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BackgroundThe Tibetan population residing in high-altitude (HA) regions has adapted to extreme hypoxic environments. However, there is limited understanding of the genetic basis of body compositions in Tibetan population adapted to HA.MethodsWe performed a genome-wide association study (GWAS) to identify genetic variants associated with HA and HA-related body composition traits. A total of 755,731 single nucleotide polymorphisms (SNPs) were genotyped using the precision medicine diversity array from 996 Tibetan college students. T-tests and Pearson correlation analysis were used to estimate the association between body compositions and altitude. The mixed linear regression identified the SNPs significantly associated with HA and HA-related body compositions. LASSO regression was used to screen for important SNPs in HA and body compositions.ResultsSignificant differences were observed in lean body mass (LBW), muscle mass (MM), total body water (TBW), standard weight (SBW), basal metabolic rate (BMR), total protein (TP), and total inorganic salt (Is) in different altitudes stratification. We identified three SNPs in EPAS1 (rs1562453, rs7589621 and rs7583392) that were significantly associated with HA (p

Aiming at the cooperative localization problem for the dynamic UAV swarm in an anchor-limited environment, an adaptive Wasserstein filter (AWF) with distance-constrained bare bones self-recovery particles (CBBP) is proposed. Firstly, to suppress the cumulative error from the inertial navigation system (INS), a position-prediction strategy based on transition particles is designed instead of using inertial measurements directly, which ensures that the generated prior particles can better cover the ground truth and provide the uncertainties of nonlinear estimation. Then, to effectively quantify the difference between the observed and the prior data, the Wasserstein measure based on slice segmentation is introduced to update the posterior weights of the particles, which makes the proposed algorithm robust against distance-measurement noise variance under the strongly nonlinear model. In addition, to solve the problem of particle impoverishment caused by traditional resampling, a diversity threshold based on Gini purity is designed, and a fast bare bones particle self-recovery algorithm with distance constraint is proposed to guide the outlier particles to the high-likelihood region, which effectively improves the accuracy and stability of the estimation. Finally, the simulation results show that the proposed algorithm is robust against cumulative error in an anchor-limited environment and achieves more competitive accuracy with fewer particles.

BackgroundThere is no clear conclusion on the immunogenicity and adverse events of concomitant administration the viral respiratory infectious disease vaccines. We aimed to evaluate the impact of concomitant administering viral respiratory infectious disease vaccines on efficiencies, safety and influencing factors.MethodsThis meta-analysis included studies from PubMed, Embase, Cochrane Central Register of Clinical Trials, Web of Science, WHO COVID-19 Research, and ClinicalTrials.gov databases. Randomized controlled trials of the adult participants concomitant administered with viral respiratory infectious disease vaccine and other vaccines were included. The main outcomes were the seroconversion rate and seroprotection rate of each vaccine. Used the Mantel–Haenszel fixed effects method as the main analysis to estimate the pooled RRs and the corresponding 95% confidence intervals. The risk of bias for each trial was assessed using the Cochrane Handbook for Systematic Reviews of Interventions, while evidence certainty was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation system.ResultsA total of 21 studies comprising 14060 participants with two types of vaccines were retained for the meta-analysis. Concomitant immunization reduced the geometric mean titer (RR: 0.858, 95% CI: (0.785 to 0.939)) and the geometric mean fold rise (0.754 (0.629 to 0.902)) in the SARS-COV-2 vaccine group but increased the seroconversion rate (1.033 (1.0002 to 1.067)) in the seasonal influenza vaccine group. Concomitant administration were influenced by the type of vaccine, adjuvant content, booster immunization, and age and gender of the recipient.ConclusionThis meta-analysis suggested that the short-term protection and safety of concomitant administered were effective. Appropriate adjuvants, health promotion and counselling and booster vaccines could improve the efficiency and safety of Concomitant vaccination.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022343709.

Triangular distributions are widely used in many applications with limited sample data, business simulations, and project management. As with other distributions, a standard way to measure deviations is to compute the standard deviation. However, the standard deviation is sensitive to outliers. In this paper, we consider and compare other deviation metrics, namely the mean absolute deviation from the mean, the median, and the quantile-based deviation. We show the simple geometric interpretations for these deviation measures and how to construct them using a compass and a straightedge. The explicit formula of mean absolute deviation from the median for triangular distribution is derived in this paper for the first time. It has a simple geometric interpretation. It is the least volatile and is always better than the standard or mean absolute deviation from the mean. Although greater than the quantile deviation, it is easier to compute with limited sample data. We present a new procedure to estimate the parameters of this distribution in terms of this deviation. This procedure is computationally simple and may be superior to other methods when dealing with limited sample data, as is often the case with triangle distributions.

One of the most common urological diseases is benign prostatic hyperplasia (BPH), with a high prevalence in the middle-aged and elderly male population. Patient's mental and physical health is affected significantly by this condition, causing them considerable discomfort. During the development of BPH, a synergistic effect occurs in response to inflammation, oxidative stress, and apoptosis induced by the activation of macrophages. The nuclear factor erythroid2-related factor 2 (Nrf2) signaling pathway can mediate macrophage activation and inhibit prostate hyperplasia by suppressing pro-inflammatory factors, anti-oxidative stress disorder, and initiating apoptosis. The purpose of this study was to review the mechanism of action of Nrf2 signaling pathway-mediated macrophage activation on the immune microenvironment of BPH and to summarize the Chinese medicine based on Nrf2 to provide an overview of BPH treatment options.

