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Abstract Metastasis is the major cause of treatment failure in patients with prostate adenocarcinoma (PRAD). Diverse programmed cell death (PCD) patterns play an important role in tumor metastasis and hold promise as predictive indicators for PRAD metastasis. Using the LASSO Cox regression method, we developed PCD score (PCDS) based on differentially expressed genes (DEGs) associated with PCD. Clinical correlation, external validation, functional enrichment analysis, mutation landscape analysis, tumor immune environment analysis, and immunotherapy analysis were conducted. The role of Prostaglandin D2 Synthase (PTGDS) in PRAD was examined through in vitro experiments, single-cell, and Mendelian randomization (MR) analysis. PCDS is elevated in patients with higher Gleason scores, higher T stage, biochemical recurrence (BCR), and higher prostate-specific antigen (PSA) levels. Individuals with higher PCDS are prone to metastasis, metastasis after BCR, BCR, and castration resistance. Moreover, PRAD patients with low PCDS responded positively to immunotherapy. Random forest analysis and Mendelian randomization analysis identified PTGDS as the top gene associated with PRAD metastasis and in vitro experiments revealed that PTGDS was considerably downregulated in PRAD cells against normal prostate cells. Furthermore, the overexpression of PTGDS was found to suppress the migration, invasion, proliferationof DU145 and LNCaP cells. To sum up, PCDS may be a useful biomarker for forecasting the possibility of metastasis, recurrence, castration resistance, and the efficacy of immunotherapy in PRAD patients. Additionally, PTGDS was identified as a viable therapeutic target for the management of PRAD.

Abstract Malaria vaccination approaches using live Plasmodium parasites are currently explored, with either attenuated mosquito-derived sporozoites or attenuated blood-stage parasites. Both approaches would profit from the availability of attenuated and avirulent parasites with a reduced blood-stage multiplication rate. Here we screened gene-deletion mutants of the rodent parasite P. berghei and the human parasite P. falciparum for slow growth. Furthermore, we tested the P. berghei mutants for avirulence and resolving blood-stage infections, while preserving sporozoite formation and liver infection. Targeting 51 genes yielded 18 P. berghei gene-deletion mutants with several mutants causing mild infections. Infections with the two most attenuated mutants either by blood stages or by sporozoites were cleared by the immune response. Immunization of mice led to protection from disease after challenge with wild-type sporozoites. Two of six generated P. falciparum gene-deletion mutants showed a slow growth rate. Slow-growing, avirulent P. falciparum mutants will constitute valuable tools to inform on the induction of immune responses and will aid in developing new as well as safeguarding existing attenuated parasite vaccines.

Lake sediment, as a solid waste, can be disposed harmlessly through combustion. The ash melting temperature is an important factor affecting boiler coking and restricting its safe and stable operation. Determining the ash melting temperature after mixing lake sediment with coal is of great significance for achieving the resource utilization of lake sediment. This paper takes the Yunnan Xiaolongtan lignite and the Dianchi Lake sediment as the research objects. The Dianchi Lake sediment is a high moisture solid waste with high nitrogen content and rich heavy metal elements. The Yunnan Dianchi Lake sediment has a large residual stock, mainly generated by a large amount of sediment entering the Dianchi Lake with river water and rainwater. It is the main source of pollution in the Dianchi Lake. However, the Xiaolongtan lignite has problems such as high production, poor coal quality, and high transportation costs, which seriously affect its utilization rate, if the local conditions can be adapted and the problems of high production, difficult treatment, and insufficient efficiency of local sediment can be solved through mixed combustion, in addition to achieving an efficient utilization of lignite, it can also alleviate the local disposal pressure. However, there is currently little research on the blending of lignite with sediment. Through experiments on the ash melting characteristics, the deformation, softening, hemisphere, and flow temperature of ash at a 5%−15% mixing ratio were determined, and the variation law of ash melting point was derived. The mineral composition, oxide, and elemental composition were obtained through XRD, XRF, and SEM-EDS detection and analysis. The crystal structure and mineral composition in the ash were confirmed, and a ternary phase diagram was constructed for thermochemical calculations. The results show that when 5% sediment is mixed, the melting temperature of the ash decreases sharply, and then the downward trend tends to be gentle. The acid-base ratio and silicon aluminum ratio are positively correlated with the downward trend. The oxides of Fe+2, Fe+3, and Ca+2 play a main role in reducing the melting point of the ash, and these oxides are prone to react with refractory minerals to form fluxing minerals such as anorthite and calcium iron pyroxene. During the low temperature stage, Ca oxides mainly promote melting, while during the high temperature stage, Fe oxides act.

In the realm of geospatial services and applications, the accuracy of address information is of utmost importance. Traditional methods of data collection, being both labor-intensive and costly, have prompted researchers to turn to Volunteered Geographic Information (VGI) for the extraction of Geographical Named Entity (GNE).Notwithstanding, prior studies have predominantly concentrated on enhancing extraction accuracy, while often overlooking the critical aspect of GNE quality. This study addresses this gap by employing a multifaceted approach. Initially, a Geographical Named Entity Semantic Model (GNESM) was constructed by improving the BERT framework and conducting ablation experiments on multiple influencing factors to verify its feasibility. Based on GNESM, a Geographical Named Entity Recognition Model (GNERM) was constructed by incremental pre-training with social media text data and fine-tuning to achieve a recognition accuracy of 90.9%. Subsequently, a Geographical Named Entity Error Correction Model (GNEECM) was constructed by training GNESM with standard GNE data and incorporating error detection and correction modules, achieving a remarkable accuracy of 96.6% in error detection and correction tasks. The experimental results convincingly demonstrate that the proposed identification and correction methods outperform all compared methods. Through the identification and correction process, this study successfully obtained high-quality GNE data, providing a reference for expanding standard address libraries and subsequent research on geographic named entity.

ABSTRACTBackground: The association between stressful life events (SLEs) and adolescent anxiety symptoms has been extensively studied, but the specific impacts of different SLEs domains remain inconclusive. Moreover, limited research has examined the role of family functioning in these associations.Objective: This study aimed to investigate the associations between various recent SLEs and adolescent anxiety symptoms and explore the role of family functioning.Methods: Data were obtained from the second phase of the Longitudinal Study of Adolescents’ Mental and Behavioral Well-being Research in Guangzhou, China. A total of 10,985 students (51.9% boys; mean [SD] age, 15.3 [1.5] years) from forty middle schools participated in the study in 2022 and completed a self-report questionnaire assessing anxiety symptoms, SLEs, and family functioning using the Generalized Anxiety Disorder-7 (GAD-7), Adolescent Self-rating Life Events Checklist (ASLEC; including five subscales: interpersonal stress, academic stress, punishment-related stress, loss-related stress, and adaptation-related stress), and the adapted Chinese version of the Family Assessment Device (FAD), respectively. Linear mixed-effects models were performed and the moderation role of family functioning was also examined.Results: The fully adjusted model revealed that a 1-SD increase in the overall ASLEC score was associated with higher levels of anxiety symptoms (β = 2.23, 95%CI: 2.15–2.32). Among various SLEs domains, the academic domain shows the most significant association (β = 2.25, 95%CI: 2.17–2.33). Family functioning exerted an independent protective influence on anxiety symptoms, with each 1-SD increase in FAD scores negatively associated with anxiety symptoms (β = −2.11, 95%CI: – 2.29 to – 1.93) in the adjusted model. Moreover, family functioning significantly buffered the impacts of overall SLEs and each domain, except for adaptation-related SLEs, on anxiety symptoms.Conclusion: Higher recent SLEs levels were associated with increased anxiety symptoms among adolescents, with academic SLEs showing the greatest association. Positive family functioning had both direct and buffering influences on anxiety symptoms.

Abstract Hydrogen (H2) has been regarded as a promising alternative to fossil‐fuel energy. Green H2 produced via water electrolysis (WE) powered by renewable energy could achieve a zero‐carbon footprint. Considerable attention has been focused on developing highly active catalysts to facilitate the reaction kinetics and improve the energy efficiency of WE. However, the stability of the electrocatalysts hampers the commercial viability of WE. Few studies have elucidated the origin of catalyst degradation. In this review, we first discuss the WE mechanism, including anodic oxygen evolution reaction (OER) and cathodic hydrogen evolution reaction (HER). Then, we provide strategies used to enhance the stability of electrocatalysts. After that, the deactivation mechanisms of the typical commercialized HER and OER catalysts, including Pt, Ni, RuO2, and IrO2, are summarized. Finally, the influence of fluctuating energy on catalyst degradation is highlighted and in situ characterization methodologies for understanding the dynamic deactivation processes are described.

