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Since the current remote sensing pre-trained models trained on optical images are not as effective when applied to SAR image tasks, it is crucial to create sensor-specific SAR models with generalized feature representations and to demonstrate with evidence the limitations of optical pre-trained models in downstream SAR tasks. The following aspects are the focus of this study: pre-training, fine-tuning, and explaining. First, we collect the current large-scale open-source SAR scene image classification datasets to pre-train a series of deep neural networks, including convolutional neural networks (CNNs) and vision transformers (ViT). A novel dynamic range adaptive enhancement method and a mini-batch class-balanced loss are proposed to tackle the challenges in SAR scene image classification. Second, the pre-trained models are transferred to various SAR downstream tasks compared with optical ones. Lastly, we propose a novel knowledge point interpretation method to reveal the benefits of the SAR pre-trained model with comprehensive and quantifiable explanations. This study is reproducible using open-source code and datasets, demonstrates generalization through extensive experiments on a variety of tasks, and is interpretable through qualitative and quantitative analyses. The codes and models are open source.

Background Periprosthetic infection is a serious complication after arthroplasty and is characterized by a long duration, recurrence, and a low cure rate. Although fungal infections are infrequent, they are often catastrophic, with an insidious onset, a long duration, atypical clinical symptoms, and imaging features in the early stage. They are easily misdiagnosed, or the diagnosis is missed, resulting in wrong treatment approaches. Case presentation This paper reports a case involving a 62-year-old female patient of Korean ethnicity with a periprosthetic infection after knee arthroplasty who underwent joint debridement. A preoperative metagenomic next-generation sequencing of joint aspirate revealed Staphylococcus epidermidis . However, postsurgical tissue cultures confirmed the fungal infection. The patient received oral voriconazole and intra-articular injection of voriconazole for antifungal treatment. Since bacterial infection could not be ruled out, we also prescribed levofloxacin. No infection recurrence was observed after more than 22 months of follow-up. In the treatment of this patient, successful short-term follow-up was achieved, but long-term efficacy still cannot be determined. Conclusions In addition to the case study, we provide an analysis of the diagnosis and treatment of fungal infection after arthroplasty, especially the efficacy of debridement, antibiotics, and implant retention for a short-term outcome.

Background Bilateral scissor bite is a rare malocclusion. For adult patients with skeletal disharmonies, orthognathic surgery is usually adopted for correction. Non-surgical orthodontic treatment is more difficult and rarely reported. During orthodontic treatment, it is necessary to reposition malpositioned molars. However, in some patients, excessive pneumatization of the maxillary sinus allows the molar roots to extend into the sinus cavity. This significantly increases the difficulty of moving the molars. Case presentation This case reports an adult patient with transverse maxillary hyperplasia, bilateral scissor bite, deep overbite, mandibular retrusion, and deviation. Additionally, due to excessive maxillary sinus pneumatization, the roots of the maxillary molars were located within the maxillary sinus. In this case, mini-implants were used in combination with palatal long-arm traction hooks of the first molars. Concurrently, the maxillary second molars were extracted, and the third molars were used as replacements. Following 45 months of treatment and a 12-month retention period, the patient displayed a normal overbite and overjet, proper molar relationships, improved facial aesthetics, and a healthy temporomandibular joint. Conclusions This case demonstrated that bodily movement of the tooth through the maxillary sinus can be achieved by using suitable force systems, correcting the bilateral scissor bite caused by transverse maxillary hyperplasia and avoiding surgical damage and complications.

When extracting entities, relations, and their associated words from scientific literature, it is imperative to consider the supporting role of feature words on the extraction results. These feature words can provide local semantic information and be combined with the global feature representation of the sentence, improving the accuracy of information extraction. However, existing methods, when fusing local semantic feature words with global features, due to ineffective distinction between the influence of feature words and non-feature words, result in limited enhancement on model performance. To solve this problem, we propose a feature words adversarial scheme (FWAS) with dual pointer method. This method implements a dynamic filtering mechanism for feature words through feature pointers, in order to semantically enhance the encoding of the original text. Simultaneously, an inverse feature pointer is designed to establish a negative weight decay mechanism, weakening interference of non-key vocabulary. During joint training, annotation information for entity relations is introduced to supervise the dual feature selection mechanism. Experimental results on three public scientific information extraction datasets demonstrate that our method consistently outperforms strong baselines, achieving up to 4.9% improvement in F1-score. This method offers a new perspective for information extraction tasks in scientific and technical literature and provides scalable optimization directions for subsequent research.

