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Importance: Although prolonged fasting has become increasingly popular, the favourable biological adaptations and possible adverse effects in humans have yet to be fully elucidated. Objective: To investigate the effects of a three-day water-only fasting, with or without exercise-induced glycogen depletion, on autophagy activation and the molecular pathways involved in cellular damage accumulation and repair in healthy humans. Design: A randomised, single-centre, two-period, two-sequence crossover trial. The primary outcome is autophagic activity, assessed as flux in peripheral blood mononuclear cells (PBMCs) measured in the context of whole blood. Secondary outcomes include changes in body composition, heart rate variability, endothelial function, and genomic, epigenomic, metabolomic, proteomic, and metagenomic adaptations to fasting in plasma, platelets, urine, stools, and PBMCs. Detailed profiling of circulating immune cell populations and their functional states will be assessed by flow cytometry. Setting: All clinical investigations will be undertaken at the Charles Perkins Centre Royal Prince Alfred Hospital clinic, University of Sydney, Australia. Participants: Twenty-four individuals aged 18 to 70 years, with a BMI of 20–40 kg/m2, free of major health conditions other than obesity. Discussion: While autophagic flux induction through fasting has garnered interest, there is a notable lack of human studies on this topic. This trial aims to provide the most detailed and integrated analysis of how three days of prolonged water-only fasting, combined with glycogen-depleting exercise, affects autophagy activation and other crucial metabolic and molecular pathways linked to cellular, metabolic, and immune health. Insights from this study may pave the way for safe and effective strategies to induce autophagy, offering potential preventive interventions for a range of chronic conditions. [ABSTRACT FROM AUTHOR]

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small objects in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-Level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small objects. Finally, we propose the Shape Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes, and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conducted comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively. [ABSTRACT FROM AUTHOR]

Breast cancer is a significant threat to life and health, which needs more safe and effective drugs to be explored. Teadenol B is a characteristic chemical component of microbial fermented tea. This study discovered that teadenol B could exhibit obvious inhibitory effects on all four different clinical subtype characteristics of breast cancer cells. Proteomic studies show that deoxycytidine triphosphate deaminase (DCTD), which could block DNA synthesis and repair DNA damage, had the most significant and consistent reduction in all four types of breast cancer cells with the treatment of teadenol B. Considering MDA-MB-231 cells exhibit poor clinical prognosis and displayed substantial statistical differences in KEGG pathway enrichment analysis results, we investigated its impact on the size and growth of MDA-MB-231 triple-negative breast tumors transplanted into nude mice and demonstrated that teadenol B significantly suppressed tumor growth without affecting body weight significantly. Finally, we found that the conversion of LC3-I to LC3-II in MDA-MB-231 increased significantly with teadenol B treatment. This proved that teadenol B could be a strong autophagy promotor, which explained the down-regulation of DCTD to some extent and may be the potential mechanism underlying teadenol B's anti-breast cancer effects. This finding provides new evidence for drinking fermented tea to prevent breast cancer and highlights the potential of teadenol B as a novel therapeutic option for breast cancer prevention and treatment, necessitating further investigations to clarify its exact target and the details involved. [ABSTRACT FROM AUTHOR]