BackgroundFamilial 46, XY Disorder of Sexual Development (DSD) was discovered in a Ph+, BCR::ABL1P210+ Acute Lymphoblastic Leukemia (ALL) female with RCBTB2::LPAR6 fusion gene. Siblings developing 46, XY DSD are extremely rare. Patients with 46, XY DSD have much higher rates of gonadal cancers. Nevertheless, the incidence of hematologic malignancies in patients with DSDs has received little attention. RCBTB2::LPAR6 is a rarely reported fusion gene in ALL.Case presentationHerein, we report a rare case of a newly diagnosed Ph+, BCR::ABL1P210+ ALL patient who was 77 years old and female by social sex. Whole Exome Sequencing (WES) and RNA sequencing revealed TET2 and NF1 mutations in addition to a rarely reported RCBTB2::LPAR6 fusion gene and 17 other genes with uncertain clinical significance. The patient was surprisingly found to have a male karyotype. On ultrasound, neither the uterus nor the ovaries were discernible. A detailed family and marital history revealed that the patient had undergone surgery at an early age for an unexplained inguinal mass. She had slow pubertal development, scanty menstruation, and few overtly feminine characteristics. She had three marriages, but none succeeded in getting pregnant. The patient had never sought therapy for infertility due to the inaccessibility of medical treatment and a lack of medical knowledge. Her sister, 73 years old and female by social sex, who had amenorrhea in adolescence and was unable to conceive, had the same experience. To our surprise, she also had a male karyotype.ConclusionsDue to the absence of long-term social attention and follow-up, studies on the incidence of hematologic malignancies in patients with 46, XY DSD are incredibly uncommon. Siblings developing 46, XY DSD is extremely rare. We report the oldest patient diagnosed with 46, XY DSD. There have not yet been any reports of familial 46, XY DSD with a concurrent diagnosis of Ph+BCR::ABL1P210+ALL with a rarely reported RCBTB2::LPAR6 fusion gene.

Abstract Breast carcinoma (BC) ranks as a predominant malignancy and constitutes the second principal cause of mortality among women globally. Epirubicin stands as the drug of choice for BC therapeutics. Nevertheless, the emergence of chemoresistance has significantly curtailed its therapeutic efficacy. The resistance mechanisms to Epirubicin remain not entirely elucidated, yet they are conjectured to stem from diminished tumor vascular perfusion and resultant hypoxia consequent to Epirubicin administration. In our investigation, we meticulously scrutinized the Gene Expression Omnibus database for EPDR1, a gene implicated in hypoxia and Epirubicin resistance in BC. Subsequently, we delineated the impact of EPDR1 on cellular proliferation, motility, invasive capabilities, and interstitial-related proteins in BC cells, employing methodologies such as the CCK-8 assay, Transwell assay, and western blot analysis. Our research further unveiled that hypoxia-induced miR-181a-5p orchestrates the regulation of BC cell duplication, migration, invasion, and interstitial-related protein expression via modulation of EPDR1. In addition, we identified TRPC1, a gene associated with EPDR1 expression in BC, and substantiated that EPDR1 influences BC cellular dynamics through TRPC1-mediated modulation of the PI3K/AKT signaling cascade. Our findings underscore the pivotal role of EPDR1 in the development of BC. EPDR1 was found to be expressed at subdued levels in BC tissues, Epirubicin-resistant BC cells, and hypoxic BC cells. The overexpression of EPDR1 curtailed BC cell proliferation, motility, invasiveness, and the expression of interstitial-related proteins. At a mechanistic level, the overexpression of hypoxia-induced miR-181a-5p was observed to inhibit the EPDR1/TRPC1 axis, thereby activating the PI3K/AKT signaling pathway and diminishing the sensitivity to Epirubicin in BC cells. In summation, our study demonstrates that the augmentation of hypoxia-induced miR-181a-5p diminishes Epirubicin sensitivity in BC cells by attenuating EPDR1/TRPC1 expression, thereby invigorating the PI3K/AKT signaling pathway. This exposition offers a theoretical foundation for the application of Epirubicin in BC therapy, marking a significant contribution to the existing body of oncological literature.

Pine wilt disease (PWD) poses a significant threat to forests due to its high infectivity and lethality. The absence of an effective treatment underscores the importance of timely detection and isolation of infected trees for effective prevention and control. While deep learning techniques combined unmanned aerial vehicle (UAV) remote sensing images offer promise for accurate identification of diseased pine trees in their natural environments, they often demand extensive prior professional knowledge and struggle with efficiency. This paper proposes a detection model YOLOv5L-s-SimAM-ASFF, which achieves remarkable precision, maintains a lightweight structure, and facilitates real-time detection of diseased pine trees in UAV RGB images under natural conditions. This is achieved through the integration of the ShuffleNetV2 network, a simple parameter-free attention module known as SimAM, and adaptively spatial feature fusion (ASFF). The model boasts a mean average precision (mAP) of 95.64% and a recall rate of 91.28% in detecting pine wilt diseased trees, while operating at an impressive 95.70 frames per second (FPS). Furthermore, it significantly reduces model size and parameter count compared to the original YOLOv5-Lite. These findings indicate that the proposed model YOLOv5L-s-SimAM-ASFF is most suitable for real-time, high-accuracy, and lightweight detection of PWD-infected trees. This capability is crucial for precise localization and quantification of infected trees, thereby providing valuable guidance for effective management and eradication efforts.

Abstract Triple‐positive breast cancer (TPBC) poorly responds to current standard neoadjuvant therapy (trastuzumab plus pertuzumab and chemotherapy). Our previous MUKDEN 01 study showed a promising total pathological complete response (tpCR) rate of 30.4% with neoadjuvant pyrotinib (pan‐human epidermal growth factor receptor tyrosine kinase inhibitor) plus dalpiciclib (cyclin‐dependent kinase 4/6 inhibitor) and letrozole, but the efficacy remains suboptimal. This pilot study (NCT05228951) explored adding trastuzumab to this triplet neoadjuvant regimen in patients with stage II–III TPBC. The primary endpoint was tpCR (ypT0/is, ypN0) rate. Between February 2022 and June 2022, 12 patients were enrolled, and seven (58%; 95% confidence interval [CI], 27.7%–84.8%) patients achieved tpCR. The rate of residual cancer burden (RCB) 0–I was 75% (95% CI, 46.8%–91.1%). The objective response rate (ORR) was 92% (95% CI, 64.6%–98.5%). Mean Ki‐67 level was significantly reduced from 45.0% (95% CI, 19.5%–70.5%) at baseline to 17.2% (95% CI, 0.7%–33.7%) after neoadjuvant therapy (p = 0.03). The most common grade 3 adverse events were diarrhea (four [33%]) and decreased neutrophil count (three [25%]). No grade 4 adverse events or treatment‐related deaths occurred. This four‐drug neoadjuvant regimen shows promising pathological response with an acceptable safety profile in patients with TPBC. A randomized controlled trial (NCT05638594) of this regimen is being conducted.

Different types of rural settlement agglomerations have been formed and mixed in space during the rural revitalization strategy implementation in China. Discriminating them from remote sensing images is of great significance for rural land planning and living environment improvement. Currently, there is a lack of automatic methods for obtaining information on rural settlement differentiation. In this paper, an improved encoder–decoder network structure, ASCEND-UNet, was designed based on the original UNet. It was implemented to segment and classify dispersed and clustered rural settlement buildings from high-resolution satellite images. The ASCEND-UNet model incorporated three components: firstly, the atrous spatial pyramid pooling (ASPP) multi-scale feature fusion module was added into the encoder, then the spatial and channel squeeze and excitation (scSE) block was embedded at the skip connection; thirdly, the hybrid dilated convolution (HDC) block was utilized in the decoder. In our proposed framework, the ASPP and HDC were used as multiple dilated convolution blocks to expand the receptive field by introducing a series of dilated rate convolutions. The scSE is an attention mechanism block focusing on features both in the spatial and channel dimension. A series of model comparisons and accuracy assessments with the original UNet, PSPNet, DeepLabV3+, and SegNet verified the effectiveness of our proposed model. Compared with the original UNet model, ASCEND-UNet achieved improvements of 4.67%, 2.80%, 3.73%, and 6.28% in precision, recall, F1-score and MIoU, respectively. The contributions of HDC, ASPP, and scSE modules were discussed in ablation experiments. Our proposed model obtained more accurate and stable results by integrating multiple dilated convolution blocks with an attention mechanism. This novel model enriches the automatic methods for semantic segmentation of different rural settlements from remote sensing images.

