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Abstract Background Therapeutic strategies based on scavenging reactive oxygen species (ROS) and suppressing inflammatory cascades are effective in improving functional recovery after spinal cord injury (SCI). However, the lack of targeting nanoparticles (NPs) with powerful antioxidant and anti-inflammatory properties hampers the clinical translation of these strategies. Here, CD44-targeting hyaluronic acid-selenium (HA-Se) NPs were designed and prepared for scavenging ROS and suppressing inflammatory responses in the injured spinal cord, enhancing functional recovery. Results The HA-Se NPs were easily prepared through direct reduction of seleninic acid in the presence of HA. The obtained HA-Se NPs exhibited a remarkable capacity to eliminate free radicals and CD44 receptor-facilitated internalization by astrocytes. Moreover, the HA-Se NPs effectively mitigated the secretion of proinflammatory cytokines (such as IL-1β, TNF-α, and IL-6) by microglia cells (BV2) upon lipopolysaccharide-induced inflammation. In vivo experiments confirmed that HA-Se NPs could effectively accumulate within the lesion site through CD44 targeting. As a result, HA-Se NPs demonstrated superior protection of axons and neurons within the injury site, leading to enhanced functional recovery in a rat model of SCI. Conclusions These results highlight the potential of CD44-targeting HA-Se NPs for SCI treatment.

Abstract This work aims to investigate how smoking exerts effect on the development of inflammatory bowel disease (IBD). A prospective cohort study and a Mendelian randomization study are first conducted to evaluate the association between smoking behaviors, smoking-related DNA methylation and the risks of Crohn’s disease (CD) and ulcerative colitis (UC). We then perform both genome-wide methylation analysis and co-localization analysis to validate the observed associations. Compared to never smoking, current and previous smoking habits are associated with increased CD (P = 7.09 × 10−10) and UC (P

ABSTRACTTo assess the clinical applicability of a semi-quantitative luciferase immunosorbent assay (LISA) for detecting antibodies against Treponema pallidum antigens TP0171 (TP15), TP0435 (TP17), and TP0574 (TP47) in diagnosing and monitoring syphilis. LISA for detection of anti-TP15, TP17, and TP47 antibodies were developed and evaluated for syphilis diagnosis using 261 serum samples (161 syphilis, 100 non-syphilis). Ninety serial serum samples from 6 syphilis rabbit models (3 treated, 3 untreated) and 110 paired serum samples from 55 syphilis patients were used to assess treatment effects by utilizing TRUST as a reference. Compared to TPPA, LISA-TP15, LISA-TP17, and LISA-TP47 showed a sensitivity of 91.9%, 96.9%, and 98.8%, specificity of 99%, 99%, and 98%, and AUC of 0.971, 0.992, and 0.995, respectively, in diagnosing syphilis. Strong correlations (rs = 0.89–0.93) with TPPA were observed. In serial serum samples from rabbit models, significant differences in the relative light unit (RLU) were observed between the treatment and control group for LISA-TP17 (days 31–51) and LISA-TP47 (day 41). In paired serum samples from syphilis patients, TRUST titres and the RLU of LISA-TP15, LISA-TP17, and LISA-TP47 decreased post-treatment (P

Summary: Background: This study investigates the associations between air pollution and colorectal cancer (CRC) risk and survival from an epigenomic perspective. Methods: Using a newly developed Air Pollutants Exposure Score (APES), we utilized a prospective cohort study (UK Biobank) to investigate the associations of individual and combined air pollution exposures with CRC incidence and survival, followed by an up-to-date systematic review with meta-analysis to verify the associations. In epigenetic two-sample Mendelian randomization analyses, we examine the associations between genetically predicted DNA methylation related to air pollution and CRC risk. Further genetic colocalization and gene–environment interaction analyses provided different insights to disentangle pathogenic effects of air pollution via epigenetic modification. Findings: During a median 12.97-year follow-up, 5767 incident CRC cases among 428,632 participants free of baseline CRC and 533 deaths in 2401 patients with CRC were documented in the UK Biobank. A higher APES score was associated with an increased CRC risk (HR, 1.03, 95% CI = 1.01–1.06; P = 0.016) and poorer survival (HR, 1.13, 95% CI = 1.03–1.23; P = 0.010), particularly among participants with insufficient physical activity and ever smokers (Pinteraction > 0.05). A subsequent meta-analysis of seven observational studies, including UK Biobank data, corroborated the association between PM2.5 exposure (per 10 μg/m3 increment) and elevated CRC risk (RR,1.42, 95% CI = 1.12–1.79; P = 0.004; I2 = 90.8%). Genetically predicted methylation at PM2.5-related CpG site cg13835894 near TMBIM1/PNKD and cg16235962 near CXCR5, and NO2-related cg16947394 near TMEM110 were associated with an increased CRC risk. Gene-environment interaction analysis confirmed the epigenetic modification of aforementioned CpG sites with CRC risk and survival. Interpretation: Our study suggests the association between air pollution and CRC incidence and survival, underscoring the possible modifying roles of epigenomic factors. Methylation may partly mediate pathogenic effects of air pollution on CRC, with annotation to epigenetic alterations in protein-coding genes TMBIM1/PNKD, CXCR5 and TMEM110. Funding: Xue Li is supported by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001), the National Nature Science Foundation of China (No. 82204019) and Healthy Zhejiang One Million People Cohort (K-20230085). ET is supported by a Cancer Research UK Career Development Fellowship (C31250/A22804). MGD is supported by the MRC Human Genetics Unit Centre Grant (U127527198).

Summary: Background: Human immunodeficiency virus (HIV) significantly increases the risk of non-Hodgkin lymphoma (NHL) development, yet the population-level impact on NHL burden is unquantified. We aim to quantify this association and estimate the global burden of HIV-associated NHL. Methods: In this meta-analysis, we searched five databases (PubMed, EMBASE, Cochrane Library, Web of Science, Scopus) from database inception up to September 13, 2023, identifying cohort, case–control, or cross-sectional studies with an effective control group to assess NHL risk among individuals with HIV infection, with two authors extracting summary data from reports. Global and regional HIV-associated population attributable fraction (PAF) and NHL disease burden were calculated based on the pooled risk ratio (RR). HIV prevalence and NHL incidence were obtained from the Joint United Nations Programme on HIV/AIDS (UNAIDS) and Global Burden of Diseases, Injuries, and Risk Factors Study 2019. Trends in NHL incidence due to HIV were assessed using age-standardised incidence rate (ASIR) and estimated annual percentage change (EAPC). This study was registered with PROSPERO (CRD42023404150). Findings: Out of 14,929 literature sources, 39 articles met our inclusion criteria. The risk of NHL was significantly increased in the population living with HIV (pooled RR 23.51, 95% CI 17.62–31.37; I2 = 100%, p

Abstract Background The proteome is a major source of therapeutic targets. We conducted a proteome-wide Mendelian randomization (MR) study to identify candidate protein markers and therapeutic targets for colorectal cancer (CRC). Methods Protein quantitative trait loci (pQTLs) were derived from seven published genome-wide association studies (GWASs) on plasma proteome, and summary-level data were extracted for 4853 circulating protein markers. Genetic associations with CRC were obtained from a large-scale GWAS meta-analysis (16,871 cases and 26,328 controls), the FinnGen cohort (4957 cases and 304,197 controls), and the UK Biobank (9276 cases and 477,069 controls). Colocalization and summary-data-based MR (SMR) analyses were performed sequentially to verify the causal role of candidate proteins. Single cell-type expression analysis, protein-protein interaction (PPI), and druggability evaluation were further conducted to detect the specific cell type with enrichment expression and prioritize potential therapeutic targets. Results Collectively, genetically predicted levels of 13 proteins were associated with CRC risk. Elevated levels of two proteins (GREM1, CHRDL2) and decreased levels of 11 proteins were associated with an increased risk of CRC, among which four (GREM1, CLSTN3, CSF2RA, CD86) were prioritized with the most convincing evidence. These protein-coding genes are mainly expressed in tissue stem cells, epithelial cells, and monocytes in colon tumor tissue. Two interactive pairs of proteins (GREM1 and CHRDL2; MMP2 and TIMP2) were identified to be involved in osteoclast differentiation and tumorigenesis pathways; four proteins (POLR2F, CSF2RA, CD86, MMP2) have been targeted for drug development on autoimmune diseases and other cancers, with the potentials of being repurposed as therapeutic targets for CRC. Conclusions This study identified several protein biomarkers to be associated with CRC risk and provided new insights into the etiology and promising targets for the development of screening biomarkers and therapeutic drugs for CRC.

