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Cooked rice and the vegetables like lettuce are common kitchen waste, which are carbonaceous materials and have the potential as feedstock for the production of activated carbon. Cooking is similar to hydrothermal treatment (HTC), which might impact the subsequent activation of kitchen waste. In this study, the HTC of lettuce, rice, or their mixture and the activation of the resulting hydrochars were conducted. The results indicated that cross-polymerization between the N-containing organics from lettuce and the sugar derivatives from rice took place in their co-HTC, which significantly increased the hydrochar yield. Activation of the hydrochar from the co-HTC generated the AC with a yield of 2 times that from direct activation of mixed lettuce/rice. However, the co-HTC facilitated aromatization, reducing reactivity with K2C2O4 in activation and producing the AC with main micropores and low specific surface area. Activation of the hydrochar from HTC of rice followed the above trend, while that from lettuce was the opposite. The organics in lettuce were thermally unstable and could not undergo sufficient aromatization. The activation of hydrochar from HTC of lettuce thus generated the AC with the lowest yield, but the highest specific surface area (1684.9 m2/g), abundant mesopores, and superior capability for adsorption of tetracycline. However, the environmental impacts and energy consumption for the production of AC from the hydrochar of lettuce were higher than that from hydrochar of co-HTC.

Abstract Background Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally, influenced by aberrant circRNA expression. Investigating circRNA-miRNA-mRNA interactions can unveil underlying mechanisms of HCC and identify potential therapeutic targets. Methods In this study, we conducted differential analyses of mRNAs, miRNAs, and circRNAs, and established their relationships using various databases such as miRanda, miRDB, and miTarBase. Additionally, functional enrichment and immune infiltration analyses were performed to evaluate the roles of key genes. We also conducted qPCR assays and western blotting (WB) to examine the expression levels of circRNA, CCL25, and MAP2K1 in both HCC cells and clinical samples. Furthermore, we utilized overexpression and knockdown techniques for circ_0000069 and conducted wound healing, transwell invasion assays, and a tumorigenesis experiment to assess the migratory and invasive abilities of HCC cells. Results Our findings revealed significant differential expression of 612 upregulated genes and 1173 downregulated genes in HCC samples compared to normal liver tissue. Additionally, 429 upregulated circRNAs and 453 downregulated circRNAs were identified. Significantly, circ_0000069 exhibited upregulation in HCC tissues and cell lines. The overexpression of circ_0000069 notably increased the invasion and migration capacity of Huh7 cells, whereas the downregulation of circ_0000069 reduced this capability in HepG2 cells. Furthermore, this effect was counteracted by CCL25 silencing or overexpression, separately. Animal studies further confirmed that the overexpression of hsa_circ_0000069 facilitated tumor growth in xenografted nude mice, while the inhibition of CCL25 attenuated this effect. Conclusion Circ_0000069 appears to promote HCC progression by regulating CCL25, suggesting that both circ_0000069 and CCL25 can serve as potential therapeutic targets.

IntroductionZhachong Shisanwei Pill (ZSP) is a classical Mongolian formula that combines 13 types of Chinese medicinal materials and has been used for treating ischemic stroke (IS) for centuries. However, the underlying molecular mechanisms have yet to be fully elucidated. The aim of this study is to explore potential mechanism of ZSP on nerve cells in cerebral ischemic injury.MethodsTo simulate the pathological process of oxidative stress following IS, an injury model using PC12 cells was induced with hydrogen peroxide (H2O2). Afterward, PC12 cells were treated with ZSP medicated serum at low, medium, and high doses. Various assays were conducted to assess cell viability and oxidative stress indicators, including lactate dehydrogenase (LDH), malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), reactive oxygen species (ROS), and mitochondrial membrane potential (MMP). Cell apoptosis was evaluated through morphological assessment and flow cytometry. Additionally, the expression levels of apoptosis-related proteins (Bcl-2, Bax, Caspase-9, Caspase-3, PARP) and signaling pathway proteins (JNK, phosphorylated JNK, ERK, phosphorylated ERK, p38, and phosphorylated p38) were measured using automated Western blotting.ResultsOur findings indicate that ZSP medicated serum preconditioning improves the condition of PC12 cells injured by H2O2. Specifically, it increased cell survival rates and reduced LDH release. Additionally, ZSP treatment decreased ROS levels and MDA content, while enhancing the activity of SOD and CAT in the injured PC12 cells. ZSP also reversed the depolarization of mitochondrial membrane potential and protected cells from apoptosis by modulating the expression of apoptosis-related proteins, including Bcl-2, Bax, Caspase-9, Caspase-3, and PARP. Furthermore, the overactivation of the MAPK signaling pathway due to H2O2-induced injury was inhibited, as evidenced by the downregulation of phosphorylated JNK, ERK, and p38 levels.DiscussionMongolian medicine ZSP demonstrates protective effects against H2O2-induced oxidative stress and apoptosis in PC12 cells. The underlying mechanism may involve the inhibition of the MAPK signaling pathway, enhancement of antioxidant enzyme activity, reduction of intracellular peroxidation levels, and suppression of intrinsic apoptosis pathways.

Abstract Electrochemical CO2 reduction has garnered significant interest in the conversion of sustainable energy to valuable fuels and chemicals. Cu‐based bimetallic catalysts play a crucial role in enhancing *CO concentration on Cu sites for efficient C─C coupling reactions, particularly for C2 product generation. To enhance Cu's electronic structure and direct its selectivity toward C2 products, a novel strategy is proposed involving the in situ electropolymerization of a nano‐thickness cobalt porphyrin polymeric network (EP‐CoP) onto a copper electrode, resulting in the creation of a highly effective EP‐CoP/Cu tandem catalyst. The even distribution of EP‐CoP facilitates the initial reduction of CO2 to *CO intermediates, which then transition to Cu sites for efficient C─C coupling. DFT calculations confirm that the *CO enrichment from Co sites boosts *CO coverage on Cu sites, promoting C─C coupling for C2+ product formation. The EP‐CoP/Cu gas diffusion electrode achieves an impressive current density of 726 mA cm−2 at −0.9 V versus reversible hydrogen electrode (RHE), with a 76.8% Faraday efficiency for total C2+ conversion and 43% for ethylene, demonstrating exceptional long‐term stability in flow cells. These findings mark a significant step forward in developing a tandem catalyst system for the effective electrochemical production of ethylene.

Background Continuous renal replacement therapy (CRRT) is commonly used for the treatment of acute kidney injury (AKI) in critically ill neonates. This study investigated the effectiveness and feasibility of CRRT for AKI in neonates who weigh ≤3 kg.Methods Data from 19 neonates with a weight ≤3 kg and AKI who underwent CRRT at two centres between January 2015 and October 2021 were collected retrospectively. Kidney function, circulatory function, complications and clinical outcomes were recorded. Repeated-measures analyses of variance, t-tests and non-parametric tests were conducted.Results The median patient age at CRRT initiation was 3 days (IQR: 1–7 days). The median patient weight at CRRT initiation was 2.67 kg (IQR: 2.20–2.85 kg). The median CCRT duration was 46 hours (IQR: 32–72 hours). The serum creatinine and blood urea nitrogen levels decreased significantly, and the mean arterial pressure increased significantly after 12 hours of CRRT and at the end of CRRT. The urinary output was significantly increased at the end of CRRT. 11 patients had thrombocytopaenia, 6 had electrolyte disorders and 3 had blocked tubes. Five patients were discharged, six died after their parents chose to discontinue treatment and eight died after active treatment. Weight at CRRT initiation and urinary output at the end of CRRT were significantly lower among patients who died than among patients who survived.Conclusions CRRT is feasible and effective for AKI in neonates who weigh ≤3 kg when accompanied by elaborate supportive care. Lower body weight and persistent oliguria may be correlated with an increased risk of poor clinical outcomes.