Owing to the challenging working conditions aboard ships, equipment may develop various defects that traditional nondestructive testing methods cannot fully detect. This study proposes a combined approach using pulsed eddy current and electromagnetic ultrasonic testing for enhanced probe detection. Utilizing COMSOL 6.2 finite-element software, a two-dimensional model of the probe was developed to optimize the probe’s design. Based on these findings, a composite probe was designed and structurally optimized, resulting in improved energy conversion efficiency. An experimental platform was established to evaluate the detection capabilities of the composite detection probes. The results indicate that the composite detection probe, as designed and optimized in this study, is effective in testing materials with various artificial defects and enhances the detection of near-surface defects in materials.

BackgroundN-lactoylphenylalanine (Lac-Phe) is a new form of “exerkines” closely related to lactate (La), which may be able to inhibit appetite. Blood flow restriction (BFR) can lead to local tissue hypoxia and increase lactate accumulation. Therefore, this study investigated the effects of combining Moderate-intensity Continuous Exercise (MICE) with BFR on Lac-Phe and appetite regulation in obese adults.MethodsThis study employed the cross-design study and recruited 14 obese adults aged 18-24 years. The participants were randomly divided into three groups and performed several tests with specific experimental conditions: (1) M group (MICE without BFR, 60%VO2max, 200 kJ); (2) B group (MICE with BFR, 60%VO2max, 200 kJ); and (3) C group (control session without exercise). Participants were given a standardized meal 60 min before exercise and a ad libitum 60 min after exercise. In addition, blood and Visual Analogue Scale (VAS) were collected before, immediately after, and 1 hour after performing the exercise.ResultsNo significant difference in each index was detected before exercise. After exercise, the primary differential metabolites detected in the M and B groups were xanthine, La, succinate, Lac-Phe, citrate, urocanic acid, and myristic acid. Apart from that, the major enrichment pathways include the citrate cycle, alanine, aspartate, and glutamate metabolism. The enhanced Lac-Phe and La level in the B group was higher than M and C groups. Hunger of the B group immediately after exercise substantially differed from M group. The total ghrelin, glucagon-like peptide-1 and hunger in the B group 1 hour after exercise differed substantially from M group. The results of calorie intake showed no significant difference among the indexes in each group.ConclusionsIn conclusion, this cross-design study demonstrated that the combined MICE and BFR exercise reduced the appetite of obese adults by promoting the secretion of Lac-Phe and ghrelin. However, the exercise did not considerably affect the subsequent ad libitum intake.

Rock zones have an important influence on the yield and quality of Wuyi rock tea. In this study, OPLS-DA combined with machine learning was used to analyze the effects of different rock zones and tea tree varieties on the physicochemical properties of rhizosphere soil, the growth of the tea tree and the quality of the tea leaves using tea trees in different rock zones. The results showed that rock zones had significant effects on rhizosphere soil physicochemical indexes, soil enzyme activities, tea tree growth and tea quality indexes, while there was little difference between different tea tree varieties. The interaction analysis showed that the physicochemical indexes of rhizosphere soil in different rock zones significantly affected tea quality, while also affecting growth indexes. The main indexes affecting tea yield and caffeine content were soil pH, available nitrogen, total phosphorus, total nitrogen and available phosphorus, while the main indexes affecting tea quality were available potassium, organic matter, total potassium, protease, polyphenol oxidase and urease. Analyses of PCA, OPLS-DA models and KNN and ANN machine learning showed that different rock zones could be effectively distinguished from each other with 100% accuracy, while different tea varieties had little difference and could not be distinguished. TOPSIS analysis found that the physicochemical indexes most affected by rock zone were available nitrogen, available potassium and sucrose, and the quality indexes most affected by rock zone were tea polyphenols and theanine. The growth index most affected by rock zone was tea yield. It was evident that the key difference between tea trees in different rock zones was yield and quality, with high yields in continent zones, and good quality in semi-rock zones and rock zones. This study provides a crucial foundation for tea-plantation management, the artificial regulation of tea yield and the quality of different rock zones of Wuyi rock tea.

Abstract Background As known, inhibition of phosphodiesterase 5 (PDE5) has the therapeutic effect on male erectile dysfunction (ED), and the processed folium of Epimedium sagittatum Maxim. (PFES) characterized by 8-isopentenyl flavonoids is a famous herb for treating ED. However, the main flavonoids inhibitory activities, structure–activity relationship (SAR) and signaling pathway have been not systematically studied so that its pharmacodynamic mechanism is unclear. Methods We aimed to initially reveal the PFES efficacy mechanism for treating ED. For the first time, 6 main 8-isopentenyl flavonoids (1–6) from PFES were isolated and identified. Then based on HPLC detection, we proposed a novel method to screen inhibitors among them. We further analyze the three-dimensional quantitative structure–activity relationship (3D-QSAR) for those inhibitors. Results The results were verified by cellular effects of the screened flavonoids. Among 6 compounds, Icariin: (1), 2-Oʹʹrhamnosylicaridide II (2) and Baohuoside I (3) were identified with significant activities (IC50 = 8.275, 3.233, 5.473 μM). Then 3D-QSAR studies showed that the replacement of C8 with bulky steric groups as isopentenyl, C3 with positive charge groups and C4' with a hydrogen bond acceptor substituent could increase inhibitory effects. In contrast, the substitution of C7 with bulky steric groups or hydrophilic groups tended to decrease the efficacies. And compounds 1, 2, 3 could increase cGMP level and decrease cytoplasmic Ca2+ of rat corpus cavernosum smooth muscle cells (CCSMCs)by activating PKG. Conclusion 8-isopentenyl flavonoids could be the main pharmacodynamic substances of PFES in the treatment for ED, and some had significant PDE5A1 inhibitory activities so as to activate cGMP/PKG/Ca2+ signaling pathway in CCSMCs, that was related to the substituents at the key sites such as C8, C3, C4ʹ and C7 in the characteristic compounds.

Neoadjuvant therapy is recommended for patients with locally advanced breast cancer. Here the authors report the results of a phase 2 clinical trial of oral neoadjuvant therapy with pyrotinib (pan-HER tyrosine kinase inhibitor), letrozole (aromatase inhibitor) and dalpiciclib (CDK4/6 inhibitor) in patients with treatment-naïve and stage II-III triple positive breast cancer.

IntroductionAccurate grading identification of tea buds is a prerequisite for automated tea-picking based on machine vision system. However, current target detection algorithms face challenges in detecting tea bud grades in complex backgrounds. In this paper, an improved YOLOv7 tea bud grading detection algorithm TBC-YOLOv7 is proposed.MethodsThe TBC-YOLOv7 algorithm incorporates the transformer architecture design in the natural language processing field, integrating the transformer module based on the contextual information in the feature map into the YOLOv7 algorithm, thereby facilitating self-attention learning and enhancing the connection of global feature information. To fuse feature information at different scales, the TBC-YOLOv7 algorithm employs a bidirectional feature pyramid network. In addition, coordinate attention is embedded into the critical positions of the network to suppress useless background details while paying more attention to the prominent features of tea buds. The SIOU loss function is applied as the bounding box loss function to improve the convergence speed of the network.ResultThe results of the experiments indicate that the TBC-YOLOv7 is effective in all grades of samples in the test set. Specifically, the model achieves a precision of 88.2% and 86.9%, with corresponding recall of 81% and 75.9%. The mean average precision of the model reaches 87.5%, 3.4% higher than the original YOLOv7, with average precision values of up to 90% for one bud with one leaf. Furthermore, the F1 score reaches 0.83. The model’s performance outperforms the YOLOv7 model in terms of the number of parameters. Finally, the results of the model detection exhibit a high degree of correlation with the actual manual annotation results ( R2 =0.89), with the root mean square error of 1.54.DiscussionThe TBC-YOLOv7 model proposed in this paper exhibits superior performance in vision recognition, indicating that the improved YOLOv7 model fused with transformer-style module can achieve higher grading accuracy on densely growing tea buds, thereby enables the grade detection of tea buds in practical scenarios, providing solution and technical support for automated collection of tea buds and the judging of grades.

