal mechanical therapy with local and systemic drugs on carotid artery and serum high-sensitivity C-reactive protein in rats with chronic periodontitis associated with atherosclerosis].

Objective: The aim of this study is to investigate the effects of serum high-sensitivity C-reactive protein (hsCRP) and the pathological changes in the carotid artery after periodontal mechanical therapy with local and systemic drugs in SD rats with chronic periodontitis (CP) associated with atherosclerosis (As).

Methods: Thirty-five SD rats were randomly divided into two groups: control group (group A) and CP+As group (group B). Group B was further divided into the natural process group (B1), the periodontal mechanical treatment group (B2), the periodontal mechanical treatment plus local drugs group (B3), and the periodontal mechanical treatment plus local and systemic drugs group (B4).