Sediments are important heavy metal sinks in lakes, crucial for ensuring water environment safety. Existing studies mainly focused on well-studied lakes, leaving gaps in understanding pollution patterns in specific basins and influencing factors.We compiled comprehensive sediment contamination data from literature and public datasets, including hydro-geomorphological, climatic, soil, landscape, and anthropogenic factors. Using advanced machine learning, we analyzed typical pollution factors to infer potential sources and migration pathways of pollutants and predicted pollution levels in basins with limited data availability. Our analysis of pollutant distribution data revealed that Cd had the most extensive pollution range, with the most severe pollution occurring in the Huaihe and Yangtze River basins. Furthermore, we identified distinct groups of driving factors influencing various heavy metals. Cd, Cr, and Pb were primarily influenced by human activities, while Cu and Ni were affected by both anthropogenic and natural factors, and Zn tended more towards natural sources. Our predictions indicated that, in addition to the typical highly polluted areas, the potential risk of Cd, Cu and Ni is higher in Xinjiang, and in Tibet and Qinghai, the potential risk of Cd, Cr, Cu and Ni is higher. Pb and Zn presented lower risks, except in the Huaihe and Yangtze River Basins. Temperature, wind, precipitation, precipitation rate, and the cation exchange capacity of soil significantly impacted the predictions of heavy metal pollution in sediments, suggesting that particulate migration, rainfall runoff, and soil erosion are likely the main pathways for pollutant migration into sediments. Considering the migration, pathways, and sources of pollutants, we propose strategies such as low-impact development and promoting sustainable transportation to mitigate pollution. This study provides the latest insights into heavy metal pollution in Chinese lake sediments, offering references for policy-making and water resource management.

Abstract To ensure the real‐time operation safety of electric vehicles (EVs), it is essential to diagnose the fault in a battery pack timely and accurately. In this paper, with considering driving condition, a battery voltage fault diagnosis method is proposed based on the real‐world operation data of EVs with a high sampling frequency. Firstly, based on driving behaviour, the driving condition of EVs is classified into four categories, and accordingly, the operation process is divided into four segments. The influencing mechanism of driving condition on battery voltage is revealed by detailed analysis on extracted operation segments. Secondly, four BP neural network (BPNN)‐based voltage prediction models are developed, respectively, for the four kinds of driving conditions. Based on the statistical analysis of prediction error and the comparison with other voltage prediction models, the superiority and stability of the four well‐trained BPNN models are verified. Thirdly, the voltage abnormity levels and thresholds for fault diagnosis are set considering driving condition differences. The effectiveness of the proposed method is verified using the actual operational data. The verification results show that the proposed method can achieve good voltage prediction and fault diagnosis for EVs under various driving conditions during the entire operation process.

Abstract Background Ethyl acetate extracts from Tetrastigma hemsleyanum (Sanyeqing) (EFT), a member of the Vitaceae plant family, have been shown to exhibit efficacy against a variety of cancers. In this light, our current study seeks to examine the mechanism of efficacy between EFT extracts and human pancreatic cancer PANC-1 cells. Methods The chemical components of EFT were analyzed by gas chromatography–mass spectrometry. The cytotoxicity of EFT on PANC-1 cells was measured using an MTT assay. In order to investigate EFT induction of cell cycle arrest, changes in cell-cycle distribution were monitored by flow cytometry. Wound healing and transwell assays were employed to investigate whether migration and invasion of PANC-1 cells were inhibited by EFT. Relative protein expression was detected using Western blot. Results GC-MS analysis of the chemical composition of EFT revealed that the majority of constituents were organic acids and their corresponding esters. EFT exhibits measurable cytotoxicity and inhibition of PANC-1 invasion. Growth inhibition was primarily attributed to downregulation of CDK2 which induces cell cycle arrest in the S-phase. Inhibition of metastasis is achieved through downregulation of mesenchymal-associated genes/activators, including ZEB1, N-cadherin, Vimentin, and Fibronectin. Meanwhile, the expression of E-cadherin was significantly increased by EFT treatment. Furthermore, downregulation of MMP-2 and MMP-9 were observed. Conclusion Treatment of PANC-1 with EFT demonstrated measurable cytotoxic effects. Furthermore, EFT evoked S phase arrest while inhibiting the migration and invasion of PANC-1 cells. Additionally, EFT inhibited the epithelial to mesenchymal transition and MMPs expression in PANC-1 cells. This study serves to confirm the strong therapeutic potential of EFT while identifying the mechanisms of action.

Abstract This study addresses the escalating risk of high-speed railway (HSR) infrastructure in China, amplified by climate warming, increased rainfall, frequent extreme weather, and geohazard events. Leveraging a georeferenced dataset of recent HSR defects obtained through an extensive literature review, we employ machine learning techniques for a quantitative multi-defect risk assessment. Climatic, geomorphological, geohydrological, and anthropogenic variables influencing HSR subgrade safety are identified and ranked. Climatic factors significantly impact frost damage and mud pumping, while geomorphological variables exhibit greater influence on settlement and uplift deformation defects. Notably, frost damage is prevalent in the northeast and northwest, mud pumping along the southeast coast, and settlement and uplift deformation in the northwest and central areas. The generated comprehensive risk map underscores high-risk zones, particularly the Menyuan Hui Autonomous and Minle County sections of the Lanzhou-Urumqi HSR, emphasizing the need for focused attention and preventive actions to mitigate potential losses and ensure operational continuity.

Background Iron metabolism plays a significant role in the development of metabolic disorders in women with polycystic ovary syndrome (PCOS). Despite the importance of hepcidin, a key iron regulator, current research on serum hepcidin levels in PCOS patients shows conflicting results.Methods PubMed, Embase, Web of Science, Cochrane Library and the China National Knowledge Infrastructure (CNKI) database were systematically searched from their inception to 9 September 2023. The search aimed to identify studies in English and Chinese that examined hepcidin levels in women with PCOS compared to healthy control subjects. Standardized mean differences (SMDs) with corresponding 95% confidence intervals (95% CIs) were calculated to evaluate the difference in serum hepcidin levels between women with and without PCOS.Results The meta-analysis included a total of 10 eligible studies, which encompassed 499 PCOS patients and 391 control subjects. The pooled analysis revealed a significant reduction in serum hepcidin levels among the PCOS patients compared to the healthy controls (SMD = −3.49, 95% CI: −4.68 to −2.30, p .05).Conclusion The serum hepcidin levels of women with PCOS were significantly lower than those of healthy controls, which suggests that serum hepcidin could be a potential biomarker for PCOS.

ObjectiveThis study aims to investigate the effects of hyperoxia exposure on TGF-β1-induced endothelial-mesenchymal transition (EndoMT) and regulatory T cell (Treg)—mediated immunomodulation in human pulmonary microvascular endothelial cells (HPMECs), which could provide a theoretical basis for further studies of the pathogenesis of bronchopulmonary dysplasia (BPD).MethodsA BPD cell model was established by exposing HPMECs to hyperoxia. Flow cytometry was used to isolate CD4 + CD3 + CD25 + CD127- Tregs from the peripheral blood samples of preterm infants. HPMECs were divided into four groups based on whether they were exposed to hyperoxia and/or co-cultured with Tregs. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) were used to test the expression levels of TGF-β1, α-SMA, Foxp3, IL-10, and reactive oxygen species (ROS).ResultsThe results showed that the expression levels of TGF-β1 and α-SMA in HPMECs increased at 24 h, 48 h, and 72 h of hyperoxia exposure. In the co-culture group of HPMECs and Tregs, Foxp3 and IL-10 expressions decreased at 48 h and 72 h of hyperoxia exposure. ROS expression increased in the hyperoxia group of HPMECs at 24 h, 48 h, and 72 h of hyperoxia exposure, which were higher than those in the hyperoxia group of HPMECs and Tregs.ConclusionThese findings suggest that hyperoxia exposure promotes EndoMT in HMPECs and inhibits the immunosuppressive effect of Tregs. Despite this, Tregs still seem could protect HPMECs from oxidative stress injury.

