Relationship between the morphology of A-1 segment of anterior cerebral artery and anterior communicating artery aneurysms

Background: The anterior communicating artery (ACoA) is one of the most frequent sites for cerebral aneurysm. The peculiar directions of projection of aneurysms offer great challenges to clinical treatment. Objetives: To establish the relationship between morphology of A-1 segment of anterior cerebral artery (ACA) and aneurismal projection. Methods: Randomly selected digital subtraction angiography data of 264 anterior communicating artery aneurysms (ACoAA) cases and 296 cases of other cerebral vascular diseases in the same period were retrospectively analyzed. Results: Among 264 ACoAA patients, the morphology of A-1 segment showed type Ⅰa in 158 sides, type Ⅰb in 11, type Ⅱa in 35, type Ⅱb in 87, type Ⅲ in 171 and absence in 66. The morphology of A-1 segment in 296 patients with other cerebral vascular diseases displayed type Ⅰa in 195 sides, type Ⅰb in 20, type Ⅱa in 47, type Ⅱ b in 74, type Ⅲ in 217 and absence in 39. The non-visualization of A-1 segment in the group of ACoAA occurred more than in the control group (χ2=11.482, p=0.001). The classifications of ACoAAs in 264 patients were confirmed as anterior-superior type in 121 cases, anterior-inferior type in 105, complicated type in 16, posterior-inferior type in 12 and posterior-superior type in 10. The correlation between morphology of A-1 segment of ACA and classifications of ACoAA was significant (p=0.000; C=0.619, p=0.000). The direction of ACoAA was downward when the A-1 segment of ACA was Type Ⅰa or Type Ⅱa, and was upward when it was Type Ⅰb or Type Ⅱb，and was upward or downward or complicated when it was Type Ⅲ. Conclusion: The relationship between morphology of A-1 segment of ACA and classification of ACoAA is clarified in the present study, which is helpful to surgical treatment.Keywords: anterior cerebral artery; morphology of A-1 segment; projection of anterior communicating artery aneurysmAfrican Health sciences Vol 14 No. 1 March 201

Exploiting Rich Event Representation to Improve Event Causality Recognition

Event causality identification is an essential task for information extraction that has attracted growing attention. Early researchers were accustomed to combining the convolutional neural network or recurrent neural network models with external causal knowledge, but these methods ignore the importance of rich semantic representation of the event. The event is more structured, so it has more abundant semantic representation. We argue that the elements of the event, the interaction of the two events, and the context between the two events can enrich the event’s semantic representation and help identify event causality. Therefore, the effective semantic representation of events in event causality recognition deserves further study. To verify the effectiveness of rich event semantic representation for event causality identification, we proposed a model exploiting rich event representation to improve event causality recognition. Our model is based on multi-column convolutional neural networks, which integrate rich event representation, including event tensor representation, event interaction representation, and context-aware event representation. We designed various experimental models and conducted experiments on the Chinese emergency corpus, the most comprehensive annotation of events and event elements, enabling us to study the semantic representation of events from all aspects. The extensive experiments showed that the rich semantic representation of events achieved significant performance improvement over the baseline model on event causality recognition, indicating that the semantic representation of events plays an important role in event causality recognition

Hybrid Neural Network for Automatic Recovery of Elliptical Chinese Quantity Noun Phrases

In Mandarin Chinese, when the noun head appears in the context, a quantity noun phrase can be reduced to a quantity phrase with the noun head omitted. This phrase structure is called elliptical quantity noun phrase. The automatic recovery of elliptical quantity noun phrase is crucial in syntactic parsing, semantic representation and other downstream tasks. In this paper, we propose a hybrid neural network model to identify the semantic category for elliptical quantity noun phrases and realize the recovery of omitted semantics by supplementing concept categories. Firstly, we use BERT to generate character-level vectors. Secondly, Bi-LSTM is applied to capture the context information of each character and compress the input into the context memory history. Then CNN is utilized to capture the local semantics of n-gramswith various granularities. Based on the ChineseAbstractMeaning Representation (CAMR) corpus and Xinhua News Agency corpus, we construct a hand-labeled elliptical quantity noun phrase dataset and carry out the semantic recovery of elliptical quantity noun phrase on this dataset. The experimental results show that our hybrid neural network model can effectively improve the performance of the semantic complement for the elliptical quantity noun phrases

Severe Pneumonia Caused by Coinfection With Influenza Virus Followed by Methicillin-Resistant Staphylococcus aureus Induces Higher Mortality in Mice

Background: Coinfection with influenza virus and bacteria is a major cause of high mortality during flu pandemics. Understanding the mechanisms behind such coinfections is of utmost importance both for the clinical treatment of influenza and the prevention and control of epidemics.Methods: To investigate the cause of high mortality during flu pandemics, we performed coinfection experiments with H1N1 influenza virus and Staphylococcus aureus in which mice were infected with bacteria at time points ranging from 0 to 7 days after infection with influenza virus.Results: The mortality rates of mice infected with bacteria were highest 0–3 days after infection with influenza virus; lung tissues extracted from these co-infected mice showed higher infiltrating cells and thicker lung parenchyma than lung samples from coinfected mice in which influenza virus was introduced at other times and sequences. The levels of interferon (IFN)-γ, tumor necrosis factor (TNF)-α, interleukin (IL)-8, and IL-6 in the 0–3 day coinfected group were significantly higher than those in the other groups (p < 0.01), as were the mRNA levels of IFN-γ, IL-6, and TNF-α. Coinfection with influenza virus and S. aureus led to high mortality rates that are directly dependent on the sequence and timing of infection by both pathogens. Moreover, coinfection following this particular schedule induced severe pneumonia, leading to increased mortality.Conclusions: Our data suggest that prevention of bacterial co-infection in the early stage of influenza virus infection is critical to reducing the risk of clinical mortality

Development of lower limb rehabilitation evaluation system based on virtual reality technology

Nowadays, with the development of the proportion of the elderly population in the world, several problems caused by the population aging gradually into people's horizons. One of the biggest problems plagued the vast majority of the elderly is hemiplegia, which leads to the vigorous development of the physical therapists. However, these traditional methods of physical therapy mainly rely on the skill of the physical therapists. In order to make up the defects of traditional methods, many research groups have developed different kinds of robots for lower limb rehabilitation training but most of them can only realize passive training which cannot adopt rehabilitation training based on the patients' individual condition effectively and they do not have a rehabilitation evaluation system to assess the real time training condition of the hemiplegic patients effectively. In order to solve the problems above, this paper proposed a lower limb rehabilitation evaluation system which is based on the virtual reality technology. This system has an easy observation of the human-computer interaction interface and the doctor is able to adjust the rehabilitation training direct at different patients in different rehabilitation stage based on this lower limb rehabilitation evaluation system. Compared with current techniques, this novel lower limb rehabilitation evaluation system is expected to have significant impacts in medical rehabilitation robot field