Application of symptom-based mind mapping combined with PBL teaching method in emergency trauma standardized resident training in MDT model.

Explore the feasibility and effectiveness of accepting mind mapping combined with problem-based learning (PBL) teaching method in the standardized training of emergency surgery residents in the multi-disciplinary team (MDT) model of emergency trauma. Eighty-nine doctors under training who rotated in the Department of Emergency Surgery of the First Affiliated Hospital of Anhui Medical University from January 2021 to January 2022 were selected as the study subjects, and randomly divided into a group receiving mind mapping combined with PBL teaching and a group receiving traditional lecture-based learning teaching. Mini-clinical evaluation exercise (Mini-CEX), direct observation of procedural skills (DOPS), teaching adherence, and satisfaction assessments were completed at the time of discharge from the department. There were no significant differences between the observation and control group trainees in terms of gender, age, education, and entry grades. Both groups of doctors were better able to participate in their respective teaching modes and made significant progress. The participants in the observation group had significantly higher Mini-CEX, DOPS, and teaching satisfaction scores than the control group (P < .05). Under the MDT model of emergency trauma, the combination of mind mapping and PBL teaching can improve the comprehensive clinical ability of the trainees more than participating in the traditional lecture-based learning teaching, which is worth promoting and implementing in the clinical standardized training.

Research on Chinese Speech Emotion Recognition Based on Deep Neural Network and Acoustic Features.

In recent years, the use of Artificial Intelligence for emotion recognition has attracted much attention. The industrial applicability of emotion recognition is quite comprehensive and has good development potential. This research uses voice emotion recognition technology to apply it to Chinese speech emotion recognition. The main purpose of this research is to transform gradually popularized smart home voice assistants or AI system service robots from a touch-sensitive interface to a voice operation. This research proposed a specifically designed Deep Neural Network (DNN) model to develop a Chinese speech emotion recognition system. In this research, 29 acoustic characteristics in acoustic theory are used as the training attributes of the proposed model. This research also proposes a variety of audio adjustment methods to amplify datasets and enhance training accuracy, including waveform adjustment, pitch adjustment, and pre-emphasize. This study achieved an average emotion recognition accuracy of 88.9% in the CASIA Chinese sentiment corpus. The results show that the deep learning model and audio adjustment method proposed in this study can effectively identify the emotions of Chinese short sentences and can be applied to Chinese voice assistants or integrated with other dialogue applications.

Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies.

With the development of the Internet, information on the stock market has gradually become transparent, and stock information is easy to obtain. For investors, investment performance depends on the amount of capital and effective trading strategies. The analysis tool commonly used by investors and securities analysts is technical analysis (TA). Technical analysis is the study of past and current financial market information, and a large amount of statistical data is used to predict price trends and determine trading strategies. Technical indicators (TIs) are a type of technical analysis that summarizes possible future trends of stock prices based on historical statistical data to assist investors in making decisions. The stock price trend is a typical time series data with special characteristics such as trend, seasonality, and periodicity. In recent years, time series deep neural networks (DNNs) have demonstrated their powerful performance in machine translation, speech processing, and natural language processing fields. This research proposes the concept of attention-based BiLSTM (AttBiLSTM) applied to trading strategy design and verified the effectiveness of a variety of TIs, including stochastic oscillator, RSI, BIAS, W%R, and MACD. This research also proposes two trading strategies that suitable for DNN, combining with TIs and verifying their effectiveness. The main contributions of this research are as follows: (1) As our best knowledge, this is the first research to propose the concept of applying TIs to the LSTM-attention time series model for stock price prediction. (2) This study introduces five well-known TIs, which reached a maximum of 68.83% in the accuracy of stock trend prediction. (3) This research introduces the concept of exporting the probability of the deep model to the trading strategy. On the backtest of TPE0050, the experimental results reached the highest return on investment of 42.74%. (4) This research concludes from an empirical point of view that technical analysis combined with time series deep neural network has significant effects in stock price prediction and return on investment.

Ultrasound Stimulation Suppresses LPS-Induced Proinflammatory Responses by Regulating NF-κB and CREB Activation in Microglial Cells.

The purpose of this study was to investigate the effects and underlying mechanisms of low-intensity pulsed ultrasound (LIPUS) against lipopolysaccharide (LPS)-induced neuroinflammation. BV-2 microglia subjected to LPS administration (1 µg/mL) were treated with LIPUS stimulation. The levels of inflammatory mediators and brain-derived neurotrophic factor (BDNF) were quantified using the western blot. The results showed that LIPUS stimulation promoted the associated cAMP response element-binding protein (CREB)/BDNF expression in the LPS-treated microglia. Meanwhile, LIPUS treatment effectively suppressed the LPS-induced production of tumor necrosis factor-α, interleukin-1ß, interleukin-6, inducible nitric oxide synthase, and cyclooxygenase-2 in the microglial cells, in addition to inhibiting the LPS-induced expressions of toll-like receptor 4 and myeloid differentiation factor 88, as well as the LPS-induced activation of c-Jun N-terminal kinase and nuclear factor kappa B. Furthermore, LIPUS significantly decreased the Bax/Bcl-2 ratio in the microglia following LPS treatment. Our data indicated that LIPUS attenuated the proinflammatory responses as well as the decline in BDNF in LPS-treated microglia. This study provides a better understanding of how LIPUS stimulation regulates anti-inflammatory actions in microglia, providing further evidence suggesting that such stimulation may be regarded as a novel strategy for the treatment of neuroinflammation.

A grammar-based semantic similarity algorithm for natural language sentences.

This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to "artificial language", such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure.

Molecular characterization of mungbean (Vigna radiata L.) starch branching enzyme I.

Mungbean (Vigna radiata L. cv. Tainan no. 5) starch branching enzyme I (SBE, EC 2.4.1.18) cDNA, VrsbeI, was cloned, and its expression was characterized. Conserved regions of the family B SBE were used to amplify a full length cDNA of 2208 bp. Phylogeny was analyzed, and the partial 3D structure and functional features were predicted. Catalytic residues were identified in the (α/ß)(8)-fold, and a unique loop from F365 to F376 between ß3/α3 was located. Gene expression of VrsbeI in seeds during growth showed that the transcript appeared from week 1 and increased substantially at week 3-4. It was cloned into the pET30 vector and expressed in E. coli BL21(DE3) pLysS cells as a soluble recombinant protein. The affinity-purified recombinant VrSBEI exhibited a specific activity of 314.6 U/mg as an active enzyme with 114-fold activity enrichment from the crude extract.