Background: Kidney-Yang deficiency syndrome (KDS) is a group of diseases related to hypothalamic-pituitary-adrenal (HPA) axis and sexual dysfunction. The folium of Epimedium brevicornu Maxim. (FEB) includes raw and prepared slices, named RFEB and PFEB, respectively. PFEB is traditionally believed to be good for tonifying kidney-Yang and improving sexual dysfunction. However, there are few studies comparing the pharmacological effects of RFEB and PFEB, and their underlying mechanisms. In this study, we aimed to compare the effects and safety of RFEB and PFEB on the HPA axis and sexual function. Additionally, the mechanisms of their roles in relation to the neuroendocrine-immune (NEI) network in the KDS model mice were explored.Methods: Male adult C57BL/6 mice were treated with corticosterone to establish a KDS mouse model, and RFEB and PFEB were administered intragastrically. Corticotropin releasing hormone (CRH), adrenocorticotropic hormone (ACTH), cyclic adenosine monophosphate (cAMP), cyclic guanosine monophosphate (cGMP), testosterone levels and oxidative damage indexes were measured. The mRNA and protein levels of CRH and ACTH in hypothalamus and pituitary, endothelial nitric oxide synthase (eNOS) and phosphodiesterase 5 (PDE5) in corpus cavernosum were examined. TNFα, IL-6, NF-κB, eNOS and PDE5 were investigated in mouse corpus cavernosum.Results: Our results showed that PFEB was more effective than RFEB in increasing corticosterone-suppressed ACTH levels, enhancing CRH levels and cAMP/cGMP ratio, and reducing oxidative damage. In vivo, PFEB significantly increased eNOS and inhibited PDE5 expression in corpus cavernosum. PFEB showed stronger protective effect on normal spleen lymphocytes from apoptosis both in vitro and in vivo. Additionally, it noticeably inhibited the levels of inflammatory cytokines in corpus cavernosum. Both RFEB and PFEB were safe and did not cause any clinical signs of toxicity in mice at the dosage of 20 times dosages of that in the Chinese Pharmacopeia.Conclusion: We demonstrated that PFEB was better than RFEB at tonifying the kidney-Yang by comparing their effects on improving the NEI network, which includes the HPA axis, immune system and corpus cavernosum. This study revealed that PFEB could significantly improve the sexual function of KDS mice by regulating the HPA axis and activating the immune system through the NEI network.

BackgroundAlthough the association between social isolation and the risk of subsequent cardiovascular disease (CVD) is well documented, most studies have only assessed social isolation at a single time point, and few studies have considered the association using repeatedly measured social isolation. ObjectiveThis study aimed to examine the association between social isolation trajectories and incident CVD in a large cohort of middle-aged and older adults. MethodsThis study used data from 4 waves (wave 1, wave 2, wave 3, and wave 4) of the China Health and Retirement Longitudinal Study. We defined the exposure period as from June 2011 to September 2015 (from wave 1 to wave 3) and the follow-up period as from September 2015 to March 2019 (wave 4). On the basis of the inclusion and exclusion criteria, our final analytic sample included 8422 individuals who had no CVD in the China Health and Retirement Longitudinal Study in waves 1 to 3 and were fully followed up in wave 4. Social isolation was ascertained using an extensively used questionnaire at 3 consecutive, biennial time points from waves 1 to 3, and individuals were assigned to 3 predefined social isolation trajectories based on their scores at each wave (consistently low, fluctuating, and consistently high). Incident CVD included self-reported physician-diagnosed heart disease and stroke combined. Cox proportional hazard models estimated the associations of social isolation trajectories with risks of incident CVD, adjusting for demographics, health behaviors, and health conditions. ResultsOf the 8422 participants (mean age 59.76, SD 10.33 years at baseline), 4219 (50.09%) were male. Most of the participants (5267/8422, 62.54%) had consistently low social isolation over time and 16.62% (1400/8422) of the participants had consistently high social isolation over the exposure period. During the 4-year follow-up, 746 incident CVDs occurred (heart disease: 450 cases and stroke: 336 cases). Compared with individuals with consistently low social isolation, those with fluctuating social isolation (adjusted hazard ratio 1.27, 95% CI 1.01-1.59) and consistently high social isolation (adjusted hazard ratio 1.45, 95% CI 1.13-1.85) had higher risks for incident CVD after adjusting for demographics (ie, age, sex, residence, and educational level), health behaviors (ie, smoking status and drinking status), and health conditions (ie, BMI; history of diabetes, hypertension, dyslipidemia, chronic kidney disease; use of diabetes medications, hypertension medications, and lipid-lowering therapy; and depressive symptoms scores). ConclusionsIn this cohort study, middle-aged and older adults with fluctuating and consistently high social isolation exposure had higher risks of the onset of CVD than those without the exposure. The findings suggest that routine social isolation screenings and efforts to improve social connectedness merit increased attention for preventing CVD among middle-aged and older adults.

Abstract Background With the population aging, multiple chronic diseases, depressive symptoms, and stroke are increasingly common among middle-aged and elderly adults worldwide. This study aimed to explore the independent associations of multiple chronic diseases and depressive symptoms as well as their combination with incident stroke in a prospective cohort of Chinese middle-aged and elderly adults, and to sensitively estimate the association between each type of chronic disease and incident stroke. Methods This study used data from the China Health and Retirement Longitudinal Study (CHARLS). A total of 8389 participants meeting the inclusion criteria at baseline (between 2011 and 2012) survey were included, and 7108 eligible participants completed the follow-up survey over 8 years (Wave 4, in 2018). Questionnaire information, physical examination, and clinical and biochemical measurements were collected. Results The mean (SD) age at baseline was 58.5 (± 9.1) years. Multiple chronic disease and depressive symptoms were independently associated with incident stroke. After adjusting for control variables, patients having 1 type of chronic disease and depressive symptoms were at 1.943 (95% CI = 1.166–3.238) times higher risk of incident stroke than those without chronic disease and depressive symptoms, and patients having at least 2 types of chronic diseases and depressive symptoms were at 3.000 (95% CI = 1.846–4.877) times higher risk of incident stroke; the magnitudes of the associations increased by the numbers of having chronic diseases and depressive symptoms. Sensitivity analyses incorporating all five types of chronic disease (i.e., hypertension, dyslipidemia, heart disease, diabetes, and chronic kidney disease) showed that the magnitude of the associations between hypertension and incident stroke was most significant. Conclusions We identified significant independent and combined longitudinal associations of multiple chronic diseases and depressive symptoms with incident stroke, and the combined associations reflected a dose–response relationship. The association between hypertension and incident stroke was strongest among the five chronic diseases.

In this work, an acylhydrazone derivative (QN62) was developed via the one-step condensation of 6-hydroxy-2-naphthoic hydrazide with quinoline-8-carboxaldehyde. The structure of the QN62 compound was characterized by 1H NMR, 13C NMR, HR-MS (ESI), and X-ray crystallography. As a dual-functional turn-on fluorescence chemosensor, QN62 exhibited rapid recognition for Zn2+ in DMSO-H2O (4:1, v/v) and Mg2+ in ethanol-H2O (9:1, v/v). The enhancement in fluorescence detection was associated with the coordination reaction between QN62 and the target ions, which promoted intramolecular charge transfer and prevented the CN isomerization process. Simultaneously, a rapid color change from colorless to yellowish-green or yellow under UV light (365 nm) was easily visible to the naked eye. Under optimal conditions, the limit of detection and limit of quantification were 32.3 nM and 97.8 nM for Zn2+ and 16.1 nM and 48.9 nM for Mg2+, respectively. The recognition mechanism was reasonably speculated based on analysis of the Job’s plot, HR-MS, 1H NMR, and density functional theoretical calculations. Utilizing silica-gel plates fabricated from the QN62 chemosensor, the visual and rapid identification of Zn2+ and Mg2+ was successfully achieved, which could provide a convenient approach for on-site detection in environmental fields. The QN62 chemosensor could also quantify trace amounts of Zn2+ and Mg2+ in water samples. Furthermore, the cell imaging experiments indicated that QN62 could effectively sense intracellular Zn2+ and Mg2+, providing potential applications in biological systems.

BackgroundHistone acetylation-related lncRNAs (HARlncRNAs) play significant roles in various cancers, but their impact on lung adenocarcinoma (LUAD) remains unclear. This study aimed to develop a new HARlncRNA-based prognostic model for LUAD and to explore its potential biological mechanisms.MethodsWe identified 77 histone acetylation genes based on previous studies. HARlncRNAs related to prognosis were screened by co-expression, univariate and multivariate analyses, and least absolute shrinkage selection operator regression (LASSO). Afterward, a prognostic model was established based on the screened HARlncRNAs. We analysed the relationship between the model and immune cell infiltration characteristics, immune checkpoint molecule expression, drug sensitivity, and tumour mutational burden (TMB). Finally, the entire sample was divided into three clusters to further distinguish between hot and cold tumours.ResultsA seven-HARlncRNA-based prognostic model was established for LUAD. The area under the curve (AUC) of the risk score was the highest among all the analysed prognostic factors, indicating the accuracy and robustness of the model. The patients in the high-risk group were predicted to be more sensitive to chemotherapeutic, targeted, and immunotherapeutic drugs. It was worth noting that clusters could effectively identify hot and cold tumours. In our study, clusters 1 and 3 were considered hot tumours that were more sensitive to immunotherapy drugs.ConclusionWe developed a risk-scoring model based on seven prognostic HARlncRNAs that promises to be a new tool for evaluating the prognosis and efficacy of immunotherapy in patients with LUAD.