Abstract Background The results of human observational studies on the correlation between gut microbiota perturbations and polycystic ovary syndrome (PCOS) have been contradictory. This study aimed to perform the first systematic review and meta-analysis to evaluate the specificity of the gut microbiota in PCOS patients compared to healthy women. Methods Literature through May 22, 2023, was searched on PubMed, Web of Science, Medline, Embase, Cochrane Library, and Wiley Online Library databases. Unreported data in diversity indices were filled by downloading and processing raw sequencing data. Systematic review inclusion: original studies were eligible if they applied an observational case-control design, performed gut microbiota analysis and reported diversity or abundance measures, sampled general pre-menopausal women with PCOS, and are longitudinal studies with baseline comparison between PCOS patients and healthy females. Systematic review exclusion: studies that conducted interventional or longitudinal comparisons in the absence of a control group. Two researchers made abstract, full-text, and data extraction decisions, independently. The Joanna Briggs Institute Critical Appraisal Checklist was used to assess the methodologic quality. Hedge’s g standardized mean difference (SMD), confidence intervals (CIs), and heterogeneity (I 2) for alpha diversity were calculated. Qualitative syntheses of beta-diversity and microbe alterations were performed. Results Twenty-eight studies (n = 1022 patients, n = 928 control) that investigated gut microbiota by collecting stool samples were included, with 26 and 27 studies having provided alpha-diversity and beta-diversity results respectively. A significant decrease in microbial evenness and phylogenetic diversity was observed in PCOS patients when compared with control participants (Shannon index: SMD = − 0.27; 95% CI, − 0.37 to − 0.16; phylogenetic diversity: SMD = − 0.39; 95% CI, -− 0.74 to − 0.03). We also found that reported beta-diversity was inconsistent between studies. Despite heterogeneity in bacterial relative abundance, we observed depletion of Lachnospira and Prevotella and enrichment of Bacteroides, Parabacteroides, Lactobacillus, Fusobacterium, and Escherichia/Shigella in PCOS. Gut dysbiosis in PCOS, which might be characterized by the reduction of short-chain fatty acid (SCFA)-producing and bile-acid-metabolizing bacteria, suggests a shift in balance to favor pro-inflammatory rather than anti-inflammatory bacteria. Conclusions Gut dysbiosis in PCOS is associated with decreased diversity and alterations in bacteria involved in microbiota-host crosstalk. Trial registration PROSPERO registration: CRD42021285206, May 22, 2023.

Fast and accurate infrared (IR) sea–sky line region (SSLR) detection can improve the early warning capability of the small targets that appear in the remote sea–sky junction. However, the traditional algorithms struggle to achieve high precision, while the learning-based ones have low detection speed. To overcome these problems, a novel learning-based algorithm is proposed; rather than detecting the sea–sky line first, the proposed algorithm directly provides SSLR, which mainly consists of three parts: Firstly, an IR sea–sky line region detection module (ISRDM) is proposed, which combines strip pooling and the connection mode of a cross-stage partial network to extract the features of the SSLR target, with an unbalanced aspect ratio, more specifically, thus improving the detection accuracy. Secondly, a lightweight backbone is presented to reduce the parameters of the model and, therefore, improve the inference speed. Finally, a Detection Head Based on the spatial-aware attention module (SAMHead) is designed to enhance the perception ability of the SSLR and further reduce the inference time. Extensive experiments conducted on three datasets with more than 26,000 frames show that the proposed algorithm achieved approximately 80% average precision (AP), outperforms the state-of-the-art algorithms in accuracy, and can realize real-time detection.

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM.

Abstract The causality of the association between depression and gastrointestinal diseases is undetermined. We conducted Mendelian randomization (MR) analyses to systematically explore the associations of depression with 24 gastrointestinal diseases. Independent genetic variants associated with depression at the genome-wide significance level were selected as instrumental variables. Genetic associations with 24 gastrointestinal diseases were obtained from the UK Biobank study, the FinnGen study, and large consortia. Multivariable MR analysis was conducted to explore the mediation effects of body mass index, cigarette smoking, and type 2 diabetes. After multiple-testing corrections, genetic liability to depression was associated with an increased risk of irritable bowel syndrome, non-alcohol fatty liver disease, alcoholic liver disease, gastroesophageal reflux, chronic pancreatitis, duodenal ulcer, chronic gastritis, gastric ulcer, diverticular disease, cholelithiasis, acute pancreatitis, and ulcerative colitis. For the causal effect of genetic liability to depression on non-alcoholic fatty liver disease, a substantial proportion was mediated by body mass index. Genetic predisposition to smoking initiation mediated half of effect of depression on acute pancreatitis. This MR study suggests that depression may play a causal role in many gastrointestinal diseases.

Promoting the recovery of neurological function in patients with traumatic spinal cord injury (TSCI) remains challenging. The balance between astrocyte-mediated neurotrophic and pro-inflammatory responses is critical for TSCI repair. Recently, the utilization of nanomaterials has been considerably explored in immunological reconstructive techniques that specifically target astrocyte-mediated inflammation, yielding positive outcomes. In this review, we aim to condense the present knowledge regarding the astrocyte-mediated inflammation following TSCI. We then review the various categories of nanomaterials utilized in the management of astrocyte-mediated inflammation in TSCI and conclude by summarizing their functions and advantages to offer novel insights for the advancement of effective clinical strategies targeting TSCI.

Objective This study aimed to explore the global burden of early-onset cancer based on the Global Burden of Disease (GBD) 2019 study for 29 cancers worldwid.Methods and analysis Incidence, deaths, disability-adjusted life years (DALYs) and risk factors for 29 early-onset cancer groups were obtained from GBD.Results Global incidence of early-onset cancer increased by 79.1% and the number of early-onset cancer deaths increased by 27.7% between 1990 and 2019. Early-onset breast, tracheal, bronchus and lung, stomach and colorectal cancers showed the highest mortality and DALYs in 2019. Globally, the incidence rates of early-onset nasopharyngeal and prostate cancer showed the fastest increasing trend, whereas early-onset liver cancer showed the sharpest decrease. Early-onset colorectal cancers had high DALYs within the top five ranking for both men and women. High-middle and middle Sociodemographic Index (SDI) regions had the highest burden of early-onset cancer. The morbidity of early-onset cancer increased with the SDI, and the mortality rate decreased considerably when SDI increased from 0.7 to 1. The projections indicated that the global number of incidence and deaths of early-onset cancer would increase by 31% and 21% in 2030, respectively. Dietary risk factors (diet high in red meat, low in fruits, high in sodium and low in milk, etc), alcohol consumption and tobacco use are the main risk factors underlying early-onset cancers.Conclusion Early-onset cancer morbidity continues to increase worldwide with notable variances in mortality and DALYs between areas, countries, sex and cancer types. Encouraging a healthy lifestyle could reduce early-onset cancer disease burden.

Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models, and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan’an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with a sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase in sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control.

Under high-frequency operating conditions, the high-speed solenoid valve (HSV) experiences energy loss and heat generation, which significantly impacts its operational lifetime. Reducing the energy loss of an HSV without compromising its opening response characteristics poses a significant challenge. To address this issue, a finite element simulation model of an HSV coupled with a current feedback model is constructed to investigate the synergistic effects of dynamic response and energy loss. Prediction models for the opening response time, HSV driving energy, and Joule energy using a back propagation neural network (BPNN) are established. Furthermore, a multi-objective optimization study on the current driving strategy using a non-dominated sorting genetic algorithm II (NSGA-II) is conducted. After optimization, although there was a 6.24% increase in the opening response time, both HSV drive energy and Joule energy were significantly reduced by 15.67% and 22.49%, respectively. The proposed multi-objective optimization method for an HSV driving strategy holds great significance for improving its working durability.

