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Object localization based on radio frequency identification (RFID) technology has promising potentials. By combining localization with its identification capability, existing applications can be enhanced and new applications can be developed for this technology. This paper starts with an overview introducing the available technologies for localization with a focus on radio frequency based technologies. The existing and potential applications of RFID localization in various industries are then summarized. Moreover, RFID localization algorithms are reviewed, which can be categorized into multilateration, Bayesian inference, nearest-neighbor, proximity, and kernel-based learning methods. Also, we present a localization case study using passive RFID technology, and it shows that objects can be successfully localized using either multilateration or Bayesian inference methods. The survey also discusses the challenges and future research on RFID localization.

Metastatic cancer accounts for over 90% of all cancer deaths, and evaluations of metastasis potential are vital for minimizing the metastasis-associated mortality and achieving optimal clinical decision-making. Computational assessment of metastasis potential based on large-scale transcriptomic cancer data is challenging because metastasis events are not always clinically detectable. The under-diagnosis of metastasis events results in biased classification labels, and classification tools using biased labels may lead to inaccurate estimations of metastasis potential. This issue is further complicated by the unknown metastasis prevalence at the population level, the small number of confirmed metastasis cases, and the high dimensionality of the candidate molecular features. Our proposed algorithm, called P ositive and unlabeled L earning from U nbalanced cases and S parse structures ( PLUS ), is the first to use a positive and unlabeled learning framework to account for the under-detection of metastasis events in building a classifier. PLUS is specifically tailored for studying metastasis that deals with the unbalanced instance allocation as well as unknown metastasis prevalence, which are not considered by other methods. PLUS achieves superior performance on synthetic datasets compared with other state-of-the-art methods. Application of PLUS to The Cancer Genome Atlas Pan-Cancer gene expression data generated metastasis potential predictions that show good agreement with the clinical follow-up data, in addition to predictive genes that have been validated by independent single-cell RNA-sequencing datasets.

In this paper, guaiacyl dehydrogenated lignin polymer (G-DHP) was synthesized using coniferin as a substrate in the presence of β-glucosidase and laccase. Carbon-13 nuclear magnetic resonance (13C-NMR) determination revealed that the structure of G-DHP was relatively similar to that of ginkgo milled wood lignin (MWL), with both containing β-O-4, β-5, β-1, β-β, and 5-5 substructures. G-DHP fractions with different molecular weights were obtained by classification with different polar solvents. The bioactivity assay indicated that the ether-soluble fraction (DC2) showed the strongest inhibition of A549 lung cancer cells, with an IC50 of 181.46 ± 28.01 μg/mL. The DC2 fraction was further purified using medium-pressure liquid chromatography. Anti-cancer analysis revealed that the D4 and D5 compounds from DC2 had better anti-tumor activity, with IC50 values of 61.54 ± 17.10 μg/mL and 28.61 ± 8.52 μg/mL, respectively. Heating electrospray ionization tandem mass spectrometry (HESI-MS) results showed that both the D4 and D5 were β-5-linked dimers of coniferyl aldehyde, and the 13C-NMR and 1H-NMR analyses confirmed the structure of the D5. Together, these results indicate that the presence of an aldehyde group on the side chain of the phenylpropane unit of G-DHP enhances its anticancer activity.

