Recent progress of electrospun nanofibers as burning dressings.

Burns are a global public health problem, which brings great challenges to public health and the economy. Severe burns often lead to systemic infection, shock, multiple organ failure, and even death. With the increasing demand for the therapeutic effect of burn wounds, traditional dressings have been unable to meet people's needs due to their single function and many side effects. In this context, electrospinning shows a great prospect on the way to open up advanced wound dressings that promote wound repairing and prevent infection. With its large specific surface area, high porosity, and similar to natural extracellular matrix (ECM), electrospun nanofibers can load drugs and accelerate wound healing. It provides a promising solution for the treatment and management of burn wounds. This review article introduces the concept of burn and the types of electrospun nanofibers, then summarizes the polymers used in electrospun nanofiber dressings. Finally, the drugs (plant extracts, small molecule drugs and nanoparticles) loaded with electrospun burn dressings are summarized. Some promising aspects for developing commercial electrospun burn dressings are proposed.

Molecular Dynamics Simulation on Solidification Microstructure and Tensile Properties of Cu/SiC Composites.

The shape of ceramic particles is one of the factors affecting the properties of metal matrix composites. Exploring the mechanism of ceramic particles affecting the cooling mechanical behavior and microstructure of composites provides a simulation basis for the design of high-performance composites. In this study, molecular dynamics methods are used for investigating the microstructure evolution mechanism in Cu/SiC composites containing SiC particles of different shapes during the rapid solidification process and evaluating the mechanical properties after cooling. The results show that the spherical SiC composites demonstrate the highest degree of local ordering after cooling. The more ordered the formation is of face-centered-cubic and hexagonal-close-packed structures, the better the crystallization is of the final composite and the less the number of stacking faults. Finally, the results of uniaxial tensile in three different directions after solidification showed that the composite containing spherical SiC particles demonstrated the best mechanical properties. The findings of this study provide a reference for understanding the preparation of Cu/SiC composites with different shapes of SiC particles as well as their microstructure and mechanical properties and provide a new idea for the experimental and theoretical research of Cu/SiC metal matrix composites.

Task Offloading Strategy for Unmanned Aerial Vehicle Power Inspection Based on Deep Reinforcement Learning.

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.

Ultrasound-guided percutaneous transhepatic one-step biliary fistulation combined with rigid choledochoscopy for recurrent hepatolithiasis.

PURPOSE: Percutaneous transhepatic one-step biliary fistulation (PTOBF) is used to treat choledocholithiasis and biliary stricture. This study aimed to evaluate the safety and efficacy of ultrasound-guided PTOBF combined with rigid choledochoscopy in the treatment of recurrent hepatolithiasis. MATERIALS AND METHODS: The clinical data of 37 consecutive patients who underwent PTOBF combined with rigid choledochoscopy for RHL from March 2020 to March 2022 at our hospital were retrospectively analyzed. RESULTS: A total of 68 percutaneous transhepatic punctures were performed in 37 patients, with a puncture success rate of 85.29% (58/68) and a dilatation success rate of 100.00% (58/58). The mean blood loss of operation was 9.84 ± 18.10 mL, the mean operation time was 82.05 ± 31.92 min, and the mean length of postoperative hospital stay was 5.59 ± 3.26 days. The initial stone clearance rate was 40.54% (15/37) and the final stone clearance rate was 100% (37/37). The incidence of postoperative complications was 10.81% (4/37), including 2 cases of pleural effusion, 1 case of hemorrhage, and 1 case of cholangitis, which recovered after treatment. During a mean follow-up period of 23 months (range 12 to 36 months), only 1 patient experienced stone recurrence. CONCLUSION: Ultrasound-guided PTOBF combined with rigid choledochoscopy in the treatment of RHL based on skilful manipulation seems to be a safe, effective and minimally invasive method with clinical application value. Further comparative studies with large sample sizes are needed in the future to confirm the reliability of its therapeutic results.

Effects of Potassium-Containing Fertilizers on Sugar and Organic Acid Metabolism in Grape Fruits.