Background and aimsHepatocellular carcinoma (HCC) is a common cause of cancer-related death in humans. Increasing evidence indicates that an imbalance in N6-methyladenosine (m6A) methylation is linked to the occurrence and development of cancer. We then developed a prognostic model as an independent risk factor with which predict the prognosis of HCC.MethodsWe obtained the gene expression and clinical data of HCC patients from the TCGA databases. The prognostic value of m6A methylation-related genes in patients who had HCC were subjected to comprehensive bioinformatics analysis. We use RiskScore=∑i=1nCoefi×Xi to construct the risk scoring formula. We collected pathological specimens from 68 patients who had HCC, and conducted immunohistochemical staining experiments on the specimens.ResultsThere was a significant correlation between candidate m6A methylation-related genes (YTHDF2, METTL14 and ZC3H13) overall survival of HCC patients. Among the 68 HCC patient specimens that underwent immunohistochemical staining, all cancer tissues were positive for METTL14, YTHDF2, and ZC3H13 staining in contrast to the adjacent tissues. We conducted a Kaplan-Meier survival analysis. The results showed that patients who had low METTL14 expression had a longer survival time than those of patients who had high METTL14 expression. Also, patients with low YTHDF2 expression had a longer survival time than patients with high YTHDF2 expression. Finally, patients with high ZC3H13 expression lived longer than those with low ZC3H13 expression. This result is consistent with the bioinformatics analysis conclusion above.ConclusionsGenerally, the prognostic model that was based on m6A methylation-related genes in this study can effectively predict the prognosis of HCC patients.

To investigate the impact of betaine on high-carbohydrate-diet-induced oxidative stress and endoplasmic reticulum (ER) stress, mandarin fish (Siniperca chuatsi) (23.73 ± 0.05 g) were fed with control (NC), betaine (BET), high carbohydrate (HC), and high carbohydrate + betaine (HC + BET) diets for 8 weeks. The results showed that betaine significantly promoted the growth of mandarin fish irrespective of the dietary carbohydrate levels. The HC diet induced oxidative stress, as evidenced by significantly elevated MDA levels. The HC diet significantly stimulated the mRNA levels of genes involved in ER stress (ire1, perk, atf6, xbp1, eif2α, atf4, chop), autophagy (ulk1, becn1, lc3b), and apoptosis (bax). However, betaine mitigated HC-diet-induced oxidative stress by modulating antioxidant enzymes and alleviated ER stress by regulating the mRNA of genes in the PERK-eIF2a-ATF4 pathway. Additionally, betaine significantly reduced the mRNA levels of becn1 and bax, along with the apoptosis rate, indicating a mitigating effect on autophagy and apoptosis. Overall, dietary betaine improved growth, attenuated HC-diet-induced oxidative stress and ER stress, and ultimately alleviated apoptosis in mandarin fish. These findings provide evidence for the use of betaine in aquafeeds to counter disruptive effects due to diets containing high carbohydrate levels.

To solve the problem of multiple sound recognition in the application of Sagnac optical fiber acoustic sensing system, a multi-source synchronous recognition algorithm was proposed, which combined the VMD (variational modal decomposition) algorithm and MFCC (Mel-frequency cepstral coefficient algorithm) algorithm to pre-process the photoacoustic sensing signal, and uses BP neural network to recognize the photoacoustic sensing signal. The modal analysis and feature extraction theory of photoacoustic sensing signal based on the VMD and MFCC algorithms were presented. The signal recognition theory analysis and system recognition program design were completed based on the BP neural network. Signal acquisition of different sounds and verification experiments of the recognition system have been carried out in a laboratory environment based on the Sagnac fiber optic sound sensing system. The experimental results show that the proposed optical fiber acoustic sensing signal recognition algorithm has a simultaneous recognition rate better than 96.5% for six types of sounds, and the optical acoustic signal recognition takes less than 5.3 s, which has the capability of real-time sound detection and recognition, and provides the possibility of further application of the Sagnac-based optical fiber acoustic sensing system.

Abstract Introduction Breast microcalcifications is a characteristic feature in diagnostic imaging and a prognostic factor of breast cancer. However, the underlying mechanisms of breast microcalcifications formation are not fully understood. Previous studies have shown that upregulation of bone morphogenetic protein 2 (BMP-2) is associated with the occurrence of microcalcifications and tumor-associated macrophages (TAMs) in the tumor microenvironment can secrete BMP-2. The aim of this study is to elucidate the role of secretion of BMP-2 by TAMs in promoting microcalcifications of breast cancer through immunohistochemical staining and co-culturing of breast cancer cells with TAMs. Methods A total of 272 patients diagnosed with primary invasive breast cancer from January 2010 to January 2012 in the First Hospital of China Medical University were included in this study. Immunohistochemical staining of CD68 (marker of entire macrophages), CD168 (marker of the M2-like macrophages) and BMP-2 were performed on 4-μm tissue microarray (TMA) sections. Following induction, THP-1 cells were differentiated to M2-like TAMs and were then co-cultured with breast cancer cells (MCF-7). Calcifications and BMP-2 expression were analyzed by Alizarin Red S staining and western blot, respectively. Results Immunohistochemical analysis showed that the expression of CD168 was significantly increased in tissues with microcalcifications and was correlated with the expression of BMP-2 and poor prognosis. The formation of cellular microcalcifications and BMP-2 expression were significantly increased in MCF-7 cells co-cultured with TAMs compared with MCF-7 cells alone. Conclusions These findings support the hypothesis that TAMs secrete BMP-2 to induce microcalcifications in breast cancer cells and influence prognosis via multiple pathways including BMP-2 and its downstream factors.

Background: More than half of Chinese patients with hormone receptor positive (HR+) ductal carcinoma in situ (DCIS) are treated with mastectomy, and usually subjected to postoperative endocrine therapy (ET). Given that long-term ET can cause severe adverse effects it is important to determine the beneficial effect and safety of post-mastectomy ET on the disease-free survival (DFS) and adverse events in patients with HR+ DCIS. Methods: To explore beneficial effect and safety of post-mastectomy ET in patients with HR+ DCIS, we performed a multicenter, population-based study. This retrospective study analyzed the DFS and adverse events in 1037 HR+ DCIS Chinese patients with or without post-mastectomy ET from eight breast centers between 2006 and 2016. The median follow-up time period was 86 months. Results: There were 791 DCIS patients receiving ET (ET group). Those patients were followed up for a median of 86 months (range, 60–177 months). There were 23 cases with tumor recurrence or distant metastasis. There were similar 5-year DFS rates and DFS between the ET and non-ET groups, even for those with high-risk factors. Conversely, 37.04% of patients suffered from adverse events after ET, which were significantly higher than those in the non-ET group. Conclusions: ET after mastectomy did not benefit patients with HR+ DCIS for their DFS, rather increased adverse events in those patients. Therefore, ET after mastectomy may not be recommended for patients with HR+ DCIS, even for those with high-risk factors, such as multifocal, microinvasive, and higher T stage. Funding: This study was supported by grants from Outstanding Scientific Fund of Shengjing Hospital (201803) and Outstanding Young Scholars of Liaoning Province (2019-YQ-10).

Walnut grading is an important step before the product enters the market. However, traditional walnut grading primarily relies on manual assessment of physiological features, which is difficult to implement efficiently. Furthermore, walnut kernel grading is, at present, relatively unsophisticated. Therefore, this study proposes a novel deep-learning model based on a spatial attention mechanism and SE-network structure to grade walnut kernels using machine vision to ensure accuracy and improve assessment efficiency. In this experiment, we found through the literature that both the lightness (L* value) and malondialdehyde (MDA) contens of walnut kernels were correlated with the oxidation phenomenon in walnuts. Subsequently, we clustered four partitionings using the L* values. We then used the MDA values to verify the rationality of these partitionings. Finally, four network models were used for comparison and training: VGG19, EfficientNetB7, ResNet152V2, and spatial attention and spatial enhancement network combined with ResNet152V2 (ResNet152V2-SA-SE). We found that the ResNet152V2-SA-SE model exhibited the best performance, with a maximum test set accuracy of 92.2%. The test set accuracy was improved by 6.2, 63.2, and 74.1% compared with that of ResNet152V2, EfficientNetB7, and VGG19, respectively. Our testing demonstrated that combining spatial attention and spatial enhancement methods improved the recognition of target locations and intrinsic information, while decreasing the attention given to non-target regions. Experiments have demonstrated that combining spatial attention mechanisms with SE networks increases focus on recognizing target locations and intrinsic information, while decreasing focus on non-target regions. Finally, by comparing different learning rates, regularization methods, and batch sizes of the model, we found that the training performance of the model was optimal with a learning rate of 0.001, a batch size of 128, and no regularization methods. In conclusion, this study demonstrated that the ResNet152V2-SA-SE network model was effective in the detection and evaluation of the walnut kernels.