BackgroundSnijders Blok-Campeau syndrome (SNIBCPS) is a rare genetic disorder characterized by facial abnormalities, hypotonia, macrocephaly, and global developmental delay (GDD) caused by mutations in CHD3 gene. There is limited information on SNIBCPS and few studies on its pathogenic gene CHD3.MethodsWe utilized whole-exome sequencing, in vitro minigene splicing assay analysis, and construction of protein models to validate the suspected pathogenic mutation. In addition, the PubMed database was searched using the keywords “Snijders Blok-Campeau syndrome,” “CHD3,” or “SNIBCPS” to summarize the gene mutations and clinical phenotypic characteristics of children with SNIBCPS.ResultsWe identified a non-frameshift variant c.3592_c.3606delGCCAAGAGAAAGATG, a splice site variant c.1708-1G>T, and two missense variants, c. 2954G>C (p.Arg985Pro) and c.3371C>T (p.A1124V), in CHD3 variants with SNIBCPS. Importantly, the c.3592_c.3606delGCCAAGAGAAAGATG, c.1708-1G>T, and c.3371C > T (p.A1124V) loci were not reported, and the children in this study also had phenotypic features of unibrow, transverse palmar creases, tracheal bronchus, and hypomelanosis of Ito (HI). The c.1708-1G>T classical splicing mutation leads to abnormal shearing of mRNA, forming a truncated protein that ultimately affects gene function.ConclusionOur findings have expanded the spectrum of genetic variants and clinical features in children with SNIBCPS. Splicing analysis of CHD3 is an important method to understand the pathogenesis of spliced cells.

Abstract Object detection techniques have been widely used in power system equipment maintenance. However, in power systems, the accuracy of object detection is limited by the scarcity of publicly available datasets and the lack of scene pertinence. In order to solve these problems, an object detection method based on cloud data fusion and transfer learning (YOLO‐DFT) for power system equipment maintenance is proposed. Illustratively, YOLO‐DFT focuses on the object detection task involving birds and humans, generating a substantial and resilient human‐bird dataset through cloud‐based data fusion to compensate for the dearth of public datasets in the power system domain. By seamlessly integrating the YOLOv5 algorithm with a transfer learning strategy, a targeted detection mechanism for specific locations is meticulously formulated. The experimental results demonstrate that YOLO‐DFT effectively addresses object detection challenges in power systems, achieving a Mean Average Precision (MAP) measure of 0.925 across all classes, thereby providing a valuable reference for the maintenance of power system equipment.

Summary: Background: Understanding the impact of CYP2D6 metabolism on paroxetine, a widely used antidepressant, is essential for precision dosing. Methods: We conducted an 8-week, multi-center, single-drug, 2-week wash period prospective cohort study in 921 Chinese Han patients with depressive or anxiety disorders (ChiCTR2000038462). We performed CYP2D6 genotyping (single nucleotide variant and copy number variant) to derive the CYP2D6 activity score and evaluated paroxetine treatment outcomes including steady-state concentration, treatment efficacy, and adverse reaction. CYP2D6 metabolizer status was categorized into poor metabolizers (PMs), intermediate metabolizers (IMs), extensive metabolizers (EMs), and ultrarapid metabolizers (UMs). The influence of CYP2D6 metabolic phenotype on paroxetine treatment outcomes was examined using multiple regression analysis and cross-ethnic meta-analysis. The therapeutic reference range of paroxetine was estimated by receiver operating characteristic (ROC) analyses. Findings: After adjusting for demographic factors, the steady-state concentrations of paroxetine in PMs, IMs, and UMs were 2.50, 1.12, and 0.39 times that of EMs, with PM and UM effects being statistically significant (multiple linear regression, P = 0.03 and P = 0.04). Sex and ethnicity influenced the comparison between IMs and EMs. Moreover, poor efficacy of paroxetine was associated with UM, and a higher risk of developing adverse reactions was associated with lower CYP2D6 activity score. Lastly, cross-ethnic meta-analysis suggested dose adjustments for PMs, IMs, EMs, and UMs in the East Asian population to be 35%, 40%, 143%, and 241% of the manufacturer's recommended dose, and 62%, 68%, 131%, and 159% in the non-East Asian population. Interpretation: Our findings advocate for precision dosing based on the CYP2D6 metabolic phenotype, with sex and ethnicity being crucial considerations in this approach. Funding: National Natural Science Foundation of China; Academy of Medical Sciences Research Unit.

Abstract Background Alzheimer’s disease (AD) is an incurable neurodegenerative disorder with a rapidly increasing prevalence worldwide. Current approaches targeting hallmark pathological features of AD have had no consistent clinical benefit. Neuroinflammation is a major contributor to neurodegeneration and hence, microglia, the brain’s resident immune cells, are an attractive target for potentially more effective therapeutic strategies. However, there is no current in vitro model system that captures AD patient-specific microglial characteristics using physiologically relevant and experimentally flexible culture conditions. Methods To address this shortcoming, we developed novel 3D Matrigel-based monocyte-derived microglia-like cell (MDMi) mono-cultures and co-cultures with neuro-glial cells (ReNcell VM). We used single-cell RNA sequencing (scRNAseq) analysis to compare the transcriptomic signatures of MDMi between model systems (2D, 3D and 3D co-culture) and against published human microglia datasets. To demonstrate the potential of MDMi for use in personalized pre-clinical strategies, we generated and characterized MDMi models from sixteen AD patients and matched healthy controls, and profiled cytokine responses upon treatment with anti-inflammatory drugs (dasatinib and spiperone). Results MDMi in 3D exhibited a more branched morphology and longer survival in culture compared to 2D. scRNAseq uncovered distinct MDMi subpopulations that exhibit higher functional heterogeneity and best resemble human microglia in 3D co-culture. AD MDMi in 3D co-culture showed altered cell-to-cell interactions, growth factor and cytokine secretion profiles and responses to amyloid-β. Drug testing assays revealed patient- and model-specific cytokine responses. Conclusion Our study presents a novel, physiologically relevant and AD patient-specific 3D microglia cell model that opens avenues towards improving personalized drug development strategies in AD.

In this paper, we investigate the cooperative economic operation control problem of the integrated energy system (IES) composed of a cluster of energy hubs (EHs) without continuous communication. To solve the problem, a dynamic event-triggered communication strategy and corresponding protocols are introduced. Specifically, a distributed EH output controller is designed based on the consensus algorithm. This enables accurate sharing of electricity and heat load power without requiring knowledge of network parameters. Furthermore, the control method ensures that the pressure is kept within an acceptable range and the frequency is restored to the reference value. By taking energy prices into account, the equal incremental principle is applied to develop a control method of devices to minimize the economic cost. Finally, the case studies are provided to substantiate the efficacy of the proposed control approach.

Efficient and accurate short-term electric load forecasting plays a significant role in energy conservation and reducing carbon emissions. Recurrent neural networks (RNN) and their derived deep learning models have continuously improved the accuracy of short-term load predictions. However, traditional deep learning models, constrained by the one-dimensional structure of time series data, struggle to capture the relationships within and between periods. And when performing load forecasting tasks, these models tend to establish temporal relationships in the time dimension while overlooking the relationships between different feature variable dimensions. In order to address both, this paper proposes a Crossformer-based TimesNet-LSTM method for short-term electric load forecasting. The proposed approach takes historical load data as input and leverages the unique structure of TimesNet to convert the one-dimensional time series into a two-dimensional space for information extraction. The Crossformer model with double attention mechanisms is then employed to capture the relationships between sequences, time, and feature variables in different dimensions. Finally, the LSTM computes the output results. Experimental calculations on publicly available datasets from Australia and the United States demonstrate the superior performance of the proposed model compared to traditional single models and other hybrid models in short-term forecasting of multidimensional electricity load data. The Mean Absolute Percentage Error (MAPE) achieved on the Australian dataset is 0.52%, while on the U.S. dataset it is 0.53%. These outstanding results highlight the universality and robustness of the model. The proposed Crossformer-based TimesNet-LSTM method not only overcomes the limitations of traditional one-dimensional deep learning models but also enhances the accuracy of short-term electric load forecasting. Its application has significant implications for energy saving and carbon emission reduction.