To identify suitable potassium fertilizers for grape (Vitis vinifera L.) production and study their mechanism of action, the effects of four potassium-containing fertilizers (complex fertilizer, potassium nitrate, potassium sulfate, and potassium dihydrogen phosphate) on sugar and organic acid metabolism in grape fruits were investigated. Potassium-containing fertilizers increased the activity of sugar and organic acid metabolism-related enzymes at all stages of grape fruit development. During the later stages of fruit development, potassium-containing fertilizers increased the total soluble solid content and the sugar content of the different sugar fractions and decreased the titratable acid content and organic acid content of the different organic acid fractions. At the ripening stage of grape fruit, compared with the control, complex fertilizer, potassium nitrate, potassium sulfate, and potassium dihydrogen phosphate increased the total soluble solid content by 1.5, 1.2, 3.5, and 3.4 percentage points, decreased the titratable acid content by 0.09, 0.06, 0.18, and 0.17 percentage points, respectively, and also increased the total potassium content in grape fruits to a certain degree. Transcriptome analysis of the differentially expressed genes (DEGs) in the berries showed that applying potassium-containing fertilizers enriched the genes in pathways involved in fruit quality, namely, carbon metabolism, carbon fixation in photosynthetic organisms, glycolysis and gluconeogenesis, and fructose and mannose metabolism. Potassium-containing fertilizers affected the expression levels of genes regulating sugar metabolism and potassium ion uptake and transport. Overall, potassium-containing fertilizers can promote sugar accumulation and reduce acid accumulation in grape fruits, and potassium sulfate and potassium dihydrogen phosphate had the best effects among the fertilizers tested.

Modification of the second PEP4-allele facilitates an industrial Saccharomyces cerevisiae to tolerate tartaric acid stress.

The practical significance of constructing robust industrial production strains against organic acid stress lies not only in improving fermentation efficiency but also in reducing manufacturing costs. In a previous study, we constructed an industrial Saccharomyces cerevisiae strain by modifying another PEP4-allele of a mutant that already had one PEP4-allele disrupted. This modification enhanced cellular tolerance to citric acid stress during growth. Unlike citric acid, which S. cerevisiae can consume, tartaric acid is often added to grape must during winemaking to increase total acidity and is not metabolizable. The results of the present study indicate that the modification of the second PEP4-allele improves the cellular tolerance of the strain with one PEP4-allele disrupted against tartaric acid stress during growth and contributes to maintaining intracellular pH homeostasis in cells subjected to tartaric acid stress. Moreover, under tartaric acid stress, a significant improvement in glucose-ethanol conversion performance, conferred by the modification of the second PEP4-allele, was observed. This study not only broadens our understanding of the role of the PEP4-allele in cellular regulation but also provides a prospective approach to reducing the concentration of sulfur dioxide used in winemaking.

Multifunctional terahertz metamaterial based on vanadium dioxide and silicon.

This paper proposes a multifunctional metamaterial device operating in the terahertz (THz) band. The metamaterial device can switch functions by using the phase transition properties of vanadium dioxide (V O 2) and the photoconductive effect of silicon. An intermediate metal layer divides the device into the I side and II side. When V O 2 is in the insulating state, the I side can achieve polarization conversion from linear polarization waves to linear polarization waves at 0.408-0.970 THz. When V O 2 is in the metal-like state, the I side can perform polarization conversion from linear polarization waves to circular polarization waves at 0.469-1.127 THz. When silicon is not excited in the absence of light, the II side can perform polarization conversion from linear polarization waves to linear polarization waves at 0.799-1.336 THz. As the light intensity increases, the II side can realize stable broadband absorption at 0.697-1.483 THz when silicon is in the conductive state. The device can be applied to wireless communications, electromagnetic stealth, THz modulation, THz sensing, and THz imaging. Moreover, it provides a fresh idea for the design of multifunctional metamaterial devices.

Unsupervised Global and Local Homography Estimation With Motion Basis Learning.

