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Wearable optical fiber sensors have great potential for development in medical monitoring. With the increasing demand for compactness, comfort, accuracy, and other features in new medical monitoring devices, the development of wearable optical fiber sensors is increasingly meeting these requirements. This paper reviews the latest evolution of wearable optical fiber sensors in the medical field. Three types of wearable optical fiber sensors are analyzed: wearable optical fiber sensors based on Fiber Bragg grating, wearable optical fiber sensors based on light intensity changes, and wearable optical fiber sensors based on Fabry–Perot interferometry. The innovation of wearable optical fiber sensors in respiration and joint monitoring is introduced in detail, and the main principles of three kinds of wearable optical fiber sensors are summarized. In addition, we discuss their advantages, limitations, directions to improve accuracy and the challenges they face. We also look forward to future development prospects, such as the combination of wireless networks which will change how medical services are provided. Wearable optical fiber sensors offer a viable technology for prospective continuous medical surveillance and will change future medical benefits.

Cardiovascular disease (CVD) is the leading cause of death worldwide and a major public health concern. CVD prediction is one of the most effective measures for CVD control. In this study, 29930 subjects with high-risk of CVD were selected from 101056 people in 2014, regular follow-up was conducted using electronic health record system. Logistic regression analysis showed that nearly 30 indicators were related to CVD, including male, old age, family income, smoking, drinking, obesity, excessive waist circumference, abnormal cholesterol, abnormal low-density lipoprotein, abnormal fasting blood glucose and else. Several methods were used to build prediction model including multivariate regression model, classification and regression tree (CART), Naïve Bayes, Bagged trees, Ada Boost and Random Forest. We used the multivariate regression model as a benchmark for performance evaluation (Area under the curve, AUC = 0.7143). The results showed that the Random Forest was superior to other methods with an AUC of 0.787 and achieved a significant improvement over the benchmark. We provided a CVD prediction model for 3-year risk assessment of CVD. It was based on a large population with high risk of CVD in eastern China using Random Forest algorithm, which would provide reference for the work of CVD prediction and treatment in China.

The origin of the temperature divergence between Holocene proxy reconstructions and model simulations remains controversial, but it possibly results from potential biases in the seasonality of reconstructions or in the climate sensitivity of models. Here we present an extensive dataset of Holocene seasonal temperatures reconstructed using 1310 pollen records covering the Northern Hemisphere landmass. Our results indicate that both summer and winter temperatures warmed from the early to mid-Holocene (~11–7 ka BP) and then cooled thereafter, but with significant spatial variability. Strong early Holocene warming trend occurred mainly in Europe, eastern North America and northern Asia, which can be generally captured by model simulations and is likely associated with the retreat of continental ice sheets. The subsequent cooling trend is pervasively recorded except for northern Asia and southeastern North America, which may reflect the cross-seasonal impact of the decreasing summer insolation through climatic feedbacks, but the cooling in winter season is not well reproduced by climate models. Our results challenge the proposal that seasonal biases in proxies are the main origin of model–data discrepancies and highlight the critical impact of insolation and associated feedbacks on temperature changes, which warrant closer attention in future climate modelling. The study reconstructed Holocene seasonal temperatures using 1,310 pollen records covering the Northern Hemisphere landmass, and show that both summer and winter temperatures peaked at ~7 ka BP, but with significant spatial variability.

C-reactive protein (CRP) is a non-specific diagnostic marker of inflammation and an evolutionarily conserved protein with roles in innate immune signaling. Natural CRP is composed of five identical globular subunits that form a pentamer, but the role of pentameric CRP (pCRP) during inflammatory pathogenesis remains controversial. Emerging evidence suggests that pCRP can be dissociated into monomeric CRP (mCRP) that has major roles in host defenses and inflammation. Here, we discuss our current knowledge of the dissociation mechanisms of pCRP and summarize the stepwise conformational transition model to mCRP to elucidate how CRP dissociation contributes to proinflammatory activity. These discussions will evoke new understanding of this ancient protein.

