Consistent Targets Provide Better Supervision in Semi-supervised Object Detection

In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD). Our core observation is that the oscillating pseudo targets undermine the training of an accurate semi-supervised detector. It not only inject noise into student training but also lead to severe overfitting on the classification task. Therefore, we propose a systematic solution, termed Consistent-Teacher, to reduce the inconsistency. First, adaptive anchor assignment~(ASA) substitutes the static IoU-based strategy, which enables the student network to be resistant to noisy pseudo bounding boxes; Then we calibrate the subtask predictions by designing a 3D feature alignment module~(FAM-3D). It allows each classification feature to adaptively query the optimal feature vector for the regression task at arbitrary scales and locations. Lastly, a Gaussian Mixture Model (GMM) dynamically revises the score threshold of the pseudo-bboxes, which stabilizes the number of ground-truths at an early stage and remedies the unreliable supervision signal during training. Consistent-Teacher provides strong results on a large range of SSOD evaluations. It achieves 40.0 mAP with ResNet-50 backbone given only 10\% of annotated MS-COCO data, which surpasses previous baselines using pseudo labels by around 3 mAP. When trained on fully annotated MS-COCO with additional unlabeled data, the performance further increases to 47.2 mAP. Our code will be open-sourced soon

UDTIRI: An Open-Source Road Pothole Detection Benchmark Suite

It is seen that there is enormous potential to leverage powerful deep learning methods in the emerging field of urban digital twins. It is particularly in the area of intelligent road inspection where there is currently limited research and data available. To facilitate progress in this field, we have developed a well-labeled road pothole dataset named Urban Digital Twins Intelligent Road Inspection (UDTIRI) dataset. We hope this dataset will enable the use of powerful deep learning methods in urban road inspection, providing algorithms with a more comprehensive understanding of the scene and maximizing their potential. Our dataset comprises 1000 images of potholes, captured in various scenarios with different lighting and humidity conditions. Our intention is to employ this dataset for object detection, semantic segmentation, and instance segmentation tasks. Our team has devoted significant effort to conducting a detailed statistical analysis, and benchmarking a selection of representative algorithms from recent years. We also provide a multi-task platform for researchers to fully exploit the performance of various algorithms with the support of UDTIRI dataset.Comment: Database webpage: https://www.udtiri.com/, Kaggle webpage: https://www.kaggle.com/datasets/jiahangli617/udtir

Chitosan-Alginate Sponge: Preparation and Application in Curcumin Delivery for Dermal Wound Healing in Rat

A biodegradable sponge, composed of chitosan (CS) and sodium alginate (SA), was successfully obtained in this work. The sponge was ethereal and pliable. The chemical structure and morphology of the sponges was characterized by FTIR and SEM. The swelling ability, in vitro drug release and degradation behaviors, and an in vivo animal test were employed to confirm the applicability of this sponge as a wound dressing material. As the chitosan content in the sponge decreased, the swelling ability decreased. All types of the sponges exhibited biodegradable properties. The release of curcumin from the sponges could be controlled by the crosslinking degree. Curcumin could be released from the sponges in an extended period for up to 20 days. An in vivo animal test using SD rat showed that sponge had better effect than cotton gauze, and adding curcumin into the sponge enhanced the therapeutic healing effect

Text classification in fair competition law violations using deep learning

IntroductionEnsuring fair competition through manual review is a complex undertaking. This paper introduces the utilization of Long Short-Term Memory (LSTM) neural networks and TextCNN to establish a text classifier for classifying and reviewing normative documents.MethodsThe experimental dataset used consists of policy measure samples provided by the antitrust division of the Guangdong Market Supervision Administration. We conduct a comparative analysis of the performance of LSTM and TextCNN classification models.ResultsIn three classification experiments conducted without an enhanced experimental dataset, the LSTM classifier achieved an accuracy of 95.74%, while the TextCNN classifier achieved an accuracy of 92.7% on the test set. Conversely, in three classification experiments utilizing an enhanced experimental dataset, the LSTM classifier demonstrated an accuracy of 96.36%, and the TextCNN classifier achieved an accuracy of 96.19% on the test set.DiscussionThe experimental results highlight the effectiveness of LSTM and TextCNN in classifying and reviewing normative documents. The superior accuracy achieved with the enhanced experimental dataset underscores the potential of these models in real-world applications, particularly in tasks involving fair competition review

Persistent Hypoglossal Artery as a Potential Risk Factor for Simultaneous Carotid and Vertebrobasilar Infarcts

Persistent hypoglossal artery (PHA), a rare embryological carotid–basilar anastomosis, is usually accompanied by hypoplastic vertebral and posterior communicating arteries, and thereby such vascular anomaly serves as the main feeder supplying the vertebrobasilar territory. Although rarely reported, simultaneous anterior and posterior territory infarcts related to PHA and carotid atherosclerosis can occur. To date, as far as we know, only 4 such cases have been previously reported in the literature. Here, we present the case of a 65-year-old female with a PHA and carotid atherosclerotic plaques, who developed acute multiterritorial infarcts involving the left carotid and vertebrobasilar territories. This case highlights that such a persistent anastomosis should be considered when multiple infarcts involving the anterior and posterior territories are encountered, and should be kept in mind when dealing with carotid atherosclerotic lesion

