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ObjectiveThe purpose of this study is to develop a multimodal model based on artificial intelligence to assist clinical doctors in the early diagnosis of necrotizing enterocolitis in newborns.MethodsThis study is a retrospective study that collected the initial laboratory test results and abdominal x-ray image data of newborns (non-NEC, NEC) admitted to our hospital from January 2022 to January 2024.A multimodal model was developed to differentiate multimodal data, trained on the training dataset, and evaluated on the validation dataset. The interpretability was enhanced by incorporating the Gradient-weighted Class Activation Mapping (GradCAM) analysis to analyze the attention mechanism of the multimodal model, and finally compared and evaluated with clinical doctors on external datasets.ResultsThe dataset constructed in this study included 11,016 laboratory examination data from 408 children and 408 image data. When applied to the validation dataset, the area under the curve was 0.91, and the accuracy was 0.94. The GradCAM analysis shows that the model's attention is focused on the fixed dilatation of the intestinal folds, intestinal wall edema, interintestinal gas, and portal venous gas. External validation demonstrated that the multimodal model had comparable accuracy to pediatric doctors with ten years of clinical experience in identification.ConclusionThe multimodal model we developed can assist doctors in early and accurate diagnosis of NEC, providing a new approach for assisting diagnosis in underdeveloped medical areas.

In this paper, we utilize group event prediction techniques for film and television communication to explore the effects of interactive cinema and scene design on participants’ feelings and the impact of film and television communication. A five-dimensional feature vector is used for quantization, and the dimensions and categories of the feature vectors can be changed to make them more consistent with the characteristics of the research object for different types of network events in the interaction scene. For the analysis and prediction of event attention, the data of the budding period before the outbreak is extracted in the preprocessing stage for the prediction of future outbreaks of the event, as the data for the analysis of attention, and the event attention model is constructed through differential equations to realize the prediction of the impact of attention. The results showed that during the video content exploration, respondents had a higher demand for experiencing the festival ritual process (5.73) and preferred the institution of making five-color glutinous rice (2.53). The interactive video scenes were designed with comforting colors (1.86), beautiful interface aesthetics, and a simple operation flow (3.11). Of the eight secondary indicators found in the documentary dissemination effect, five indicators, including content theme and video duration, significantly impacted the number of interactions with the video. There is a significant difference between interactive behavior and emotional experience, and interactive respondents can feel the emotional experience more.

Stability of compounds in the human plasma is crucial for maintaining sufficient systemic drug exposure and considered an essential factor in the early stages of drug discovery and development. The rapid degradation of compounds in the plasma can result in poor in vivo efficacy. Currently, there are no open-source software programs for predicting human plasma stability. In this study, we developed an attention-based graph neural network, PredPS to predict the plasma stability of compounds in human plasma using in-house and open-source datasets. The PredPS outperformed the two machine learning and two deep learning algorithms that were used for comparison indicating its stability-predicting efficiency. PredPS achieved an area under the receiver operating characteristic curve of 90.1%, accuracy of 83.5%, sensitivity of 82.3%, and specificity of 84.6% when evaluated using 5-fold cross-validation. In the early stages of drug discovery, PredPS could be a helpful method for predicting the human plasma stability of compounds. Saving time and money can be accomplished by adopting an in silico-based plasma stability prediction model at the high-throughput screening stage. The source code for PredPS is available at https://bitbucket.org/krict-ai/predps and the PredPS web server is available at https://predps.netlify.app.

It is difficult to implement modeling for overall construction due to the complex structure and distinct environmental influences for the high-speed elevator so researchers rarely achieve end-to-end control for vibration suppression. Meanwhile, the dynamic parameters of the hoist component (the hoist rope) contain more environmental disturbance characteristics than the traction machine. In this paper, a sensitivity analysis of data channels of hoist rope that can help to clarify the effective control loop based on experimental results is implemented. The authors combined a neural network-based (NN-based) regression model and attention module for predicting hoisting rope vibration to achieve over 95% accuracy, and determine the channel data that contributes most to vibration prediction. [ABSTRACT FROM AUTHOR]

Affective computing plays an important role in simulating human affects through multimedia stimuli. To provide appropriate responses and quantify emotions, it is essential to identify and interpret emotions accurately. Often the audience is influenced by media content and it has an impact on the audience's emotions. The authors aim to develop a machine vision based smart affective system that investigates the correlation between the basic facial expressions and corresponding ratings given by the audience. For the same, a notion of emotion vector is introduced to measure the elicited emotion. The idea of measuring attention is also suggested to find if the con- tent could keep the audience engaged. Our results reveal that such systems are useful in capturing and analyzing individual and collective sentiment.

Objective: The purpose of this study is to develop a multimodal model based on artificial intelligence to assist clinical doctors in the early diagnosis of necrotizing enterocolitis in newborns., Methods: This study is a retrospective study that collected the initial laboratory test results and abdominal x-ray image data of newborns (non-NEC, NEC) admitted to our hospital from January 2022 to January 2024.A multimodal model was developed to differentiate multimodal data, trained on the training dataset, and evaluated on the validation dataset. The interpretability was enhanced by incorporating the Gradient-weighted Class Activation Mapping (GradCAM) analysis to analyze the attention mechanism of the multimodal model, and finally compared and evaluated with clinical doctors on external datasets., Results: The dataset constructed in this study included 11,016 laboratory examination data from 408 children and 408 image data. When applied to the validation dataset, the area under the curve was 0.91, and the accuracy was 0.94. The GradCAM analysis shows that the model's attention is focused on the fixed dilatation of the intestinal folds, intestinal wall edema, interintestinal gas, and portal venous gas. External validation demonstrated that the multimodal model had comparable accuracy to pediatric doctors with ten years of clinical experience in identification., Conclusion: The multimodal model we developed can assist doctors in early and accurate diagnosis of NEC, providing a new approach for assisting diagnosis in underdeveloped medical areas., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Cui, Changrong, Maomin, Hui and Xiuxiang.)