The understanding of heat transport in nonequilibrium thermodynamics is an important research frontier, which is crucial for implementing novel thermodynamic devices, such as heat engines and refrigerators. The convection, conduction, and radiation are the well-known basic ways to transfer thermal energy. Here, we demonstrate a different mechanism of phonon heat transport between two spatially separated nanomechanical resonators coupled by the cavity-enhanced long-range interactions. The single trajectory for thermalization and non-equilibrium dynamics is monitored in real-time. In the strong coupling regime, the instant heat flux spontaneously oscillates back and forth in the nonequilibrium steady states. The universal bound on the precision of nonequilibrium steady-state heat flux, i.e. the thermodynamic uncertainty relation, is verified in such a temperature gradient driven far-off equilibrium system. Our results give more insight into the heat transfer with nanomechanical oscillators, and provide a playground for testing fundamental theories in non-equilibrium thermodynamics. Heat flux is well understood on macroscopic scales, however when the system size is reduced, novel phenomena are induced by fluctuations. Here, the authors demonstrate phonon heat transport between two nanomechanical resonators coupled by cavity enhanced interactions exhibiting an oscillating heat flux.

Combating bacterial infections is one of the most important applications of nanomedicine. In the past two decades, significant efforts have been committed to tune physicochemical properties of nanomaterials for the development of various novel nanoantibiotics. Among which, metal nanoclusters (NCs) with well-defined ultrasmall size and adjustable surface chemistry are emerging as the next-generation high performance nanoantibiotics. Metal NCs can penetrate bacterial cell envelope more easily than conventional nanomaterials due to their ultrasmall size. Meanwhile, the abundant active sites of the metal NCs help to catalyze the bacterial intracellular biochemical processes, resulting in enhanced antibacterial properties. In this review, we discuss the recent developments in metal NCs as a new generation of antimicrobial agents. Based on a brief introduction to the characteristics of metal NCs, we highlight the general working mechanisms by which metal NCs combating the bacterial infections. We also emphasize central roles of core size, element composition, oxidation state, and surface chemistry of metal NCs in their antimicrobial efficacy. Finally, we present a perspective on the remaining challenges and future developments of metal NCs for antibacterial therapeutics. Graphical Abstract

Electronic skin is an important means through which robots can obtain external information. A novel flexible tactile sensor capable of simultaneously detecting the contact position and force was proposed in this paper. The tactile sensor had a three-layer structure. The upper layer was a specially designed conductive film based on indium-tin oxide polyethylene terephthalate (ITO-PET), which could be used for detecting contact position. The intermediate layer was a piezoresistive film used as the force-sensitive element. The lower layer was made of fully conductive material such as aluminum foil and was used only for signal output. In order to solve the inconsistencies and nonlinearity of the piezoresistive properties for large areas, a Radial Basis Function (RBF) neural network was used. This includes input, hidden, and output layers. The input layer has three nodes representing position coordinates, X, Y, and resistor, R. The output layer has one node representing force, F. A sensor sample was fabricated and experiments of contact position and force detection were performed on the sample. The results showed that the principal function of the tactile sensor was feasible. The sensor sample exhibited good consistency and linearity. The tactile sensor has only five lead wires and can provide the information support necessary for safe human—computer interactions.

The precise classification of crop types using hyperspectral remote sensing imaging is an essential application in the field of agriculture, and is of significance for crop yield estimation and growth monitoring. Among the deep learning methods, Convolutional Neural Networks (CNNs) are the premier model for hyperspectral image (HSI) classification for their outstanding locally contextual modeling capability, which facilitates spatial and spectral feature extraction. Nevertheless, the existing CNNs have a fixed shape and are limited to observing restricted receptive fields, constituting a simulation difficulty for modeling long-range dependencies. To tackle this challenge, this paper proposed two novel classification frameworks which are both built from multilayer perceptrons (MLPs). Firstly, we put forward a dilation-based MLP (DMLP) model, in which the dilated convolutional layer replaced the ordinary convolution of MLP, enlarging the receptive field without losing resolution and keeping the relative spatial position of pixels unchanged. Secondly, the paper proposes multi-branch residual blocks and DMLP concerning performance feature fusion after principal component analysis (PCA), called DMLPFFN, which makes full use of the multi-level feature information of the HSI. The proposed approaches are carried out on two widely used hyperspectral datasets: Salinas and KSC; and two practical crop hyperspectral datasets: WHU-Hi-LongKou and WHU-Hi-HanChuan. Experimental results show that the proposed methods outshine several state-of-the-art methods, outperforming CNN by 6.81%, 12.45%, 4.38% and 8.84%, and outperforming ResNet by 4.48%, 7.74%, 3.53% and 6.39% on the Salinas, KSC, WHU-Hi-LongKou and WHU-Hi-HanChuan datasets, respectively. As a result of this study, it was confirmed that the proposed methods offer remarkable performances for hyperspectral precise crop classification.

