The eye-computer interaction technology based on electro-oculogram provides the users with a convenient way to control the device, which has great social significance. However, the eye-computer interaction is often disturbed by the involuntary eye movements, resulting in misjudgment, affecting the users’ experience, and even causing danger in severe cases. Therefore, this paper starts from the basic concepts and principles of eye-computer interaction, sorts out the current mainstream classification methods of voluntary/involuntary eye movement, and analyzes the characteristics of each technology. The performance analysis is carried out in combination with specific application scenarios, and the problems to be solved are further summarized, which are expected to provide research references for researchers in related fields.
Medical whole-body positron emission tomography (PET), one of the most successful molecular imaging technologies, has been widely used in the fields of cancer diagnosis, cardiovascular disease diagnosis and cranial nerve study. But, on the other hand, the sensitivity, spatial resolution and signal-noise-ratio of the commercial medical whole-body PET systems still have some shortcomings and a great room for improvement. The sensitivity, spatial resolution and signal-noise-ratio of PET system are largely affected by the performances of the scintillators and the photo detectors. The design of a PET system is usually a trade-off in cost and performance. A better image quality can be achieved by optimizing and balancing the key components which affect the system performance the most without dramatically increases in cost. With the development of the scintillator, photo-detector and high speed electronic system, the performance of medical whole-body PET system would be dramatically improved. In this paper, we report current progresses and discuss future directions of the developments of technologies in medical whole-body PET system.
Objective To evaluate the risk factors for cognitive impairment and their interactions in acute ischemic stroke (IS) patients. Methods IS patients admitted to the Department of Neurology, the People’s Hospital of Mianyang between January 2019 and January 2022 were selected. Patients were divided into a cognitive impairment group and a cognitive normal group. The demographic characteristics and clinical data of the subjects were collected, and the traditional risk factors for cognitive impairment were determined by univariate and multivariate logistic regression analysis. The multifactor dimensionality reduction test was used to detect the possible interactions between risk factors. Results A total of 255 patients were included. Among them, 88 cases (34.5%) in the cognitive impairment group and 167 cases (65.5%) in the cognitive normal group. The results of factor logistic regression analysis showed that after adjusting for covariates, big and medium infarction volume, severe IS, moderate to severe carotid artery stenosis as well as high hypersensitive C-reactive protein (hs-CRP) were associated with post-IS cognitive impairment (P<0.05). The cognitive impairment increased by 22.632 times [odds ratio=22.632, 95% confidence interval (5.980, 85.652), P<0.001] in patients with big and medium infarction volume, severe IS and high hs-CRP. Conclusions The cognitive impairment is common in acute IS. Patients with big and medium infarction volume, non-mild stroke, carotid artery stenosis, high hs-CRP, and non-right sided infarction are prone to cognitive impairment, and there are complex interactions among these risk factors.
Speech expression is an important high-level cognitive behavior of human beings. The realization of this behavior is closely related to human brain activity. Both true speech expression and speech imagination can activate part of the same brain area. Therefore, speech imagery becomes a new paradigm of brain-computer interaction. Brain-computer interface (BCI) based on speech imagery has the advantages of spontaneous generation, no training, and friendliness to subjects, so it has attracted the attention of many scholars. However, this interactive technology is not mature in the design of experimental paradigms and the choice of imagination materials, and there are many issues that need to be discussed urgently. Therefore, in response to these problems, this article first expounds the neural mechanism of speech imagery. Then, by reviewing the previous BCI research of speech imagery, the mainstream methods and core technologies of experimental paradigm, imagination materials, data processing and so on are systematically analyzed. Finally, the key problems and main challenges that restrict the development of this type of BCI are discussed. And the future development and application perspective of the speech imaginary BCI system are prospected.
In order to address the problem of traditional dolphin adjuvant therapy such as high cost and its limitation in time and place, this paper introduces a three-dimensional virtual dolphin adjuvant therapy system based on virtual reality technology. By adopting Oculus wearable three-dimensional display, the system combined natural human-computer interaction based on Leap Motion with high-precision gesture recognition and cognitive training, and achieved immersive three-dimensional interactive game for child rehabilitation training purposes. The experimental data showed that the system can effectively improve the cognitive and social abilities of those children with autism spectrum disorder, providing a useful exploration for the rehabilitation of those children.
