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Abstract Background Cancer survivors face elevated risks of heart failure (HF) and death, with cardiac dysfunction being a significant concern. Current evaluations often emphasize systolic function while insufficiently addressing diastolic function. This study aims to investigate the prevalence of diastolic dysfunction and assess its prognostic implications in long-term cancer survivors. Methods We analyzed participants from the Atherosclerosis Risk in Communities (ARIC) Study with complete echocardiographic assessments and documented cancer histories. Diastolic function was classified by guideline criteria: normal (≤ 1 abnormal parameter), indeterminate (2 abnormal parameters), and dysfunction (≥ 3 abnormal parameters). The primary outcomes were incident HF and all-cause death. Diastolic dysfunction prevalence was compared between cancer survivors and non-cancer participants after propensity score matching. Cox regression, Kaplan–Meier, and restricted cubic spline (RCS) analyses were used to assess associated risks. Results A total of 5322 participants were included, with 18.4% (N = 979) being cancer survivors. The mean age of cancer survivors at echocardiography was 76.3 (5.10) years, with a median of 12.17 years since diagnosis. There were no significant differences in diastolic dysfunction prevalence (12.26% vs 10.73%, P = 0.29) after matching. Cox regression revealed a graded association between diastolic dysfunction and risks of HF and death. Fully adjusted hazard ratios were 2.59 (95% CI: 1.59–4.20, P

Abstract The aim of this research was to determine the residues of dioxins and dioxin-like polychlorinated biphenyls in cultured eel and find out the source of pollution and the distribution pattern of eels. One hundred samples of eel, 4 fodder samples, and 12 environmental samples (water, plants, and soil) were collected from 4 cities and counties in Jiangxi Province, China. The contents of 17 kinds of dioxins (PCDD/Fs) and 12 kinds of dioxin-like polychlorinated biphenyls (dl-PCBs) were determined by high-resolution gas chromatography-tandem mass spectrometry, and the exposure risk was evaluated by using risk index. The total toxicity equivalence quantity (TEQ) of dioxin and dioxin-like polychlorinated biphenyls in eel and fodder samples collected in the study area were 0.65 ± 0.31 pg/g and 0.10 ± 0.02 pg/g, respectively. Dl-PCBs were the main dioxin pollution in eel and fodder samples. 2,3',4,4',5-pentachlorodiphenyl and 2,3,3',4,4'-pentachlorobiphenyl were the main contributing monomers. The environmental samples were mainly polluted by polychlorinated dibenzo-p-dioxins (PCDDs), with the main contributing monomer being Octachlorodibenzodioxins (OCDD), while 3,3',4,4',5-pentachlorobiphenyl and 2,3,4,7,8-pentachlorodibenzofuran were the main toxic compounds in eel and fodder. The dioxin pollution of eels cultured in Jiangxi Province was mainly from fodder polluted via dl-PCBs. The meat segment VI (tail) exhibited a strong enrichment effect of polychlorinated benzofurans (PCDFs). It should be the key part for dioxins and dioxin-like polychlorinated biphenyls assessment. Further, the results were helpful to improve the edible safety of eel products and the efficiency of the risk assessment of dioxins and dioxin-like polychlorinated biphenyls in fish. Graphical Abstract

In this paper, we propose a novel approach to coordinate-based acoustic emission (AE) source localization to address the challenges of limited and imbalanced datasets from fiber-optic AE sensors used for structural health monitoring (SHM). We have developed a hybrid deep learning model combining four generative adversarial network (GAN) variants for data augmentation with an adapted inception neural network for regression-based prediction. The experimental setup features a single fiber-optic AE sensor based on a tightly coiled fiber-optic Fabry-Perot interferometer formed by two identical fiber Bragg gratings. AE signals were generated using the Hsu-Nielsen pencil lead break test on a grid-marked thin aluminum plate with 35 distinct locations, simulating real-world structural monitoring conditions in bounded isotropic plate-like structures. It is demonstrated that the single-sensor configuration can achieve precise localization, avoiding the need for a multiple sensor array. The GAN-based signal augmentation expanded the dataset from 900 to 4500 samples, with the Wasserstein distance between the original and synthetic datasets decreasing by 83% after 2000 training epochs, demonstrating the high fidelity of the synthetic data. Among the GAN variants, the standard GAN architecture proved the most effective, outperforming other variants in this specific application. The hybrid model exhibits superior performance compared to non-augmented deep learning approaches, with the median error distribution comparisons revealing a significant 50% reduction in prediction errors, accompanied by substantially improved consistency across various AE source locations. Overall, this developed hybrid approach offers a promising solution for enhancing AE-based SHM in complex infrastructures, improving damage detection accuracy and reliability for more efficient predictive maintenance strategies.