Abstract Background Ovarian function suppression (OFS) is indicated in premenopausal women with early or metastasis breast cancer, which may be achieved with similar effect by gonadotropin-releasing hormone agonists (GnRHa) or ovarian ablation (OA). We examined whether there were differences in major depressive symptoms outcomes and its associated factors between gonadotropin-releasing hormone agonists (GnRHa) and ovarian ablation (OA) in premenopausal breast cancer patients. Methods Premenopausal breast cancer patients from seven hospitals who received OFS participated in the study between June 2019 and June 2020. The correlated variable was the type of ovarian suppression, categorized as either OA (n = 174) or GnRHa (n = 389). Major depressive symptoms was evaluated using the Patient Health Questionnaire (PHQ-9), and the Female Sexual Function Index questionnaire was used to assess sexual function. Results A total of 563 patients completed the surveys. The mean PHQ-9 sum score was slightly lower in the GnRHa cohort than in the OA cohort (11.4 ± 5.7 vs. 12.8 ± 5.8, P = 0.079). There were significantly fewer patients with major depressive symptoms (PHQ-9 ≥ 15) in the GnRHa cohort (31.1% vs. 40.2%, Exp (B)=1.805, P=0.004). Further, breast-conserving surgery and sexual dysfunction were negatively correlated with major depressive symptoms [mastectomy vs. breast-conserving: Exp (B) = 0.461, P

To comprehensively understand the spatial characteristics and pollution hazards of heavy metals in rice in Fujian Province, the contents of Cd, Hg, As, Pb, Cr, Ni, Cu, and Zn in 260 rice samples were measured based on the field investigation. Their spatial distribution patterns and clustering characteristics were explored by geostatistical methods, and their pollution-health risks were revealed by the pollution index method and exposure evaluation model. The results showed that the average contents of eight heavy metals in rice were below the food safety limits, but the excess rates of As, Cu, and Cd samples reached 33.08%, 22.69%, and 18.85%, respectively. In terms of spatial characteristics, the contents of Cd, Hg, and Ni were higher in the southeastern and northwestern regions, while the contents of As, Cu, and Cr were higher in the southwestern parts of Fujian Province. The hotspots of the corresponding heavy metals were also concentrated in the above-mentioned areas, showing an obvious spatial clustering effect. The pollution risk assessment showed that the single factor pollution index of heavy metals in rice decreased in the order of As (0.536) > Hg (0.505) > Cu (0.421) > Cd (0.378) > Cr (0.318) > Zn (0.304) > Pb (0.286) > Ni (0.116), and the Nemerow comprehensive pollution index was 0.623, which was generally at a safe level with low pollution risk. Furthermore, the non-carcinogenic risk index of heavy metals for adults and children were 3.558 and 6.014, and the carcinogenic risk index were 0.0050 and 0.0084, respectively, with extremely serious health hazards. In contrast, the health damage of heavy metals was higher in children than that in adults, with As and Cd in rice as the pivotal elements. Therefore, attention should be paid to the future monitoring and agricultural management of heavy metal pollution in rice in the region.

Since impurities produced during walnut processing can cause serious harm to human health, strict quality control must be carried out during production. However, most detection equipment still uses photoelectric detection technology to automatically sort heterochromatic particles, which is unsuitable for detecting endogenous foreign bodies with similar colors. Therefore, this paper proposes an improved YOLOv4 deep learning object detection algorithm, WT-YOLOM, for detecting endogenous impurities in walnuts—namely, oily kernels, black spot kernels, withered kernels, and ground nutshells. In the backbone of the model, a lightweight MobileNet module was used as the encoder for the extraction of features. The spatial pyramid pooling (SPP) structure was improved to spatial pyramid pooling—fast (SPPF), and the model size was further reduced. Loss function was replaced in this model with a more comprehensive SIoU loss. In addition, efficient channel attention (ECA) mechanisms were applied after the backbone feature map to improve the model’s recognition accuracy. This paper compares the recognition speed and accuracy of the WT-YOLOM algorithm with the Faster R-CNN, EfficientDet, CenterNet, and YOLOv4 algorithms. The results showed that the average precision of this model for different kinds of endogenous impurities in walnuts reached 94.4%. Compared with the original model, the size was reduced by 88.6%, and the recognition speed reached 60.1 FPS, which was an increase of 29.0%. The metrics of the WT-YOLOM model were significantly better than those of comparative models and can significantly improve the detection efficiency of endogenous foreign bodies in walnuts.

In recent years, the Chinese tourism industry has developed rapidly, leading to significant changes in the relationship between people and space patterns in scenic regions. To attract more tourists, the surrounding environment of a scenic region is usually well developed, attracting a large number of human activities, which creates a cognitive range for the scenic region. From the perspective of tourism, tourists’ perceptions of the region in which tourist attractions are located in a city usually differ from the objective region of the scenic spots. Among them, social media serves as an important medium for tourists to share information about scenic spots and for potential tourists to learn scenic spot information, and it interacts to influence people’s perceptions of the destination image. Extracting the names of tourist attractions from social media data and exploring their spatial distribution patterns is the basis for research on the cognitive region of tourist attractions. This study takes Hangzhou, a well-known tourist city in China, as a case study to explore the human cognitive region of its popular scenic spots. First, we propose a Chinese tourist attraction name extraction model based on RoBERTa-BiLSTM-CRF to extract the names of tourist attractions from social media data. Then, we use a multi-distance spatial clustering method called Ripley’s K to filter the extracted tourist attraction names. Finally, we combine road network data and polygons generated using the chi-shape algorithm to construct the vague cognitive regions of each scenic spot. The results show that the classification indicators of our proposed tourist attraction name extraction model are significantly better than those of previous toponym extraction models and algorithms (precision = 0.7371, recall = 0.6926, F1 = 0.7141), and the extracted vague cognitive regions of tourist attractions also generally conform to people’s habitual cognition.

Accurate recognition of the flowering stage is a prerequisite for flower yield estimation. In order to improve the recognition accuracy based on the complex image background, such as flowers partially covered by leaves and flowers with insignificant differences in various fluorescence, this paper proposed an improved CR-YOLOv5s to recognize flower buds and blooms for chrysanthemums by emphasizing feature representation through an attention mechanism. The coordinate attention mechanism module has been introduced to the backbone of the YOLOv5s so that the network can pay more attention to chrysanthemum flowers, thereby improving detection accuracy and robustness. Specifically, we replaced the convolution blocks in the backbone network of YOLOv5s with the convolution blocks from the RepVGG block structure to improve the feature representation ability of YOLOv5s through a multi-branch structure, further improving the accuracy and robustness of detection. The results showed that the average accuracy of the improved CR-YOLOv5s was as high as 93.9%, which is 4.5% better than that of normal YOLOv5s. This research provides the basis for the automatic picking and grading of flowers, as well as a decision-making basis for estimating flower yield.

With the increasing demand of location-based services, channel state information (CSI) has attracted great interest because of the fine-grained information it provides. In this paper, we propose an original network structure, which exploits both the local information and global information in CSI amplitude for fingerprint localization. First, we validate the correlation between adjacent subcarriers and introduce the position-dependent local feature (PDL-feature). Next, local connection based deep neural network (LC-DNN) is designed to improve positioning performance by extracting and exploiting the correlation between adjacent subcarriers for indoor localization. LC-DNN consists of locally-connected layer and fully-connected layer. In the locally-connected layer, the variation of CSI amplitude in local frequency range is extracted and spliced for rich information. The frequency range and the times of extraction are determined by receptive field length and step size respectively. In the fully-connected layer, not only global features of CSI amplitude are further extracted, but also the function between features and position coordinates is obtained. Experiments are conducted to validate the effectiveness of LC-DNN and investigate the influence of hyper parameters on localization. Moreover, the positioning performance of LC-DNN is compared with four methods based on deep neural networks (DNNs). Results show that LC-DNN performs well in positioning accuracy and stability, with the mean error of 0.78m.