Summary: While a few works have shown that Mettl3 plays oncogenic roles in hepatocellular carcinoma (HCC), its function in early HCC tumorigenesis remains unclear. In Mettl3flox/flox; Alb-Cre knockout mice, Mettl3 loss leads to aberrant hepatocyte homeostasis and liver damage. Importantly, Mettl3 deletion dramatically accelerates liver tumorigenesis in various HCC mouse models. Depletion of Mettl3 in adult Mettl3flox/flox mice through TBG-Cre administration also enhances liver tumor development, while overexpression of Mettl3 inhibits hepatocarcinogenesis. Mechanistically, aggravated tumorigenesis upon Mettl3 deletion is a consequence of hepatocyte dedifferentiation and hyperproliferation via m6A-mediated modulation on Hnf4α and cell cycle genes. In contrast, by using Mettl3flox/flox; Ubc-Cre mice, depletion of Mettl3 in established HCC ameliorates tumor progression. Additionally, Mettl3 is overexpressed in HCC tumors compared with adjacent non-tumor tissues. The present findings define a tumor-suppressive role of Mettl3 in liver tumorigenesis, indicating its potentially opposite stage-dependent functions in HCC initiation versus progression.

Abstract Background With the growing number of traditional posterior open surgery, the incidence of failed back surgery syndrome (FBSS) increases gradually. We aimed to investigate the incidence and risk factors for FBSS following open posterior lumbar surgery for degenerative lumbar disease (DLD). Method A multivariable regression analysis was performed for 333 consecutive patients to identify potential risk factors for FBSS. Clinical outcomes were evaluated by the validated North American Spine Society (NASS) Questionnaire and numerical rating scale (NRS) for pain. Demographics, diagnostic characteristics, surgical data, radiographic parameters for each patient were analyzed. Result 16.8% of the included patients were classified as FBSS. Univariate analysis showed that age, hypertension, symptom location, intermittent claudication, preoperative pain NRS-leg, HIZ, Modic changes (MCs), surgical strategy and postoperative rehabilitation were related to FBSS. Multivariable logistic regression analysis demonstrated that preoperative NRS-leg (OR:0.80, 95%CI:0.71–0.91, P = 0.001), hypertension (OR: 2.22, 95%CI: 1.10–4.51, P = 0.027), intermittent claudication with waking distance > 100 m (OR: 4.07, 95%CI: 1.75–9.47, P = 0.001) and waking distance ≤ 100 m (OR: 12.43, 95%CI: 5.54–27.92, P

Abstract Heart health monitoring based on wearable devices is often contaminated by various noises to varying degrees. Using signal quality indicators (SQIs) to achieve signal quality assessment (SQA) is among the most promising ways to solve this problem, but the performance of SQIs in expressing ECG quality features contaminated by motion artifact (MA) noise remains disappointing. Here, we present a novel SQA method that fuses the proposed depth local dual-view (DLDV) features and the dual-input transformer (DI-Transformer) framework to improve the recognition ability of MA-contaminated ECGs. The proposed DLDV features are to identify subtle differences between MA and ECG through depth local amplitude and phase angle features. When it fuses with the temporal relationship features extracted by DI-Transformer, its accuracy is significantly improved compared to the SQIs-based methods. In addition, we also verify the robustness and the accuracy of DLDV features on four traditional classifiers. Finally, we conduct our experiments on the two datasets. On the PhysioNet/Computing in Cardiology Challenge dataset, the DLDV features (Acc = 95.49%) outperform the combination of six SQIs features (Acc = 91.26%). When combined with our DI-Transformer, it delivered an accuracy of 99.62%, outperforming the state-of-the-art SQA methods. On the artificial testset constructed by MA noise, our DI-Transformer outperforms four traditional methods and also delivered an accuracy of 97.69%.

High-precision extraction of river boundaries in Synthetic Aperture Radar (SAR) images is of great significance in monitoring rivers. In this paper, the detection of the health of the Yellow River after the rainstorm in 20 July, 2021 in Zhengzhou is the focus of this paper. The refined-Lee filtering concept and the filtering characteristics of the convolution operation are combined, and an optimized internal weight convolution kernel Refined-Lee Kernel is proposed according to the geometric characteristics of the river channel. A novel river extraction deep neural network model, the River-Net, is also proposed. To verify the effectiveness of the proposed model, this article utilized 20 m resolution Interferometric Wideswath (IW) image data obtained from the European Space Agency Sentinel-1 satellite before and after the 20 July rainstorm in Zhengzhou, employing the images before the rainstorm to train the model. The model, after training, was used to extract the Yellow River channel and analyze the rise of the river after the rainstorm. Experimental results show that the proposed model can extract river channels from SAR images more accurately than trendy semantic segmentation models. The model has important application value for flood disaster detection and evaluation.

Fragmented mitochondria are a frequent hallmark of cancer, but the cause and consequence are less clear. The authors demonstrate that elevated FUNDC2 causes mitochondrial fragmentation through inhibition of MFN1 in hepatocellular carcinoma and that knockdown of FUNDC2 inhibits liver tumorigenesis in mice.

Summary: Background: Identifying new drug targets for inflammatory bowel disease (IBD) is urgently needed. The proteome is a major source of therapeutic targets. We conducted a proteome-wide Mendelian randomization (MR) and colocalization analyses to identify possible targets for IBD. Methods: Summary-level data of 4907 circulating protein levels were extracted from a large-scale protein quantitative trait loci study including 35,559 individuals. Genetic associations with IBD and its subtypes were obtained from the Inflammatory Bowel Disease Genetics Consortium (25,024 cases and 34,915 controls), the FinnGen study (7206 cases and 253,199 controls), and the UK Biobank study (7045 cases and 449,282 controls). MR analysis was conducted to estimate the associations between protein and IBD risk. The colocalization analysis was used to examine whether the identified proteins and IBD shared casual variants. Findings: Genetically predicted levels of 3, and 5 circulating proteins were associated with IBD and ulcerative colitis (UC), respectively. With high supporting evidence of colocalization, genetically predicted MST1 (macrophage stimulating 1) and HGFAC (hepatocyte growth factor activator) levels were inversely associated with IBD risks. The associations of STAT3 (signal transducer and activator of transcription 3), MST1, CXCL5 (C-X-C motif chemokine ligand 5), and ITPKA (inositol-trisphosphate 3-kinase A) with the risk of UC were supported by colocalization analysis. Interpretation: The proteome-wide MR investigation identified many proteins associated with the risk of IBD. MST1, HGFAC, STAT3, ITPKA, and CXCL5 deserve further investigation as potential therapeutic targets for IBD. Funding: SCL is supported by research grants from the Swedish Research Council for Health, Working Life and Welfare (Forte; grant no. 2018-00123) and the Swedish Research Council (Vetenskapsrådet; grant no. 2019-00977). XYW is supported by research grants from the National Natural Science Foundation of China (81970494) and Key Project of Research and Development Plan of Hunan Province (2019SK2041). XL is supported by research grants from the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001).

In this study, the nonlocal, nonordinary state-based peridynamics (NOSBPD) is introduced as a novel regularization technique to study the strain localization and progressive failure of soil slopes due to strain softening behavior. The NOSBPD formulations are introduced first, and then the strain-softening Mohr–Coulomb model is incorporated into the nonlocal framework. The implicit formulations of the nonlinear NOSBPD model under the plane strain condition combined with the energy dissipation-based arc-length method to avoid the limitations of the force and displacement control in the nonlinear analysis are given. Numerical examples, including the plane strain biaxial experiments and slope models, validate the effectiveness of the proposed method in terms of alleviating mesh sensitivity. The results show that this nonlocal method can remove the mesh dependence, and the localized shear band is related to the nonlocal parameter δ. The proposed method involves being able to capture strength mobilization in different parts of the sliding surface during the progressive failure process of the soil slope. It can avoid the dilemma that the peak strength is too dangerous and the residual strength is too conservative due to the rigid body assumption in the limit equilibrium method.