In this paper, we introduce a new framework for unsupervised deep homography estimation. Our contributions are 3 folds. First, unlike previous methods that regress 4 offsets for a homography, we propose a homography flow representation, which can be estimated by a weighted sum of 8 pre-defined homography flow bases. Second, considering a homography contains 8 Degree-of-Freedoms (DOFs) that is much less than the rank of the network features, we propose a Low Rank Representation (LRR) block that reduces the feature rank, so that features corresponding to the dominant motions are retained while others are rejected. Last, we propose a Feature Identity Loss (FIL) to enforce the learned image feature warp-equivariant, meaning that the result should be identical if the order of warp operation and feature extraction is swapped. With this constraint, the unsupervised optimization can be more effective and the learned features are more stable. With global-to-local homography flow refinement, we also naturally generalize the proposed method to local mesh-grid homography estimation, which can go beyond the constraint of a single homography. Extensive experiments are conducted to demonstrate the effectiveness of all the newly proposed components, and results show that our approach outperforms the state-of-the-art on the homography benchmark dataset both qualitatively and quantitatively. Code is available at https://github.com/megvii-research/BasesHomo.

Prediction model of dental caries in 12-year-old children in Sichuan Province based on machine learning.

OBJECTIVES: The machine learning algorithm was used to construct a prediction model of children's dental caries to determine the risk factors of dental caries in children and put forward targeted measures and policy suggestions to improve children's oral health. METHODS: Stratified cluster random sampling was adopted in this study. In accordance with different policies and measures in Sichuan Province, 12-year-old students from 3-4 middle schools in eight cities of Sichuan Province were randomly selected for questionnaire survey, oral examination, and physical examination. Multivariate logistic regression analysis of risk factors for dental caries in 12-year-old children was conducted. The dataset was randomly divided into training set and validation set at a ratio of 7∶3. Four machine learning algorithms, including random forest, decision tree, extreme gradient boosting (XGBoost), and Logistic regression, were constructed using R version 4.1.1, and the prediction effects of the four prediction models were evaluated using the area under receiver operating characteristic curve (AUC). RESULTS: A total of 4 439 children aged 12 years were included in this study. The incidence of permanent teeth caries was 50.93%. The results of multivariate logistic regression analysis showed that body mass index, highest educational background of the father, highest educational background of the mother, whether to brush teeth, how many times a day, use of toothpaste when brushing teeth, duration of brushing teeth, mouthwash after meals, eating before going to bed after brushing teeth, sweet drinks, snacks, going to dental clinic to examine teeth, and age of brushing teeth were the factors influencing children's dental caries (P<0.05). The AUC values predicted by random forest, decision tree, Logistic regression, and XGBoost were 0.840, 0.755, 0.799, and 0.794, respectively. In the random forest model, the variable with the highest contribution was eating before bed after brushing. CONCLUSIONS: A prediction model of dental caries in children was established on the basis of random forest, showing good prediction effect. Taking preventive measures for the main factors affecting the occurrence of dental caries in children is beneficial.

A chemical perspective on the modulation of TEAD transcriptional activities: Recent progress, challenges, and opportunities.

TEADs are transcription factors and core downstream components of the Hippo pathway. Mutations of the Hippo pathway and/or dysregulation of YAP/TAZ culminate in aberrant transcriptional activities of TEADs, which were considered as key contributing factors of mesotheliomas, fibrotic diseases, Alzheimer's diseases, Huntington's diseases, suppressive immune response, and drug resistance, among others. To modulate transcriptional activities of TEADs, several pharmacological approaches have been pursued, including TEAD/YAP protein-protein interaction inhibitors, TEAD PBP inhibitors, and TEAD activators. As summarized in this review, a large number of inhibitors and activators of TEADs have been reported with decent in vitro potencies, a few exerted robust and compelling in vivo efficacies, and three that are undergoing clinical trials for the treatment of human cancers. Despite clinical advancement of the TEAD PBP inhibitors, development of other types TEAD inhibitors and activators generally lags behind. Information showcased herein might benefit discovery of next generation TEAD modulators for treatment of human oncological diseases and beyond.