Abstract Compared with electrical neural networks, optical neural networks (ONNs) have the potentials to break the limit of the bandwidth and reduce the consumption of energy, and therefore draw much attention in recent years. By far, several types of ONNs have been implemented. However, the current ONNs cannot realize the acceleration as powerful as that indicated by the models like quantum neural networks. How to construct and realize an ONN with the quantum speedup is a huge challenge. Here, we propose theoretically and demonstrate experimentally a new type of optical convolutional neural network by introducing the optical correlation. It is called the correlated optical convolutional neural network (COCNN). We show that the COCNN can exhibit “quantum speedup” in the training process. The character is verified from the two aspects. One is the direct illustration of the faster convergence by comparing the loss function curves of the COCNN with that of the traditional convolutional neural network (CNN). Such a result is compatible with the training performance of the recently proposed quantum convolutional neural network (QCNN). The other is the demonstration of the COCNN’s capability to perform the QCNN phase recognition circuit, validating the connection between the COCNN and the QCNN. Furthermore, we take the COCNN analog to the 3-qubit QCNN phase recognition circuit as an example and perform an experiment to show the soundness and the feasibility of it. The results perfectly match the theoretical calculations. Our proposal opens up a new avenue for realizing the ONNs with the quantum speedup, which will benefit the information processing in the era of big data.

The discovery of a lead compound against Candida albicans is urgently needed because of the lack of clinically available antifungal drugs and the increase in drug resistance. Herein, a β-carboline alkaloid methylaervine (MET) exhibited potential activity against C. albicans (MIC = 16–128 μg/mL), no hemolytic toxicity, and a low tendency to induce drug resistance. An antifungal mechanism study indicated that MET effectively inhibited the biofilm formation and disrupted the mature biofilm. Moreover, filamentation formation and spore germination were also weakened. The electron microscopy analysis revealed that MET could damage the cell structure, including the cell wall, membrane, and cytoplasm. In particular, the permeability and integrity of the cell membrane were destroyed. When it entered the fungi cell, it interfered with the redox homeostasis and DNA function. Overall, MET can inhibit the growth of C. albicans from multiple channels, such as biofilm, filamentation, cell structure, and intracellular targets, which are difficult to mutate at the same time to generate drug resistance. This work provides a promising lead compound for the creation of new antifungal agents against C. albicans.

Due to the insulation saturation effect of a vacuum gap, the series connection is the main method to improve the capability of the triggered vacuum switch (STVS) to withstand voltage. In this paper, a double-gap surface flashover triggered multistage vacuum switch (DMVS) is constructed based on a removable vacuum chamber. The DMVS consists of a surface flashover triggered vacuum gap (STG) and a self-breakdown vacuum gap (SBG) in series. The trend in DMVS conduction delay time under different operating parameters and gap distances is firstly measured through experiments, and then the conduction delay time characteristics of DMVS and single-gap STVS compared. The test results show that increasing the trigger current and operating voltage and decreasing the gap distance of each vacuum gap can optimize the conduction delay time of the DMVS, but the voltage division ratio between the STG and the SBG has no obvious influence on the conduction delay time. The comparison result between the DMVS and the STVS shows that increasing the number of series gaps increases the conduction delay time but, for a certain vacuum gap distance, DMVS exhibits superior conduction delay time characteristics compared to STVS.

Quantum metrology, such as quantum phase estimation, can surpass classical sensing limits, reaching the Heisenberg‐scaling precision. So far, this kind of metrology has been thought to be only implementable with the quantum systems, which, however, are fragile to environmental noise and hardly contribute to the practical detections. Herein, it is demonstrated both theoretically and experimentally that the parameter encoded by the optical phase can also be estimated at the Heisenberg scaling in classical optics. Inspired by the quantum‐entanglement‐enhanced sensing scheme, the estimation is performed by using classically correlated beams as probes, and obtaining the probes readout after their interaction with the target system. Because the correlated beams considered are spatially separable, a distributed phase estimation scheme is given, which can sense the linear combinations of the phase shifts induced by distinct systems. The results of our experiments show an error reduction up to 3.89 dB below the classical limit when the correlated beam number for probing is 6, approaching the Heisenberg limit. Compared with quantum strategies, the proposal shows a better robustness against the environmental disturbance and keeps their performances even when the correlated beam number is relatively large. Hence, it indicates promising practical applications in the future.

Abstract Background Small extracellular vesicles (sEVs) have great potential as new biomarkers in liquid biopsy. However, due to the limitations of sEVs extraction and component analysis procedures, further clinical applications of sEVs are hampered. Carcinoembryonic antigen (CEA) is a commonly used broad-spectrum tumor marker that is strongly expressed in a variety of malignancies. Results In this study, CEA+ sEVs were directly separated from serum using immunomagnetic beads, and the nucleic acid to protein ultraviolet absorption ratio (NPr) of CEA+ sEVs was determined. It was found that the NPr of CEA+ sEVs in tumor group was higher than that of healthy group. We further analyzed the sEV-derived nucleic acid components using fluorescent staining and found that the concentration ratio of double-stranded DNA to protein (dsDPr) in CEA+ sEVs was also significantly different between the two groups, with a sensitivity of 100% and a specificity of 41.67% for the diagnosis of pan-cancer. The AUC of dsDPr combined with NPr was 0.87 and the ACU of dsDPr combined with CA242 could reach 0.94, showing good diagnostic performance for pan-cancer. Conclusions This study demonstrates that the dsDPr of CEA+ sEVs can effectively distinguish sEVs derived from tumor patients and healthy individuals, which can be employed as a simple and cost-effective non-invasive screening technology to assist tumor diagnosis.

Hyperspectral image (HSI) classification, incorporating both spatial and spectral information, is a crucial topic in earth observation and land cover analysis. However, ground objects with similar spectral attributes are still the challenges for finer classifications. Recently, deep learning-based multimodality fusion provides promising solutions by exploiting LiDAR data with its geometric information to fuse with spectral attributes. However, the labor-intensive and time-consuming multimodality data annotation limits the performance of supervised deep learning technologies. How to address the semantic disparity between the LiDAR data and HSIs, and learning transferable representations for cross-scene classifications are still challenging. In this paper, we propose a multimodal fusion relational network with meta-learning (MFRN-ML) to solve these challenges. Specifically, the MFRN-ML incorporates the multimodal learning and few-shot learning (FSL) into a three-stage task-based learning framework to learn the transferable cross-modality representations for few-shot HSI and LiDAR collaborative classification. First, a multimodal fusion relational network, composed of a cross-modality feature fusion module and a relation learning module, is proposed to address the challenge of limited annotations in multimodal learning in a data-adaptive way. Then, a three-stage task-based learning framework can train the network to learn transferable representations with few labeled samples for cross-scene classification. We perform experiments on four multimodal datasets collected by different sensors. Compared with existing supervised, semi-supervised, and meta-learning methods, MFRN-ML attains state-of-the-art performances in few-shot tasks. Particularly, our method shows promising generalization ability on unseen categories across different domains.

ObjectiveDisruption in the delicate symphony of genes, microRNA (miRNA), or protein expression can result in the dysregulation of the immune system, leading to the devastating consequences such as lupus nephritis (LN). The capacity of exosomes to transport miRNAs between cells and modify the phenotype of recipient cells implies their involvement in persistent kidney inflammation. This study unveils identifying two previously undiscovered exosomal miRNAs in the serum of LN patients, offering potential solutions to the current challenges in LN diagnosis and management.MethodsInitially, we used a reagent-based kit to isolate serum exosomes from patients with Systemic lupus erythematosus (SLE) and used Trizol method for total RNA extraction. Subsequently, we employed small RNA sequencing to screen for differential expression profiles of exosomal small RNAs. The RT-qPCR method was used to individually validate samples in both the screening and validation cohorts, enabling the identification of candidate small RNAs; specific to LN. We assessed the diagnostic potency using receiver operating characteristic (ROC) curve, and explored the biological roles of miRNAs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses.ResultsCompared to SLE patients without LN, SLE patients accompanied by LN exhibited significantly spiked levels of exosomal hsa-miR-4796-5p and hsa-miR-7974. The duo of miRNAs, hsa-miR-4796-5p and hsa-miR-7974, exhibited promising potential as biomarkers for diagnosing LN, with an AUC exceeding 0.8. Correlation analysis revealed a strong positive association between these miRNAs and proteinuria, as well as the SLE Disease Activity Index (SLEDAI) score. Moreover, the levels of two miRNAs in LN patients were significantly elevated in comparison to other autoimmune nephritis conditions, such as immunoglobulin A nephropathy (IgAN) and diabetic nephropathy (DN). Furthermore, the bioinformatics analysis indicated that this miRNAs duo can play a pivotal role in the regulation of immune processes by modulating signal pathways, such as the mTOR and PI3K-Akt signaling pathway.ConclusionThis study provides a new ground that serum exosomal miRNAs can effectively identify and predict LN in SLE patients.