Biochar (BC) usually has abundant surface functional groups, well-developed pore structures and high specific surface areas, which can combine with transition metals for peroxymonosulfate (PMS) activation to degrade organics. In this paper, BC modified with Cu/CuO was prepared by a modified impregnation pyrolysis method using peanut shells as raw materials. The morphology, structure and physicochemical properties were analyzed. Results showed that the originally smooth BC surface was modified into a rough structure with distributed metal particles, and the specific surface area of the modified Cu/CuO-BC700 (i.e., Cu/CuO-BC) increased from the initial 22.57 to 192.64 m2/g. The Cu/CuO-BC was employed for PMS activation and tetracycline (TC) degradation, achieving a removal efficiency of 93.2% at TC initial concentration 20 mg/L, PMS concentration 0.5 mM and catalyst dosage 0.1 g/L after 30 min. The influence of co-existing anions in the actual water on TC degradation followed the order of HCO3− > H2PO4− > Cl−, and HA had an inhibitory effect on TC degradation. A variety of active species participated in TC degradation, and the free radical pathway played a dominant role. Furthermore, the Cu/CuO-BC could maintain the degradation efficiency of TC up to 80% even after five consecutive cycles. The Cu/CuO-BC maintained high activity through redox reactions between catalytically generated active species and the cycling of metal ions (Cu+/Cu2+).

Early diagnosis of HIV-1 infection and immediate initiation of combination antiretroviral therapy (cART) are important for achieving better virological suppression and quicker immune reconstitution. However, no serological HIV-1 recency testing assay has been approved for clinical use, and the real-world clinical outcomes remain to be explored for the subjects with HIV-1 recent infection (RI) or long-term infection (LI) when antiretroviral therapy is initiated. In this study, a HIV-1 rapid recent-infection testing strip (RRITS) was developed and incorporated into the recent infection testing algorithms (RITAs) to distinguish HIV-1 RI and LI and to assess their clinical outcomes including virological response, the recovery of CD4+ T-cell count and CD4/CD8 ratio and the probability of survival. We found that the concordance between our RRITS and the commercially available LAg-Avidity EIA was 97.13% and 90.63% when detecting the longitudinal and cross-sectional HIV-1 positive samples, respectively. Among the 200 HIV-1 patients analyzed, 22.5% (45/200) of them were RI patients and 77.5% (155/200) were chronically infected and 30% (60/200) of them were AIDS patients. After cART, 4.1% (5/155) of the LI patients showed virological rebound, but none in the RI group. The proportion of CD4+ T-cell count >500 cells/mm3 was significantly higher in RI patients than in LI after 2 years of cART with a hazard ratio (HR) of 2.6 (95% CI: 1.9, 3.6, p

Precise genotyping is necessary to understand epidemiology and clinical manifestations of Chlamydia trachomatis infection with different genotypes. Next-generation high-throughput sequencing (NGHTS) has opened new frontiers in microbial genotyping, but has been clinically characterized in only a few settings. This study aimed to determine C. trachomatis genotypes in particular mixed-genotype infections and their association with clinical manifestations and to characterize the sensitivity and accuracy of NGHTS. Cervical specimens were collected from 8,087 subjects from physical examination center (PEC), assisted reproductive technology center (ART) and gynecology clinics (GC) of Chenzhou Hospital of China. The overall prevalence of C. trachomatis was 3.8% (311/8087) whereas a prevalence of 2.8, 3.7 and 4.8% was found in PEC, ART and GC, respectively. The most frequent three C. trachomatis genotypes were E (27.4%, 83/303), F (21.5%, 65/303) and J (18.2%, 55/303). Moreover, NGHTS identified 20 (6.6%, 20/303) mixed-genotype infections of C. trachomatis. Genotype G was more often observed in the subjects with pelvic inflammatory disease than genotype E (adjusted OR = 3.61, 95%CI, 1.02–12.8, p = 0.046). Mixed-genotype infection was associated with severe vaginal cleanliness (degree IV) with an adjusted OR of 5.17 (95%CI 1.03–25.9, p = 0.046) whereas mixed-genotype infection with large proportion of minor genotypes was associated with cervical squamous intraepithelial lesion (SIL) with an adjusted OR of 5.51 (95%CI 1.17–26.01, p = 0.031). Our results indicated that NGHTS is a feasible tool to identity C. trachomatis mixed-genotype infections, which may be associated with worse vaginal cleanliness and cervical SIL.

Diffuse idiopathic skeletal hyperostosis (DISH) is characterized by the calcification and ossification of ligaments and tendons. Progressive dysphagia caused by DISH-related anterior cervical osteophytes and deteriorating dysphagia caused by DISH combined with neurological dysfunction resulting from the posterior longitudinal ligament is rare. The initial diagnosis is misleading and patients often consult several specialists before spine surgeons. This study aims to provide a comprehensive review of the literature on this challenging pathological association. We also present a case illustration where a 53-year-old man presented with progressive dysphagia and foreign body sensation in the pharynx, accompanied by a neurological numbness defect in the right upper limb. Radiography and computed tomography confirmed the existence of osteophytes at the anterior edge of the C4–C7 pyramid and ossification of the posterior longitudinal ligament, in which the giant coracoid osteophyte could be seen at the anterior edge of the C4–C5 pyramid. The anterior cervical osteophyte was removed, and decompression and fusion were performed. The symptoms were relieved postoperatively. No recurrence of symptoms was found during the six-month follow-up. Spine surgeons should consider progressive dysphagia caused by DISH-related osteophytes at the anterior edge of the cervical spine as it is easily misdiagnosed and often missed on the first evaluation. When combined with ossification of the posterior longitudinal ligament, following cervical osteophyte resection it is necessary to consider stabilizing the corresponding segments via fusion.

Glioblastoma (GBM) is the most lethal primary brain tumor in the central nervous system with limited therapeutic strategies to prolong the survival rate in clinic. TNF-related apoptosis-inducing ligand (TRAIL)-based strategy has been demonstrated to induce cell death in an extensive spectrum of tumor cells, including GBM, while a considerable proportion of malignant cells are resistant to TRAIL-induced apoptosis. MiR-137 is highly expressed in the brain, but significantly decreases with advanced progression of GBM. However, the functional link between miR-137 and TRAIL-induced apoptosis in GBM cells has not been established. Here, GBM cells were transfected with miR-137, and gene expression levels were examined by qRT-PCR and western blot. Apoptotic cells were measured by Annexin-V staining and TUNEL assay. Our data showed that miR-137 sensitizes GBM cells to the TRAIL-mediated apoptosis. Mechanistically, we identified that XIAP is a bona fide target of miR-137, which is essential for miR-137-regulated sensitivity of TRAIL-induced cell death in GBM cells. Finally, in a xenograft model, combined utilization of miR-137 and TRAIL potently suppresses tumor growth in vivo. Collectively, we demonstrate that a miR-137-XIAP axis is required for the sensitivity of TRAIL-induced cell death and shed a light on the avenue for the treatment of GBM.

The existence of tumor heterogeneity is widely recognized; however, heterogeneity of the antitumor response in multiple tumor nodules in the same patient has not been reported. Sintilimab, a monoclonal antiprogrammed cell death receptor-1 (PD-1) antibody, was used to treat patients with unresectable hepatocellular carcinoma (HCC). In the present study, we report a case of therapeutic heterogeneity in relapsed HCC with lung metastases. A 57-year-old female patient was diagnosed with HCC and underwent radical hepatectomy. One and a half years later, imaging scans found multiple metastatic tumors in the lung, which were accompanied by an increased α-fetoprotein (AFP) level. The patient then started to receive sintilimab. In the first 6 months after sintilimab treatment, all the metastatic nodules regressed gradually and ultimately disappeared, except for one nodule, which remained stable in the following 3 months. Finally, the patient underwent pulmonary lobectomy to remove the remaining nodule. Thereafter, follow-up visits showed the AFP level decreased to normal and imaging scans showed no signs of recurrence, confirming that the patient exhibited a clinically complete response. Pathological assessments showed that in the primary tumor site, the tumor comprised moderately differentiated HCC with a few infiltrated cytotoxic T cells and negative PD-L1 expression. While in the metastatic site, the nodule was composed of poorly differentiated HCC with cytotoxic T-cell infiltration with few cells inside the tumor and expressed PD-L1 in some areas of the tumor. There were dynamic alterations of PD-L1 expression and cytotoxic T-cell infiltration in the primary and relapsed HCC lesions after anti-PD-1 treatment. This case presented the heterogeneities of both the tumor microenvironment and the following antitumor response among the metastatic nodules in the same patient and revealed the importance of comprehensive therapy in cancer treatment.