Pedestrian trajectory prediction is an important task in practical applications such as automatic driving and surveillance systems. It is challenging to effectively model social interactions among pedestrians and capture temporal dependencies. Previous methods typically emphasized social interactions among pedestrians but ignored the temporal consistency of predictions and suffered from superfluous interactions by dense undirected graphs, resulting in a considerable deviance from reality. In addition, autoregressive approaches predicted future locations conditioning on previous predictions one by one, which would lead to error accumulation and time consuming. To address these issues, we present Non-autoregressive Sparse Transformer (NaST) networks for pedestrian trajectory prediction. Specifically, NaST models sparse spatial interactions and sparse temporal dependency via a sparse spatial transformer and a sparse temporal transformer separately. Different from previous predictions such as RNN-based approaches, the transformer decoder works in non-autoregressive pattern and predicts all the future locations at one time from a query sequence, which could avoid the error accumulation and be less computationally intensive. We evaluate our proposed method on the ETH and UCY datasets, and the experimental results show our method outperforms comparative state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Object detection has drawn the attention of many researchers due to its wide application in computer vision-related applications. In this paper, a novel model is proposed for object detection. Firstly, a new neck is designed for the proposed detection model, including an efficient SPPNet (Spatial Pyramid Pooling Network), a modified NLNet (Non Local Network) and a lightweight adaptive feature fusion module. Secondly, the detection head with double residual branch structure is presented to reduce the delay of a decoupled head and improve the detection ability. Finally, these improvements are embedded in YOLOX as plug-and-play modules for forming a high-performance detector, EYOLOX (EfficientYOLOX). Extensive experiments demonstrate that the EYOLOX achieves significant improvements, which increases YOLOX-s from 40.5% to 42.2% AP on the MS COCO dataset with a single GPU. Moreover, the performance of the detection of EYOLOX also outperforms YOLOv6 and some SOTA methods with the same number of parameters and GFLOPs. In particular, EYOLOX has only been trained on the COCO-2017 dataset without using any other datasets, and only the pre-training weights of the backbone part are loaded. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is the second most common hematologic malignancy. MM stem cells (MMSCs) are thought to be the main causes of in vivo engraftment and eventual recurrence. As a notable new technology, cold atmospheric plasmas (CAPs) show a promising anti-tumor effect, due to their production of various ROS. In this study, we found that different types of plasma could inhibit MM's ability to form cell colonies, suppress MM in vivo engraftment, and extend survival times. We demonstrated that NAC (a ROS scavenger) could block ROS increases and reverse the inhibition of MM's cell-colony-formation ability, which was induced by the plasma treatment. By using a stem cell signaling array, we found that the Notch pathway was inhibited by the plasma treatment; this was further confirmed by conducting real-time PCRs of three MM cell lines. Together, these results constitute the first report of plasma treatment inhibiting MM in vivo engraftment and prolonging survival time by suppressing the Notch pathway via ROS regulation. [ABSTRACT FROM AUTHOR]

In recent years, the emerging technology of cold atmospheric pressure plasma (CAP) has grown rapidly along with the many medical applications of cold plasma (e.g., cancer, skin disease, tissue repair, etc.). Plasma-activated liquids (e.g., culture media, water, or normal saline, previously exposed to plasma) are being studied as cancer treatments, and due to their advantages, many researchers prefer plasma-activated liquids as an alternative to CAP in the treatment of cancer. In this study, we showed that plasma-activated-saline (PAS) treatment significantly inhibited tumor growth, as compared with saline, in melanoma, and a low-pH environment had little effect on tumor growth in vivo. In addition, based on an ultra-high-performance liquid tandem chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) analysis of tumor cell metabolism, the glycerophospholipid metabolic pathway was the most susceptible metabolic pathway to PAS treatment in melanoma in vitro and in vivo. Furthermore, PAS also inhibited cell proliferation in vivo in oral tongue squamous-cell cancer and non-small-cell lung cancer. There were few toxic side effects in the three animal models, and the treatment was deemed safe to use. In the future, plasma-activated liquids may serve as a potential therapeutic approach in the treatment of cancer. [ABSTRACT FROM AUTHOR]

Object detection has been playing a significant role in computer vision for a long time, but it is still full of challenges. In this paper, we propose a novel object detection framework based on relationship among different objects and the scene-level information of the whole image to cope with the problem that some strongly correlated objects are difficult to be recognized. Our motivation is to enrich the semantics of object detection feature by a scene-level information branch and a relationship branch. There are three important changes of our framework over traditional detection methods: representation of relationship, scene-level information as the prior knowledge and the fusion of the above two information. Extensive experiments are carried out on PASCAL VOC and MS COCO databases. The experimental results show that the detection performance can be improved by introducing relationship and scene-level information, and our proposed model achieve better performance than several classical and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Currently, how to deal with the massive garbage produced by various human activities is a hot topic all around the world. In this paper, a preliminary and essential step is to classify the garbage into different categories. However, the mainstream waste classification mode relies heavily on manual work, which consumes a lot of labor and is very inefficient. With the rapid development of deep learning, convolutional neural networks (CNN) have been successfully applied to various application fields. Therefore, some researchers have directly adopted CNNs to classify garbage through their images. However, compared with other images, the garbage images have their own characteristics (such as inter-class similarity, intra-class variance and complex background). Thus, neglecting these characteristics would impair the classification accuracy of CNN. To overcome the limitations of existing garbage image classification methods, a Depth-wise Separable Convolution Attention Module (DSCAM) is proposed in this paper. In DSCAM, the inherent relationships of channels and spatial positions in garbage image features are captured by two attention modules with depth-wise separable convolutions, so that our method could only focus on important information and ignore the interference. Moreover, we also adopt a residual network as the backbone of DSCAM to enhance its discriminative ability. We conduct the experiments on five garbage datasets. The experimental results demonstrate that the proposed method could effectively classify the garbage images and that it outperforms some classical methods. [ABSTRACT FROM AUTHOR]