Abstract Aggregation‐induced emission (AIE) is a phenomenon where a molecule that is weakly or non‐luminescent in a diluted solution becomes highly emissive when aggregated. AIE luminogens (AIEgens) hold promise in diverse applications like bioimaging, chemical sensing, and optoelectronics. Investigation in AIE luminescence is also critical for understanding aggregation kinetics as the aggregation process is an essential component of AIE emission. Experimental investigation of AIEgen aggregation is challenging due to the fast timescale of the aggregation and the amorphous aggregate structures. Computer simulations such as molecular dynamics (MD) simulation provide a valuable approach to complement experiments with atomic‐level knowledge to study the fast dynamics of aggregation processes. However, individual simulations still struggle to systematically elucidate heterogeneous kinetics of the formation of amorphous AIEgen aggregates. Kinetic network models (KNMs), constructed from an ensemble of MD simulations, hold great potential in addressing this challenge. In these models, dynamic processes are modeled as a series of Markovian transitions occurring among metastable conformational states at discrete time intervals. In this perspective article, we first review previous studies to characterize the AIEgen aggregation kinetics and their limitations. We then introduce KNMs as a promising approach to elucidate the complex kinetics of aggregations to address these limitations. More importantly, we discuss our perspective on linking the output of KNMs to experimental observations of time‐resolved AIE luminescence. We expect that this approach can validate the computational predictions and provide great insights into the aggregation kinetics for AIEgen aggregates. These insights will facilitate the rational design of improved AIEgens in their applications in biology and materials sciences.

Existing data-driven object detection methods use the Constant False Alarm Rate (CFAR) principle to achieve more robust detection performance using supervised learning. This study systematically proposes a data-driven target detection framework based on the measured echo data from the ground early warning radar for low-altitude slow dim target detection. This framework addresses two key problems in this field: (1) aiming at the problem that current data-driven object detection methods fail to make full use of feature representation learning to exert its advantages, a representation learning method of echo temporal dependency is proposed, and two implementations, including unsupervised- and supervised-learning are given; (2) Low-altitude slow dim targets show extreme sparsity in the radar detection range, such unevenness of target-clutter sample scale causes the trained model to seriously tilt to the clutter samples, resulting in the decision deviation. Therefore, we further propose incorporating the data balancing policy of abnormal detection into the framework. Finally, ablation experiments are performed on the measured X-band echo data for each component in the proposed framework. Experimental results completely validate the effectiveness of our echo temporal representation learning and balancing policy. Additionally, under real sequential validation, our proposed method achieves comprehensive detection performance that is superior to multiple CFAR methods.

The spread of the FluA virus poses significant public health concerns worldwide. Fluorescent lateral flow immunoassay (LFIA) test strips have emerged as vital tools for the early detection and monitoring of influenza infections. However, existing quantitative virus-detection methods, particularly those utilizing smartphone-based sensing platforms, encounter accessibility challenges in resource-limited areas and among the elderly population. Despite their advantages in speed and portability, these platforms often lack user-friendliness for these demographics, impeding their widespread utilization. To address these challenges, this study proposes leveraging the optical pick-up unit (OPU) sourced from commercial optical drives as a readily available fluorescence excitation module for the quantitative detection of antibodies labeled with quantum-dot fluorescent microspheres. Additionally, we utilize miniaturized and high-performance optical components and 3D-printed parts, along with a customized control system, to develop an affordable point-of-care testing (POCT) device. Within the system, a stepping motor scans the test strip from the T-line to the C-line, enabling the calculation of the fluorescence-intensity ratio between the two lines. This simple yet effective design facilitates rapid and straightforward field or at-home testing for FluA. The proposed prototype platform demonstrates promising performance, achieving a limit of detection (LOD) of 2.91 ng/mL, a total detection time of no more than 15 min, and dimensions of 151 mm × 11.2 mm × 10.8 mm3. We believe that the proposed approach holds great potential for improving access to an accurate influenza diagnosis.