The PV/T-GSHP heating control system is proposed to address the energy consumption problem of building heating in northern China. By using TRNSYS to establish an automatic control model, the control of the heat pump start/stop, water supply temperature on the load side of the tank, and high temperature water return to the building can achieve the purpose of being energy saving, providing stable heating, and providing control of high temperature heating. The results show that the PV/T-GSHP control system has an annual fluctuation range of 0.01~0.17 kW in the heating period for a typical building heat supply and building heat load, and the system energy efficiency and energy consumption are increased by 14.5% and 33.91%, respectively, compared with the GSHP system, and the annual value of cost is reduced by 8.49% and 19.09% compared with the GSHP and traditional coal-fired boiler system, which shows that the PV/T-GSHP control system is more economical. It can be seen that the PV/T-GSHP control system has better heating stability, energy efficiency, and economy, and it provides theoretical significance for the current clean heating automatic control scheme in Chinese northern areas.

Over the years, remote sensing technology has become an important means to obtain accurate agricultural production information, such as crop type distribution, due to its advantages of large coverage and a short observation period. Nowadays, the cooperative use of multi-source remote sensing imagery has become a new development trend in the field of crop classification. In this paper, the polarimetric components of Sentinel-1 (S-1) decomposed by a new model-based decomposition method adapted to dual-polarized SAR data were introduced into crop classification for the first time. Furthermore, a Dual-Channel Convolutional Neural Network (DC-CNN) with feature extraction, feature fusion, and encoder-decoder modules for crop classification based on S-1 and Sentinel-2 (S-2) was constructed. The two branches can learn from each other by sharing parameters so as to effectively integrate the features extracted from multi-source data and obtain a high-precision crop classification map. In the proposed method, firstly, the backscattering components (VV, VH) and polarimetric components (volume scattering, remaining scattering) were obtained from S-1, and the multispectral feature was extracted from S-2. Four candidate combinations of multi-source features were formed with the above features. Following that, the optimal one was found on a trial. Next, the characteristics of optimal combinations were input into the corresponding network branches. In the feature extraction module, the features with strong collaboration ability in multi-source data were learned by parameter sharing, and they were deeply fused in the feature fusion module and encoder-decoder module to obtain more accurate classification results. The experimental results showed that the polarimetric components, which increased the difference between crop categories and reduced the misclassification rate, played an important role in crop classification. Among the four candidate feature combinations, the combination of S-1 and S-2 features had a higher classification accuracy than using a single data source, and the classification accuracy was the highest when two polarimetric components were utilized simultaneously. On the basis of the optimal combination of features, the effectiveness of the proposed method was verified. The classification accuracy of DC-CNN reached 98.40%, with Kappa scoring 0.98 and Macro-F1 scoring 0.98, compared to 2D-CNN (OA reached 94.87%, Kappa scored 0.92, and Macro-F1 scored 0.95), FCN (OA reached 96.27%, Kappa scored 0.94, and Macro-F1 scored 0.96), and SegNet (OA reached 96.90%, Kappa scored 0.95, and Macro-F1 scored 0.97). The results of this study demonstrated that the proposed method had significant potential for crop classification.

Rice is one of the most important staple foods in the world, feeding more than 50% of the global population. However, rice is also a significant emitter of greenhouse gases and plays a role in global climate change. As a result, quickly and accurately obtaining the rice mapping is crucial for ensuring global food security and mitigating global warming. In this study, we proposed an automated rice mapping method called automated rice mapping using V-shaped phenological features of rice (Auto-RMVPF) based on the time-series Sentinel-1A images, which are composed of four main steps. First, the dynamic threshold method automatically extracts abundant rice samples by flooding signals. Second, the second-order difference method automatically extracts the phenological period of rice based on the scattering feature of rice samples. Then, the key “V” feature of the VH backscatter time series, which rises before and after rice transplanting due to flooding, is used for rice mapping. Finally, the farmland mask is extracted to avoid interference from non-farmland features on the rice map, and the median filter is applied to remove noise from the rice map and obtain the final spatial distribution of rice. The results show that the Auto-RMVPF method not only can automatically obtain abundant rice samples but also can extract the accurate phenological period of rice. At the same time, the accuracy of rice mapping is also satisfactory, with an overall accuracy is more than 95% and an F1 score of over 0.91. The overall accuracy of the Auto-RMVPF method is improved by 2.8–12.2% compared with support vector machine (SVM) with an overall accuracy of 89.9% (25 training samples) and 92.2% (124 training samples), random forest (RF) with an overall accuracy of 82.8% (25 training samples) and 88.3% (124 training samples), and automated rice mapping using synthetic aperture radar flooding signals (ARM-SARFS) with an overall accuracy of 89.9%. Altogether, these experimental results suggest that the Auto-RMVPF method has broad prospects for automatic rice mapping, especially for mountainous regions where ground samples are often not easily accessible.

Soil moisture is a crucial factor in the field of meteorology, hydrology, and agricultural sciences. In agricultural production, surface soil moisture (SSM) is crucial for crop yield estimation and drought monitoring. For SSM inversion, a synthetic aperture radar (SAR) offers a trustworthy data source. However, for agricultural fields, the use of SAR data alone to invert SSM is susceptible to the influence of vegetation cover. In this paper, based on Sentinel-1 microwave remote sensing data and Sentinel-2 optical remote sensing data, a convolution neural network optimized by sparrow search algorithm (SSA-CNN) was suggested to invert farmland SSM. The feature parameters were first extracted from pre-processed remote sensing data. Then, the correlation analysis between the extracted feature parameters and field measured SSM data was carried out, and the optimal combination of feature parameters for SSM inversion was selected as the input data of the subsequent models. To enhance the performance of the CNN, the hyper-parameters of CNN were optimized using SSA, and the SSA-CNN model was built for SSM inversion based on the obtained optimal hyper-parameter combination. Three typical machine learning approaches, including generalized regression neural network, random forest, and CNN, were used for comparison to show the efficacy of the suggested method. With an average coefficient of determination of 0.80, an average root mean square error of 2.17 vol.%, and an average mean absolute error of 1.68 vol.%, the findings demonstrated that the SSA-CNN model with the optimal feature combination had a better accuracy among the 4 models. In the end, the SSM of the study region was inverted throughout four phenological periods using the SSA-CNN model. The inversion results indicated that the suggested method performed well in local situations.

Coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 variants is a new and unsolved threat; therefore, it is an urgent and unmet need to develop a simple and rapid method for detecting and tracking SARS-CoV-2 variants. The spike gene of SARS-CoV-2 was amplified by isothermal recombinase-aided amplification (RAA) followed by the cleavage of CRISPR-Cas12a in which five allele-specific crRNAs and two Omicron-specific crRNAs were designed to detect and distinguish major SARS-CoV-2 variants of concerns (VOCs), including alpha, beta, delta variants, and Omicron sublineages BA.1 and BA.2. The whole reaction can be carried out in one tube at 39°C within 1.5–2 h, and the results can be read out by a fluorescence meter or naked eyes. Our results show that the RAA/CRISPR-Cas12a-based assay could readily distinguish the signature mutations, i.e., K417N, T478K, E484K, N501Y, and D614G, with a sensitivity of 100.0% and a specificity of 94.9–100.0%, respectively. The assay had a low limit of detection (LOD) of 104 copies/reaction and a concordance of 92.59% with Sanger sequencing results when detecting 54 SARS-CoV-2 positive clinical samples. The two Omicron-specific crRNAs can readily and correctly distinguish Omicron BA.1 and BA.2 sublineages with a LOD of as low as 20 copies/reaction. Furthermore, no cross-reaction was observed for all crRNAs analyzed when detecting clinical samples infected with 11 common respiratory pathogens. The combination of isothermal amplification and CRISPR-Cas12a-mediated assay is suitable for rapid detection of major SARS-CoV-2 variants in point-of-care testing and in resource-limiting settings. This simple assay could be quickly updated for emerging variants and implemented to routinely monitor and track the spread of SARS-CoV-2 variants.

