Lightweight Detection Methods for Insulator Self-Explosion Defects.

The accurate and efficient detection of defective insulators is an essential prerequisite for ensuring the safety of the power grid in the new generation of intelligent electrical system inspections. Currently, traditional object detection algorithms for detecting defective insulators in images face issues such as excessive parameter size, low accuracy, and slow detection speed. To address the aforementioned issues, this article proposes an insulator defect detection model based on the lightweight Faster R-CNN (Faster Region-based Convolutional Network) model (Faster R-CNN-tiny). First, the Faster R-CNN model's backbone network is turned into a lightweight version of it by substituting EfficientNet for ResNet (Residual Network), greatly decreasing the model parameters while increasing its detection accuracy. The second step is to employ a feature pyramid to build feature maps with various resolutions for feature fusion, which enables the detection of objects at various scales. In addition, replacing ordinary convolutions in the network model with more efficient depth-wise separable convolutions increases detection speed while slightly reducing network detection accuracy. Transfer learning is introduced, and a training method involving freezing and unfreezing the model is employed to enhance the network's ability to detect small target defects. The proposed model is validated using the insulator self-exploding defect dataset. The experimental results show that Faster R-CNN-tiny significantly outperforms the Faster R-CNN (ResNet) model in terms of mean average precision (mAP), frames per second (FPS), and number of parameters.

Text Recognition Model Based on Multi-Scale Fusion CRNN.

Scene text recognition is a crucial area of research in computer vision. However, current mainstream scene text recognition models suffer from incomplete feature extraction due to the small downsampling scale used to extract features and obtain more features. This limitation hampers their ability to extract complete features of each character in the image, resulting in lower accuracy in the text recognition process. To address this issue, a novel text recognition model based on multi-scale fusion and the convolutional recurrent neural network (CRNN) has been proposed in this paper. The proposed model has a convolutional layer, a feature fusion layer, a recurrent layer, and a transcription layer. The convolutional layer uses two scales of feature extraction, which enables it to derive two distinct outputs for the input text image. The feature fusion layer fuses the different scales of features and forms a new feature. The recurrent layer learns contextual features from the input sequence of features. The transcription layer outputs the final result. The proposed model not only expands the recognition field but also learns more image features at different scales; thus, it extracts a more complete set of features and achieving better recognition of text. The results of experiments are then presented to demonstrate that the proposed model outperforms the CRNN model on text datasets, such as Street View Text, IIIT-5K, ICDAR2003, and ICDAR2013 scenes, in terms of text recognition accuracy.

Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning.

DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.

Uveal Effusion Syndrome as the Initial Manifestation of Primary Sjögren's Syndrome and Review of the Literature.

Purpose: The authors report a38-year-old woman with primary Sjögren's syndrome who initially showed recurrent blurred vision caused by uveal effusion syndrome and later developed dry mouth, dry eyes, and arthralgia. During the 5-year-course of disease, the patient's 3-time-onset was all manifested as blurred vision after decreased immunity. Despite the initial absence of sufficient immunological evidence, the final presence of positive serum anti-SS-A, rheumatoid factors, ANA, and inflammatory findings in minor salivary gland biopsy indicated primary Sjögren's syndrome.Methods: Retrospective review of a case note.Conclusions: The manifestation of UES requires further exploration of its real pathogenesis, and the possibility of systemic disease should never be excluded.

Effects of probiotics combined with early enteral nutrition on endothelin-1 and C-reactive protein levels and prognosis in patients with severe traumatic brain injury.

OBJECTIVE: To investigate the effects of probiotics combined with early enteral nutrition on levels of endothelin-1 (ET-1), C-reactive protein (CRP), and inflammatory factors, and on the prognosis of patients with severe traumatic brain injury (TBI). METHODS: We enrolled 76 adults with severe TBI. The patients were divided randomly into two equal groups administered enteral nutrition with and without probiotics, respectively. Demographic and clinical data including age, sex, Glasgow Coma Scale score, Sequential Organ Failure Score, Acute Physiology, Chronic Health Score, hospitalization, mortality, and infections were recorded. RESULTS: Serum levels of inflammatory factors gradually decreased with increasing treatment time in both groups. However, ET-1 at 15 days, and interleukin (IL)-6, IL-10, tumor necrosis factor (TNF)-α, and CRP at 7 and 15 days decreased significantly more in the combined treatment group. Hospitalization duration and pulmonary infection rates were also significantly reduced in the combined compared with the enteral nutrition alone group. GCS scores at 15 days were significantly lower in the combined compared with the enteral nutrition group. CONCLUSION: Probiotics combined with early enteral nutrition could reduce serum levels of ET-1, CRP, and IL-6, IL-10, and TNF-α, and could thus improve the recovery of patients with severe TBI.

Prediction of BAP1 Expression in Uveal Melanoma Using Densely-Connected Deep Classification Networks.

Uveal melanoma is the most common primary intraocular malignancy in adults, with nearly half of all patients eventually developing metastases, which are invariably fatal. Manual assessment of the level of expression of the tumor suppressor BRCA1-associated protein 1 (BAP1) in tumor cell nuclei can identify patients with a high risk of developing metastases, but may suffer from poor reproducibility. In this study, we verified whether artificial intelligence could predict manual assessments of BAP1 expression in 47 enucleated eyes with uveal melanoma, collected from one European and one American referral center. Digitally scanned pathology slides were divided into 8176 patches, each with a size of 256 × 256 pixels. These were in turn divided into a training cohort of 6800 patches and a validation cohort of 1376 patches. A densely-connected classification network based on deep learning was then applied to each patch. This achieved a sensitivity of 97.1%, a specificity of 98.1%, an overall diagnostic accuracy of 97.1%, and an F1-score of 97.8% for the prediction of BAP1 expression in individual high resolution patches, and slightly less with lower resolution. The area under the receiver operating characteristic (ROC) curves of the deep learning model achieved an average of 0.99. On a full tumor level, our network classified all 47 tumors identically with an ophthalmic pathologist. We conclude that this deep learning model provides an accurate and reproducible method for the prediction of BAP1 expression in uveal melanoma.

A case report of Werner's syndrome with bilateral juvenile cataracts.

BACKGROUND: To report a case of Werner's syndrome with bilateral juvenile cataracts. CASE PRESENTATION: Review of the clinical, laboratory, photographic, genetic testing of the patient. A 26-year-old Chinese man presented with impaired vision in both eyes for more than a year. Anterior segment examination of both eyes revealed cataract. According to the ocular symptoms and systemic signs, including low body weight, a short stature, a bird-like face, atrophic and scleroderma-like skin, in addition to the juvenile cataracts, the clinical diagnosis of Werner's syndrome was made. Next-generation sequencing identified a homozygous WRN mutation in this patient. CONCLUSIONS: The ocular and systemic findings in this patient in combination with the homozygous WRN mutation indicated the definitive Werner's syndrome diagnosis.