ABSTRACTIn recent years, deep learning methods have been widely used for the classification of hyperspectral images. However, their limited availability under the condition of small samples remains a serious issue. Moreover, the current mainstream approaches based on convolutional neural networks do well in local feature extraction but are also restricted by its limited receptive field. Hence, these models are unable to capture long-distance dependencies both on spatial and spectral dimension. To address above issues, this paper proposes a global-local U-Net augmented by transformers (TrmGLU-Net). First, whole hyperspectral images are input to the model for end-to-end training to capture the contextual information. Then, a transformer-augmented U-Net is designed with alternating transformers and convolutional layers to perceive both global and local information. Finally, a superpixel-based label expansion method is proposed to expand the labels and improve the performance under the condition of small samples. Extensive experiments on four hyperspectral scenes demonstrate that TrmGLU-Net has better performance than other advanced patch-level and image-level methods with limited training samples. The relevant code will be opened at https://github.com/sssssyf/TrmGLU-Net

Background Norepinephrine (NE) is recommended for children and full-term neonates (born at >37 gestational weeks) with septic shock. Meanwhile, data on the effectiveness of NE in preterm neonates are still limited. This study aimed to evaluate the clinical efficacy of NE in preterm neonates with dopamine-resistant shock compared with that in full-term neonates.Methods This was a single-centre, retrospective (January 2010–December 2020) cohort study of neonates with persistent shock despite adequate fluid resuscitation and dopamine or dobutamine administration at ≥10 μg/kg/min. Medical records of neonates treated with NE were retrospectively reviewed to collect respiratory and haemodynamic parameters and results of arterial blood gas (ABG) tests before and 8 hours after NE infusion. The effectiveness of NE was assessed using changes in clinical parameters and multiple regression models for mortality among subgroups of preterm and full-term neonates.Results Ninety-two neonates (76% preterm) who received NE infusion were included in the study. NE infusion was started after a median of 7 hours (IQR 2–19 hours) after shock onset. Among the preterm neonates, the maximum dose of NE infusion was 0.5 (IQR 0.3–1.0) µg/kg/min with a median duration of 45 (IQR 24.0–84.5) hours. Haemodynamic dysfunction was ameliorated with increased blood pressure, decreased heart rate and improved ABG results. Preterm neonates with septic shock tended to have a reduced response to NE; however, preterm neonates with persistent pulmonary hypertension of the newborn tended to have a better response. Thirty-four (37%) neonates died in our cohort. The timing, dose and duration of NE use were not associated with neonatal mortality.Conclusions Although using NE effectively improves clinical parameters in preterm neonates with dopamine-resistant shock, our study is underpowered to identify the association between NE infusion and mortality in preterm neonates with dopamine-resistant shock.

IntroductionTomato yellow leaf curl virus (TYLCV), which is a typical member of the genus Begomovirus, causes severe crop yield losses worldwide. RNA interference (RNAi) is an important antiviral defense mechanism in plants, but whether plant beneficial microbes used as biocontrol agents would modulate RNAi in defense against TYLCV remains unclear.MethodsHere, we employed whole-transcriptome, bisulfite, and small RNA sequencing to decipher the possible role of Bacillus amyloliquefaciens Ba13 as a bacterial biocontrol agent against TYLCV in RNAi modulation.ResultsPotted tomato plants were exposed to whiteflies for natural viral infection 14 days after bacterial inoculation. Compared with non-inoculated controls, the abundance of TYLCV gene in the leaves of inoculated plants decreased by 70.1% at 28 days post-infection, which mirrored the pattern observed for plant disease index. The expression of the ARGONAUTE family genes (e.g., AGO3, AGO4, AGO5, and AGO7) involved in antiviral defense markedly increased by 2.44–6.73-fold following bacterial inoculation. The methylation level at CpG site 228 (in the open reading frame region of the RNA interference suppressing gene AV2) and site 461 (in the open reading frame regions of AV1 and AV2) was 183.1 and 63.0% higher in inoculated plants than in non-inoculated controls, respectively. The abundances of 10 small interfering RNAs matched to the TYLCV genome were all reduced in inoculated plants, accompanied by enhancement of photosystem and auxin response pathways.DiscussionThe results indicate that the application of Ba. amyloliquefaciens Ba13 enhances plant resistance to TYLCV through RNAi modulation by upregulating RNAi-related gene expression and enhancing viral genome methylation.

Fatigue is always accompany with the driving task, which have been extensively investigated for driver monitoring and traffic safety. While many scholars dedicate to the study of fatigue detection methods with higher accuracy, but the basic correlation between detection methods and fatigue cause or prevention receive relatively little attention.This study systematically reviews the authors’ studies of fatigue influential factors, fatigue identification and measurement, and fatigue prediction; and then structurally and comparably describes the research of driver fatigue behavior from the above three components within the literature of interest.Time-related indicators are usually considered as the main driver fatigue influential factors, and driving environment and vehicle performance are also found to be contributive to driver fatigue. The elastic control of driving time and rest time is an effective measure for the prevention of driver fatigue. Sensitivity analysis can test the correlation between measurements of fatigue identification and fatigue level, and then ensure the performance of measurements. Models that consider time-related factors based on bio-mathematic model theory can be used for real-time fatigue level prediction and characterizing the fatigue dynamics in the planned travel time. Driver individual differences should be considered for the fatigue behavior research as the performance of fatigue detection model and prediction models could vary greatly within drivers from different population.This review described the structure of driver fatigue behavior studies, and the link between fatigue influential indicators, fatigue identification, prediction. The effect of fatigue identification should be further explored, not just for detection or high accuracy.

The beam self-cleaning effect in graded-index multimode optical fibers has several interesting potential applications, such as high-resolution nonlinear imaging and mode-locked fiber lasers. Most experimental and theoretical studies of beam self-cleaning have neglected the role of the state of polarization of light. In this work, we fill this gap by reporting extensive experimental investigations of beam self-cleaning in multimode fibers, for beams with different input states of light polarization. We found that the state of polarization undergoes a complex evolution, which may lead either to full conservation of the input state of polarization, or to nearly complete light depolarization at the fiber output. The former outcome is compelling for applications based on the beam self-cleaning effect, such as multimode mode-locked fiber lasers for micromachining, and multimode devices for microscopy and endoscopy. The latter result, instead, permits to test the limits of validity of current purely scalar theoretical approaches for describing nonlinear propagation in multimode fibers.

Abstract Background Colorectal cancer (CRC) is the third most common cancer in the world, and a strong relationship exists between CRC and gut microbiota, which affects the occurrence, development, and metastasis of cancer. Bioinformatics-based analyses revealed that the abundance of Parvimonas micra (P. micra) in the feces of patients with cancer is significantly higher than that in healthy people. Therefore, an important relationship may exist between P. micra and CRC. Methods We first confirmed that P. micra can promote the proliferation of cell lines through cell experiments and mouse models. Then we selected the signaling pathways and content of exosomes to promote the development of CRC by transcriptomics and microRNA sequencing. Finally, we confirmed that P. micra promoted CRC development through miR-218-5p/Ras/ERK/c-Fos pathway through the in vivo and in vitro experiments. Results First, it was confirmed by in vitro and in vivo experiments that P. micra can promote the development of CRC. Transcriptome analysis after the coincubation of bacteria and cells revealed that P. micra promoted cell proliferation by activating the Ras/ERK/c-Fos pathway. Furthermore, microRNA sequencing analysis of the cells and exosomes showed that miR-218-5p and protein tyrosine phosphatase receptor R (PTPRR) were the key factors involved in activating the Ras/ERK/c-Fos pathway, and the miR-218-5p inhibitor was used to confirm the role of microRNA in xenograft mice. Conclusion This experiment confirmed that P. micra promoted the development of CRC by upregulating miR-218-5p expression in cells and exosomes, inhibiting PTPRR expression, and ultimately activating the Ras/ERK/c-Fos signaling pathway.