Egg products after thermal treatment possess a unique flavor and are favored by consumers. In this study, the key aroma-active compounds of egg yolk products and their formation mechanism during thermal treatment were investigated. The volatile aroma compounds in egg yolks were monitored using an electronic nose, gas chromatography-mass spectrometry (GC–MS) and gas chromatography–olfactometry–mass spectrometry (GC–O–MS), and the lipid molecular species were explored using ultra-high-performance liquid chromatography– mass spectrometry with a Q-Exactive HF-X Orbitrap (UPLC-Q-Exactive HF-X). A total of 68 volatile compounds were identified. Boiled eggs mainly derived their flavor from hexanal, 2-pentyl-furan, 2-butanone, 3-methyl-butanal and heptane. Meanwhile, fried eggs relied mainly on 14 compounds, the most important of which were 2-ethyl-3-methyl-pyrazine, 3-ethyl-2,5-dimethyl-pyrazine, 2-ethyl-3,5-dimethyl-pyrazine, nonanal and 2,3-diethyl-5-methyl-pyrazine, providing a baked and burnt sugar flavor. A total of 201 lipid molecules, belonging to 21 lipid subclasses, were identified in egg yolks, and 13 oxidized lipids were characterized using a molecular network. Phosphoethanolamines (PEs) containing polyunsaturated fatty acids were the primary flavor precursors contributing to the development of egg yolks’ flavor, participating in lipid oxidation reactions and the Maillard reaction and regulating the production of aldehydes and pyrazine compounds. This study provides reference and guidance for the development of egg yolk flavor products.

The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention. However, its intricately discontinuous spike mechanism brings difficulty to the optimization of the deep SNN. Since the surrogate gradient method can greatly mitigate the optimization difficulty and shows great potential in directly training deep SNNs, a variety of direct learning-based deep SNN works have been proposed and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these direct learning-based deep SNN works, mainly categorized into accuracy improvement methods, efficiency improvement methods, and temporal dynamics utilization methods. In addition, we also divide these categorizations into finer granularities further to better organize and introduce them. Finally, the challenges and trends that may be faced in future research are prospected.

Objective • This study aimed to investigate the clinical impact of goal-oriented, evidence-based nursing in preventing perioperative stress injuries. Methods • A total of 380 patients undergoing surgery were allocated into either the control or study group. The study group received goal-oriented, evidence-based nursing, while the control group received routine nursing care. Various perioperative indicators, including operating time, position change time, intraoperative bleeding, and length of hospitalization, were assessed and compared between the two groups. Additionally, the Mini-Nutritional Assessment (MNA) score, Munro score, incidence of stress injuries, and nursing satisfaction rate were compared. Patients with perioperative pressure sores (PS) were further evaluated using the Pressure Ulcer Healing Score (PUSH), Braden Stress Injury Scale (Braden), visual analogue scale of pain (VAS), and wound healing time. Results • The study group exhibited higher MNA levels during and after the operation, while Munro levels were lower compared to the control group (P < .05). The study group demonstrated a shorter length of stay and quicker body position changes than the control group. Incidence of pressure sores (PS) was lower in the study group, accompanied by higher nursing satisfaction. PS patients in the study group had lower VAS and PUSH scores, higher Braden scores, and shorter wound healing times than those in the control group. Conclusion • This study highlights the efficacy of goal- oriented, evidence-based nursing in reducing perioperative stress injuries, advocating its adoption for improved care and patient outcomes. However, the single-center design limits generalizability, necessitating further validation. Ultimately, this approach signifies a step forward in nursing practice, promising better patient recovery and satisfaction. [ABSTRACT FROM AUTHOR]