Abstract To investigate the ability of the ratio of blood urea nitrogen (BUN) to serum albumin ratio (BAR) in patients with sepsis in intensive care units (ICUs) to predict the prognosis of short-and long-term death. Data are from the Marketplace for Intensive Care Medical Information IV (MIMIC-IV v2.0) database for patients with sepsis as defined by SEPSIS-3. The primary outcome was 30-day mortality and the secondary outcome was 360-day mortality. Kaplan–Meier (KM) survival curves were plotted to describe differences in BAR mortality in different subgroups and area under the curve (AUC) analysis was performed to compare the predictive value of sequential organ failure assessment (SOFA), BAR, blood urea nitrogen (BUN) and albumin. Multivariate Cox regression models and subgroup analysis were used to determine the correlation between BAR and 30-day mortality and 360-day mortality. A total of 7656 eligible patients were enrolled in the study with a median BAR of 8.0 mg/g, including 3837 in the ≤ 8.0 group and 3819 in the BAR > 8.0 group, with 30-day mortality rates of 19.1% and 38.2% (P

To solve the problem of industrial control network (ICN) security situation prediction, this paper proposes a security situation prediction model for ICN based on evidential reasoning (ER) and belief rule base (BRB). First, this paper analyzes multiple factors influencing the security situation of the ICN, establishes a framework for security situation assessment, employs the ER algorithm for attribute fusion, and derives the security situation value of the ICN. Second, using historical data combined with expert knowledge, a security situation prediction model for ICN based on the BRB is constructed. Additionally, an extended projection covariance matrix adaptive evolution strategy (EP-CMA-ES) optimization algorithm is proposed, which is employed to optimize the parameters of the prediction model. The model not only comprehensively uses qualitative knowledge and quantitative data, but also integrates more uncertain information, and the reasoning process is interpretable. It also solves the subjectivity problem of expert knowledge, overcomes the problem of small amount of data caused by the difficulty of collecting industrial control safety data, and improves the accuracy of model prediction. Finally, prediction experiments were conducted on industrial datasets, confirming the feasibility and effectiveness of the security situation prediction model for ICN and the EP-CMA-ES optimization algorithm proposed in this paper.

The traditional support vector machine (SVM) requires manual feature extraction to improve classification performance and relies on the expressive power of manually extracted features. However, this characteristic poses limitations in complex Industrial Internet of Things (IIoT) environments. Traditional manual feature extraction may fail to capture all relevant information, thereby restricting the application effectiveness of SVM in IIoT settings. CNN-RNN, as a deep learning network capable of simultaneously extracting spatial and temporal features, can alleviate researchers’ burden. In this paper, we propose a novel intrusion detection system (IDS) framework based on anomalies, called CRSF. The framework’s pre-training part employs a dimension transformation function to process input data into two-dimensional images. Two-dimensional convolutional kernels are then employed to extract spatial features, and the feature sequences are passed to an RNN to capture richer temporal features. After sufficient pre-training, SVM is used as a classifier to map the pre-training data from the feature space to a high-dimensional space and learn nonlinear decision boundaries, enabling the framework to accurately differentiate feature representations of different classes. Simulation experiments on the TON_IoT-Datasets demonstrate the effectiveness of the CRSF framework in intrusion detection. When using the “linear” kernel function in SVM, the framework achieves an accuracy, F1-score, and AUC of 0.9959, 0.9959, and 0.9977, respectively, indicating its capability and superiority in intrusion detection.

The network security status assessment (NSSA) method can evaluate the network security status of cloud manufacturing systems (CMSs), which is of great significance to reduce the network security risk and loss of CMSs. At present, the NSSA of CMSs suitable for semiquantitative and uncertain information conditions is commonly used, which has certain limitations and low accuracy. This paper proposes an NSSA method for CMSs based on semiquantitative information. First, through the detailed analysis of the influencing factors of the network security status of the CMSs, the security evaluation indicators are selected, and the evaluation framework containing multilevel indicators is established by combining semiquantitative information. Second, the evaluation level is established according to the data distribution and the properties of the indicators. Third, a process of data fusion reasoning based on an evidential reasoning algorithm is designed, and a strong reference fusion case is given. Finally, the effectiveness of the proposed security state assessment algorithm in the NSSA of CMSs is verified and analyzed by simulation experiments. The experimental results show that the proposed method can make full use of semiquantitative information and uncertain information to evaluate the network security status of CMSs, and the evaluation results can reflect the actual security status of CMSs.

Abstract The incidence of thyroid nodules is increasing year by year. Accurate determination of benign and malignant nodules is an important basis for formulating treatment plans. Ultrasonography is the most widely used methodology in the diagnosis of benign and malignant nodules, but diagnosis by doctors is highly subjective, and the rates of missed diagnosis and misdiagnosis are high. To improve the accuracy of clinical diagnosis, this paper proposes a new diagnostic model based on deep learning. The diagnostic model adopts the diagnostic strategy of localization-classification. First, the distribution laws of the nodule size and nodule aspect ratio are obtained through data statistics, a multiscale localization network structure is a priori designed, and the nodule aspect ratio is obtained from the positioning results. Then, uncropped ultrasound images and nodule area image are correspondingly input into a two-way classification network, and an improved attention mechanism is used to enhance the feature extraction performance. Finally, the deep features, the shallow features, and the nodule aspect ratio are fused, and a fully connected layer is used to complete the classification of benign and malignant nodules. The experimental dataset consists of 4021 ultrasound images, where each image has been labeled under the guidance of doctors, and the ratio of the training set, validation set, and test set sizes is close to 3:1:1. The experimental results show that the accuracy of the multiscale localization network reaches 93.74%, and that the accuracy, specificity, and sensitivity of the classification network reach 86.34%, 81.29%, and 90.48%, respectively. Compared with the champion model of the TNSCUI 2020 classification competition, the accuracy rate is 1.52 points higher. Therefore, the network model proposed in this paper can effectively diagnose benign and malignant thyroid nodules.