BackgroundA disintegrin and metalloproteinase with thrombospondin motifs 10 (ADAMTS10) plays a role in extracellular matrix and correlates with Weill–Marchesani syndrome. However, its role in gastric cancer remains unknown. Thus, we started this research to unveil the role of ADAMTS10 in gastric cancer (GC).MethodsThe expression of ADAMTS10 in GC was analyzed by immunohistochemical staining and quantitative RT-PCR (qRT-PCR). The effects of ADAMTS10 inhibiting GC cell progression were conducted by functional experiments in vitro and in vivo. Flow cytometry was used to discover changing of cell cycle, apoptosis and ROS by ADAMTS10 in GC cell. Western blot was applied to identify targets of ADAMTS10. Western blot, qRT-PCR and flow cytometry were applied to discover the effect of ADAMT10 on THP1.ResultsADAMTS10 expression was downregulated in GC tissue and patients with low ADAMTS10 levels had poorer overall survival. ADAMTS10 overexpression altered cell cycle, promoted apoptosis, and inhibited proliferation, migration, and invasion in vitro and in vivo. ADAMTS10 regulated TXNIP and ROS through the JAK/STAT/c-MYC pathway. Decreasing TXNIP and ROS reversed the inhibitory effect of ADAMTS10 on cell migration and invasion in vitro. ADAMTS10 secreted by GC cells was absorbed by THP1 and regulated TXNIP and ROS in THP1. ADAMTS10 secreted by GC cells inhibited macrophage M2 polarization.ConclusionsThese results suggest that ADAMTS10 targets TXNIP and ROS via the JAK/STAT/c-MYC pathway and that may play important roles in GC progression and macrophage polarization which indicates that ADAMTS10 can be a potential survival marker for gastric cancer.

Flexible and stretchable transparent electrodes are widely used in smart display, energy, wearable devices and other fields. Due to the limitations of flexibility and stretchability of indium tin oxide electrodes, alternative electrodes have appeared, such as metal films, metal nanowires, and conductive meshes. However, few of the above electrodes can simultaneously have excellent flexibility, stretchability, and optoelectronic properties. Nanofiber (NF), a continuous ultra-long one-dimensional conductive material, is considered to be one of the ideal materials for high-performance transparent electrodes with excellent properties due to its unique structure. This paper summarizes the important research progress of NF flexible transparent electrodes (FTEs) in recent years from the aspects of NF electrode materials, preparation technology and application. First, the unique advantages and limitations of various NF materials are systematically discussed. Then, we summarize the preparation technology of various advanced NF FTEs, and point out the future development trend. We also discuss the application of NFs in solar cells, supercapacitors, electric heating equipments, sensors, etc, and analyze its development potential in flexible electronic equipment, as well as problems that need to be solved. Finally, the challenges and future development trends are proposed in the wide application of NF FTEs in the field of flexible optoelectronics. The key properties, advantages, and limitations of various NF materials for FTEs are reviewed and their potential for industrial production is evaluated. Various additive manufacturing technologies for preparing nanofibre-based FTEs are summarized, and their unique benefits and gaps requiring improvement are analyzed. The latest application advances of NF-based FTEs are reviewed, and the future development directions of intelligent flexible optoelectronic devices are analyzed. The current challenges of NF-based FTEs are summarized, and their future development directions and a series of promising research strategies are proposed.

Land use change detection (LUCD) serves as a crucial technical cornerstone for natural resource management and ecological environment monitoring, playing an indispensable role in advancing the modernization of national governance capacities. Nonetheless, severe interference from radiometric variations on feature representation readily induces spurious changes and thus a high false alarm rate. Additionally, the challenge of balancing discriminative feature extraction and fine-grained contextual modeling leads to fragmented change regions and missed detection. To address these issues and eliminate the reliance on annotated samples, a novel framework is proposed for unsupervised LUCD, integrating superpixel-based deep feature analysis with a dense conditional random field (CRF). Firstly, relative radiometric correction and band-wise maximum stacking fusion are performed on the bi-temporal images. A simple non-iterative clustering (SNIC) algorithm is adopted to generate homogeneous superpixels with cross-temporal consistency. Then, a deep feature coupling mining mechanism is introduced to implement spatial–spectral feature extraction and in-depth parsing of invariant semantic information. Meanwhile, the difference confidence map based on dual features is constructed using superpixel-level discriminant vectors to enhance the separability. Finally, leveraging homogeneous units with spatial correspondence, a task-specific redesign of a global optimization model is established to achieve the precise extraction of change regions, which incorporates difference confidence, spatial adjacency relationship, and cross-temporal feature similarity into the dense CRF. The experimental results demonstrate that the proposed method achieves an average overall accuracy of over 90% across all datasets with excellent comprehensive performance, striking a well-balanced trade-off in practical applicability. It can effectively suppress salt-and-pepper noise, significantly improve the recall rate of change regions (maintaining at approximately 90%), and exhibit favorable superiority and robustness in complex land cover scenarios.

