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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.

The high volatility of the price of cobalt and the geopolitical limitations of cobalt mining have made the elimination of Co a pressing need for the automotive industry 1 . Owing to their high energy density and low-cost advantages, high-Ni and low-Co or Co-free (zero-Co) layered cathodes have become the most promising cathodes for next-generation lithium-ion batteries 2 , 3 . However, current high-Ni cathode materials, without exception, suffer severely from their intrinsic thermal and chemo-mechanical instabilities and insufficient cycle life. Here, by using a new compositionally complex (high-entropy) doping strategy, we successfully fabricate a high-Ni, zero-Co layered cathode that has extremely high thermal and cycling stability. Combining X-ray diffraction, transmission electron microscopy and nanotomography, we find that the cathode exhibits nearly zero volumetric change over a wide electrochemical window, resulting in greatly reduced lattice defects and local strain-induced cracks. In-situ heating experiments reveal that the thermal stability of the new cathode is significantly improved, reaching the level of the ultra-stable NMC-532. Owing to the considerably increased thermal stability and the zero volumetric change, it exhibits greatly improved capacity retention. This work, by resolving the long-standing safety and stability concerns for high-Ni, zero-Co cathode materials, offers a commercially viable cathode for safe, long-life lithium-ion batteries and a universal strategy for suppressing strain and phase transformation in intercalation electrodes. A compositionally complex (high-entropy) doping strategy is proposed to fabricate zero-strain high-Ni and Co-free layered cathodes with superior structural and mechanical stabilities and long cycle life.

Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

Globally, prostate cancer ranks second in cancer burden of the men. It occurs more frequently in black men compared to white or Asian men. Usually, high rates exist for men aged 60 and above. In this review, we focus on the Wnt/β-catenin signal transduction pathway in prostate cancer since many studies have reported that β-catenin can function as an oncogene and is important in Wnt signaling. We also relate its expression to the androgen receptor and MMP-7 protein, both critical to prostate cancer pathogenesis. Some mutations in the androgen receptor also impact the androgen-β-catenin axis and hence, lead to the progression of prostate cancer. We have also reviewed MiRNAs that modulate this pathway in prostate cancer. Finally, we have summarized the impact of Wnt/β-catenin pathway proteins in the drug resistance of prostate cancer as it is a challenging facet of therapy development due to the complexity of signaling pathways interaction and cross-talk.

Convolutional neural networks provide an ideal solution for hyperspectral image (HSI) classification. However, the classification effect is not satisfactory when limited training samples are available. Focused on “small sample” hyperspectral classification, we proposed a novel 3D-2D-convolutional neural network (CNN) model named AD-HybridSN (Attention-Dense-HybridSN). In our proposed model, a dense block was used to reuse shallow features and aimed at better exploiting hierarchical spatial–spectral features. Subsequent depth separable convolutional layers were used to discriminate the spatial information. Further refinement of spatial–spectral features was realized by the channel attention method and spatial attention method, which were performed behind every 3D convolutional layer and every 2D convolutional layer, respectively. Experiment results indicate that our proposed model can learn more discriminative spatial–spectral features using very few training data. In Indian Pines, Salinas and the University of Pavia, AD-HybridSN obtain 97.02%, 99.59% and 98.32% overall accuracy using only 5%, 1% and 1% labeled data for training, respectively, which are far better than all the contrast models.

Controlling the nucleation and growth of lithium metal is essential for realizing fast-charging batteries. Here we report the growth of single-crystalline seeds that results in the deposition of dense lithium, even at high current densities. Contrary to the widely accepted practice of using a lithiophilic surface to achieve dendrite-free deposition, we employ a lithiophobic surface made of a nanocomposite of LiF and Fe to deposit hexagonal crystals, which induce subsequent dense lithium deposition. The nanocomposites have uniform Fe sites for nucleation while LiF enables rapid lithium transport. A cell using a 3 mAh cm −2 LiNi 0.8 Co 0.1 Mn 0.1 O 2 (LiNMC811) cathode, onefold excess of lithium and 3 g Ah −1 electrolyte cycles at a 1 C rate for more than 130 cycles with 80% capacity retention, a 550% improvement over the baseline cells. Our findings advance the understanding of lithium nucleation and pave the way for realizing high-energy, fast-charging Li-metal batteries. Controlling the nucleation and growth is essential for enabling long-life Li-metal batteries. Here the authors report the growth of faceted single-crystalline Li seeds on a lithiophobic Fe/LiF composite substrate that enables dense Li deposition under fast-charging conditions.

High-nickel content cathode materials offer high energy density. However, the structural and surface instability may cause poor capacity retention and thermal stability of them. To circumvent this problem, nickel concentration-gradient materials have been developed to enhance high-nickel content cathode materials’ thermal and cycling stability. Even though promising, the fundamental mechanism of the nickel concentration gradient’s stabilization effect remains elusive because it is inseparable from nickel’s valence gradient effect. To isolate nickel’s valence gradient effect and understand its fundamental stabilization mechanism, we design and synthesize a LiNi 0.8 Mn 0.1 Co 0.1 O 2 material that is compositionally uniform and has a hierarchical valence gradient. The nickel valence gradient material shows superior cycling and thermal stability than the conventional one. The result suggests creating an oxidation state gradient that hides the more capacitive but less stable Ni 3+ away from the secondary particle surfaces is a viable principle towards the optimization of high-nickel content cathode materials. High-nickel content cathode materials suffer issues of structural and surface instability. Herewith authors show that introduction of a nickel valence gradient enhances the thermal and cycle stability of high-nickel content cathode materials.