Indoor positioning and navigation is an essential field of location-based service. Non-line of sight (NLOS) error restricts the accuracy of indoor positioning. Many researchers have studied the localization problem in indoor NLOS environments, but there is still a problem that NLOS error cannot be mitigated in unknown areas. To solve the above problems, this paper proposes a method of constructing virtual base stations in unknown areas (UA-VBS), and presents the corresponding positioning algorithm to calculate the location of the user equipment. Firstly, the base stations are selected and the initial positioning is carried out. Then, multiple virtual base stations are constructed according to the user equipment positions in the first three steps. The LOS base stations and virtual base stations participate in the TDOA calculation together, and calculate the base stations’ combination with the minimum residual and the corresponding positioning result. Finally, the pedestrian dead reckoning fusion weight is updated by the residual value, and the accurate positioning result in NLOS environment is obtained. Simulation and experimental results show that the proposed algorithm has high positioning accuracy and stability in NLOS environment.

The calculation of the frost-heaving pressure is one of the key issues in the frost-resistant design of tunnels in cold regions. To reveal the frost heave mechanism of partial ponding behind the tunnel linings, a three-dimensional geo-mechanical model test was developed that only considered the frost heave load. A self-designed water bladder equipment was set up to simulate the partial ponding. The frost heave response characteristics of the surrounding rock with partial ponding were simulated to analyze the change law of temperature field and frost-heaving pressure under freezing by using air-conditioning to reduce the ambient temperature of the model test. The changes in the internal temperature of the surrounding rock and the evolution of the frost-heaving pressure over time under different thicknesses of partial ponding and different levels of surrounding rock were compared. The theoretical calculation value of frost-heaving pressure and the test values of the model test were compared and analyzed using the numerical simulation method. The results showed that the temperature of the surrounding rock presented three-stage changes, which showed a lagging characteristic relative to the environmental temperature. The empirical equation for the relationship between the frost-heaving pressure and time was obtained by nonlinear fitting of the experimental results. Taking advantage of the regular tetrahedron calculation model, the paper established a semiempirical frost-heaving pressure model considering time and space effects, which was identical with the frost-heaving phenomenon from the experiments. Theoretical analysis, experiments, and numerical simulation show that the frost-heaving pressure increased along with the depth of partial ponding and the elastic resistance coefficient of surrounding rock, which could provide references for revealing the frost-heaving pressure of partial ponding behind tunnels of cold regions and has a significant meaning in the engineering application.

Geomechanical model tests provide an intuitive and convenient method for observing physical phenomenon due to their easy implementation compared to in situ tests and prototype tests. The success of model tests depends heavily on the appropriate selection of model materials and proportions. Therefore, a new similar material is developed by utilizing the orthogonal experimental design method to conduct a series of proportioning tests. The new material is mixed with barite powder, iron ore powder, quartz sand, liquid paraffin, rosin, gypsum powder, and water. The physical and mechanical properties are studied through uniaxial compressive tests, Brazilian splitting tests, and direct shear tests. The influences of various raw material factors on the parameters of the similar material are systematically studied through range analysis. The results demonstrate that the mechanical parameters of similar materials have wide variation ranges; the adjustment range is 42.0–279.0 MPa for the elastic modulus, 0.37–5.37 MPa for the uniaxial compressive strength and 2.23–2.65 g/cm3 for the density. The new similar material has illustrated advantages in terms of performance stability, low price, and convenient production, which can simulate the similar relationship with different geomechanical model tests. The similar material is applied to a 3D geomechanical model test of the foundation pit excavation of Shenzhen metro station, which proves that the similar material can realistically reflect the change of earth pressure in the excavation process. With the deepening of excavation, the earth pressure curve shows significant fluctuations, and as the retaining structure is displaced, the parts with large earth pressure changes should be strengthened. The research methods and results can provide reference for further geological engineering research.

Address matching, which aims to match an input descriptive address with a standard address in an address database, is a key technology for achieving data spatialization. The construction of today’s smart cities depends heavily on the precise matching of Chinese addresses. Existing methods that rely on rules or text similarity struggle when dealing with nonstandard address data. Deep-learning-based methods often require extracting address semantics for embedded representation, which not only complicates the matching process, but also affects the understanding of address semantics. Inspired by deep transfer learning, we introduce an address matching approach based on a pretraining fine-tuning model to identify semantic similarities between various addresses. We first pretrain the address corpus to enable the address semantic model (abbreviated as ASM) to learn address contexts unsupervised. We then build a labelled address matching dataset using an address-specific geographical feature, allowing the matching problem to be converted into a binary classification prediction problem. Finally, we fine-tune the ASM using the address matching dataset and compare the output with several popular address matching methods. The results demonstrate that our model achieves the best performance, with precision, recall, and an F1 score above 0.98.

Microbial remediation is a promising technique to reduce Cd accumulation in rice (Oryza sativa). In present study, a set of pot experiments were conducted to evaluate the effects of Cd-tolerate Pseudomonas TCd-1 inoculation on rice Cd uptake, soil enzyme activities and Cd bioavailability in the rhizosphere soils under Cd contaminated conditions. The results showed that at the ripening stage, with the inoculation of TCd-1, Cd contents in root, culm, leaf, hull and brown rice significantly reduced by 60.7%, 47.7%, 50.6%, 58.1% and 47.9%, respectively, and the cadmium bioconcentration factor (BCF) of rice lowered by 66.2% under 5 mg kg−1 Cd treatment. At the meantime, in the rhizosphere soils, pH increased by 0.05, the contents of exchangeable Cd (EX-Cd) and Fe-Mn oxides (OX-Cd) increased by 107.8% and 33.5%, whereas organic matter (OM-Cd) and residual (Res-Cd) decreased by 31.9% and 60.0%, respectively. The activity of acid phosphatase (ACP) increased by 28.3%, catalase (CAT), saccharase (SUC) activity decreased by 28.5% and 26.0%. Similarly, the Cd contents in root, culm, leaf, hull and brown rice reduced by 42.1%, 42.5%, 58.0%, 50.3%, and 68.8%, respectively, and the BCF lowered by 57.1%, under 10 mg kg−1 Cd treatment. Simultaneously, the soil pH increased by 0.06, the activities of CAT, SUC, urease (URE), ACP decreased by 26.4%, 34.6%, 63.8% and 15.3%, respectively. Furthermore, the correlation analysis showed that the inoculation of TCd-1 changed the correlation between rice Cd content and the biomass of roots, leaves, soil pH, CAT, PPO, URE activities, OM-Cd in rhizosphere soils. It suggested that Pseudomonas TCd-1 effectively reduced Cd uptake and Cd accumulation in rice was closely linked to the changes of soil pH, enzyme activities and Cd availability.

Objective: Recent studies on follicular regulatory T (Tfr) and follicular helper T (Tfh) cells suggest that they may participate in the pathogenesis of rheumatoid arthritis (RA). Here, we examine Tfr-like and Tfh-like cells and their subsets in RA and assess the correlations between these subsets with B cells and cytokines related to the pathogenesis of RA and their clinical significance.Methods: The study population consisted of 18 healthy controls and 47 RA patients (17 new onset, 57.00 ± 11.73 years; 30 treated RA patients, 57.56 ± 1.97 years). Disease activity scores in 28 joints were calculated. The positive rates of rheumatoid factor (RF) and anticyclic citrullinated peptide antibodies (anti-CCP) were 82.9 and 89.4%, respectively. Cell subsets were analyzed using flow cytometry, and serum cytokine levels were measured using cytometric bead array.Results: Tfh-like and PD-1+ Tfh-like cells were elevated, and the distribution of Tfh-like cell subsets was altered with increased Tfh17-like and Tfh1/17-like cells in RA patients. The receiver operating characteristics curves for Tfh-like, Tfh17-like, Tfh1/17-like, and PD-1+ Tfh-like cells indicate improved RA diagnostic potential. RA patients had decreased regulatory T (Treg), Tfr-like, and memory Tfr-like (mTfr-like) cells and increased Tfh-like/Treg, Tfh-like/Tfr-like, and Tfh-like/mTfr-like cell ratios. Tfh-like cells and their subsets, including Tfh1-like, Tfh2-like, Tfh1/17-like, and PD-1+ Tfh-like cells, were positively correlated with B cells. Tfh-like/Treg, Tfh-like/Tfr-like, and Tfh-like/mTfr-like cell ratios were positively correlated with B cells in new-onset RA. Interleukin (IL)-2, IL-4, IL-17, interferon-γ, and tumor necrosis factor-α were positively correlated with Tfr-like and mTfr-like cells. IL-2 and IL-10 were positively correlated with Tfh-like and Tfh2-like cells. IL-4 was positively correlated with Tfh-like cells.Conclusions: Tfh-like and PD-1+ Tfh-like cells are increased, whereas Treg, Tfr-like, and mTfr-like cells are decreased in RA, leading to an imbalance in Tfh-like/Treg, Tfh-like/Tfr-like, and Tfh-like/mTfr-like cell ratios. Tfh-like cells and a portion of their subsets as well as Tfh-like/Treg, Tfh-like/Tfr-like, and Tfh-like/mTfr-like cell ratios are closely related to B cells. Dysfunction of cell subsets leads to abnormal levels of cytokines involved in the pathogenesis of RA. The altered distributions of Tfh-like cell subsets, especially Tfh1/17-like cells, represent potential therapeutic targets for treatment of RA.