Stability of compounds in the human plasma is crucial for maintaining sufficient systemic drug exposure and considered an essential factor in the early stages of drug discovery and development. The rapid degradation of compounds in the plasma can result in poor in vivo efficacy. Currently, there are no open-source software programs for predicting human plasma stability. In this study, we developed an attention-based graph neural network, PredPS to predict the plasma stability of compounds in human plasma using in-house and open-source datasets. The PredPS outperformed the two machine learning and two deep learning algorithms that were used for comparison indicating its stability-predicting efficiency. PredPS achieved an area under the receiver operating characteristic curve of 90.1%, accuracy of 83.5%, sensitivity of 82.3%, and specificity of 84.6% when evaluated using 5-fold cross-validation. In the early stages of drug discovery, PredPS could be a helpful method for predicting the human plasma stability of compounds. Saving time and money can be accomplished by adopting an in silico-based plasma stability prediction model at the high-throughput screening stage. The source code for PredPS is available at https://bitbucket.org/krict-ai/predps and the PredPS web server is available at https://predps.netlify.app., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Using computer vision technology, aiming at intelligently analyzing the attention of people in the mission, through the acquisition and analysis of face image videos and corresponding EEG signals, a continuous facial image information corresponding to the EEG information sample library is established. Under the premise of using the EEG information to supervise the attention degree of facial images ,a method that uses facial information to judge the attention degree is applied. According to the recognition results of SVM (Support Vector Machine) classifiers trained under multiple distributed samples ,there is a correlation between facial information and its attention degree. Therefore, it is feasible to use facial images to analyze personas attention in task, and it provides objective data for subsequent e-valuation of condition monitoring. [ABSTRACT FROM AUTHOR]

The gargantuan plethora of opinions, facts, and tweets on financial business offers the opportunity to test and analyze the influence of such text sources on future directions of stocks. It also creates though the necessity to distill via statistical technology the informative elements of this prodigious and indeed colossal data source. Using mixed text sources from professional platforms, blog fora, and stock message boards, we distill via different lexica sentiment variables. These are employed for an analysis of stock reactions: volatility, volume, and returns. An increased sentiment, especially for those with negative prospection, will influence volatility as well as volume. This influence is contingent on the lexical projection and different across Global Industry Classification Standard (GICS) sectors. Based on review articles on 100 S&P 500 constituents for the period of October 20, 2009, to October 13, 2014, we project into BL, MPQA, LM lexica and use the distilled sentiment variables to forecast individual stock indicators in a panel context. Exploiting different lexical projections to test different stock reaction indicators we aim at answering the following research questions:Are the lexica consistent in their analytic ability?To which degree is there an asymmetric response given the sentiment scales (positive v.s. negative)?Are the news of high attention firms diffusing faster and result in more timely and efficient stock reaction?Is there a sector specific reaction from the distilled sentiment measures?We find that there is significant incremental information in the distilled news flow and the sentiment effect is characterized as an asymmetric, attention-specific, and sector-specific response of stock reactions. [ABSTRACT FROM PUBLISHER]

Affective computing plays an important role in simulating human affects through multimedia stimuli. To provide appropriate responses and quantify emotions, it is essential to identify and interpret emotions accurately. Often the audience is influenced by media content and it has an impact on the audience's emotions. The authors aim to develop a machine vision based smart affective system that investigates the correlation between the basic facial expressions and corresponding ratings given by the audience. For the same, a notion of emotion vector is introduced to measure the elicited emotion. The idea of measuring attention is also suggested to find if the con- tent could keep the audience engaged. Our results reveal that such systems are useful in capturing and analyzing individual and collective sentiment. [ABSTRACT FROM AUTHOR]

Landmark image classification is a challenging task due to the various circumstances, e.g., illumination, viewpoint, zoom in/out and occlusion under which landmark images are taken. Most existing approaches utilize features extracted from the whole image including both landmark and non-landmark areas. However, non-landmark areas introduce redundant and noisy information. In this paper, we propose a novel approach to improve landmark image classification consisting of three steps. First, an attention-based 3-D reconstruction method is proposed to reconstruct sparse 3-D landmark models. Second, the sparse 3-D models are projected onto iconic images in order to identify images of the hot regions. For a landmark, hot regions are parts of a landmark which attract photographers' attention and are popularly captured in photos. These hot region images are later used to enhance reconstructed sparse 3-D models. Third, the landmark regions are obtained through mapping the enhanced 3-D models to landmark images. A k-dimensional tree (kd-tree) is then constructed for each landmark based on scale invariant feature transform (SIFT) features refid="ref5"/ extracted from the landmark area to classify unlabeled images into pre-defined landmark categories. The proposed method is evaluated using 291 661 images of 51 landmarks. Experiments of comparison indicate that our method outperforms bag-of-words (BoW) based approach refid="ref25"/ 18.5% and method of spatial-pyramid-matching using sparse-coding (ScSPM) refid="ref3"/ 8.4%. [ABSTRACT FROM PUBLISHER]