BackgroundEffective personal protective equipment (PPE) contribute to the prevention of COVID-19 infection. However, it is necessary to evaluate the potential risk of different medical protections in the isolation ward of COVID-19.ObjectivesWe aimed to explore the dynamics in physiological indexes of medical staff under primary and secondary PPE in the isolation ward of COVID-19 and provide the scientific basis for determining the safe work strategy.Materials and MethodsIn this study, 30 female nurses were selected to simulate medical work under the primary or secondary PPE, respectively. The oral temperature, axillary temperature, heart rate, respiratory rate, blood oxygen saturation, and blood pressure were measured and recorded every 20 min. The subjective adverse symptoms were recorded every 30 min. The blood glucose and weight of the individuals were measured and recorded before and after the trial.ResultsThe results indicated that the median trial persistence time in the participants with moderate-intensity work wearing the secondary PPE (70.0 min) was much lower than that with moderate-intensity work wearing the primary PPE (180 min) and with light-intensity work wearing the primary PPE (110 min; p < 0.05). Importantly, the heart rate, oral/axillary temperature, and respiratory rate of physiological indexes of the participants under moderate-intensity work wearing the secondary PPE increased significantly faster than the primary PPE (p < 0.001), while blood oxygen saturation decreased significantly faster than the primary PPE (p < 0.001). In addition, the proportions of subjective adverse symptoms (such as dry mouth, dizziness, palpitations, and anhelation) were much higher than primary PPE (p < 0.001). The average sweat volume and blood glucose consumption of participants under moderate-intensity work wearing primary PPE were higher than secondary PPE (p < 0.001).ConclusionThe combination of an exacerbated workload and secondary PPE worn by COVID-19 healthcare workers increases the change in physiological indicators, and in some cases the adverse symptoms, which can affect and even suspend their medical work. For any medical institution, there is room for improvement in terms of bioethics of a “Job Well Done” to reduce the risks of medical activities under secondary PPE.

BackgroundThe endoscopic modified Lothrop procedure (EMLP) is an important procedure used to address frontal and anterior skull-base lesions. Two techniques were established, namely, the inside-out approach and the outside-in approach. The former technique take the frontal recess and the first olfactory filament (FOF) as key landmarks while the latter use the FOF as posterior boundary. In some cases, however, these two landmarks are not available. Therefore, we supplement the outside-in approach and named it trans-nasion-complex approach (TNCA) for EMLP that can be performed without locating these two landmarks.MethodsTwo dry human skulls were used to observe the bony nasion complex. Then, five colored silicon-injected human head specimens were dissected via TNCA for EMLP. Finally, the outcomes of patients who underwent TNCA were reviewed.ResultsThe nasion complex is an osseous complex that consists of the nasion and its adjacent structures, including the bilateral root of nasal bones, nasal process of frontal bones, anterior portion of the perpendicular plate of the ethmoid bone that connects with the inferior aspect of the nasal bones, and portions of the bilateral frontal process of the maxillary bones. Surgical landmarks for TNCA include the anterior superior portion of the nasal septum, anterior margin and axilla of the middle turbinate, frontal process of the maxilla bone, nasal process of the frontal bone and upper part of the nasal bone. These structures form a “mushroom sign” during cadaveric dissection and surgery. Twenty-one patients underwent TNCA, of whom 9 had tumors and 12 had chronic rhinosinusitis with nasal polyps (CRSwNP). None of them had major complications.ConclusionTNCA is expected to be a safe, and direct route for EMLP. Adequate understanding of the nasion complex and “mushroom sign” will be helpful to complete TNCA.

Soil moisture is an important land environment characteristic that connects agriculture, ecology, and hydrology. Surface soil moisture (SSM) prediction can be used to plan irrigation, monitor water quality, manage water resources, and estimate agricultural production. Multi-source remote sensing is a crucial tool for assessing SSM in agricultural areas. The field-measured SSM sample data are required in model building and accuracy assessment of SSM inversion using remote sensing data. When the SSM samples are insufficient, the SSM inversion accuracy is severely affected. An SSM inversion method suitable for a small sample size was proposed. The alpha approximation method was employed to expand the measured SSM samples to offer more training data for SSM inversion models. Then, feature parameters were extracted from Sentinel-1 microwave and Sentinel-2 optical remote sensing data, and optimized using three methods, which were Pearson correlation analysis, random forest (RF), and principal component analysis. Then, three common machine learning models suitable for small sample training, which were RF, support vector regression, and genetic algorithm-back propagation neural network, were built to retrieve SSM. Comparison experiments were carried out between various feature optimization methods and machine learning models. The experimental results showed that after sample augmentation, SSM inversion accuracy was enhanced, and the combination of utilizing RF for feature screening and RF for SSM inversion had a higher accuracy, with a coefficient of determination of 0.7256, a root mean square error of 0.0539 cm3/cm3, and a mean absolute error of 0.0422 cm3/cm3, respectively. The proposed method was finally used to invert the regional SSM of the study area. The inversion results indicated that the proposed method had good performance in regional applications with a small sample size.

The extraction of water stream based on synthetic aperture radar (SAR) is of great significance in surface water monitoring, flood monitoring, and the management of water resources. However, in recent years, the research mainly uses the backscattering feature (BF) to extract water bodies. In this paper, a feature-fused encoder–decoder network was proposed for delineating the water stream more completely and precisely using both the BF and polarimetric feature (PF) from SAR images. Firstly, the standard BFs were extracted and PFs were obtained using model-based decomposition. Specifically, the newly model-based decomposition, more suitable for dual-pol SAR images, was selected to acquire three different PFs of surface water stream for the first time. Five groups of candidate feature combinations were formed with two BFs and three PFs. Then, a new feature-fused encoder–decoder network (FFEDN) was developed for mining and fusing both BFs and PFs. Finally, several typical areas were selected to evaluate the performance of different combinations for water stream extraction. To further verify the effectiveness of the proposed method, two machine learning methods and four state-of-the-art deep learning algorithms were utilized for comparison. The experimental results showed that the proposed method using the optimal feature combination achieved the highest accuracy, with a precision of 95.21%, recall of 91.79%, intersection over union (IoU) score of 87.73%, overall accuracy (OA) of 93.35%, and average accuracy (AA) of 93.41%. The results showed that the performance was higher when BF and PF were combined. In short, in this study, the effectiveness of PFs for water stream extraction was verified and the proposed FFEDN can further improve the accuracy of water stream extraction.

Synthetic aperture radar (SAR), as an active microwave sensor, can inevitably receive radio frequency interference (RFI) generated by various electromagnetic equipment. When the SAR system receives RFI, it will affect SAR imaging and limit the application of SAR images. As a kind of RFI mitigation method, notch filtering method is a classical method with high efficiency and robust performance. However, the notch filtering methods pay no attention to the protection of useful signals. This paper proposed a modified 2-D notch filter based on image segmentation for RFI mitigation with signal-protected capability. (1) The adaptive gamma correction (AGC) approach was utilized to enhance the SAR image with RFI in the range-frequency and azimuth-time domain. (2) The modified selective binary and Gaussian filtering regularized level set (SBGFRLS) model was utilized to further process the image after AGC to accurately extract the contour of the useful signals with interference, which is more conducive to protecting the useful signals without interference. (3) The Generalized Singular Value Thresholding (GSVT) based low-rank sparse decomposition (LRSD) model was utilized to separate the RFI signals and the useful signals. Then, the useful signals were restored to the raw data. The simulation experiments and measured data experiments show that the proposed method can effectively mitigate RFI and protect the useful signals whether there are RFI with single source or multiple sources.