There are a lot of non-blind image deblurring methods, especially with the total variation (TV) model-based method. However, how to choose the parameters adaptively for regularization is a major open problem. We proposed a very novel method that is based on the TV deep network to learn the best parameters adaptively for regularization. We used deep learning and prior knowledge to set up a TV-based deep network and calculate the parameters of regularization, such as biases and weights. Therefore, we used the idea of a deep network to update these parameters automatically to avoid sophisticated calculations. Our experimental results by our proposed network are significantly better than several other methods, in respect of detail retention and anti-noise performance. At the same time, we can achieve the same effect with a minimum number of training sets, thus speeding up the calculation.

With the increasing popularity of location-based services, channel state information (CSI) has received widespread attention in positioning due to the fine-grained information it provides. However, the raw amplitudes of CSI are especially sensitive to noise and easily effected by the frequency-selective fading. As the expanding of test area, positioning is disturbed by random noise and insufficient resolution of features severely. In this paper, we propose Cluster-Mapping (C-Map), an adaptive pre-processing system for CSI amplitude-based fingerprint localization. This system models positioning as a classification problem, an adaptive processing mechanism is constructed according to the characteristics of CSI amplitude. C-Map mainly contains two parts: dynamic denoising and feature enhancement. In the dynamic denoising, effective features are screened out by clustering iteratively, without specifying parameters according to the experimental environment. In the feature enhancement, polynomial fitting with regularization is used to reduce jagged fluctuations, then the trend of variation over 30 sub-carriers are described by combining derivation with nonlinear kernel function. Extensive experiments are conducted in typical environments to verify the superior performance of C-Map for the preprocessing of CSI amplitude. Compared with the combination of mean and multidimensional scaling (MDS), the average positioning error of C-Map is reduced 28.1% in comprehensive indoor environment, and 19% in garage. Furthermore, compared with the combination of DBSCAN and principal component analysis (PCA), the average positioning error of C-Map is reduced 33.1% in comprehensive indoor environment, and 28% in garage.

Retailer may exhibit irrationality when facing the risk of demand uncertainty; therefore, we consider four retailer behavioral preferences: risk neutral (RN), waste aversion (WA), stockout aversion (SA), and stockout-waste aversion (SW). The decision-making and contract choice of upstream and downstream enterprises in cases where demand depends on supplier’s effort are studied. The results show that if the retailer has only SA or RN preferences, then the supplier prefers to choose a wholesale price contract, while the retailer does the opposite, if the retailer has only WA, then the supplier prefers to choose a revenue sharing contract, but the retailer’s contract choice depends on the degree of waste aversion, and if the retailer has SW, then the contract choice of upstream and downstream enterprises is related to the degree of waste aversion and stockout aversion.

Background/Aims: This study aimed to explore the prognostic value of aldehyde dehydrogenase 1 (ALDH1) expression and vasculogenic mimicry (VM) in patients with breast cancer. Methods: ALDH1 expression and the presence of VM were examined by immunohistochemistry and CD31/PAS double staining, respectively, using formalin-fixed paraffin-embedded tissues from 202 breast cancer patients. The mean follow-up period ranged from 15 to 115 months. The Kaplan-Meier method was used to plot survival curves. Prognostic values were assessed by multivariate analysis using the Cox regression model. Results: ALDH1 expression was strongly associated with VM (P = 0.005). ALDH1 expression was positively correlated with histological grade (P = 0.011). Both ALDH1 expression and VM were negatively related to the status of the estrogen receptor and progesterone receptor and were statistically increased in triple-negative breast cancer. Patients with ALDH1 expression or VM displayed poorer disease-free survival (DFS) and overall survival (OS) than ALDH1-negative or VM-negative patients, with the worst OS and DFS observed in ALDH1/VM-double-positive patients. ALDH1-positive and VM-positive were independent survival risk factors for DFS and OS. Conclusion: ALDH1 expression and VM are correlated with the survival rate of patients with breast cancer. ALDH1 and VM, either alone or together, are prognostic factors in patients with breast cancer.

Methyltransferase-like 3 (METTL3), a predominantly catalytic enzyme in the N6-methyladenosine (m6A) methyltransferase system, is dysregulated and plays a dual role (oncogene or tumor suppressor) in different human cancers. The expression and pro- or anticancer role of METTL3 in different cancers remain controversial. METTL3 is implicated in many aspects of tumor progression, including tumorigenesis, proliferation, invasion, migration, cell cycle, differentiation, and viability. Most underlying mechanisms involve multiple signaling pathways that rely on m6A-dependent modification. However, METTL3 can also modulate the cancer process by directly promoting the translation of oncogenes via interaction with the translation initiation machinery through recruitment of eukaryotic translation initiation factor 3 subunit h (eIF3h). In this review, we summarized the current evidence on METTL3 in diverse human malignancies and its potential as a prognostic/ therapeutic target.

Abstract Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation. Despite the rapid developments, image noise and artifacts still remain a major issue in the low dose protocol. In this paper, a deep learning based method named Improved GoogLeNet is proposed to remove streak artifacts due to projection missing in sparse-view CT reconstruction. Residual learning is used in GoogLeNet to study the artifacts of sparse-view CT reconstruction, and then subtracts the artifacts obtained by learning from the sparse reconstructed images, finally recovers a clear correction image. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. The results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.

This paper investigates a sliding mode controller based on quantum particle swarm optimization algorithm (QPSO) to solve the nonlinearity of electro-hydraulic servo systems, external disturbance problems, and jitter of sliding mode controller. The electro-hydraulic servo system state space equations are established, constructing the sliding surface according to the tracking error and obtaining the output of the sliding mode controller. The ITAE metric is used as an adaptation function of the QPSO algorithm to evaluate the parameters in the sliding mode controller, which has good engineering utility and parameter selectivity. The QPSO algorithm is used to increase the randomicity of the search and to expand the search space, which can effectively prevent falling into a local optimum solution. Finally, a comparative simulation is presented to illustrate global search performance of QPSO algorithm and the effectiveness and applicability of the proposed control method.

Localization estimation and clock synchronization are important research directions in the application of wireless sensor networks. Aiming at the problems of low positioning accuracy and slow convergence speed in localization estimation methods based on message passing, this paper proposes a low-complexity distributed cooperative joint estimation method suitable for dynamic networks called multi-Gaussian variational message passing (M-VMP). The proposed method constrains the message to be a multi-Gaussian function superposition form to reduce the information loss in the variational message passing algorithm (VMP). Only the mean, covariance and weight of each message need to be transmitted in the network, which reduces the computational complexity while ensuring the information completeness. The simulation results show that the proposed method is superior to the VMP algorithm in terms of position accuracy and convergence speed and is close to the sum-product algorithm over a wireless network (SPAWN) based on non-parametric belief propagation, but the computational complexity and communication load are significantly reduced.

Vehicle positioning with 5G can effectively compensate for the lack of vehicle positioning based on GNSS (Global Navigation Satellite System) in urban canyons. However, there is also a large ranging error in the non-line of sight (NLOS) propagation of 5G. Aiming to solve this problem, we consider a new time delay estimation algorithm called non-line of sight cancellation multiple signal classification (NC-MUSIC). This algorithm uses cross-correlation to identify and cancel the NLOS signal. Then, an unsupervised multipath estimation method is used to estimate the number of multipaths and extract the noise subspace. The MUSIC spectral function can be calculated by the noise subspace. Finally, the time delay of the direct path is estimated by searching the peak of MUSIC spectral function. This paper adopts the 5G channel model developed by 3GPP TR38.901 for simulation experiments. The experiment results demonstrated that the proposed algorithm has obvious advantages in terms of NLOS propagation for urban canyon environments. It provided a high-precision time delay estimation algorithm for observed time difference of arrival (OTDOA), joint angle of arrival (AOA) ranging, and other positioning methods in the 5G vehicle positioning method, which can effectively improve the positioning accuracy of 5G vehicle positioning in urban canyon environments.