Head smut is a soil-borne fungal disease caused by Sporisorium reilianum that infects maize tassels and ears. This disease poses a tremendous threat to global maize production. A previous study found markedly different and stably heritable tassel symptoms in some maize inbred lines with Sipingtou blood after infection with S. reilianum. In the present study, 55 maize inbred lines with Sipingtou blood were inoculated with S. reilianum and classified into three tassel symptom types (A, B, and C). Three maize inbred lines representing these classes (Huangzao4, Jing7, and Chang7-2, respectively) were used as test materials to investigate the physiological mechanisms of tassel formation in infected plants. Changes in enzyme activity, hormone content, and protein expression were analyzed in all three lines after infection and in control plants. The activities of peroxidase (POD), superoxide dismutase (SOD), and phenylalanine-ammonia-lyase (PAL) were increased in the three typical inbred lines after inoculation. POD and SOD activities showed similar trends between lines, with the increase percentage peaking at the V12 stage (POD: 57.06%, 63.19%, and 70.28% increases in Huangzao4, Jing7, and Chang7-2, respectively; SOD: 27.01%, 29.62%, and 47.07% in Huangzao4, Jing7, and Chang7-2, respectively. These were all higher than in the disease-resistant inbred line Mo17 at the same growth stage); this stage was found to be key in tassel symptom formation. Levels of gibberellic acid (GA3), indole-3-acetic acid (IAA), and abscisic acid (ABA) were also altered in the three typical maize inbred lines after inoculation, with changes in GA3 and IAA contents tightly correlated with tassel symptoms after S. reilianum infection. The differentially expressed proteins A5H8G4, P09233, and Q8VXG7 were associated with changes in enzyme activity, whereas P49353, P13689, and P10979 were associated with changes in hormone contents. Fungal infection caused reactive oxygen species (ROS) and nitric oxide (NO) bursts in the three typical inbred lines. This ROS accumulation caused biofilm disruption and altered host signaling pathways, whereas NO signaling triggered strong secondary metabolic responses in the host and altered the activities of defense-related enzymes. These factors together resulted in the formation of varying tassel symptoms. Thus, interactions between S. reilianum and susceptible maize materials were influenced by a variety of signals, enzymes, hormones, and metabolic cycles, encompassing a very complex regulatory network. This study preliminarily identified the physiological mechanisms leading to differences in tassel symptoms, deepening our understanding of S. reilianum-maize interactions.

Multiple studies have indicated that distinct metabolites are involved in the occurrence and development of osteopenia (ON) and osteoporosis (OP); however, these metabolites in OP and ON have not yet been classified and standardized. This systematic review and meta-analysis included 21 articles aiming to investigate the distinct metabolites in patients with ON and OP. The quality of the included articles was generally high; seventeen studies had >7 stars, and the remaining four received 6 stars. This systematic review showed that three metabolites (phosphatidylcholine (PC) (lipid metabolites), galactose (carbohydrate metabolites), and succinic acid (other metabolites)) increased, four (glycylglycine (gly-gly), cystine (amino acids), sphingomyelin (SM) (lipid metabolites) and glucose (carbohydrate metabolites)) decreased, and five (glutamine, hydroxyproline, taurine (amino acids), lysophosphatidylcholine (LPC) (lipid metabolites), and lactate (other metabolites)) had conflicting directions in OP/ON. The results of the meta-analysis show that gly-gly (MD = −0.77, 95%CI −1.43 to −0.11, p = 0.02) and cystine (MD = −5.52, 95%CI −7.35 to −3.68, p < 0.00001) decreased in the OP group compared with the healthy control group. Moreover, LPC (MD = 1.48, 95%CI 0.11 to 2.86, p = 0.03) increased in the OP group compared with the healthy control group. These results indicate that distinct metabolites were associated with ON and OP, which could be considered a predictor for OP.

Azathioprine is clinically used as an immunosuppressant for treating autoimmune diseases. However it has narrow therapeutic indices due to frequent myelosuppression. Polymorphic variants of genes coding for thiopurine S-methyltransferase (TPMT) and nucleoside diphosphate-linked moiety X motif 15 (NUDT15) are critical determinants of AZA intolerance, and the differences in frequencies of the two genetic variants exist among people of different ethnicities. Most reports regarding NUDT15 variant, AZA-induced myelosuppression occurred in patients with inflammatory bowel disease and acute lymphoblastic leukemia. Moreover, detailed clinical characteristics were not frequently reported. Here we present the case of a young Chinese female with the NUDT15 c.415C>T (rs116855232, TT) homozygous variant and wild-type TPMT*2 (rs1800462), TPMT*3B (rs1800460), and TPMT*3C (rs1142345) who received high doses of AZA (2.3 mg/kg/d) for systematic lupus erythematosus and had not been told to undergo routine blood cell counts during AZA ingestion. The patient had suffered from severe AZA-induced myelosuppression and alopecia. Moreover, dynamic changes in blood cell counts and responses to treatment were observed. We also conducted a systematic review of published case reports of patients exclusively with NUDT15 c.415C>T homozygous or heterozygous variants to review the characteristics of dynamic changes in blood cells so as to provide reference information for clinical treatment.

Abstract Rolling Bearing is a key component of the transmission of rotating machinery, and it is widely used in industrial fields. Therefore, it is of vital importance to evaluate the performance and reliability of rolling bearing. Aiming at the interference problems faced by rolling bearings during operation, a performance evaluation model based on the evidential reasoning (ER) rule is proposed in this article. Firstly, the time domain and frequency domain characteristic indicators of bearing vibration signals are taken as evaluation indicators, and the evaluation system is constructed. Secondly, various indicator information is unified into a belief structure, and the reliability and the weight of the indicators are fully considered in the ER rule. Thirdly, to simulate the complex working environment of rolling bearings, the perturbation analysis method is adopted. After determining the maximum perturbation error and perturbation coefficient, the performance reliability of the rolling bearing is analysed, and a performance reliability evaluation model considering perturbation is proposed. Finally, based on the whole-life open data set of rolling bearing from the University of Cincinnati, the validity and reliability of the proposed model are verified in performance analysis.