Construction of nZVI@PES metal-organic frameworks to catalyze peroxymonosulfate for removal of naproxen

Naproxen, as one of the typical drugs and personal care products, has been widely detected in the environment due to its extensive production and use. Heterogeneous advanced oxidation technology based on persulfate activation has been more and more applied in the degradation of toxic and harmful pollutants in recent years. In particular, iron based catalytic materials are widely used in persulfate activation because of their environmental protection, low cost and high reactivity, but there are some defects in the application process such as easy oxidation and agglomeration. In this work, polymeric polyether sulfone (PES) material was introduced as organic framework, and the nZVI@PES metal-organic frameworks (MOFs) was constructed by solvent surface dissolution, phase conversion and surface reduction loading methods. Peroxymonosulfate (PMS) was further activated to degrade naproxen in aqueous environment. The synergic degradation performance of the nZVI@PES MOFs /PMS system on naproxen was evaluated

Efficacy of three lung cancer prediction models in diagnosing benign and malignant pulmonary nodules

Background: The predictive models for malignant lung nodules have been developed, but need further validation and optimization for broader clinical use. This study aimed to compare the diagnostic efficacy of the Mayo model, Peking University People’s Hospital (PKUPH) model, and the lung cancer biomarker panel (LCBP) model in distinguishing between benign and malignant pulmonary nodules, providing valuable clinical research data for the early diagnosis of lung cancer. Methods: Clinical and imaging data of patients diagnosed with pulmonary nodules at Meizhou People’s Hospital from March 2021 through January 2023 were collected. Data from patients with benign pulmonary nodules during the same period, who served as negative referents, were also gathered. The Mayo model, PKUPH model, and LCBP model were used to clinically validate lung cancer prediction rates. The receiver operating characteristic (ROC) curves and statistical significance comparing the areas under the curve (AUCs) for each model were evaluated. Results: A total of 428 patients were included: 160 females and 268 males. The noncancer group included 218 cases (50.93%), and the cancer group included 210 cases (49.07%). The AUC values of the three models were as follows: Mayo model, 0.783; PKUPH model, 0.726; and LCBP model, 0.759. (I) For the Mayo model, at the maximum Youden index, the concordance rate was 74.3%, the sensitivity 85.71%, the specificity 63.30%, the positive predictive value 69.23%, the negative predictive value 82.14%, the positive likelihood ratio 2.335, and the negative likelihood ratio 0.226. (II) For the PKUPH model, at the maximum Youden index, the concordance rate was 70.3%, the sensitivity 84.29%, the specificity 56.88%, the positive predictive value 65.31%, the negative predictive value 78.98%, the positive likelihood ratio 1.955, and the negative likelihood ratio 0.276. (III) For the LCBP model, at the maximum Youden index, the concordance rate was 75.0%, the sensitivity 72.38%, the specificity 77.52%, the positive predictive value 75.62%, the negative predictive value 74.45%, the positive likelihood ratio 3.220, and the negative likelihood ratio 0.356. Conclusions: All three predictive models exhibit clinical applicability, with minimal differences in diagnostic efficacy. The LCBP model outperformed both the Mayo and PKUPH models in diagnostic performance, showing greater diagnostic value for the Chinese population. However, there is still room for optimization in each model

Establishment of a reverse genetics system for an epidemic strain of porcine rotavirus JXAY01 type G5P[23]I12

Porcine rotavirus is one of the most important pathogens causing diarrhea in newborn piglets, and the genome of this virus contains 11 double-stranded RNA segments, which are easy to be recombined among strains to produce new strains with different antigenic properties. The reverse genetics system is an informative tool for studying virus biology. Recently, adaptable plasmid-based reverse genetics systems were developed for the porcine rotavirus OSU strain; however, such systems have not been developed for epidemic porcine rotavirus genotypes in China. In this study, we successfully established a reverse genetic system based on an epidemic strain of porcine rotavirus JXAY01 isolated in recent years, which was characterized by a specific genotype constellation: G5-P[23]-I12-R1-C1-M1-A8-N1-T7-E1-H1. 11 gene segments of porcine rotavirus JXAY01 were cloned into plasmid vectors similar to the SA11 system. JXAY01 genome segment plasmids were co-transfected with 10 complementary SA11 genome plasmids, and 11 monoreassortant strains were successfully rescued. Viral replication analyses of the parental SA11 strain and the monoreassortant strains showed that the structural protein replacement monoreassortants had reduced cell proliferation compared with the parental SA11 and non-structural protein replacement strains. The recombinant rJXAY01 strain could be successfully rescued using 11 pRG-JXAY01 plasmids. Whole genome sequencing showed 12 amino acid differences between the isolate JXAY01 and the recombinant rJXAY01, but there was no significant difference in their in vitro replication ability. This study reports the reverse genetic system, which lays the foundation for further understanding of porcine rotavirus molecular biology and novel vaccine development