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Objective Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical dictionaries. We demonstrate the pipeline by curating a Unified Medical Language System (UMLS)-colloquial symptom dictionary from COVID-19-related tweets as proof of concept. Methods COVID-19-related tweets from February 1, 2020, to April 30, 2022 were used. The pipeline includes three modules: a named entity recognition module to detect symptoms in tweets; an entity normalization module to aggregate detected entities; and a mapping module that iteratively maps entities to Unified Medical Language System concepts. A random 500 entity samples were drawn from the final dictionary for accuracy validation. Additionally, we conducted a symptom frequency distribution analysis to compare our dictionary to a pre-defined lexicon from previous research. Results We identified 498 480 unique symptom entity expressions from the tweets. Pre-processing reduces the number to 18 226. The final dictionary contains 38 175 unique expressions of symptoms that can be mapped to 966 UMLS concepts (accuracy = 95%). Symptom distribution analysis found that our dictionary detects more symptoms and is effective at identifying psychiatric disorders like anxiety and depression, often missed by pre-defined lexicons. Conclusions This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data. The final lexicon's high accuracy, validated by medical professionals, underscores the potential of this methodology to reliably interpret, and categorize vast amounts of unstructured social media data into actionable medical insights across diverse linguistic and regional landscapes. [ABSTRACT FROM AUTHOR]

The identification of drug-target interactions (DTI) is a valuable step in the drug discovery and repositioning process. However, traditional laboratory experiments are time-consuming and expensive. Computational methods have streamlined research to determine DTIs. The application of deep learning methods has significantly improved the prediction performance for DTIs. Modern deep learning methods can leverage multiple sources of information, including sequence data that contains biological structural information, and interaction data. While useful, these methods cannot be effectively applied to each type of information individually (e.g., chemical structure and interaction network) and do not take into account the specificity of DTI data such as low- or zero-interaction biological entities. To overcome these limitations, we propose a method called MFA-DTI (Multi-feature Fusion Adopted framework for DTI). MFA-DTI consists of three modules: an interaction graph learning module that processes the interaction network to generate interaction vectors, a chemical structure learning module that extracts features from the chemical structure, and a fusion module that combines these features for the final prediction. To validate the performance of MFA-DTI, we conducted experiments on six public datasets under different settings. The results indicate that the proposed method is highly effective in various settings and outperforms state-of-the-art methods. • A novel DTI prediction model that explores both the graph and structural features of drugs and proteins is proposed. • A graph processing algorithm for biological networks is designed. • A module for generating sequence structure vectors is developed. • The results on 6 public datasets show it outperforms existing methods in all observed metrics. [ABSTRACT FROM AUTHOR]

Tomb murals are different from cave temple murals and temple murals, as they are underground cultural relics, their painting style is unique, solemn, and austere, and the performance image is characterised by simple colours, low contrast, and fewer survivors. During the digital restoration process, it is important to have sufficient reference samples to ensure the accuracy of the restoration. In addition, the style of mural paintings in the tombs varies greatly from other styles of murals and types of word paintings. Therefore, learning the unique artistic style of tomb murals, providing stylistically consistent training samples for digital restoration, and overcoming the problems of dim lighting and complex surface granularity of tomb murals are all necessary for research. This paper proposes a generative adversarial network algorithm that separates and fuses style features to enhance the generative network's ability to acquire image information. The algorithm extracts underlying and surface style feature details of the image to be tested and conducts fusion generation experiments. The generative network's parsing layer modifies the input noise tensor and optimises the corresponding weights to prevent misalignment between drawing lines and fresco cracks. Finally, to optimise the fresco generation effect, we add the corresponding loss function in the discriminator. The tomb murals dataset was established for experiments and tests, and quantitatively and qualitatively analysed with other style migration models, and SSIM, FID, LPIPS and NIQE were used as evaluation indexes. The results were 0.97, 269.579, 0.425 and 3.250, respectively, and the effect of style migration of this paper's method was significantly higher than that of the control group model. [ABSTRACT FROM AUTHOR]

In the process of workpieces production, surface defects are prone to occur, and these defects come in a wide variety and are often intermixed, making defect detection and classification exceptionally challenging. With the development of artificial intelligence and deep learning, to tackle this problem, this paper introduces an enhanced single shot multibox detector algorithm based on MobileNetV2 for the detection of surface defects. The method utilizes MobileNetv2 as the backbone of the feature extraction network to obtain six feature layers with different detection scales from the baseline network, that is, the original 1 × 1 feature prediction layer is deleted and a 75 × 75 feature prediction layer is added, which is closer to the specific features of the defects on the surface of the workpiece. In the additional feature layer, two parallel dilated convolution structures are connected, introducing depth‐separable convolution and dilated convolution, combining skip connections and pixel‐wise addition operations. A special feature fusion structure is proposed to perform feature fusion for small, medium and large target detection layers, which effectively solves the problem of missed and false detection. Moreover, it refines candidate bounding box aspect ratios within the training set through the utilization of the K‐means clustering algorithm, ensuring a better match with real boxes. The experimental results demonstrate the effectiveness of the enhanced model, and the mean average precision value reaches 88.72%. Compared to other state‐of‐the‐art detection methods, it exhibits superior capabilities. [ABSTRACT FROM AUTHOR]