In recent years, the wide use of deep learning based methods has greatly improved the classification performance of hyperspectral image (HSI). As an effective method to improve the performance of deep convolution networks, attention mechanism is also widely used for HSI classification tasks. However, the majority of the existing attention mechanisms for HSI classification are based on the convolution layer, and the classification accuracy still has margins for improvement. Motivated by the latest self attention mechanism in natural language processing, a deep transformer is proposed for HSI classification in this paper. Specifically, deep transformer along the spectral dimension and the spatial dimension are explored respectively. Then, a deep spatial-spectral transformer (DSS-TRM) is proposed to improve the classification performance of HSI. The contribution of this paper is to make full use of self attention mechanism, that is to use transformer layer instead of convolution layer. More importantly, a DSS-TRM is proposed to realize end-to-end HSI classification. Extensive experiments are conducted on three HSI data sets. The experimental results demonstrates that the proposed DSS-TRM could outperform the traditional convolutional neural networks and attention based methods.

Abstract Background The association between oral dysbiosis and chronic kidney disease (CKD) has gained increasing attention in recent years. Diabetes and hypertension are the most common conditions in CKD. However, a case–control study with matched confounding variables on the salivary microbiome in CKD and the influence of diabetes and hypertension on the microbiome has never been reported. Methods In our study, we compared the salivary microbiome profile between patients with CKD and healthy controls (HC) using 16S ribosomal DNA sequencing and examine its association with diabetes, hypertension, and immunity. Results We observed that the bacterial community was skewed in the saliva of CKD, with increased Lautropia and Pseudomonas, and decreased Actinomyces, Prevotella, Prevotella 7, and Trichococcus. No difference in the bacterial community between the CKD patients complicated with and without diabetes, and between those with and without hypertension. Prevotella 7 declined in CKD patients with/without hypertension with respect to HC, while Pseudomonas increased in CKD patients with/without hypertension. Pseudomonas was negatively associated with immunoglobin G in CKD patients. Both CKD patients with positive and negative antistreptolysin O had declined Prevotella 7 and Trichococcus compared to HC, whereas increased Pseudomonas. Conclusions Our study identifies a distinct bacterial saliva microbiome in CKD patients characterized by alteration in composition. We unravel here that the co-occurrence diseases of diabetes and hypertension are not associated with specific bacterial alterations, suggesting that bacterial dysbiosis in saliva plays a role in renal damage regardless of the occurrence of diabetes and hypertension.

BackgroundTo establish antibiotic preregimes and administration routes for studies on urinary microbiota.Methods and materialsAntibiotics for enteritis (Abx-enteritis) and UTIs (Abx-UTI) were administered via gavage and/or urinary catheterisation (UC) for 1 and/or 2 weeks. The effects of these Abx on the urinary microbiota of rats were examined via 16S rRNA sequencing and urine culture, including anaerobic and aerobic culture. Additionally, the safety of the Abx was examined.ResultsAbx-enteritis/Abx-UTI (0.5 g/L and 1 g/L) administered via gavage did not alter the microbial community and bacterial diversity in the urine of rats (FDR > 0.05); however, Abx-UTI (1 g/L) administered via UC for 1 and 2 weeks altered the urinary microbial community (FDR < 0.05). Rats administered Abx-UTI (1 g/L) via UC for 1 week demonstrated a distinct urinary microbiota in culture. Abx-enteritis/Abx-UTI administered via gavage disrupted the microbial community and reduced bacterial diversity in the faeces of rats (FDR < 0.05), and Abx-UTI administered via UC for 2 weeks (FDR < 0.05) altered the fecal microbiota. Abx-UTI (1 g/L) administered via UC did not alter safety considerations. In addition, we noticed that UC did not induce infections and injuries to the bladder and kidney tissues.ConclusionsAdministration of Abx-UTI via UC for 1 week can be considered a pre-treatment option while investigating the urinary microbiota.

ABSTRACT Recently, microbiota dysbiosis in lung cancer has attracted immense attention. Studies on lung microbes are mostly based on sequencing, which has left the potentially functional bacteria with extremely low abundance uncovered. In this study, we characterized and compared the lung and oral cavity microbiotas using culturomics and 16S rRNA gene sequencing. Of the 198 bacteria identified at the species level from bronchoalveolar lavage fluid (BALF) samples, Firmicutes was predominant (39.90%). Twenty bacterial species isolated from BALF samples were present in at least half of the patients and were also highly abundant in oral samples. Of all isolated strains, Streptococcus and Veillonella were highly dominant. The abundance of Prevotella and Veillonella decreased from the oral cavity to the lung, whereas that of Pseudomonas increased. Linear discriminant analysis effect size demonstrated that Prevotella was more abundant in the healthy samples than in the cancerous ones, which is in accordance with the isolation of Prevotella oralis only from the healthy group using culturomics. Moreover, Gemella sanguinis and Streptococcus intermedius were isolated only from the non-small-cell lung cancer (NSCLC) group, and 16S rRNA gene sequencing showed that they were higher in the NSCLC than in the small-cell lung cancer group. Furthermore, while Bacillus and Castellaniella were enriched in lung adenocarcinoma, Brucella was enriched in lung squamous cell carcinoma. Overall, alterations were observed in the microbial community of patients with lung cancer, whose diversity might be site and pathology dependent. Using culturomics and 16S rRNA gene amplicon sequencing, this study has provided insights into pulmonary and oral microbiota alterations in patients with lung cancer. IMPORTANCE The relationship between lung microbiota and cancer has been explored based on DNA sequencing; however, culture-dependent approaches are indispensable for further studies on the lung microbiota. In this study, we applied a comprehensive approach combining culturomics and 16S rRNA gene amplicon sequencing to detect members of the microbiotas in saliva and BALF samples from patients with unilateral lobar masses. We found alterations in the microbial community of patients with lung cancer, whose diversity might be site and pathology dependent. These features may be potential bacterial biomarkers and new targets for lung cancer diagnosis and treatment. In addition, a lung and oral microbial biobank from lung cancer patients was established, which represents a useful resource for studies of host-microbe interactions.

This research investigates the visible upconversion luminescence which is induced by multiphoton absorption of soft glass fiber defects. The study of this phenomenon has thus far been restricted to standard silica fibers. We observed the emission of green and cyan light as a consequence of fiber material ionization. We investigate both the commercial ZBLAN step index and in-house-made tellurite nanostructured graded-index fibers. For the latter, the analysis of the luminescence signal permits us to determine the core and cladding refractive index difference. Upconversion luminescence is a powerful tool for characterizing soft glass fibers and a promising platform for innovative photonic technologies and mid-IR applications.

Abstract The surface tension of a molten sample can be evaluated based on its resonant frequency with various levitation techniques. Under a 1-G condition, the use of levitation forces to counteract gravity will cause the levitated sample’s resonant frequency to differ from that under microgravity. A mathematical relationship to correct for this deviation is not available for a sample levitated with aerodynamic levitation (ADL), which raises issues on the validity of surface tension measurements done with ADL. In this study, we compared the surface tension of molten Al2O3 obtained using the front tracking (FT) simulation method, the drop-bounce method with ADL, and the oscillating drop method with ADL. The drop-bounce method simulates microgravity by allowing the sample to free-fall over a period of tens of milliseconds. Based on the results of this comparison, we determined that the surface tension of molten materials measured with ground-based ADL with the oscillating drop method, calculated using the resonant frequency of the l=2 m=0 mode, only shows a small deviation from that obtained under microgravity.