Glioblastomas are highly heterogeneous brain tumors. Despite the availability of standard treatment for glioblastoma multiforme (GBM), i.e., Stupp protocol, which involves surgical resection followed by radiotherapy and chemotherapy, glioblastoma remains refractory to treatment and recurrence is inevitable. Moreover, the biology of recurrent glioblastoma remains unclear. Increasing evidence has shown that intratumoral heterogeneity and the tumor microenvironment contribute to therapeutic resistance. However, the interaction between intracellular heterogeneity and drug resistance in recurrent GBMs remains controversial. The aim of this study was to map the transcriptome landscape of cancer cells and the tumor heterogeneity and tumor microenvironment in recurrent and drug‐resistant GBMs at a single‐cell resolution and further explore the mechanism of drug resistance of GBMs. We analyzed six tumor tissue samples from three patients with primary GBM and three patients with recurrent GBM in which recurrence and drug resistance developed after treatment with the standard Stupp protocol using single‐cell RNA sequencing. Using unbiased clustering, nine major cell clusters were identified. Upregulation of the expression of stemness‐related and cell‐cycle‐related genes was observed in recurrent GBM cells. Compared with the initial GBM tissues, recurrent GBM tissues showed a decreased proportion of microglia, consistent with previous reports. Finally, vascular endothelial growth factor A expression and the blood–brain barrier permeability were high, and the O6‐methylguanine DNA methyltransferase‐related signaling pathway was activated in recurrent GBM. Our results delineate the single‐cell map of recurrent glioblastoma, tumor heterogeneity, tumor microenvironment, and drug‐resistance mechanisms, providing new insights into treatment strategies for recurrent glioblastomas. We observed upregulation of the expression of stemness‐related and cell‐cycle‐related genes in recurrent GBM cells. Further, we observed that recurrent GBM tissues showed a decreased proportion of microglia, consistent with previous reports, and that vascular endothelial growth factor A expression and the blood–brain barrier permeability were high, and the O6‐methylguanine DNA methyltransferase‐related signaling pathway was activated in recurrent GBM. Our results provide new insights into treatment strategies for recurrent glioblastomas.

Precise information on strawberry fruit distribution is of significant importance for optimizing planting density and formulating harvesting strategies. This study applied a combined analysis of kernel density estimation and nearest neighbor techniques to estimate fruit distribution density from YOLOdetected strawberry images. Initially, an improved yolov8n strawberry object detection model was employed to obtain the coordinates of the fruit centers in the images. The results indicated that the improved model achieved an accuracy of 94.7% with an mAP@0.5~0.95 of 87.3%. The relative error between the predicted and annotated coordinates ranged from 0.002 to 0.02, demonstrating high consistency between the model predictions and the annotated results. Subsequently, based on the strawberry center coordinates, the kernel density estimation algorithm was used to estimate the distribution density in the strawberry images. The results showed that with a bandwidth of 200, the kernel density estimation accurately reflected the actual strawberry density distribution, ensuring that all center points in high-density regions were consistently identified and delineated. Finally, to refine the strawberry distribution information, a comprehensive method based on nearest neighbor analysis was adopted, achieving target area segmentation and regional density estimation in the strawberry images. Experimental results demonstrated that when the distance threshold ϵ was set to 600 pixels, the correct grouping rate exceeded 94%, and the regional density estimation results indicated a significant positive correlation between the number of fruits and regional density. This study provides scientific evidence for optimizing strawberry planting density and formulating harvesting sequences, contributing to improved yield, harvesting efficiency, and reduced fruit damage. In future research, this study will further explore dynamic models that link fruit distribution density, planting density, and fruit growth status.