•The first study to identify genes associated with voxel-mirrored homotopic connectivity (VMHC) alterations in patients with schizophrenia using the transcription-neuroimaging association analysis approach.•This study determined the biological functions of the identified genes, as well as the cell types, brain regions and developmental stages in which they were enriched.•We also found cognitive processes related to VMHC alterations in schizophrenia.•The findings offer novel insights into the genetic mechanisms of schizophrenia-related VMHC alterations. It has been revealed that abnormal voxel-mirrored homotopic connectivity (VMHC) is present in patients with schizophrenia, yet there are inconsistencies in the relevant findings. Moreover, little is known about their association with brain gene expression profiles. In this study, transcription-neuroimaging association analyses using gene expression data from Allen Human Brain Atlas and case-control VMHC differences from both the discovery (meta-analysis, including 9 studies with a total of 386 patients and 357 controls) and replication (separate group-level comparisons within two datasets, including a total of 258 patients and 287 controls) phases were performed to identify genes associated with VMHC alterations. Enrichment analyses were conducted to characterize the biological functions and specific expression of identified genes, and Neurosynth decoding analysis was performed to examine the correlation between cognitive-related processes and VMHC alterations in schizophrenia. In the discovery and replication phases, patients with schizophrenia exhibited consistent VMHC changes compared to controls, which were correlated with a series of cognitive-related processes; meta-regression analysis revealed that illness duration was negatively correlated with VMHC abnormalities in the cerebellum and postcentral/precentral gyrus. The abnormal VMHC patterns were stably correlated with 1287 genes enriched for fundamental biological processes like regulation of cell communication, nervous system development, and cell communication. In addition, these genes were overexpressed in astrocytes and immune cells, enriched in extensive cortical regions and wide developmental time windows. The present findings may contribute to a more comprehensive understanding of the molecular mechanisms underlying VMHC alterations in patients with schizophrenia.

With the development of economic globalization, the problem of unequal distribution of globalization dividends among and within countries has become increasingly serious, and reverse globalization has a great impact on the national economy and export trade. This paper uses the KOF Globalization Index and the world input-output tables in World Input-Output Database (WIOD), and empirically studies the transformation of a country’s export trade and export structure in the context of reverse globalization from the perspectives of world, country, industry, subdivided manufacturing and service industry. The results show that reverse globalization has a significant non-linear negative effect on economic development and export trade. Compared with developed and European Union (EU) countries, the exports of developing and non-EU countries are more affected by reverse globalization shocks. Reverse globalization has the greatest inhibition on the secondary industry exports, followed by the tertiary industry. The suppressive effects on the exports of 12 subdivided manufacturing and 14 subdivided service in China are significantly greater than that of the United States, but most of sub-industry exports in the United States are more sensitive. Besides, China’s exports of high-product-complexity industry such as metal products, medicinal chemicals, electrical and optical products and mechanical equipments are greatly affected by reverse globalization, while the exports of water transportation, construction, land transportation are relatively less restrained.

Current detection methods for apple leaf diseases still suffer some challenges, such as the high number of parameters, low detection speed and poor detection performance for small dense spots, which limit the practical applications in agriculture. Therefore, an efficient and accurate model for apple leaf disease detection based on YOLOv5 is proposed and named EADD-YOLO. In the EADD-YOLO, the lightweight shufflenet inverted residual module is utilized to reconstruct the backbone network, and an efficient feature learning module designed through depthwise convolution is proposed and introduced to the neck network. The aim is to reduce the number of parameters and floating point of operations (FLOPs) during feature extraction and feature fusion, thus increasing the operational efficiency of the network with less impact on detection performance. In addition, the coordinate attention module is embedded into the critical locations of the network to select the critical spot information and suppress useless information, which is to enhance the detection accuracy of diseases with various sizes from different scenes. Furthermore, the SIoU loss replaces CIoU loss as the bounding box regression loss function to improve the accuracy of prediction box localization. The experimental results indicate that the proposed method can achieve the detection performance of 95.5% on the mean average precision and a speed of 625 frames per second (FPS) on the apple leaf disease dataset (ALDD). Compared to the latest research method on the ALDD, the detection accuracy and speed of the proposed method were improved by 12.3% and 596 FPS, respectively. In addition, the parameter quantity and FLOPs of the proposed method were much less than other relevant popular algorithms. In summary, the proposed method not only has a satisfactory detection effect, but also has fewer parameters and high calculation efficiency compared with the existing approaches. Therefore, the proposed method provides a high-performance solution for the early diagnosis of apple leaf disease and can be applied in agricultural robots. The code repository is open-sourced at https://github.com/AWANWY/EADD-YOLO.