The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs. For such a complex situation, this paper analyses adversarial attackers’ ability to deceive IDSs used in the IIoT and proposes the evaluation of an IDS with function-discarding adversarial attacks in the IIoT (EIFDAA), a framework that can evaluate the defence performance of machine learning-based IDSs against various adversarial attack algorithms. This framework is composed of two main processes: adversarial evaluation and adversarial training. Adversarial evaluation can diagnose IDS that is unfitting in adversarial environments. Then, adversarial training is used to treat the weak IDS. In this framework, five well-known adversarial attacks, the fast-gradient sign method (FGSM), basic iterative method (BIM), projected gradient descent (PGD), DeepFool and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) are used to convert attack samples into adversarial samples to simulate the adversarial environment. This study evaluates the capability of mainstream machine learning techniques as intrusion detection models to defend against adversarial attacks, and retrains these detectors to improve the robustness of IDSs through adversarial training. In addition, the framework includes an adversarial attack model that discards the attack function of the attack samples in the IIoT. Through the experimental results on the X-IIoTID dataset, the dropped adversarial detection rate of these detectors to nearly zero demonstrates that an adversarial attack has black-box attack capabilities for these IDSs. Additionally, the improved IDSs retrained with adversarial samples can effectively defend against adversarial attackers while maintaining the original detection rate for the attack samples. EIFDAA is expected to be a solution that can be applied to IDS for improving the robustness in the IIoT.

The traditional Transformer model primarily employs a self-attention mechanism to capture global feature relationships, potentially overlooking local relationships within sequences and thus affecting the modeling capability of local features. For Support Vector Machine (SVM), it often requires the joint use of feature selection algorithms or model optimization methods to achieve maximum classification accuracy. Addressing the issues in both models, this paper introduces a novel network framework, CTSF, specifically designed for Industrial Internet intrusion detection. CTSF effectively addresses the limitations of traditional Transformers in extracting local features while compensating for the weaknesses of SVM. The framework comprises a pre-training component and a decision-making component. The pre-training section consists of both CNN and an enhanced Transformer, designed to capture both local and global features from input data while reducing data feature dimensions. The improved Transformer simultaneously decreases certain training parameters within CTSF, making it more suitable for the Industrial Internet environment. The classification section is composed of SVM, which receives initial classification data from the pre-training phase and determines the optimal decision boundary. The proposed framework is evaluated on an imbalanced subset of the X-IIOTID dataset, which represent Industrial Internet data. Experimental results demonstrate that with SVM using both “linear” and “rbf” kernel functions, CTSF achieves an overall accuracy of 0.98875 and effectively discriminates minor classes, showcasing the superiority of this framework.

(1) Many studies have attempted to utilize metabolomic approaches to explore potential biomarkers for the early detection of osteoarthritis (OA), but consistent and high-level evidence is still lacking. In this study, we performed a systematic review and meta-analysis of differential small molecule metabolites between OA patients and healthy individuals to screen promising candidates from a large number of samples with the aim of informing future prospective studies. (2) Methods: We searched the EMBASE, the Cochrane Library, PubMed, Web of Science, Wan Fang Data, VIP Date, and CNKI up to 11 August 2022, and selected relevant records based on inclusion criteria. The risk of bias was assessed using the Newcastle–Ottawa quality assessment scale. We performed qualitative synthesis by counting the frequencies of changing directions and conducted meta-analyses using the random effects model and the fixed-effects model to calculate the mean difference and 95% confidence interval. (3) Results: A total of 3798 records were identified and 13 studies with 495 participants were included. In the 13 studies, 132 kinds of small molecule differential metabolites were extracted, 58 increased, 57 decreased and 17 had direction conflicts. Among them, 37 metabolites appeared more than twice. The results of meta-analyses among four studies showed that three metabolites increased, and eight metabolites decreased compared to healthy controls (HC). (4) Conclusions: The main differential metabolites between OA and healthy subjects were amino acids (AAs) and their derivatives, including tryptophan, lysine, leucine, proline, phenylalanine, glutamine, dimethylglycine, citrulline, asparagine, acetylcarnitine and creatinine (muscle metabolic products), which could be potential biomarkers for predicting OA.

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China, has led to the rapid development of Coronavirus disease 2019 (COVID-19) pandemic. COVID-19 represents a fatal disease with a great global public health importance. This study aims to develop a three-parameter Weibull mathematical model using continuous functions to represent discrete COVID-19 data. Subsequently, the model was applied to quantitatively analyze the characteristics for the mortality of COVID-19, including the age, sex, the length of symptom time to hospitalization time (SH), hospitalization date to death time (HD) and symptom time to death time time (SD) and others. A three-parameter mathematical model was developed by combining the reported cases in the Data Repository from the Center for Systems Science and Engineering at Johns Hopkins University and applied to estimate and analyze the characteristics for mortality of COVID-19. We found that the scale parameters of males and females were 5.85 and 5.45, respectively. Probability density functions in both males and females were negative skewness. 5% of male patients died under the age of 43.28 (44.37 for females), 50% died under 69.55 (73.25 for females), and 95% died under 86.59 (92.78 for females). The peak age of male death was 67.45 years, while that of female death was 71.10 years. The peak and median values of SH, HD and SD in male death were correspondingly 1.17, 5.18 and 10.30 days, and 4.29, 11.36 and 16.33 days, while those in female death were 1.19, 5.80 and 12.08 days, and 4.60, 12.44 and 17.67 days, respectively. The peak age of probability density in male and female deaths was 69.55 and 73.25 years, while the high point age of their mortality risk was 77.51 and 81.73 years, respectively. The mathematical model can fit and simulate the impact of various factors on IFR. From the simulation results of the model, we can intuitively find the IFR, peak age, average age and other information of each age. In terms of time factors, the mortality rate of the most susceptible population is not the highest, and the distribution of male patients is different from the distribution of females. This means that Self-protection and self-recovery in females against SARS-CoV-2 virus might be better than those of males. Males were more likely to be infected, more likely to be admitted to the ICU and more likely to die of COVID-19. Moreover, the infection fatality ration (IFR) of COVID-19 population was intrinsically linked to the infection age. Public health measures to protect vulnerable sex and age groups might be a simple and effective way to reduce IFR.