ObjectiveTo investigate the correlation between expression of stromal interaction molecule 1 (STIM1) and tumor malignant degree or lymph node metastasis in patients with gastric cancer. MethodsA total of 83 patients with gastric cancer treated in the Affiliated Hospital of Southwest Medical University and Sichuan Mianyang 404 Hospital from October 2018 to April 2021 were collected. The expression of STIM1 protein in the gastric cancer tissues and the corresponding adjacent normal gastric tissues was detected by immunohistochemistry method. Meanwhile the correlation between the expression of STIM1 protein and clinicopathologic features or postoperative lymph node status of the patients with gastric cancer was analyzed. ResultsThe positive rate of STIM1 protein expression in the gastric cancer tissues was 95.2% (79/83), including 62 (74.7%) patients with high expression (STIM1 scoring 5–7) and 21 (25.3%) patients with low expression (STIM1 scoring 2–4), which in the corresponding adjacent normal gastric tissues was 41.0% (34/83), the difference was statistically significant (χ2=58.078, P<0.001). The expression of STIM1 protein was not related to gender, age, and tumor size of the patients with gastric cancer (P>0.05), while the proportions of the patients with high expression of STIM1 protein in the gastric cancer patients with low/undifferentiated tumor, T3+T4 of infiltration depth, TNM stage Ⅲ, and lymph node metastasis were higher than those with high/medium differentiation (χ2=11.052, P=0.001), T1+T2 of infiltration depth (χ2=24.720, P<0.001), TNM stage Ⅰ+Ⅱ (χ2=9.980, P=0.002), and non-lymph node metastasis (χ2=6.097, P=0.014). The expression intensity of STIM1 protein was positively correlated with the number of lymph node metastasis (r=0.552, Z=–3.098, P=0.002) and the rate of lymph node metastasis (r=0.561, Z=–6.387, P<0.001). ConclusionsPositive rate of STIM1 protein expression in gastric cancer tissues is relatively high. STIM1 protein expression in gastric cancer tissue is closely related to tumor malignancy and lymph node metastasis, so it might play an important role in progression of gastric cancer.
Objective To explore the influencing factors of internet game addiction among middle school students. Methods Students from a certain district in Sichuan between September 2022 and March 2023 were included as participants. Basic information such as gender, age, whether the subjects were only children, place of residence, parental education, and subjective economic status were investigated. The nine-item Internet Gaming Disorder Scale-short form was used to investigate whether participants had internet game addiction, and the Berkman-Syme Social Network Index was used to evaluate the participants’ social level. Multiple linear regression analysis was used to conduct multivariate analysis to explore the influencing factors of internet game addiction. Results A total of 594 questionnaires were distributed, and 592 valid questionnaires were ultimately obtained. The detection rate of internet game addiction was 12.0%. Multiple linear regression analysis showed that gender (t=?8.281, P<0.001), age (t=3.211, P=0.001), subjective economic status in the region (t=2.025, P=0.043), and social level (t=?4.239, P<0.001) were the influencing factors of online game addiction. Due to the P value was close to the set test level (0.05), subjective economic status in the region was not considered an influencing factor of internet game addiction. Conclusion Teenagers with male gender, older age, and lower social skills are more likely to develop addiction to internet games.
Atherosclerosis is a complex disease characterized by lipid accumulation in the vascular wall and influenced by multiple genetic and environmental factors. To understand the mechanisms of molecular regulation related to atherosclerosis better, a protein interaction network was constructed in the present study. Genes were collected in nucleotide database and interactions were downloaded from Biomolecular Object Network Database (BOND). The interactional data were imported into the software Cytoscape to construct the interaction network, and then the degree characteristics of the network were analyzed for Hub proteins. Statistical significance pathways and diseases were figured out by inputting Hub proteins to KOBAS2.0. The complete pathway network related to atherosclerosis was constructed. The results identified a series of key genes related to atherosclerosis, which would be the potential promising drug targets for effective prevention.
Electronic skin has shown great application potential in many fields such as healthcare monitoring and human-machine interaction due to their excellent sensing performance, mechanical properties and biocompatibility. This paper starts from the materials selection and structures design of electronic skin, and summarizes their different applications in the field of healthcare equipment, especially current development status of wearable sensors with different functions, as well as the application of electronic skin in virtual reality. The challenges of electronic skin in the field of wearable devices and healthcare, as well as our corresponding strategies, are discussed to provide a reference for further advancing the research of electronic skin.
Protein structure determines function, and structural information is critical for predicting protein thermostability. This study proposes a novel method for protein thermostability prediction by integrating graph embedding features and network topological features. By constructing residue interaction networks (RINs) to characterize protein structures, we calculated network topological features and utilize deep neural networks (DNN) to mine inherent characteristics. Using DeepWalk and Node2vec algorithms, we obtained node embeddings and extracted graph embedding features through a TopN strategy combined with bidirectional long short-term memory (BiLSTM) networks. Additionally, we introduced the Doc2vec algorithm to replace the Word2vec module in graph embedding algorithms, generating graph embedding feature vector encodings. By employing an attention mechanism to fuse graph embedding features with network topological features, we constructed a high-precision prediction model, achieving 87.85% prediction accuracy on a bacterial protein dataset. Furthermore, we analyzed the differences in the contributions of network topological features in the model and the differences among various graph embedding methods, and found that the combination of DeepWalk features with Doc2vec and all topological features was crucial for the identification of thermostable proteins. This study provides a practical and effective new method for protein thermostability prediction, and at the same time offers theoretical guidance for exploring protein diversity, discovering new thermostable proteins, and the intelligent modification of mesophilic proteins.