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This article discusses a case study of a 78-year-old Chinese woman who developed erythrodermic scabies, a severe and rare form of scabies, after receiving systemic corticosteroid treatment. Erythroderma is a condition characterized by skin inflammation, and its occurrence in patients with scabies is uncommon. The patient was initially misdiagnosed with senile pruritus, eczema, and hypereosinophilic dermatitis, and her condition worsened despite treatment with antihistamines and corticosteroids. The diagnosis of erythrodermic scabies was confirmed through dermoscopy and microscopy, and the patient was successfully treated with ivermectin and sulfur ointment. The article emphasizes the importance of accurately diagnosing scabies to avoid misdiagnosis and inappropriate treatment. [Extracted from the article]

Designing a natural, usable, and efficient gesture‐based virtual locomotion interface in immersive environments has been challenging. Freehand‐based virtual locomotion technique (VLT) has the advantage of eliminating the need to see the handhold devices and has been implemented in many applications. Translation control and direction control are two essential components of freehand‐based VLT; however, the transfer function of turning speed in direction control has not been well explored. In this article, we systematically investigate the effects of turning speed on freehand‐based virtual locomotion for the first time. A transfer function that maps the position of the hand to the turning speed is defined, and three parameters related to the turning speed are investigated. In the experiment, participants are asked to travel in the virtual environment from a first‐person perspective with different parameters. Statistical analysis of the experimental results reveals the relationship between the turning speed and the traveling performance as well as the traveling experience. [ABSTRACT FROM AUTHOR]

Severe visual fatigue symptoms occur while users view three‐dimensional (3D) content. These symptoms adversely affect the user experience and hinder the widespread application of 3D media. Existing studies mainly used electroencephalography (EEG) power (based on frequency domain analysis) to evaluate 3D visual fatigue (3DVF), but their results were often contradictory and confusing. To address this problem, we investigated the relationship between EEG features and visual fatigue from the perspective of both time and time‐frequency domain. Random‐dot stereograms (RDS) with various disparities were employed to elicit depth‐related visual evoked potentials (VEPs). The experiment lasted about 27 min for each subject to induce fatigue, and the EEG was recorded synchronously. VEPs of different fatigue levels were processed to extract both time (such as P120 and N170 components in VEPs) and time‐frequency (like event‐related spectral perturbation [ERSP]) domain characteristics. A significant increase in P120 amplitude was observed in the parietal region, and time‐frequency analysis also revealed a significant ERSP decrease of theta bands in the central‐frontal region with a latency of 200 to 300 ms when subjects suffered fatigue. Our results confirm the hypothesis that activity of theta decreased with 3DVF level, or rather, we found ERSPs of theta bands on FC4 and Cz in the 200‐ to 300‐ms window decreased significantly across four fatigue levels. Furthermore, we first reported the relationship between P120 amplitude and 3DVF, which implied this feature could be considered as a feature of 3DVF evaluation. In summary, our studies could provide a new insight for 3DVF evaluation using EEG. [ABSTRACT FROM AUTHOR]

Three‐dimension (3D) display has become increasingly popular in many fields. However, watching 3D content continuously can lead to visual fatigue that is harmful to users' vision system. Visual fatigue assessment aims at monitoring users' brain states based on the electroencephalogram (EEG) signals to identify different fatigue levels and avoid severe fatigue. Most of existing studies on the modeling of visual fatigue assessment rely on manual features extracted from EEG, which is time‐consuming and needs prior knowledge. Convolutional Neural Networks (CNNs) which have been used in computer vision, speech recognition have attracted increasing interest. There is still a lack of attempts to employ end‐to‐end EEG analysis on visual fatigue assessment. In this paper, we propose a deep learning model DeepFatigueNet to perform automatic feature extraction and classification from raw single‐channel EEG. The DeepFatigueNet is evaluated on our own visual fatigue dataset and compared with the state‐of‐the‐art deep learning methods for EEG‐based tasks. The overall accuracy of DeepFatigueNet reaches 75.9% on the three‐classification task exceeding other models. The experimental results demonstrate the effectiveness of our model and show the potential of deep convolutional neural networks for end‐to‐end visual fatigue assessment. [ABSTRACT FROM AUTHOR]

Abstract: Duration of viewing was considered as one of the most important factors of 3D visual fatigue. However, their relationship was not well studied. We proposed to use brain monitoring techniques to reveal more details about the variety of brain activity while watching three‐dimensional (3D) content. We investigated the spectral patterns in the electroencephalogram related to various duration of viewing and showed the power spectral density varied significantly along with the development of fatigue over occipital lobe, parietal lobe, and right frontal lobe. Moreover, we first described the trend of brain activity during 3D content viewing and revealed the location of significant components related to 3D visual fatigue. We believe that the absolute power describes more information and may be a better indicator of 3D visual fatigue assessment compared with relative power. We also first revealed the cortex location that is related to 3D visual fatigue. Our findings may help to define appropriate electroencephalogram features for online 3D visual fatigue detection while participants viewing 3D content. [ABSTRACT FROM AUTHOR]