To further our understanding of the change in association between lignin and carbohydrates after kraft pulping, isotope-labeled kraft pulp (KP) was prepared using 13C and D double-isotope-labeled wheat straw, and it was subjected to enzymatic hydrolysis and ionic liquid treatment to explore the linkages between lignin and carbohydrate complexes in wheat straw. Isotope abundance determination showed that 13C and D abundances in the experimental groups were substantially higher than those in the control group, indicating that the injected exogenous coniferin-[α-13C], coniferin-[γ-13C], and d-glucose-[6-D2] were effectively absorbed and metabolized during wheat internode growth. Solid-state CP/MAS 13C-NMR spectroscopy showed that lignin was mainly linked to polysaccharides via acetal, benzyl ether, and benzyl ester bonds. Kraft pulp (KP) from the labeled wheat straw was degraded by cellulase. The obtained residue was fractionated using the ionic liquid DMSO/TBAH to separate the cellulose–lignin complex (KP-CLC) and xylan–lignin complex (KP-XLC). X-ray diffractometer determination showed that the KP-CLC regenerated cellulose type II from type I after the ionic liquid conversion. The 13C-NMR spectrum of Ac-En-KP-CLC showed that the cellulose–lignin complex structure was chemically bonded between the lignin and cellulose through acetal and benzyl ether bonds. The 13C-NMR spectrum of En-KP-XLC showed a lignin–hemicellulose complex structure, wherein lignin and xylan were chemically bonded by benzyl ether and acetal bonds. These results indicate that the cross-linking between lignin and carbohydrates exists in lignocellulosic fibers even after kraft pulping.

M-type phospholipase A2 receptor (PLA2R) is a major auto-antigen of primary membranous nephropathy(PMN). Anti-PLA2R antibody levels are closely associated with disease severity and therapeutic effectiveness. Analysis of PLA2R antigen epitope reactivity may have a greater predictive value for remission compared with total PLA2R-antibody level. This study aims to elucidate the relationship between domain-specific antibody levels and clinical outcomes of PMN. This retrospective analysis included 87 patients with PLA2R-associated PMN. Among them, 40 and 47 were treated with rituximab (RTX) and cyclophosphamide (CTX) regimen, respectively. The quantitative detection of -immunoglobulin G (IgG)/-IgG4 targeting PLA2R and its epitope levels in the serum of patients with PMN were obtained through time-resolved fluorescence immunoassays and served as biomarkers in evaluating the treatment effectiveness. A predictive PMN remission possibility nomogram was developed using multivariate logistic regression analysis. Discrimination in the prediction model was assessed using the area under the receiver operating characteristic curve (AUC-ROC).Bootstrap ROC was used to evaluate the performance of the prediction model. After a 6-month treatment period, the remission rates of proteinuria, including complete remission and partial remission in the RTX and CTX groups, were 70% and 70.21% (P = 0.983), respectively. However, there was a significant difference in immunological remission in the PLA2R-IgG4 between the RTX and CTX groups (21.43% vs. 61.90%, P = 0.019). Furthermore, we found differences in PLA2R-CysR-IgG4(P = 0.030), PLA2R-CTLD1-IgG4(P = 0.005), PLA2R-CTLD678-IgG4(P = 0.003), and epitope spreading (P = 0.023) between responders and non-responders in the CTX group. Multivariate logistic analysis showed that higher levels of urinary protein (odds ratio [OR], 0.49; 95% confidence interval [CI], 0.26-0.95; P = 0.035) and higher levels of PLA2R-CTLD1-IgG4 (OR, 0.79; 95%CI,0.62-0.99; P = 0.041) were independent risk factors for early remission. A multivariate model for estimating the possibility of early remission in patients with PMN is presented as a nomogram. The AUC-ROC of our model was 0.721 (95%CI, 0.601-0.840), in consistency with the results obtained with internal validation, for which the AUC-ROC was 0.711 (95%CI, 0.587-0.824), thus, demonstrating robustness. Cyclophosphamide can induce immunological remission earlier than rituximab at the span of 6 months. The PLA2R-CTLD1-IgG4 has a better predict value than total PLA2R-IgG for remission of proteinuria at the 6th month.