Objective To explore the effects of Icaritin (ICA) on the regulation of osteocyte exosomal miRNAs and to promote the understanding of the potential molecular mechanisms involved in bone repair by ICA. Methods MLO-Y4 cells were treat with PBS or 10 µM ICA for 24 h and the supernatant was collected. Exosomes were isolated and purified according to standard methods, and identified by transmission electron microscopy, nanoparticle tracking analysis and protein blotting. Exosomal miRNAs were analysed by RNA sequencing. Results Osteocyte exosomes were successfully isolated and characterised. MiRNA sequencing showed that two known exosomal miRNAs (miR-128-3p, miR-30a-5p) were significantly up-regulated and two were significantly down-regulated (miR-5112, miR-1285) after ICA intervention. Conclusion Based on the findings, ICA regulates several miRNAs of osteocytes, which deepen our understanding of the therapeutic effects and mechanisms of ICA on skeletal diseases. The translational potential of this article Osteocytes are the most abundant cell type in bone tissue, of which the impact on bone homeostasis is still not clear. This study explored the impact of icaritin on osteocytes and their derived exosomes. By doing so, we hope to contribute to the understanding the therapeutic potential of ICA and osteocytes in maintaining bone health and treating conditions such as osteoporosis.

Huanglongbing (HLB), also known as citrus greening disease, represents a severe and imminent threat to the global citrus industry. With no complete cure currently available, effective control strategies are crucial. This article presents a transmission model of HLB, both with and without nutrient injection, to explore methods for controlling disease spread. By calculating the basic reproduction number (R0) and analyzing threshold dynamics, we demonstrate that the system remains globally stable when R0<1, but persists when R0>1. Sensitivity analyses reveal factors that significantly impact HLB spread on both global and local scales. We also propose a comprehensive optimal control model using the pontryagin minimum principle and validate its feasibility through numerical simulations. Results show that while removing infected trees and spraying insecticides can significantly reduce disease spread, a combination of measures, including the production of disease-free budwood and nursery trees, nutrient solution injection, removal of infected trees, and insecticide application, provides superior control and meets the desired control targets. These findings offer valuable insights for policymakers in understanding and managing HLB outbreaks.

Background Follicular thyroid carcinoma (FTC) is the second most common cancer of the thyroid gland and has a greater propensity for haematogenous metastasis. However, the preoperative differentiation of FTC from follicular thyroid adenoma (FTA) is not well established. Certain ultrasound characteristics are associated with an increased risk of thyroid malignancy, but mainly for papillary thyroid cancers and not for FTC. Objectives This retrospective study aimed to evaluate the ultrasound characteristics of FTC and the value of ultrasound characteristics in differentiating FTC from FTA. Methods A total of 96 patients with pathologically confirmed FTC or FTA who underwent preoperative thyroid ultrasound were included in this study. The ultrasound and pathological characteristics were evaluated. Results Our data revealed that the incidences of lesions with tubercle-in-nodule, spiculated/microlobulated margins, mixed vascularization, egg-shell calcification, central stellate scarring, extension toward the capsule and chronic lymphocytic thyroiditis were significantly higher in the FTC group (all p  < 0.05). After adjusting for confounding factors, lesions with mixed vascularization (odds ratio [OR]: 2.038, P  = 0.019), central stellate scarring (OR: 87.992, P  = 0.007), extension toward the capsule (OR: 22.587, P  = 0.010), and chronic lymphocytic thyroiditis (OR: 9.195, P  = 0.006) were independently associated with FTC. Furthermore, combined with chronic lymphocytic thyroiditis, mixed vascularization, central stellate scarring, and extension toward the capsule showed high discriminatory accuracy in predicting FTC (AUC: 0.914; sensitivity: 96.5%; specificity: 71.8%; p  < 0.001). Conclusions In combination with chronic lymphocytic thyroiditis, mixed vascularization, central stellate scarring, and extension toward the capsule have greater accuracy in differentiating FTCs from FTAs.