Abstract The three-dimensional target position is vital to automatic driving, which can efficiently detect the geometrical shape of obstacles and achieve automatic environment recognition for both indoor and outdoor scenarios. In this paper, we propose an RFID 3D localization prototype based on the polarization phase model. To eliminate the impacts of tag angle, we leverage the polarization effect to obtain polarized phases. After that, we use polarized phase decomposition to get pure phases, i.e., the phase corresponding to the line of sight path. Hence, we can calculate the raw phases using the HMFCW algorithm. Finally, we can estimate the target’s 3D position according to unwrapped phase information. In this way, we implement a low-cost, high-precision, easy-for-deployment 3D position solution using commercial UHF RFID hardware. The experiments show that the method can obtain effective positioning results in various environments and achieve positioning accuracy with an average error of about 9 cm.

Lizhe Zhu et al. use a quasi Markov State Model built from extensive molecular dynamics simulations to elucidate the dynamics of RISC-mRNA interactions. Their results provide further insight on how a target nucleotide is positioned before recognition by Argonaute.

Basement excavation may induce deformations of the adjacent tunnels. The response of existing tunnels to basement excavation considering the critical unloading ratio is rarely studied. In this study, a three-dimensional numerical model is established to investigate basement–tunnel interaction. Then, the numerical model is validated by simulating the centrifuge model test. Thereafter, the influences of basement geometry and tunnel location relative to the basement on the vertical deformation of the tunnel are studied. The results show that the vertical deformation of the tunnel increases linearly with the unloading ratio, which describes the degree of excavation depth above the tunnel. But there exists a critical unloading ratio of 0.6, beyond which the vertical deformation of the tunnel increases significantly. On this basis, an empirical model is proposed to predict the vertical deformation of the tunnel considering the unloading ratio.

Classically, ice nucleation on foreign surfaces is considered a one-step process. Here, Li et al. uncover a two-barrier pathway which becomes competitive at lower temperatures, facilitated by synergistic, entropic effects of rhombic and hexagonal ice structures.

Introduction The prevalence of heart failure (HF) is increasing. Exercise-based cardiac rehabilitation (CR) reduces mortality and further improves the prognosis of patients with HF. However, the effect of different modes of CR delivery on HF remains unclear. Thus, the purpose of this study is to find out the relative efficacy and safety of different modes of CR delivery for individuals with HF using a network meta-analysis.Methods and analysis We will perform a systematic review and network meta-analysis of randomised controlled trials which compare different modes of exercise-based CR delivery for patients with HF. Databases including Embase, Medline, the Cochrane Central Register of Controlled Trials and Web of Science will be searched up to May 2022. The primary outcomes will focus on the functional capacity and the health-related quality of life (hr-QOL). Functional capacity will be evaluated by peak oxygen consumption (mL/kg/min) and 6 min walking test (metres). The Minnesota Living with Heart Failure questionnaire, Short Form-36, Psychometric properties of the Kansas City cardiomyopathy questionnaire and EuroQol five dimensions questionnaire will serve as measures of hr-QOL. As secondary outcomes, we will assess hospital admissions (all-cause and cardiac) and all-cause mortality, which required a minimum follow-up of 6 months, as well as adverse events during exercise training. The risk of bias for individual studies will be evaluated according to the Cochrane Handbook. The quality of evidence will be assessed by the Grading of Recommendations, Assessment, Development and Evaluation approach.Ethics and dissemination This study does not require ethics approval as it is based on published trials. Results of this systematic review and network meta-analysis will be submitted to a peer-reviewed journal for future publication.Trial registration number CRD42021278351.

