Notes

Ischemic heart problems as well as cardioprotection: Concentrate on estrogenic hormone environment and also microvascular well being.
Considering that https://www.selleckchem.com/products/cp-91149.html of Weibo may contain a lot of irrelevant information and sometimes even lack some important components of topic, three retouching mechanisms are proposed to improve the conciseness, richness and readability of topic sentence extraction results. We validate our approach on natural disaster and social hot event datasets from Sina Weibo. The experimental results show that the polished topic summary not only reflects the exact relationship between topic sentences and natural disasters or social hot events, but also has rich semantic information. More importantly, we can almost grasp the basic elements of natural disaster or social hot event from the topic sentence, so as to help the government guide disaster relief or meet the needs of users for quickly obtaining information of social hot events.On the basis of logistic models of forest restoration, we consider the influence of population pressure on forest restoration and establish a reaction diffusion model with Holling II functional responses. We study this reaction diffusion model under Dirichlet boundary conditions and obtain a positive equilibrium. In the square region, we analyze the existence of Turing instability and Hopf bifurcation near this point. The square patterns and mixed patterns are obtained when steady-state bifurcation occurs, the hyperhexagonal patterns appears in Hopf bifurcation.Biomedical named entity recognition (Bio-NER) is the prerequisite for mining knowledge from biomedical texts. The state-of-the-art models for Bio-NER are mostly based on bidirectional long short-term memory (BiLSTM) and bidirectional encoder representations from transformers (BERT) models. However, both BiLSTM and BERT models are extremely computationally intensive. To this end, this paper proposes a temporal convolutional network (TCN) with a conditional random field (TCN-CRF) layer for Bio-NER. The model uses TCN to extract features, which are then decoded by the CRF to obtain the final result. We improve the original TCN model by fusing the features extracted by convolution kernel with different sizes to enhance the performance of Bio-NER. We compared our model with five deep learning models on the GENIA and CoNLL-2003 datasets. The experimental results show that our model can achieve comparative performance with much less training time. The implemented code has been made available to the research community.Based on ecological significance, a delayed diffusive predator-prey system with food-limited and nonlinear harvesting subject to the Neumann boundary conditions is investigated in this paper. Firstly, the sufficient conditions of the stability of nonnegative constant steady state solutions of system are derived. The existence of Hopf bifurcation is obtained by analyzing the associated characteristic equation and the conditions of Turing instability are derived when the system has no delay. Furthermore, the occurrence conditions the Hopf bifurcation are discussed by regarding delay expressing the gestation time of the predator as the bifurcation parameter. Secondly, by using upper-lower solution method, the global asymptotical stability of a unique positive constant steady state solution of system is investigated. Moreover, we also give the detailed formulas to determine the direction, stability of Hopf bifurcation by applying the normal form theory and center manifold reduction. Finally, numerical simulations are carried out to demonstrate our theoretical results.The coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in the end of 2019, and soon became a serious public health threat globally. Due to the unobservability, the time interval between transmission generations (TG), though important for understanding the disease transmission patterns, of COVID-19 cannot be directly summarized from surveillance data. In this study, we develop a likelihood framework to estimate the TG and the pre-symptomatic transmission period from the serial interval observations from the individual transmission events. As the results, we estimate the mean of TG at 4.0 days (95%CI 3.3-4.6), and the mean of pre-symptomatic transmission period at 2.2 days (95%CI 1.3-4.7). We approximate the mean latent period of 3.3 days, and 32.2% (95%CI 10.3-73.7) of the secondary infections may be due to pre-symptomatic transmission. The timely and effectively isolation of symptomatic COVID-19 cases is crucial for mitigating the epidemics.The combination of medical field and big data has led to an explosive growth in the volume of electronic medical records (EMRs), in which the information contained has guiding significance for diagnosis. And how to extract these information from EMRs has become a hot research topic. In this paper, we propose an ELMo-ET-CRF model based approach to extract medical named entity from Chinese electronic medical records (CEMRs). Firstly, a domain-specific ELMo model is fine-tuned on a common ELMo model with 4679 raw CEMRs. Then we use the encoder from Transformer (ET) as our model's encoder to alleviate the long context dependency problem, and the CRF is utilized as the decoder. At last, we compare the BiLSTM-CRF and ET-CRF model with word2vec and ELMo embeddings to CEMRs respectively to validate the effectiveness of ELMo-ET-CRF model. #link# With the same training data and test data, the ELMo-ET-CRF outperforms all the other mentioned model architectures in this paper with 85.59% F1-score, which indicates the effectiveness of the proposed model architecture, and the performance is also competitive on the CCKS2019 leaderboard.Anomaly detection has been widely researched in financial, biomedical and other areas. However, most existing algorithms have high time complexity. Another important problem is how to efficiently detect anomalies while protecting data privacy. In this paper, we propose a fast anomaly detection algorithm based on local density estimation (LDEM). The key insight of LDEM is a fast local density estimator, which estimates the local density of instances by the average density of all features. The local density of each feature can be estimated by the defined mapping function. Furthermore, we propose an efficient scheme named PPLDEM based on the proposed scheme and homomorphic encryption to detect anomaly instances in the case of multi-party participation. Compared with existing schemes with privacy preserving, our scheme needs less communication cost and less calculation cost. From security analysis, our scheme will not leak privacy information of participants. And experiments results show that our proposed scheme PPLDEM can detect anomaly instances effectively and efficiently, for example, the recognition of activities in clinical environments for healthy older people aged 66 to 86 years old using the wearable sensors.
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