In an anti-thrombotic pressure circulatory device, relays and solenoid valves serve as core execution units. Thus the therapeutic efficacy and patient safety of the device will directly depend on their performance. A new type of testing system for relays and solenoid valves used in the anti-thrombotic device has been developed, which can test action response time and fatigue performance of relay and solenoid valve. PC, data acquisition card and test platform are used in this testing system based on human-computer interaction testing modules. The testing objectives are realized by using the virtual instrument technology, the high-speed data acquisition technology and reasonable software design. The two sets of the system made by relay and solenoid valve are tested. The results proved the universality and reliability of the testing system so that these relays and solenoid valves could be accurately used in the anti-thrombotic pressure circulatory equipment. The newly-developed testing system has a bright future in the aspects of promotion and application prospect.
To solve the complex interaction problems of hepatitis disease classification, we proposed a lasso method (least absolute shrinkage and selection operator method) with feature interaction. First, lasso penalized function and hierarchical convex constraint were added to the interactive model which is newly defined. Then the model was solved with the convex optimal method combining Karush-Kuhn-Tucker (KKT) condition with generalized gradient descent. Finally, the sparse solution of the main effect features and interactive features were derived, and the classification model was implemented. The experiments were performed on two liver data sets and proved that features interaction contributed to the classification of liver diseases. The experimental results showed that the feature interaction lasso method was of strong explanatory ability, and its effectiveness and efficiency were superior to those of lasso, of all pair-wise lasso, support vector machine (SVM) method, K nearest neighbor (KNN) method, linear discriminant analysis (LDA) classification method, etc.
Features and interaction between features of liver disease is of great significance for the classification of liver disease. Based on least absolute shrinkage and selection operator (LASSO) and interaction LASSO, the generalized interaction LASSO model is proposed in this paper for liver disease classification and compared with other methods. Firstly, the generalized interaction logistic classification model was constructed and the LASSO penalty constraints were added to the interactive model parameters. Then the model parameters were solved by an efficient alternating directions method of multipliers (ADMM) algorithm. The solutions of model parameters were sparse. Finally, the test samples were fed to the model and the classification results were obtained by the largest statistical probability. The experimental results of liver disorder dataset and India liver dataset obtained by the proposed methods showed that the coefficients of interaction features of the model were not zero, indicating that interaction features were contributive to classification. The accuracy of the generalized interaction LASSO method is better than that of the interaction LASSO method, and it is also better than that of traditional pattern recognition methods. The generalized interaction LASSO method can also be popularized to other disease classification areas.
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.
Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.
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.
In order to promote the openness, transparency and standardization of clinical trials, improve the scientific and reliability of results, and reserve the manpower, material, and financial resources in the process of clinical trials, this study constructed an integrated intelligent management platform for clinical trials, which could carry out various types of clinical trials such as randomized controlled trials, non-randomized controlled trials, cohort studies, case-control studies, and cross-sectional studies simultaneously. The platform covers the whole process of scheme design, recruitment, follow-up, data analysis, and quality control. This paper mainly introduced the practical needs, design concept, basic framework and technical highlights to provide auxiliary tools for promoting the standardization and intelligence of clinical trials with energy saving and optimal efficiency.
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.
Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.