The interaction perception between aroma and oral chewing during food consumption has always been a hot topic in exploring consumers’ preferences and purchase desires. A chewing simulation system was set to find out the effect of key saliva components and chewing time on odorants released with grilled eel meat. Odor release did not always enhance with the degree of chewing, or the amount of saliva released. The breaking up of the tissue structure of the fish meat by the teeth encourages the release of odorants and the participation of saliva partially blocks this process. The release of pyrazine, alcohol, and acid compounds in grilled eel meat peaked within 20–60 s after chewing. Sufficient exposure of saliva to grilled eel meat will inhibit aromatic, ketone, ester, hydrocarbon, and sulfur compounds release. 3-methyl-2-butanol contributed to the subtle aroma differences that arise before and after eating grilled eel meat. Naphthalene, 2-acetylthiazole, 2-decenal, 2-undecanone, 5-ethyldihydro-2(3H)-furanone were the main odorants released in large quantities in the early stages of eating grilled eel and affected the top note. Consequently, the results provided the odorants information in aroma perception during grilled eel consumption and benefited the objective evaluation of grilled eel product optimization.

Background: Considerable evidence implicates myeloid-derived suppressor cells (MDSCs) promote tumor progression and drug resistance. Sorafenib is the standard first-line therapy for advanced hepatocellular carcinoma (HCC). Clinical evidence indicates that sorafenib resistance is associated with increased MDSCs, by which MDSCs exerts these effects is obscure. This study aimed to investigate the mechanism of sorafenib resistance mediated by MDSCs. Methods: A syngeneic mouse-liver cancer cell line BNL was subcutaneously injected to build a tumor-bearing mouse model, and syngeneic MDSCs were adoptive transferred into the tumor-bearing mouse. Tumor tissue was obtained, and transcriptomic analysis of the tumor was carried out on RNAseq data. A coculture system was used to verify the crosstalk between MDSCs and BNL cells. Results: Adoptive MDSCs transfer into tumor-bearing mice induced an increase of tumor-infiltrating MDSCs, which led to tumor growth and impaired antitumor activity of sorafenib in BNL HCC models. MDSCs transfer contributed to tumor fibrosis and tumor-associated fibroblast (CAF) activation, associated with fibroblast growth factor (FGF1) upregulation. In contrast, MDSC depletion by anti-Ly6G+ reduced fibrosis and increased sorafenib antitumor efficacy. Intriguingly, tumor-infiltrating MDSCs barely expressed FGF1. IL-6 derived from MDSCs increased FGF1 expression in BNL liver cancer cells, and anti-IL-6 attenuated this effect in vitro. MAPK pathway, one of the sorafenib targets, is the downstream signaling of FGF1 and is reactivated by MDSCs-mediated FGF1 upregulation. Conclusions: Our finding demonstrated that MDSCs led to tumor growth and sorafenib resistance via FGF1 upregulation and subsequent indirect CAF activation. We offered a novel mechanism of MDSCs-driven HCC progression and sorafenib resistance.

Background: There is an urgent need for effective treatment for advanced hepatocellular carcinoma (HCC). Sorafenib, a multi-kinase inhibitor, is the first-line treatment for advanced HCC, which has a modest efficacy due to severe adverse effects (AEs), acquired resistance, and others. Jianpi Huayu Decoction (JHD) is a traditional Chinese medicine formulation, effective as an alternative and complementary therapy of HCC. Combined sorafenib with JHD is a potential approach to overcome sorafenib resistance. Methods: Effect and AEs of clinical-dose sorafenib combined JHD were evaluated in a subcutaneous model. Diarrhea, the most frequently reported AEs, was evaluated by diarrhea score. Illumina NovaSeq analyzed the gut microbiota (GM) composition of the mice. Results: JHD administration enhanced anti-tumor response to sorafenib and prevented the occurrence of sorafenib-induced diarrhea and body weight loss. Proinflammatory GM Helicobacter positively correlated with sorafenib treatment while anti-inflammatory GM, Muribaculum, Fusicatenibacter, and Dorea positively correlated with JHD treatment. In addition, JHD decreased the infiltration of inflammatory cells and downregulated signaling of interleukin 6/signal transducer and activator of the transcription-3 pathway. Conclusions: Our finding suggested that JHD with microbiota modulation properties could significantly potentiate sorafenib and provide a promising treatment for advanced HCC.