BackgroundPrevious studies suggest that physiological feedback can be an effective method for emotion regulation (ER). However, studies on the specific effects of physiological feedback have shown conflicting results due to inconsistencies in study designs. Therefore, we present this systematic review to further validate the effectiveness of physiological feedback for ER, clarify its specific effects, as well as summarize the factors that influence its effectiveness.MethodThis systematic review following PRISMA guidelines covers all studies using physiological feedback in emotions. A literature search was conducted in Web of Science, PubMed, PsychINFO, China National Knowledge Infrastructure, and WANFANG DATA. And a standardized quality assessment was performed.ResultsWe identified 27 relevant articles (25 studies), and the majority of these studies showed a significant regulatory effect of physiological feedback on different emotions. The feedback's content, explanation, authenticity, real-time capability, and modality were the key factors that influenced its effects, and this technology will achieve its optimal ER effect when these factors are considered comprehensively.ConclusionsThese findings further confirmed the effectiveness of physiological feedback as an ER method, as well as providing key factors that should be addressed in its application. Meanwhile, due to the limitations of these studies, more well-designed studies are still needed.

Supporting ecological protection and restoration has been at the heart of China’s ambitious Yangtze River conservation strategy. Knowledge of the current status of heavy metal distribution is important for planning remediation practices and allocation to waste treatment facilities. Through an extensive and systematic review of literatures, this study depicts the up-to-date spatial distribution and characteristics of typical heavy metals in soils of the Yangtze River Basin, China. A total of 7,694 geo-referenced records of heavy metal in soils of the Yangtze River Basin were compiled from the literatures published between 2000 and 2020. The results show the spatially-heterogeneous concentrations of Zn, Cu, Pb, Cr, Ni, As, Hg and Cd. The degree of heavy metal pollution was relatively higher in the middle reaches, while it was relatively lower in the upstream and downstream. According to the limits set by the state to ensure agricultural production and maintain human health, the average concentration of Cd greatly exceeded its limit. Overall, a certain number of heavily polluted areas were found to occur in regions with frequent human economic activities, posing potential health risks. The carcinogenic and non-carcinogenic risks of children are 1.4 times and 1.6 times higher than those of adults, respectively, and the heavy metal with the highest risk to human health was Cr. This study provides an important basis for the field of soil pollution prevention and control in the Yangtze River Basin. It updates the current understanding of the spatial pattern of major pollutants in a large ecologically protected region in China, which is conducive to the precise prevention and control of public health risks.

Transfer RNA-derived small RNAs (tsRNAs) tRF-LeuCAG-002 (ts3011a RNA) is a novel class of non-coding RNAs biomarker for pancreatic cancer (PC). Reverse transcription polymerase chain reaction (RT-qPCR) has been unfit for community hospitals that are short of specialized equipment or laboratory setups. It has not been reported whether isothermal technology can be used for detection, because the tsRNAs have rich modifications and secondary structures compared with other non-coding RNAs. Herein, we have employed a catalytic hairpin assembly (CHA) circuit and clustered regularly interspaced short palindromic repeats (CRISPR) to develop an isothermal and target-initiated amplification method for detecting ts3011a RNA. In the proposed assay, the presence of target tsRNA triggers the CHA circuit that transforms new DNA duplexes to activate collateral cleavage activity of CRISPR-associated proteins (CRISPR-Cas) 12a, achieving cascade signal amplification. This method showed a low detection limit of 88 aM at 37 °C within 2 h. Moreover, it was demonstrated for the first time that, this method is less likely to produce aerosol contamination than RT-qPCR by simulating aerosol leakage experiments. This method has good consistency with RT-qPCR in the detection of serum samples and showed great potential for PC-specific tsRNAs point-of-care testing (POCT).

Abstract Background Amyotrophic lateral sclerosis (ALS) is a multifactorial neurodegenerative disease characterised by the loss of upper and lower motor neurons. Increasing evidence indicates that neuroinflammation mediated by microglia contributes to ALS pathogenesis. This microglial activation is evident in post-mortem brain tissues and neuroimaging data from patients with ALS. However, the role of microglia in the pathogenesis and progression of amyotrophic lateral sclerosis remains unclear, partly due to the lack of a model system that is able to faithfully recapitulate the clinical pathology of ALS. To address this shortcoming, we describe an approach that generates monocyte-derived microglia-like cells that are capable of expressing molecular markers, and functional characteristics similar to in vivo human brain microglia. Methods In this study, we have established monocyte-derived microglia-like cells from 30 sporadic patients with ALS, including 15 patients with slow disease progression, 6 with intermediate progression, and 9 with rapid progression, together with 20 non-affected healthy controls. Results We demonstrate that patient monocyte-derived microglia-like cells recapitulate canonical pathological features of ALS including non-phosphorylated and phosphorylated-TDP-43-positive inclusions. Moreover, ALS microglia-like cells showed significantly impaired phagocytosis, altered cytokine profiles, and abnormal morphologies consistent with a neuroinflammatory phenotype. Interestingly, all ALS microglia-like cells showed abnormal phagocytosis consistent with the progression of the disease. In-depth analysis of ALS microglia-like cells from the rapid disease progression cohort revealed significantly altered cell-specific variation in phagocytic function. In addition, DNA damage and NOD-leucine rich repeat and pyrin containing protein 3 (NLRP3) inflammasome activity were also elevated in ALS patient monocyte-derived microglia-like cells, indicating a potential new pathway involved in driving disease progression. Conclusions Taken together, our work demonstrates that the monocyte-derived microglia-like cell model recapitulates disease-specific hallmarks and characteristics that substantiate patient heterogeneity associated with disease subgroups. Thus, monocyte-derived microglia-like cells are highly applicable to monitor disease progression and can be applied as a functional readout in clinical trials for anti-neuroinflammatory agents, providing a basis for personalised treatment for patients with ALS.

Abstract To address the existing problems of capsule networks in deep feature extraction and spatial‐spectral feature fusion of hyperspectral images, this paper proposes a hyperspectral image classification method that combines a deep residual 3D capsule network and Markov random field. Based on this method, the deep spatial‐spectral features of hyperspectral images are extracted using the deep residual 3D convolutional structure, the vector capsules of the features are obtained by the initial capsule layer and mapped into probability capsules via the 3D dynamic routing mechanism to construct the classification probability map, and the spatial structure of the classification results is regularised by the Markov random field to further improve the classification accuracy and performance of the images. Two sets of benchmark hyperspectral images, namely Indian Pines and Pavia University data sets, were used to conduct comparative experiments and ablation study. The experimental results showed that, compared with the conventional convolutional neural network and existing capsule network models, the proposed method not only improves the classification accuracy of the images but also partly eliminates the category noise and affords a more regular classification probability map.

ObjectiveLupus nephritis (LN) is one of the most severe organ manifestations of systemic lupus erythematosus (SLE). Early identification of renal disease in SLE is important. Renal biopsy is currently recognized as the gold standard for diagnosing LN, however, it is invasive and inconvenient for dynamic monitoring. Urine has been considered more promising and valuable than blood in identifying inflamed kidney tissue. Here, we determine whether the signatures of tRNA-derived small noncoding RNA (tsRNA) in urinary exosomes can serve as novel biomarkers for the diagnosis of LN.MethodstsRNA sequencing was performed in exosome extracted from pooled urine of 20 LN patients and 20 SLE without LN, and the top 10 upregulated tsRNAs were screened as candidate markers of LN. The candidate urinary exosomal tsRNAs were primarily elected by TaqMan probe-based quantitative reverse transcription-PCR (RT-PCR) in 40 samples (20 LN and 20 SLE without LN) in the training phase. In the validation phase, selected tsRNAs from the training phase were further confirmed in a larger cohort (54 LN patients and 39 SLE without LN). Receiver operating characteristic curve (ROC) analysis was conducted to evaluate the diagnostic efficacy.ResultsUpregulated levels of tRF3-Ile-AAT-1 and tiRNA5-Lys-CTT-1 in the urinary exosomes were observed in LN compared with SLE without LN (P < 0.0001 and P < 0.001) and healthy controls (P < 0.01 and P < 0.01), with the area under the curve (AUC) of 0.777 (95% CI: 0.681-0.874, sensitivity 79.63%, specificity 66.69%) and 0.715 (95% CI: 0.610-0.820, sensitivity 66.96%, specificity 76.92%) for discriminating LN from SLE without LN patients. SLE patients with mild activity and moderate to severe activity had higher levels of urinary exosome derived tRF3-Ile AAT-1 (P = 0.035 and P < 0.001) and tiRNA5-Lys-CTT-1 (P = 0.021 and P < 0.001) compared with patients with no activity. Moreover, bioinformatics analysis revealed that both of the tsRNAs regulate the immune process by modulating metabolism and signal pathway.ConclusionIn this study, we demonstrated that urinary exosome tsRNAs can be served as noninvasive biomarkers for the efficient diagnosis and prediction of nephritis in SLE.