Switchable Multifunctional Terahertz Metamaterials Based on the Phase-Transition Properties of Vanadium Dioxide.

Currently, terahertz metamaterials are studied in many fields, but it is a major challenge for a metamaterial structure to perform multiple functions. This paper proposes and studies a switchable multifunctional multilayer terahertz metamaterial. Using the phase-transition properties of vanadium dioxide (VO2), metamaterials can be controlled to switch transmission and reflection. Transmissive metamaterials can produce an electromagnetically induced transparency-like (EIT-like) effect that can be turned on or off according to different polarization angles. The reflective metamaterial is divided into I-side and II-side by the middle continuous VO2 layer. The I-side metamaterials can realize linear-to-circular polarization conversion from 0.444 to 0.751 THz when the incident angle of the y-polarized wave is less than 30°. The II-side metamaterials can realize linear-to-linear polarization conversion from 0.668 to 0.942 THz when the incident angle of the y-polarized wave is less than 25°. Various functions can be switched freely by changing the conductivity of VO2 and the incident surface. This enables metamaterials to be used as highly sensitive sensors, optical switches, and polarization converters, which provides a new strategy for the design of composite functional metamaterials.

Contour Transformer Network for One-Shot Segmentation of Anatomical Structures.

Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gathering the requisite expert-labeled image annotations in a scalable manner remains a main obstacle. Therefore, annotation-efficient methods that permit to produce accurate anatomical structure segmentation are highly desirable. In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism. We formulate anatomy segmentation as a contour evolution process and model the evolution behavior by graph convolutional networks (GCNs). Training the CTN model requires only one labeled image exemplar and leverages additional unlabeled data through newly introduced loss functions that measure the global shape and appearance consistency of contours. On segmentation tasks of four different anatomies, we demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning methods. With minimal human-in-the-loop editing feedback, the segmentation performance can be further improved to surpass the fully supervised methods.

Degraded Image Semantic Segmentation with Dense-Gram Networks.

Degraded image semantic segmentation is of great importance in autonomous driving, highway navigation systems, and many other safety-related applications and it was not systematically studied before. In general, image degradations increase the difficulty of semantic segmentation, usually leading to decreased semantic segmentation accuracy. Therefore, performance on the underlying clean images can be treated as an upper bound of degraded image semantic segmentation. While the use of supervised deep learning has substantially improved the state of the art of semantic image segmentation, the gap between the feature distribution learned using the clean images and the feature distribution learned using the degraded images poses a major obstacle in improving the degraded image semantic segmentation performance. The conventional strategies for reducing the gap include: 1) Adding image-restoration based pre-processing modules; 2) Using both clean and the degraded images for training; 3) Fine-tuning the network pre-trained on the clean image. In this paper, we propose a novel Dense-Gram Network to more effectively reduce the gap than the conventional strategies and segment degraded images. Extensive experiments demonstrate that the proposed Dense-Gram Network yields stateof-the-art semantic segmentation performance on degraded images synthesized using PASCAL VOC 2012, SUNRGBD, CamVid, and CityScapes datasets.

Small Object Sensitive Segmentation of Urban Street Scene with Spatial Adjacency Between Object Classes.

Recent advancements in deep learning have shown exciting promise in the urban street scene segmentation. However, many objects, such as poles and sign symbols, are relatively small and they usually cannot be accurately segmented since the larger objects usually contribute more to the segmentation loss. In this paper, we propose a new boundary-based metric that measures the level of spatial adjacency between each pair of object classes and find that this metric is robust against object size induced biases. We develop a new method to enforce this metric into the segmentation loss. We propose a network, which starts with a segmentation network, followed by a new encoder to compute the proposed boundary-based metric, and then trains this network in an end-to-end fashion. In deployment, we only use the trained segmentation network, without the encoder, to segment new unseen images. Experimentally, we evaluate the proposed method using CamVid and CityScapes datasets and achieve a favorable overall performance improvement and a substantial improvement in segmenting small objects.