(1) Background: Many studies have attempted to explore potential biomarkers for the early detection of gout, but consistent and high levels of evidence are lacking. In this study, metabolomics was used to summarize the changes of metabolites in the literature and explore the potential value of metabolites in predicting the occurrence and development of gout. (2) Methods: We searched the databases including the EMBASE, the Cochrane Library, PubMed, Web of Science, VIP Date, Wanfang Data, and CNKI, and the screening was fulfilled on 30 July 2022. The records were screened according to the inclusion criteria and the risk of bias was assessed. Qualitative analysis was performed for all metabolites, and meta-analysis was performed for metabolite concentrations using random effects to calculate the Std mean difference and 95% confidence interval. (3) Results: A total of 2738 records were identified, 33 studies with 3422 participants were included, and 701 metabolites were identified. The qualitative analysis results showed that compared with the healthy control group, the concentration of 56 metabolites increased, and 22 metabolites decreased. The results of the meta-analysis indicated that 17 metabolites were statistically significant. (4) Conclusions: Metabolites are associated with gout. Some specific metabolites such as uric acid, hypoxanthine, xanthine, KYNA, guanosine, adenosine, creatinine, LB4, and DL-2-Aminoadipic acid have been highlighted in the development of gout.

Earthquake forecasting is the process of forecasting the time, location, and magnitude of an earthquake, hoping to gain some time to prepare to reduce the disasters caused by earthquakes. In this paper, the possible relationship between the maximum electron density, the corresponding critical frequency, and the occurrence of earthquakes is explored by means of radio occultation data based on mechanism analysis and actual earthquake-nearby data. A new disturbance frequency index is proposed in this paper as a novel method to help forecast earthquakes. Forecasting of the location and timing of earthquakes is based on the connection between proven new frequency distributions and earthquakes. The effectiveness of this index is verified by backtracking observation around the 2022 Ya’an earthquake. Using this index, occultation data can forecast the occurrence of earthquakes five days ahead of detection, which can help break the bottleneck in earthquake forecasting.

Background: Lung cancer is one of the most serious malignant tumors endangering human health and life. This study focused on evaluating the association between single nucleotide polymorphisms (SNPs) of the glutaminase (GLS) and lung cancer susceptibility in the Chinese Han population. Methods: A total of 684 lung cancer patients and 684 healthy individuals were enrolled. Five GLS SNPs (rs143584207 C/A, rs117985587 T/C, rs74271715 G/T, rs2355570 G/A, and rs6713444 A/G) were screened as candidate genetic loci. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated to assess the association between GLS SNPs and lung cancer susceptibility. False-positive report probability (FPRP) analysis further verified whether the positive results deserved attention. Finally, the multi-factor dimensionality reduction (MDR) method was applied to analyze the interactions between SNPs. Results: The overall analysis revealed that GLS rs143584207 and rs6713444 were significantly associated with lung cancer susceptibility. The subgroup and clinical information analyses further revealed that GLS rs143584207 and rs6713444 could remarkably reduce lung cancer susceptibility in different subgroups (age >60, females, body mass index (BMI)

Tuberculosis (TB) is an important health issue in the world. Although the relation of SLC11A1 polymorphisms with TB risk has been extensively studied, it has not been reported in the northwest Chinese Han population. Therefore, this study aimed to investigate the relationships between five polymorphisms in or near the SLC11A1 gene and susceptibility to TB. The Agena MassARRAY platform was conducted for genotyping from 510 TB patients and 508 healthy controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were analyzed through logistic regression adjustment age and gender to assess the relationships between polymorphisms and TB risk. Our results identified that rs7608307 was related to increased TB risk in males (CT vs. CC: OR = 1.69, 95%CI: 1.12–2.56, p = 0.013; CT-TT vs. CC: OR = 1.61, 95%CI: 1.08–2.41, p = 0.020) and age ≤41 group (CT vs. CC: OR = 1.66, 95%CI: 1.04–2.65, p = 0.035), respectively. The SNP rs13062 was associated with the TB risk both in males (p = 0.012) and age >41 group (p = 0.021). In addition, we observed that the CC genotype of rs4674301 was correlated with increased TB risk in females (p = 0.043). Our results demonstrated the relationships between polymorphisms (rs7608307, rs4674301, and rs13062) in or near the SLC11A1 gene and age- and sex-specific TB risk in the northwest Chinese Han population.

Herein, two-ligand nickel-based MOF (JUC-85Ni) have been prepared by choosing high N-containing and S-containing ligand. On this basis of the research, the NiS2 nanoparticles dispersed N and S co-doped carbon materials (NiS2@NSC) are fabricated through the sulfidation of JUC-85Ni precursors with thioacetamide (TAA). The rationally designed NiS2@NSC catalysts are conducted a test for catalytically reducing 4-nitrophenol (4-NP) to environment friendly 4-aminophenol (4-AP) under NaBH4 alkaline condition. The superior features endows the catalyst with outstanding catalytic activity and recycling performance, and support it great potential for wastewater treatment.

Abstract Nasopharyngeal carcinoma (NPC) is a rare malignancy, with the unique geographical and ethnically characteristics of distribution. Gene chip and bioinformatics have been employed to reveal regulatory mechanisms in current functional genomics. However, a practical solution addressing the unresolved aspects of microarray data processing and analysis have been long pursuit. This study developed a new method to improve the accuracy of identifying key biomarkers, namely Unit Gamma Measurement (UGM), accounting for multiple hypotheses test statistics distribution, which could reduce the dependency problem. Three mRNA expression profile of NPC were selected to feed UGM. Differentially expressed genes (DEGs) were identified with UGM and hub genes were derived from them to explore their association with NPC using functional enrichment and pathway analysis. 47 potential DEGs were identified by UGM from the 3 selected datasets, and affluent in cysteine-type endopeptidase inhibitor activity, cilium movement, extracellular exosome etc. also participate in ECM-receptor interaction, chemical carcinogenesis, TNF signaling pathway, small cell lung cancer and mismatch repair pathway. Down-regulation of CAPS and WFDC2 can prolongation of the overall survival periods in the patients. ARMC4, SERPINB3, MUC4 etc. have a close relationship with NPC. The UGM is a practical method to identify NPC-associated genes and biomarkers.