Background In recent years, excellent results have suggested an association between the “brain-gut” axis and Alzheimer’s disease (AD) progression, yet the role of the “brain-gut” axis in AD pathogenesis still remains obscure. Herein, we provided a potential link between the central and peripheral neuroinflammatory disorders in AD progression. Methods The Morris water maze (MWM) test, immunohistochemistry, ELISA, ProcartaPlex Multiplex immunoassay, multiple LC-MS/MS methods, and the V3-V4 regions of 16S rRNA genes were applied to explore potential biomarkers. Results In Tg-APP/PS1 mice, gut dysbiosis and lipid metabolism were highly associated with AD-like neuroinflammation. The combination of inflammatory factors (IL-6 and INF-γ), phosphatidylcholines (PCs) and SCFA-producing bacteria were expected to be early diagnostic biomarkers for AD. Huanglian Jiedu decoction (HLJDD) suppressed gut dysbiosis and the associated Aβ accumulation, harnessed neuroinflammation and reversed cognitive impairment. Conclusion Together, our findings highlighted the roles of neuroinflammation induced by gut dysbiosis and lipid metabolism disorder in AD progression. This integrated metabolomics approach showed its potential to understand the complex mechanisms of HLJDD in the treatment of AD.

Primary membranous nephropathy is a widely recognized autoimmune disease associated with podocyte antigens; the most important autoantigen is PLA2R1. PLA2R1 and HLA-DQA1 play important roles in the production of pathogenic antibodies. The purpose of this study was to observe the relationship between gene polymorphisms and primary membranous nephropathy and explore the clinical functional clues of PLA2R1 and HLA-DQA1 genes affecting treatment responsiveness. The study enrolled 89 patients with primary membranous nephropathy and 91 healthy people as a control. Single-nucleotide polymorphism loci (seven on PLA2R1 and two on HLA-DQA1) were identified using the PCR-Sanger technique. The patients were followed up until the 12th month, and relevant clinical data were collected. The relationship between these single-nucleotide polymorphism loci and primary membranous nephropathy remission was analyzed. Genotypic and allelic frequency distributions for six single-nucleotide polymorphisms within PLA2R1 (rs4664308, rs3792189, rs3792192, rs1870102, rs17831251, and rs35771982) and one in HLA-DQA1 (rs2187668) were associated with morbidity of primary membranous nephropathy. Single-nucleotide polymorphisms rs1870102, rs17831251, and rs2187668 were statistically significant in the genetic model analysis. The odds ratio for primary membranous nephropathy in patients carrying rs2187668 GG and rs1870102 AA was 52.875. We found that PLA2R1 single-nucleotide polymorphism rs36771982 was related to proteinuria remission at the 12th month, and found in further analysis that PLA2R1 single-nucleotide polymorphisms rs3792189, rs3792192, rs17831251, and rs35771982 were related to treatment response in the RTX group. In this study, we found several PLA2R1 and HLA-DQA1 single-nucleotide polymorphism loci associated with primary membranous nephropathy morbidity and that some PLA2R1 single-nucleotide polymorphism loci were related to the treatment response of patients with primary membranous nephropathy.