3D display has become increasingly popular in many fields. It can provide incredible immersion compared with 2D display. There are many researches referring to the change of user about no matter subjective experience or objective physiology signal while watching 3D content. Similarly, in this paper, we study the variation of user's EEG when the user watched 3D content. However, we only focus on parallax cue and avoid other cues of 3D content by using Random Dot Stereogram(RDS) as visual stimulus. We designed an experiment which asked the subject to watch RDS with nine kinds of parallaxes(0,±0.5,±1.1,±1.3,±1.7). The users watched one RDS for 1 minute and then closed eyes and rest for 1 minute every time. During watching, EEG signal with 30‐channel was recorded. The result suggests that significant variation of the user's EEG data occurred in the Alpha (8–13Hz) and Beta(14–30Hz) frequency band by different parallax's comparison. And we found not only parallax but also electrode position can impact the user's EEG data significantly. Accurately, the EEG data has a significant change in six electrodes position in both frequency bands including CZ, PZ, OZ, O2 in international 10–20 system. It means those electrode positions we detect can be regarded as typical positions where we record EEG signal. It will simplify the process of EEG signal acquisition when we need to study the relationship between parallax cue of 3D display and the user further. [ABSTRACT FROM AUTHOR]

In this paper, we designed an experiment to observe changes for different fatigue level in human brain when watching random dot stereogram (RDS) using the method of EEG. The subjects were asked to watch 3D content for 30 minutes and the disparity of RDS changed every 10 seconds. During this watching task, subjects should rate their extent of fatigue during the experiment on the scale of one to five only when they felt worse or better. 30 channels electroencephalogram (EEG) signals were recorded through the experiment. Finally, the relationship between various fatigue levels and disparities which revealed by EEG signals were investigated. Our results show that the power of waveband, namely alpha increased with the fatigue level increasing from one to four during watching RDS, but decreased in level five. We have employed logistic regression to model fatigue level and achieved classification accuracy of 94.18%. Our research has important implications for the continuous monitoring of user's fatigue in the future. [ABSTRACT FROM AUTHOR]

The NdIII coordination compounds [Nd(4-pytza)3(H2O)2] ·2H2O ( 1) and [Nd(4-pytza)2(H2O)4]Cl ·2H2O ( 2) [H4-pytza = 5-(4-pyridyl)tetrazole-2-acetic acid] were synthesized by reactions of K4-pytza and NdCl3 ·6H2O at different pH values. Single crystal X-ray diffraction analysis reveals that 4-pytza ligands in 1 in a μ1,3-COO syn- syn or μ1,1,3-COO bridging mode coordinate to two central NdIII atoms to display a dinuclear unit, which is connected by one of these 4-pytza ligands acting in end-to-end bridging mode to form a 1D ladder-like chain. Different from 1, each 4-pytza in 2 with a μ1,3-COO syn- anti bridging mode coordinates to two NdIII atoms to display a 1D zigzag chain. Furthermore, the luminescence properties of 1 and 2 were investigated at room temperature in the solid state. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) displays become more and more popular in many fields, because they can provide amazing visual effects. However, visual fatigue as one of the critical factors has seriously impeded the wide range of applications of 3D technology. Electrocardiograph (ECG) and electrooculogram (EOG) have been widely used for monitoring visual fatigue. In this paper, one more objective and effective visual fatigue evaluation model is proposed. Subjective scores (SS), heart rates (HR), blink frequency (BF), Sympathetic activity, Vagal activity, Sympathetic-vagal ratio (a measure of autonomic balance), Average of NN intervals (NNMEAN) and Standard deviation of NN intervals (SDNN) of all subjects were collected to analyze the change of visual fatigue during the continuous viewing 3D/2D movie. The results showed that SS, HR, BF, Sympathetic activity and SDNN all increase with visual fatigue while NN-MEAN and Vagal activity decrease. As shown by the result of subjective scoring, the visual fatigue has an overall trend of increasing when viewing both 2D and 3D videos and it is higher in 3D condition than in 2D. Based on the results above, two models were built to predict visual fatigue from above indicates during continuous viewing 2D and 3D video processes respectively. The performance of the models makes a good prediction of visual fatigue. [ABSTRACT FROM AUTHOR]