Terahertz is a new radiation source with many unique advantages. In recent years, its application has rapidly expanded to various fields, but there are few studies on the individual effects of terahertz. In this study, we investigated the behavioral effects of terahertz radiation on C57BL/6 mice, and we conducted an open field test, an elevated plus maze test, a light–dark box test, a three-chamber social test, and a forced swim test to explore the effects of terahertz radiation on mice from a behavioral perspective. The results show that terahertz wave may increase anti-anxiety, anti-depression, and social interaction in mice. [ABSTRACT FROM AUTHOR]

Graph Convolutional Networks (GCNs) have attracted a lot of attention and shown remarkable performance for action recognition in recent years. For improving the recognition accuracy, how to build graph structure adaptively, select key frames and extract discriminative features are the key problems of this kind of method. In this work, we propose a novel Adaptive Attention Memory Graph Convolutional Networks (AAM-GCN) for human action recognition using skeleton data. We adopt GCN to adaptively model the spatial configuration of skeletons and employ Gated Recurrent Unit (GRU) to construct an attention-enhanced memory for capturing the temporal feature. With the memory module, our model can not only remember what happened in the past but also employ the information in the future using multi-bidirectional GRU layers. Furthermore, in order to extract discriminative temporal features, the attention mechanism is also employed to select key frames from the skeleton sequence. Extensive experiments on Kinetics, NTU RGB+D and HDM05 datasets show that the proposed network achieves better performance than some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Access to healthcare data such as electronic health records (EHR) is often restricted by laws established to protect patient privacy. These restrictions hinder the reproducibility of existing results based on private healthcare data and also limit new research. Synthetically-generated healthcare data solve this problem by preserving privacy and enabling researchers and policymakers to drive decisions and methods based on realistic data. Healthcare data can include information about multiple in- and out- patient visits of patients, making it a time-series dataset which is often influenced by protected attributes like age, gender, race etc. The COVID-19 pandemic has exacerbated health inequities, with certain subgroups experiencing poorer outcomes and less access to healthcare. To combat these inequities, synthetic data must "fairly" represent diverse minority subgroups such that the conclusions drawn on synthetic data are correct and the results can be generalized to real data. In this article, we develop two fairness metrics for synthetic data, and analyze all subgroups defined by protected attributes to analyze the bias in three published synthetic research datasets. These covariate-level disparity metrics revealed that synthetic data may not be representative at the univariate and multivariate subgroup-levels and thus, fairness should be addressed when developing data generation methods. We discuss the need for measuring fairness in synthetic healthcare data to enable the development of robust machine learning models to create more equitable synthetic healthcare datasets. [ABSTRACT FROM AUTHOR]

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden. We propose a lightweight fusion distillation network (LFDN) for image deblurring and deraining to solve the above problems. The proposed LFDN is designed as an encoder–decoder architecture. In the encoding stage, the image feature is reduced to various small-scale spaces for multi-scale information extraction and fusion without much information loss. Then, a feature distillation normalization block is designed at the beginning of the decoding stage, which enables the network to distill and screen valuable channel information of feature maps continuously. Besides, an information fusion strategy between distillation modules and feature channels is also carried out by the attention mechanism. By fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring and deraining results with a smaller number of parameters and outperform the existing methods in model complexity. [ABSTRACT FROM AUTHOR]

The growing demand for intelligent equipment has greatly inspired the development of flexible devices. Thus, disparate flexible multifunctional devices, including pressure sensitive flexible/stretchable displays, have drawn worldwide research attention. Electrodes maintaining conductivity and mechanical strength against deformations are indispensable components in all prospective applications. In this work, a flexible pressure mapping sensor array is developed based on patterned Ag-nanofibers (Ag-NFs) electrode through electrospinning and lithography. The metallic Ag layer is sputtered onto the electrospinning polyvinyl alcohol (PVA) NFs. A uniform and super conductive electrode layer with outstanding mechanical performance is thus formed after dissolving PVA. Followed by the traditional lithography method, a patterned electrode array (4 × 4 sensors) is obtained. Based on the newly developed triboelectric nanogenerator (TENG) technology, a flexible pressure-mapping sensor with excellent stability towards bending deformations is further demonstrated. Moreover, a letter "Z" is successfully visualized by this pressure sensor array, encouraging more human–machine interactive implementations, such as multi-functional tactile screens. [ABSTRACT FROM AUTHOR]

Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN requires a large amount of labeled data to achieve a good performance but labeling data is a time-consuming and laborious work. Since active learning can effectively reduce the labeling effort, we propose a new intelligent active learning method for deep learning, which is called multi-view active learning based on double-branch network (MALDB). Different from most existing active learning methods, our proposed MALDB first integrates two Bayesian convolutional neural networks (BCNNs) with different structures as two branches of a classifier to learn the effective features for each sample. Then, MALDB performs data analysis on unlabeled dataset and queries the useful unlabeled samples based on different characteristics of two branches to iteratively expand the training dataset and improve the performance of classifier. Finally, MALDB combines multiple level information from multiple hidden layers of BCNNs to further improve the stability of sample selection. The experiments are conducted on five extensively used datasets, Fashion-MNIST, Cifar-10, SVHN, Scene-15 and UIUC-Sports, the experimental results demonstrate the validity of our proposed MALDB. [ABSTRACT FROM AUTHOR]

Cold atmospheric plasma (CAP) has been widely used in biomedicine during the last two decades. While direct plasma treatment has been reported to promote wound healing, its application can be uneven and inconvenient. In this study, we first activated water with a portable dielectric barrier discharge plasma device and evaluated the inactivation effect of plasma-activated water (PAW) on several kinds of bacteria that commonly infect wounds. The results show that PAW can effectively inactivate these bacteria. Then, we activated tap water and examined the efficacy of PAW on wound healing in a mouse model of full-thickness skin wounds. We found that wound healing in mice treated with PAW was significantly faster compared with the control group. Histological analysis of the skin tissue of mice wounds showed a significant reduction in the number of inflammatory cells in the PAW treatment group. To identify the possible mechanism by which PAW promotes wound healing, we analyzed changes in the profiles of wound bacteria after PAW treatment. The results show that PAW can significantly reduce the abundance of wound bacteria in the treatment group. The results of biochemical blood tests and histological analysis of major internal organs in the mice show that PAW had no obvious side effects. Taken together, these results indicate that PAW may be a new and effective method for promoting wound healing without side effects. [ABSTRACT FROM AUTHOR]

With the increasing demand of achieving comprehensive perception in every aspect of life, optical fibers have shown great potential in various applications due to their highly-sensitive, highly-integrated, flexible and real-time sensing capabilities. Among various sensing mechanisms, plasmonics based fiber-optic sensors provide remarkable sensitivity benefiting from their outstanding plasmon–matter interaction. Therefore, surface plasmon resonance (SPR) and localized SPR (LSPR)-based hybrid fiber-optic sensors have captured intensive research attention. Conventionally, SPR- or LSPR-based hybrid fiber-optic sensors rely on the resonant electron oscillations of thin metallic films or metallic nanoparticles functionalized on fiber surfaces. Coupled with the new advances in functional nanomaterials as well as fiber structure design and fabrication in recent years, new solutions continue to emerge to further improve the fiber-optic plasmonic sensors' performances in terms of sensitivity, specificity and biocompatibility. For instance, 2D materials like graphene can enhance the surface plasmon intensity at the metallic film surface due to the plasmon–matter interaction. Two-dimensional (2D) morphology of transition metal oxides can be doped with abundant free electrons to facilitate intrinsic plasmonics in visible or near-infrared frequencies, realizing exceptional field confinement and high sensitivity detection of analyte molecules. Gold nanoparticles capped with macrocyclic supramolecules show excellent selectivity to target biomolecules and ultralow limits of detection. Moreover, specially designed microstructured optical fibers are able to achieve high birefringence that can suppress the output inaccuracy induced by polarization crosstalk and meanwhile deliver promising sensitivity. This review aims to reveal and explore the frontiers of such hybrid plasmonic fiber-optic platforms in various sensing applications. [ABSTRACT FROM AUTHOR]

The relationship between synthetic factors and the resulting structures is critical for rational synthesis of zeolites and related microporous materials. In this paper, we develop a new feature selection method for synthetic factor analysis of (6,12)-ring-containing microporous aluminophosphates (AlPOs). The proposed method is based on a maximum weight and minimum redundancy criterion. With the proposed method, we can select the feature subset in which the features are most relevant to the synthetic structure while the redundancy among these selected features is minimal. Based on the database of AlPO synthesis, we use (6,12)-ring-containing AlPOs as the target class and incorporate 21 synthetic factors including gel composition, solvent and organic template to predict the formation of (6,12)-ring-containing microporous aluminophosphates (AlPOs). From these 21 features, 12 selected features are deemed as the optimized features to distinguish (6,12)-ring-containing AlPOs from other AlPOs without such rings. The prediction model achieves a classification accuracy rate of 91.12% using the optimal feature subset. Comprehensive experiments demonstrate the effectiveness of the proposed algorithm, and deep analysis is given for the synthetic factors selected by the proposed method.