Introduction: Distributed wideband jamming (interference) is commonly used in navigation countermeasure. Due to the limited volume of GNSS (Global Navigation Satellite System) array antenna receiver, the number of interferences usually exceeds the number of array elements. At present, the anti-jamming capability and mechanism of Global Navigation Satellite System array antenna against distributed sup-DOF (Degree of Freedom) interference have not been fully studied.Methods and innovation: To solve this problem, this paper analyzes the characteristics of GNSS System array antenna against sup-Degree of Freedom interference by formula derivation and simulation. Firstly, the definition of sup-Degree of Freedom interference of Global Navigation Satellite System array antenna is proposed from the perspective of spatial anti-jamming; Secondly, the directional characteristics of Global Navigation Satellite System array antenna for sup-Degree of Freedom interference suppression are analyzed.Results: The results show that the performance of sup-Degree of Freedom interference suppression is sensitive to the direction and distribution of interference. On the one hand, the residual interference power varies from interference direction and distribution, while the minimum value of which is zero and the maximum value is the sum of interference power. On the other hand, the suppression performance of UCA (Uniform Circular Array) and central Uniform Circular Array is periodic along azimuth. If the number of elements on the circumference is M (M ≥ 3), the period of the suppression performance is 4π/M/3+(−1M+1.Discussion: The conclusion of this paper show the upper and lower bounds of sup-Degree of Freedom interference suppression performance and the variation rule in azimuth, which can be used in the fields such as interference deployment, anti-jamming performance evaluation and anti-jamming algorithm development.

Although tissue engineering offered new approaches to repair bone defects, it remains a great challenge to create a bone-friendly microenvironment and rebuild bone tissue rapidly by a scaffold with a bionic structure. In this study, a multifunctional structurally optimized hydrogel scaffold was designed by integrating polyvinyl alcohol (PVA), gelatin (Gel), and sodium alginate (SA) with aspirin (ASA) and nano-hydroxyapatite (nHAP). The fabrication procedure is through a dual-crosslinking process. The chemical constitution, crystal structure, microstructure, porosity, mechanical strength, swelling and degradation property, and drug-release behavior of the hydrogel scaffold were analyzed. Multi-hydrogen bonds, electrostatic interactions, and strong “egg-shell” structure contributed to the multi-network microstructure, bone tissue-matched properties, and desirable drug-release function of the hydrogel scaffold. The excellent performance in improving cell viability, promoting cell osteogenic differentiation, and regulating the inflammatory microenvironment of the prepared hydrogel scaffold was verified using mouse pre-osteoblasts (MC3T3-E1) cells. And the synergistic osteogenic and anti-inflammatory functions of aspirin and nano-hydroxyapatite were also verified. This study provided valuable insights into the design, fabrication, and biological potential of multifunctional bone tissue engineering materials with the premise of constructing a bone-friendly microenvironment.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and Clustered Regularly Interspaced Short Palindromic Repeats-Associated Proteins (CRISPR-Cas) have promising prospects in the field of nucleic acid molecular diagnostics. However, Clustered Regularly Interspaced Short Palindromic Repeats-based fluorescence detection technology is mainly hindered by proteins with conjugated double bonds and autofluorescence, resulting in high fluorescence background, low sensitivity and incompatible reaction systems, which are not conducive to automatic clinical testing. Chemiluminescence (CL) detection technology has been applied mainly owing to its greatly high sensitivity, as well as low background and rapid response. Therefore, we developed a rapid, ultrasensitive and economical detection system based on Clustered Regularly Interspaced Short Palindromic Repeats-Clustered Regularly Interspaced Short Palindromic Repeats-Associated Proteins 13a combined with magnetic beads (MBs) and chemiluminescence (CL) (Cas13a-MB-CL) to detect Influenza A (H7N9), an acute respiratory tract infectious disease. The carboxyl functionalized magnetic beads (MBs-COOH) were covalently coupled with aminated RNA probe while the other end of the RNA probe was modified with biotin. Alkaline phosphatase labeled streptavidin (SA-ALP) binds with biotin to form magnetic beads composites. In presence of target RNA, the collateral cleavage activity of Cas13a was activated to degrade the RNA probes on MBs and released Alkaline phosphatase from the composites. The composites were then magnetically separated followed by addition of ALP substrate Disodium 2-chloro-5-{4-methoxyspiro [1,2-dioxetane-3,2′-(5′-chloro) tricyclo (3.3.1.13,7) decan]-4-yl}-1-phenyl phosphate (CDP-star), to generate the chemiluminescence signal. The activity of Associated Proteins 13a and presence of target RNA was quantified by measuring the chemiluminescence intensity. The proposed method accomplished the detection of H7N9 within 30 min at 25°C. When combined with Reverse Transcription- Recombinase Aides Amplification (RT-RAA), the low detection limit limit of detection was as low as 19.7 fM (3S/N). Our proposed MB-Associated Proteins 13a-chemiluminescence was further evaluated to test H7N9 clinical samples, showing superior sensitivity and specificity.

Abstract High-dimensional linear regression model is the most popular statistical model for high-dimensional data, but it is quite a challenging task to achieve a sparse set of regression coefficients. In this paper, we propose a simple heuristic algorithm to construct sparse high-dimensional linear regression models, which is adapted from the shortest-solution guided decimation algorithm and is referred to as ASSD. This algorithm constructs the support of regression coefficients under the guidance of the shortest least-squares solution of the recursively decimated linear models, and it applies an early-stopping criterion and a second-stage thresholding procedure to refine this support. Our extensive numerical results demonstrate that ASSD outperforms LASSO, adaptive LASSO, vector approximate message passing, and two other representative greedy algorithms in solution accuracy and robustness. ASSD is especially suitable for linear regression problems with highly correlated measurement matrices encountered in real-world applications.

Abstract Lenvatinib is the first target drug approved for advanced hepatocellular carcinoma (HCC). However, the development of drug resistance is common, and the mechanisms of lenvatinib resistance and resistant targets in HCC are poorly understood. By using CRISPR/Cas9 library screening, we screened out two key resistance genes, neurofibromin 1(NF1), and dual specificity phosphatase 9 (DUSP9), as critical drivers for lenvatinib resistance in HCC. With RNAi knockdown and CRISPR/Cas9 knockout models, we further clarified the mechanisms by which NF1 loss reactivates the PI3K/AKT and MAPK/ERK signaling pathways, while DUSP9 loss activates the MAPK/ERK signaling pathways, thereby inactivating FOXO3, followed by degradation of FOXO3, finally induced lenvatinib resistance. We also screened out trametinib, a small molecule pathway inhibitor for MEK, that can be used to reverse resistance induced by NF1 and DUSP9 loss in HCC cells. Trametinib was still able to halt HCC growth even when NF1 was knocked out in mice. Collectively, the findings indicate that NF1 and DUSP9 takes critical role in lenvatinib resistance and may be novel specific targets and predictive markers for lenvatinib resistance in HCC.