As the emerging member of zero-dimension transition metal dichalcogenide, WSe2 quantum dots (QDs) have been applied to memristors and exhibited better resistance switching characteristics and miniaturization size. However, low power consumption and high reliability are still challenges for WSe2 QDs-based memristors as synaptic devices. Here, we demonstrate a high-performance, superlow power consumption memristor device with the structure of Ag/WSe2 QDs/La0.3Sr0.7MnO3/SrTiO3. The device displays excellent resistive switching memory behavior with a ROFF/RON ratio of ~5 × 103, power consumption per switching as low as 0.16 nW, very low set, and reset voltage of ~0.52 V and~ -0.19 V with excellent cycling stability, good reproducibility, and decent data retention capability. The superlow power consumption characteristic of the device is further proved by the method of density functional theory calculation. In addition, the influence of pulse amplitude, duration, and interval was studied to gradually modulating the conductance of the device. The memristor has also been demonstrated to simulate different functions of artificial synapses, such as excitatory postsynaptic current, spike timing-dependent plasticity, long-term potentiation, long-term depression, and paired-pulse facilitation. Importantly, digit recognition ability based on the WSe2 QDs device is evaluated through a three-layer artificial neural network, and the digit recognition accuracy after 40 times of training can reach up to 94.05%. This study paves a new way for the development of memristor devices with advanced significance for future low power neuromorphic computing.

ObjectivesTraumatic orbital apex syndrome (TOAS) is an uncommon but severe ocular complication of craniomaxillofacial fracture. The optimal surgical strategy for TOAS has not been determined. To investigate the endoscopic anatomy of the orbital apex region, propose a protocol for simultaneous endoscopic endonasal decompression of the optic canal, superior orbital fissure, and proper orbital apex (EEDCFA) for TOAS and report its use in two patients.MethodsAn endoscopic endonasal approach was utilized to dissect the orbital apex region in two silicon-injected adult cadaveric heads. The details of the procedure used for decompression of the orbital apex were determined. The effects of this procedure were determined in two patients with TOAS who underwent simultaneous decompression of the optic canal, superior orbital fissure, and proper orbital apex.ResultsThe orbital apex consisted of three portions, the contents of the optic canal superomedially; the contents of the superior orbital fissure inferolaterally; and the converging portion, or proper orbital apex, anteriorly. From an endoscopic endonasal approach, the optic nerve, superior orbital fissure, and orbital apex convergence prominences were found to form a π-shaped configuration. This π-shaped configuration was indicative of the orbital apex and was an important landmark for decompression of the orbital apex. Endonasal decompression of the orbital apex in the two patients resulted in the satisfactory recovery of extraocular mobility, with no surgical complications.ConclusionsEEDCFA is feasible, effective, and safe for patients with TOAS caused by direct compression of displaced fracture segments. The π-shaped configuration is a valuable landmark for EEDCFA.

Abstract Background Ischemia-reperfusion injury of the spinal cord (SCII) often leads to unalterable neurological deficits, which may be associated with apoptosis induced by oxidative stress and inflammation. Astaxanthin (AST) is a strong antioxidant and anti-inflammatory agent with multitarget neuroprotective effects. This study aimed to investigate the potential therapeutic effects of AST for SCII and the molecular mechanism. Methods Rat models of SCII with abdominal aortic occlusion for 40 min were carried out to investigate the effects of AST on the recovery of SCII. Tarlov’s scores were used to assess the neuronal function; HE and TUNEL staining were used to observe the pathological morphology of lesions. Neuron oxidative stress and inflammation were measured using commercial detection kits. Flow cytometry was conducted to assess the mitochondrial swelling degree. Besides, Western blot assay was used to detect the expression of PI3K/Akt/GSK-3β pathway-related proteins, as well as NOX2 and NLRP3 proteins. Results The results demonstrated that AST pretreatment promoted the hind limb motor function recovery and alleviated the pathological damage induced by SCII. Moreover, AST significantly enhanced the antioxidative stress response and attenuated mitochondrial swelling. However, AST pretreatment hardly inhibited the levels of proinflammatory cytokines after SCII. Most importantly, AST activated p-Akt and p-GSK-3β expression levels. Meanwhile, cotreatment with LY294002 (a PI3K inhibitor) was found to abolish the above protective effects observed with the AST pretreatment. Conclusion Overall, these results suggest that AST pretreatment not only mitigates pathological tissue damage but also effectively improves neural functional recovery following SCII, primarily by alleviating oxidative stress but not inhibiting inflammation. A possible underlying molecular mechanism of AST may be mainly attributed to the activation of PI3K/Akt/GSK-3β pathway.

ABSTRACT A big challenge for the control of COVID-19 pandemic is the emergence of variants of concern (VOCs) or variants of interest (VOIs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which may be more transmissible and/or more virulent and could escape immunity obtained through infection or vaccination. A simple and rapid test for SARS-CoV-2 variants is an unmet need and is of great public health importance. In this study, we designed and analytically validated a CRISPR-Cas12a system for direct detection of SARS-CoV-2 VOCs. We further evaluated the combination of ordinary reverse transcription-PCR (RT-PCR) and CRISPR-Cas12a to improve the detection sensitivity and developed a universal system by introducing a protospacer adjacent motif (PAM) near the target mutation sites through PCR primer design to detect mutations without PAM. Our results indicated that the CRISPR-Cas12a assay could readily detect the signature spike protein mutations (K417N/T, L452R/Q, T478K, E484K/Q, N501Y, and D614G) to distinguish alpha, beta, gamma, delta, kappa, lambda, and epsilon variants of SARS-CoV-2. In addition, the open reading frame 8 (ORF8) mutations (T/C substitution at nt28144 and the corresponding change of amino acid L/S) could differentiate L and S lineages of SARS-CoV-2. The low limit of detection could reach 10 copies/reaction. Our assay successfully distinguished 4 SARS-CoV-2 strains of wild type and alpha (B.1.1.7), beta (B.1.351), and delta (B.1.617.2) variants. By testing 32 SARS-CoV-2-positive clinical samples infected with the wild type (n = 5) and alpha (n = 11), beta (n = 8), and delta variants (n = 8), the concordance between our assay and sequencing was 100%. The CRISPR-based approach is rapid and robust and can be adapted for screening the emerging mutations and immediately implemented in laboratories already performing nucleic acid amplification tests or in resource-limited settings. IMPORTANCE We described CRISPR-Cas12-based multiplex allele-specific assay for rapid SARS-CoV-2 variant genotyping. The new system has the potential to be quickly developed, continuously updated, and easily implemented for screening of SARS-CoV-2 variants in resource-limited settings. This approach can be adapted for emerging mutations and implemented in laboratories already conducting SARS-CoV-2 nucleic acid amplification tests using existing resources and extracted nucleic acid.

Background: Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and is associated with very high mortality. Emerging studies have shown that N6-methyladenosine (m6A)-related long non-coding (lnc) RNAs play crucial roles in tumor prognosis and the tumor immune microenvironment (TME). We aimed to explore the expression patterns of different m6A-related lncRNAs concerning patient prognosis and construct an m6A-related lncRNA prognostic model for LUAD.Methods: The prognostic value of m6A-related lncRNAs was investigated in LUAD samples from The Cancer Genome Atlas (TCGA). Potential prognostic m6A-related lncRNAs were selected by Pearson’s correlation and univariate Cox regression analysis. Patients were divided into clusters using principal component analysis and the m6A-related lncRNA prognostic signature was calculated using least absolute shrinkage and selection operator (LASSO) Cox regression analysis.Results: Based on 91 prognostic m6A-related lncRNAs, we identified two m6A-related-lncRNA pattern clusters with different overall survival (OS) and different TMEs. We subsequently verified our findings multidimensionally by constructing a 13 m6A-related lncRNA prognostic signature (m6A-LPS) to calculate the risk score, which was robust in different subgroups. The receiver operating characteristic (ROC) curves and concordance index demonstrated that m6A-LPS harbored a promising ability to predict OS in TCGA data set and independent GSE11969 cohort. The risk score was also related to OS, TME, and clinical stage, and the risk score calculated by our model was also identified as independent prognostic predictive factors for LUAD patients after adjustment for age, smoking, gender, and stage. Enrichment analysis indicated that malignancy and drug resistance-associated pathways were more common in cluster2 (LUAD-unfavorable m6A-LPS). Furthermore, the results indicated that the signaling pathway enriched by the target gene of 13 m6A-related lncRNAs may be associated with metastasis and progression of cancer according to current studies.Conclusion: The current results indicated that different m6A-related-lncRNA patterns could affect OS and TME in patients with LUAD, and the prognostic signature based on 13 m6A-related lncRNAs may help to predict the prognosis in LUAD patients.