Adversarial examples generated by perturbing raw data with carefully designed, imperceptible noise have emerged as a primary security threat to artificial intelligence systems. In particular, black-box adversarial attack algorithms, which only rely on model input and output to generate adversarial examples, are easy to implement in real scenarios. However, previous research on black-box attacks has primarily focused on multi-class classification models, with relatively few studies on black-box attack algorithms for multi-label classification models. Multi-label classification models exhibit significant differences from multi-class classification models in terms of structure and output. The former can assign multiple labels to a single sample, with these labels often exhibiting correlations, while the latter classifies a sample as the class with the highest confidence. Therefore, existing multi-class attack algorithms cannot directly attack multi-label classification models. In this paper, we study the transplantation methods of multi-class black-box attack algorithms to multi-label classification models and propose the multi-label versions for eight classic black-box attack algorithms, which include three score-based attacks and five decision-based (label-only) attacks, for the first time. Experimental results indicate that the transplanted black-box attack algorithms demonstrate effective attack performance across various attack types, except for extreme attacks. Especially, most transplanted attack algorithms achieve more than 60% success rate on the ML-GCN model and more than 30% on the ML-LIW model under the hiding all attack type. However, the performance of these transplanted attack algorithms shows variation among different attack types due to the correlations between labels.

The aim of this study was to investigate the effect of Zhiqiao Gancao decoction (ZQGCD) on hyperalgesia in lumbar disc herniation (LDH) and its mechanism. The potential mechanism of ZQGCD's therapeutic effect on LDH was investigated through network pharmacology, which involved screening the targets of eight components that were absorbed into the bloodstream. The effects of CCR2 inhibitors and ZQGCD-containing serum on the excitability of the CCL2/CCR2 signaling pathway and dorsal root ganglion neurons (DRGn) were investigated in vitro. The effects of CCR2 inhibitors and ZQGCD on the expression of the CCL2/CCR2 signaling pathway and ASIC3 in the rat intervertebral disc and dorsal root ganglion (DRG), the degree of disc degeneration, the threshold of foot retreat, and the latency of foot retreat in LDH rats were examined in vivo. The binding affinities and interaction modes between CCR2 and the components absorbed into the blood were analyzed using the AutodockVina 1.2.2 software. Network pharmacology revealed that ZQGCD could treat LDH through a mechanism involving the chemokine signaling pathway. It was observed that the CCR2 inhibitor and ZQGCD-containing serum downregulated CCR2 and ASIC3 expression and decreased cell excitability in DRGn. The CCL2/CCR2 signaling pathway was activated in the degenerated intervertebral disc and DRG of LDH rats, increased the expression of ASIC3, and decreased the mechanical allodynia domain and thermal hyperalgesia domain. However, a CCR2 inhibitor or ZQGCD could ameliorate the above changes in LDH rats. The target proteins, CCL2 and CCR2, exhibited a robust affinity for the eight components that were absorbed into the bloodstream. The CCL2/CCR2 pathway was activated in the intervertebral disc and DRG of LDH rats. This was accompanied by upregulation of ASIC3 expression, increased excitability of DRGn, and the occurrence of hyperalgesia. ZQGCD improves hyperalgesia in LDH rats by inhibiting the CCL2/CCR2 pathway and downregulating ASIC3 expression.

Deep neural networks (DNNs) are highly sensitive to small, meticulously crafted perturbations, which have been utilized in adversarial attacks, threatening the reliability of DNNs in practical applications. Current adversarial attack methods rely heavily on expert design, requiring significant researcher effort. In this paper, we introduce LLM-GA, a Large Language Model-based Gradient Attack method, specifically designed for generating adversarial attacks against multi-label classification models. Based on the prompts and algorithm templates provided by attackers, LLM-GA can automatically generate ideas for attack algorithms and produce corresponding code implementations. This significantly improves the efficiency of designing adversarial attack algorithms. To generate more effective attack algorithms, LLM-GA leverages evolutionary algorithms to iteratively refine its ideas. Experimental results demonstrate that LLM-GA outperforms three existing gradient-based adversarial attacks in terms of both attack success rate and perturbation size. The code for this paper is available https://github.com/liuyujiang123/LLM-GA .