The noncontact and wide-band characteristics of the ultrasonic signal excited by the laser make it widely used in nondestructive detection. In this paper, the finite element method is used to simulate multiple ultrasonic signal modes and the responses of them to metal defects. The simulation shows that the changes of excited Rayleigh-wave (RW) and shear-wave (SW) can be used to research the surface crack and internal defect of the sample respectively. By analyzing the reflected wave of RW and SW at the inspection point, the effects of surface crack and internal defect on the ultrasonic signals of the two modes are quantitatively compared. The results show that the time delay of the Rayleigh echo has a linear relationship to the width and transverse position of the crack, and the peak-valley difference of Rayleigh echo at the inspection point and the crack depth can also be fitted linearly. In addition, the Y component displacement of the bottom reflected SW also decays with the change of the internal defect. This simulation model provides a theoretical basis for further experimental verification basis.

Air-quality monitoring and forecasting are crucial for atmosphere pollution control and management. We propose an innovative data-driven framework for air quality index (AQI) prediction by integrating GNSS radio occultation (GNSS-RO) observation and weather modeling. Empowered by the state-of-the-art machine learning approach, our method can effectively predict regional AQI with a comparable accuracy much more quickly than the traditional numerical modeling and simulation approach. In a real case study using a representative region of China, our data-driven approach achieves a 2000 times speedup; meanwhile, the prediction error measured by rRMSE is only 2.4%. We investigate further the effects of different models, hyperparameters, and meteorological factors on the performance of our AQI prediction framework, and reveal that wind field and atmospheric boundary-layer height are important influencing factors of AQI. This paper showcases a direct application of GNSS-RO observation in assisting in forecasting regional AQI. From a machine learning point of view, it provides a new way to leverage the unique merits of GNSS atmospheric remote sensing technology with the help of the more traditional weather forecasting modeling approach.

Machine learning is becoming prevalent increasingly for reservoir characteristics analysis in the petroleum industry. This investigation proposes an alternative way for evaluating interwell connectivity in oil fields utilizing machine learning. In this study, three-dimensional convolutional neural network (CNN) was utilized to establish a deep learning model, which can invert interwell connectivity combining with dynamic production data. Different from traditional methods that try to construct mathematical formulas to calculate the connectivity among wells basing on physical laws, deep learning model can capture autonomously the changing characteristics of dynamic production data by training continuously and provide a potential to characterize the interwell connectivity accurately without physical model. At the same time, the back propagation (BP) neural network has also been built to analyze the prediction performance, which are compared with CNN. The results demonstrate that CNN has better performance in predicting the connectivity with the overall AARD below 15.35%. Moreover, the connectivity predicted by CNN is closest to the real connectivity factor compared with some traditional methods. The evaluation method on interwell connectivity proposed by this paper provides effective guidance for the secondary development of both conventional and unconventional reservoirs.

Ion-complementary self-assembling peptides have potential in delivering hydrophobic drugs. This study involved two self-assembling peptides, RADA16-I and RVDV16-I, of which RVDV16-I was a novel self-assembling peptide with different hydrophobic side chains designed from RADA16-I. The purpose of this study was to observe the interaction between different self-assembling peptides and emodin through fluorescence spectrophotometry, CD, SEM and AFM; to construct a preliminary suspension in-situ hydrogel delivery system for emodin with the self-assembling peptides; and to investigate the drug-loading and drug-releasing properties of the self-assembling peptides on emodin. The results showed that both peptides can interact with emodin and the interaction was dominated by hydrophobic interaction. The aqueous solutions of both self-assembling peptides can form relatively stable suspensions with emodin under mechanical stirring, and the suspension can form in-situ hydrogel under physiological condition. In vitro release of emodin from the hydrogels showed a manner of sustained release to some extent. Cell viability studies showed inherent proliferation inhibiting effects of emodin on tumor cells was maintained or enhanced through the in-situ hydrogels. The self-assembling peptides RADA16-I and RVDV16-I had showed promising drug-loading and drug-releasing performance for hydrophobic drugs. It is reasonable to exploit self-assembling peptides as drug carriers for their great potential to improve delivery of hydrophobic drugs.

The flowering time and plant height of soybean are important agronomic characters, which control the adaptability and yield of soybean. R2R3 MYB transcription factor plays an important regulatory role in plant growth and development. In this study, soybean GmGAMYB gene of R2R3-MYB type was induced by long-days (LDs). GmGAMYB showed higher transcriptional levels in the flowers, leaves and pods of soybean. Overexpression of GmGAMYB in transgenic soybean showed earlier flowering time and maturity in LDs and short-days (SDs). GmGAMYB interacted with GmGBP1 and might promote flowering time by up-regulating the expression of GmFULc gene in soybean. Moreover, the expression level of GmGAMYB was also induced by gibberellins (GAs) and the plant height of GmGAMYB-ox plants was significantly increased, which was caused by the enlargement of internode cell in stem. Furthermore, GmGAMYB overexpression led to increased GA sensitivity in the hypocotyl of soybean seedlings compared with WT. GmGAMYB may be a positive regulator of GA response of promoting plant height by up-regulating the expression of GmGA20ox gene in soybean. Together, our studies preliminarily showed that the partial functions of GmGAMYB in regulating flowering time and GA pathway.