Soil moisture is an important parameter affecting environmental processes such as hydrology, ecology, and climate. Synthetic aperture radar (SAR) microwave remote sensing is an important means of farmland surface soil moisture (SSM) measurement. The inversion of farmland SSM by microwave remote sensing is greatly affected by vegetation cover. To address this problem, a multisource remote sensing inversion method of farmland SSM based on feature optimization and machine learning is proposed in this paper. Six typical machine learning algorithms suitable for small sample training, including random forest, radial basis function neural network, generalized regression neural network, support vector regression, genetic algorithm–back propagation neural network, and extreme learning machine, were selected in this paper. The features extracted from Sentinel-1/2 and Radarsat-2 remote sensing data were analyzed by Pearson correlation, and those with high correlation coefficients were selected to form the optimal feature subset as the input for the subsequent machine learning models. Then, the SSM collaborative inversion models under different machine learning algorithms were constructed, and comparative experiments were set up to select the optimal prediction model. The models’ accuracy under different feature parameters were studied, and the difference in the performance between the dual-polarization SAR data and the quad-polarization SAR data in SSM inversion was explored. The experimental results showed that among the six models, the random forest model had a higher inversion accuracy, with a coefficient of determination of 0.6395 and a root mean square error of 0.0264 cm3/cm3. Meanwhile, the inversion accuracy could be greatly improved after feature optimization, and the inversion accuracy of the quad-polarization SAR data combined with optical remote sensing data, was better than that of the dual-polarization SAR data combined with optical remote sensing data.

A longitudinal serological study to investigate the seropositive frequency, incidence, and antibody dynamics of Chlamydia trachomatis infection in the general population of China is urgently needed in order to optimize the strategies for surveillance and precise prevention of C. trachomatis infection. This longitudinal study enrolled 744 subjects aged 18–65 years from Jidong Community of Northern China from 2014 to 2018. Seropositive frequency, incidence, and reinfection of C. trachomatis were determined by detecting antibody against C. trachomatis Pgp3 using “in-house” luciferase immunosorbent assay (LISA). The dynamic of anti-Pgp3 antibody was analyzed using the Generalized Estimating Equation (GEE) model. The overall Pgp3 seropositive frequency among the 18–65-year-old population was 28.1% (95% CI 24.9–31.5), and significantly increased from 12.0% in those aged 18–29 years to 48.6% in the 60–65 years old. The seropositive frequency was slightly higher in women than in men (31.3% vs. 25.4%) without statistical significance. The C. trachomatis incidence and reinfection rate were 11 and 14 per 1,000 person-years, respectively, and showed no significant difference with respect to age, gender, ethnicity, marital status, and education levels. Furthermore, anti-Pgp3 antibody remained detectable in 93.3% (195/209) of the seropositive subjects during the 5 years of follow-up. The overall decay rate for anti-Pgp3 antibody for CT-infected persons was −0.123 Log2 RLU/year, which was dramatically slower than in CT new infection (−3.34 Log2 RLU/year) or reinfection (−1.1 Log2 RLU/year). In conclusion, at least one quarter of the people aged 18–65 years have been infected with C. trachomatis over their lifetime while all age groups are susceptible to C. trachomatis infection in the community of Northern China. Therefore, comprehensive prevention strategies are urgently needed.

Crop classification is an important part of crop management and yield estimation. In recent years, neural networks have made great progress in synthetic aperture radar (SAR) crop classification. However, the insufficient number of labeled samples limits the classification performance of neural networks. In order to solve this problem, a new crop classification method combining geodesic distance spectral similarity measurement and a one-dimensional convolutional neural network (GDSSM-CNN) is proposed in this study. The method consisted of: (1) the geodesic distance spectral similarity method (GDSSM) for obtaining similarity and (2) the one-dimensional convolutional neural network model for crop classification. Thereinto, a large number of training data are extracted by GDSSM and the generalized volume scattering model which is based on radar vegetation index (GRVI), and then classified by 1D-CNN. In order to prove the effectiveness of the GDSSM-CNN method, the GDSSM method and 1D-CNN method are compared in the case of a limited sample. In terms of evaluation and verification of methods, the GDSSM-CNN method has the highest accuracy, with an accuracy rate of 91.2%, which is 19.94% and 23.91% higher than the GDSSM method and the 1D-CNN method, respectively. In general, the GDSSM-CNN method uses a small number of ground measurement samples, and it uses the rich polarity information in multi-temporal fully polarized SAR data to obtain a large number of training samples, which can quickly improve the accuracy of classification in a short time, which has more new inspiration for crop classification.

The human kinome comprises 538 kinases playing essential functions by catalyzing protein phosphorylation. Annotation of subcellular distribution of the kinome greatly facilitates investigation of normal and disease mechanisms. Here, we present Kinome Atlas (KA), an image-based map of the kinome annotated to 10 cellular compartments. 456 epitope-tagged kinases, representing 85% of the human kinome, were expressed in HeLa cells and imaged by immunofluorescent microscopy under a similar condition. KA revealed kinase family-enriched subcellular localizations and discovered a collection of new kinase localizations at mitochondria, plasma membrane, extracellular space, and other structures. Furthermore, KA demonstrated the role of liquid-liquid phase separation in formation of kinase condensates. Identification of MOK as a mitochondrial kinase revealed its function in cristae dynamics, respiration, and oxidative stress response. Although limited by possible mislocalization due to overexpression or epitope tagging, this subcellular map of the kinome can be used to refine regulatory mechanisms involving protein phosphorylation.

This paper proposes a new method for fast human motion capture based on a single RGB-D sensor. By leveraging the human pose detection results for reinitializing the ICP-based sequential human motion tracking algorithm when tracking failure happens, our system achieves a highly robust and accurate human motion tracking performance even for fast motion. Moreover, the calculation and utilization of semantic tracking loss enable body-part-level motion tracking refinement, which is better than whole-body refinement. Finally, a simple yet effective post-processing method, semantic bidirectional motion blending, for single-view human motion capture is proposed to further improve the tracking accuracy, especially under severe occlusions and fast motion. The results and experiments demonstrate that the proposed method achieves highly accurate and robust human motion capture performance in a very efficient way. Applications include AR&VR, human motion analysis, movie, and gaming.

The paper further develops a modeling concept of three-phase, three-legged transformer with tank walls represented as a distributed parameter structure. The circuital models proposed replicate accurately all possible zero-sequence impedances and losses of a three-winding transformer. Several model versions are compared to each other and to a conventional topological model with respect to their transient behavior during inrush and short circuit events, and in the presence of geomagnetically induced current (GIC). It was found that in all the cases considered, except that with a large GIC, all the models yield similar results. The reliability of the models is due to the representation of the tank walls behavior in a physical way, allowing one to observe the field distribution over the wall thickness as a function of transformer excitation. The modeled results are in a close agreement with positive and zero sequence data measured on a 25 MVA transformer as well as with inrush current test on a 300 kVA unit.

Currently, there are many works exploring how to more fully and efficiently use multi-scale feature maps of deep convolutional neural networks to improve the performance of object detection. But most of these works are devoted to predicting, respectively, on multi-scale feature maps or blending multi-scale feature maps for enriching representation. In this paper, we present a new method of cross fusing feature, named multi-semantic pyramids (MSP), for detecting different-scale objects. Various scale objects are predicted, respectively, by corresponding semantic pyramids (SP), each SP can produce rich semantic features for predicting via reusing inherent multi-scale feature maps from the network backbone. Through promoting the reuse of inherent feature layers, our MSP can improve detection performance with marginal extra cost. In addition, since the reuse connection of the MSP facilitates the conduction of the gradient, the convergence of the network is greatly improved. The experimental results on the PASCAL VOC and COCO datasets illustrate that our MSP can achieve more competitive detection accuracy.

Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Learning (ML) methods. Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. We also pay more attention to the most popular networks, such as U-Net and its modified models, and novel networks, such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. In the end, an in-depth discussion is presented, along with conclusions and future trends, on the limitations and challenges of DL for water-body segmentation.