A multiwavelength fiber laser based on erbium-doped twin-core fiber (ED-TCF) and nonlinear optical loop mirror (NOLM) is proposed and demonstrated in this paper. The ED-TCF is employed as comb filter and gain medium simultaneously. The NOLM serves as an intensity equalizer. A fiber laser with 8 wavelength outputs is achieved at room temperature. The optical signal to noise ratio (OSNR) is larger than 40 dB. The fluctuation of peak power and wavelength is less than 1 dB and within 0.015 nm, respectively. This fiber laser has the advantages of simple structure and stable operation.

Abstract Aims Stroke is a complicated neurological disease and the second leading cause of death in the world. We aimed to investigate the association between CYP24A1 genetic polymorphisms and ischemic stroke risk. Methods In this case–control study, four single‐nucleotide polymorphisms of CYP24A1 were selected and genotyped by MassARRAY platform in Chinese Han population. Odds ratios and 95% confidence intervals were calculated via logistic regression analysis with adjustment in genetic models. Results Our results indicated that CYP24A1 variant (rs1570669) was associated with the decreased risk of ischemic stroke (OR = 0.60, p 64 years. The rs2762934 had an increased ischemic stroke susceptibility (OR = 1.62, p = .033); however, rs1570669 might reduce stroke risk (OR = 0.61, p = .015) in age ≤64 years. The rs1570669 depressed ischemic stroke susceptibility both in female and male patients (OR = 0.46, p = .002; OR = 0.69, p = .033, respectively), and rs2296241 would weaken the risk in male (OR = 0.63, p = .012). The rs1570669 was associated with decreased risk of ischemic stroke with hypertension (OR = 0.56, p = .042). Conclusion Our study gave the evidences that CYP24A1 genetic polymorphisms were significantly associated with ischemic stroke patients, which would provide useful information of assessment or possible diagnostic markers for ischemic stroke.

With the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper presents an alternative method to predict vertical heterogeneity of the reservoir utilizing various deep neural networks basing on dynamic production data. A numerical simulation technique was adopted to obtain the required dataset, which contains dynamic production data calculated under different heterogeneous reservoir conditions. Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. On the basis of model validation, the results show that machine learning methods have excellent performance in predicting heterogeneity with the RMSE of 12.71 mD, which effectively estimated the permeability of the entire reservoir. Moreover, the overall AARD of the predictive result obtained by the CNN method was controlled at 11.51%, revealing the highest accuracy compared with BP and LSTM neural networks. And the permeability contrast, an important parameter to characterize heterogeneity, can be predicted precisely as well, with a derivation of below 10%. This study proposed a potential for vertical heterogeneity prediction in reservoir basing on machine learning methods.

Abstract Background Kidney cancer is the predominant form of malignancy of the kidney and accounts for approximately 3%–4% of all cancers. Renal cell cancer (RCC) represents more than 85% of kidney cancer. It has been reported that genetic factors may predispose individuals to RCC. This study evaluated the association between Acylphosphatase 2 (ACYP2) gene polymorphisms and RCC risk in the Han Chinese population. Methods Twelve single‐nucleotide polymorphisms (SNPs) in ACYP2 were genotyped using the Agena MassARRAY platform from 293 RCC patients and 495 controls. The Chi‐squared test, genetic models, haplotype, and stratification analyses were used to evaluate the association between SNPs and the risk of RCC. The relative risk was estimated using the odds ratio (OR) and 95% confidence interval (CI). Results We observed that the rs6713088 allele G (OR = 1.26, 95% CI: 1.03–1.53, p = .023) and rs843711 allele T (OR = 1.29, 95% CI: 1.06–1.57, p = .010) were associated with increased RCC risk. Genetic model analyses found that rs843711 was significantly associated with an increased RCC risk under the recessive model and log‐additive model after adjusting for age and gender. Haplotype analysis showed that the haplotype “TTCTCGCC” (OR = 0.67, 95% CI: 0.48–0.94, p = .021) was associated with a decreased risk of RCC in the Han Chinese population. Stratification analysis also found that rs6713088 and rs843711 were significantly associated with increased RCC risk. Conclusion In summary, the results suggested that ACYP2 polymorphisms could be used as a genetic marker for RCC. Additional functional and association studies are required to validate our results.

Background:Cytochrome P450 3A4 (CYP3A4) regulates pharmacokinetic and pharmacodynamic interactions during the process of drug absorption and metabolism, suggesting CYP3A4 plays an important role in drug addiction. However, the association between CYP3A4 polymorphisms and drug addiction risk is still not clear.Methods: This case-control study included 504 drug addicts and 501 healthy controls from Xi'an, China. Four single nucleotide polymorphisms (SNP) in CYP3A4 (rs3735451, rs4646440, rs35564277, and rs4646437) were genotyped by Agena MassARRAY platform. After adjusting by age and gender, we calculated odd ratios (OR) and 95% confidence intervals (CI) by logistic regression to estimate the association between CYP3A4 polymorphisms and drug addiction risk.Results: We found rs4646440 and rs4646437 were associated with decreased risk of drug addiction in codominant (rs4646440: OR = 0.41, 95%CI = 0.19–0.92, p = 0.030; rs4646437: OR = 0.19, 95%CI = 0.04–0.87, p = 0.032) and recessive (rs4646440: OR = 0.41, 95%CI = 0.19–0.91, p = 0.028; rs4646437: OR = 0.20, 95%CI = 0.04–0.90, p = 0.036) models. Rs3735451 and rs4646437 were associated with drug addiction risk in the subgroup of middle-aged people (44 < age ≤ 59) and elderly people (age ≥ 60), individually. For men, rs3735451, rs4646440, and rs4646437 had strong relationship with decreased risk of drug addiction (p < 0.05). The effects of rs3735451 on drug addiction risk were related to drug-using time (p < 0.05). We also observed one block (rs4646440 and rs35564277) in haplotype analysis.Conclusion:CYP3A4 polymorphisms were associated with drug addiction risk among the Chinese Han population.

Abstract Background Tuberculosis (TB) is a significant worldwide health problem, and is caused by Mycobacteria tuberculosis. Recent studies have suggested that FOXO3 plays vital roles in the risk of immune–related infectious diseases such as TB. Methods and Results The present study aimed to evaluate FOXO3 genetic variants and TB risk. We recruited 510 TB patients and 508 healthy controls in this study. All subjects were genotyped with the Agena MassARRAY platform. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression adjusted for age and gender. Our result revealed that rs3800229 T/G and rs4946935 G/A genotypes significantly increased the risk of TB (OR = 1.34, 95% CI = 1.04–1.74, p = 0.026; OR = 1.34, 95% CI = 1.03–1.73, p = 0.029, respectively). In stratified analysis according to gender and age, we observed that rs3800229 T/G and rs4946935 G/A genotypes were associated with an increase the risk of TB among males and age ≤41 years, respectively (OR = 1.47, 95% CI = 1.06–2.04, p = 0.022 and OR = 1.45, 95% CI = 1.05–2.02, p = 0.025). Conclusions Our study showed that rs3800229 and rs4946935 in FOXO3 were associated with a risk of TB in the Chinese population.

A scheme for controlling the frequency difference of output pulse pair with double frequency shift loops is proposed. The frequency shift system includes two loop elements of 20 and 200 MHz. The first one carries out a single selective positive frequency shift of 1–20 MHz, and the second one can satisfy a single fixed positive frequency shift of 200 MHz. The reverse cascade technology of two acousto-optic crystals is introduced to solve the limitation of the small frequency shift of crystal size. A multichannel synchronization signal completes the time domain control of each acousto-optic modulator. Finally, the frequency shift difference of the output pulse pair ranges of 0–2 GHz, and the frequency shift accuracy is 5 MHz.

Nucleic acid extraction is crucial for PCR detection of pathogenic bacteria to ensure food safety. In this study, a new magnetic extraction method was developed using 3D printing and magnetic silica beads (MSBs) to extract the target DNA from a large volume of bacterial sample and combined with microfluidic PCR to determine the bacteria. After proteinase K was added into a bacterial sample to lyse the bacteria and release the DNA, it was continuous-flow injected into the serpentine channel of the extraction chip, where magnetic silica bead chains had been formed in advance using a homogeneous magnetic field generated by two concentric semicircle magnets to capture the MSBs. Then, the flowing DNA was captured by the MSB chains, washed with alcohol, dried with gas, and eluted with deionized water to obtain the purified and concentrated DNA. Finally, the extracted DNA templates were injected into a microfluidic PCR chip with lyophilized amplification reagents and determined using a commercial qPCR device. The experimental results showed that the DNA extraction efficiency was more than 90%, and the lower detection limit of Salmonella was 102 CFU/mL. This new Salmonella detection method is promising to provide the rapid, sensitive, and simultaneous detection of multiple foodborne pathogens.

A Ni/meso-MgO catalyst with high surface area and small Ni nanoparticles was synthesized and investigated for hydrogen production by steam reforming of phenol for the first time. Compared to conventional Ni/MgO, the Ni/meso-MgO catalyst showed higher catalytic activity and stability. X-ray Diffraction, N2 adsorption, hydrogen temperature programmed reduction, transmission electron microscopy and thermal gravimetry results indicated that the Ni/meso-MgO catalyst had higher surface area than Ni/MgO and Ni particles of Ni/meso-MgO were narrowly distributed in the range of 5~6 nm with an average size of 5.3 nm, while Ni particles of Ni/MgO were in the range of 6~10 nm with an average size of 7.92 nm. The small and uniform Ni nanoparticles in Ni/meso-MgO were attributed to the high surface area and the confinement effect of the mesoporous structure of meso-MgO, which could effectively limit the growth of the active metal and stabilize Ni particles during the procedure of NiO reduction. The mesoporous structure of Ni/meso-MgO also played an important role in suppressing Ni nanoparticle sintering and carbon deposition during the steam reforming of phenol reaction.

Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir.

Human errors are one of the major contributors of accidents. In order to improve the safety performance, human errors have to be addressed. Human Factors Analysis and Classification System (HFACS) has been developed as an analytical framework for the investigation of the role of human errors in aviation accidents. However, the HFACS framework did not reveal the relationships describing the effect among diverse factors at different levels. Similarly, its interior structure was not exposed. As a result, it is difficult to identify critical paths and key factors. Therefore, an improved Human Factors Analysis and Classification System in the construction industry (I-HFACS) was developed in this study. An analytical I-HFACS mechanism was designed to interpret how activities and decisions made by upper management lead to operator errors and subsequent accidents. Critical paths were highlighted. Similarly, key human factors were identified, that is, “regulatory factors,” “organizational process,” “supervisory violations,” “adverse spiritual state,” “skill underutilization,” “skill-based errors,” and “violations.” Findings provided useful references for the construction industry to improve the safety performance.

A facile synthetic method was developed for a novel acid-sensitive camptothecin norcantharidin acid ester derivative I. The total yield can reach 71%. This method provides several advantages, including high yield and simple working procedure for the synthesis of analogues. The new synthetic compound I has been shown to exhibit better solubility and similar activity against tumour cell lines.

Objective: To report a rare liveborn case with a giant, septated, chest wall lymphangioma that underwent prenatal expectation treatment. Case report: A case of giant fetal chest wall cystic lymphangioma was diagnosed prenatally at 19 weeks gestation. Expectation treatment was performed, carefully after prenatal counseling, ruling out other structural abnormalities. At 38 weeks gestation, ultrasound showed a multilocular, subcutaneous cystic mass of 12.3 cm × 9.2 cm × 11.0 cm located on the left chest wall and left upper arm. The tumor was surgically removed 4 days after birth, and no recurrence was observed in the following 18 months. Conclusion: Our experience suggests that a large, septated fetal lymphangioma may still merit prenatal expectation treatment if there is no evidence for chromosomal and structural abnormality.