Search

Abstract Objective To assess the effectiveness of Internet-based self-help interventions in treating depression in adolescents and young adults. Methods A systematic search was conducted across six databases, including PubMed, to identify randomized controlled trials (RCTs) that satisfied the specified inclusion and exclusion criteria. The intervention measure consisted of Internet-based self-help interventions. Results A total of 23 randomized controlled trials (RCTs) were included in this analysis. Meta-analysis indicated that Internet-based self-help therapies significantly reduced depression scores in adolescents and young adults. (OR = -0.68, 95%CI [-0.88, -0.47], P

To effectively cope with the ever-growing intricacy of railway Industry Foundation Classes (IFC) file sizes, the adoption of a partial model approach has consistently been recommended as a highly effective strategy. This approach facilitates the efficient handling of increasing data volumes, enhancing the management capabilities of IFC files for purposes such as data storage, exchange, and transmission. This paper proposes an algorithm that adopts an iterative extraction methodology, grounded on the hierarchical IFC model’s tree structure to produce the desired partial models. Its primary objective is to minimize the size of large IFC files by selectively extracting the indispensable models. By leveraging solely the data structure of the IFC files, this approach circumvents the necessity for file format conversion or reliance on Model View Definitions (MVD). During the extraction of the required models, two types of related attributes are simultaneously extracted, and the extraction of relationship entity properties is optimized to enhance the extraction efficiency. To assess the effectiveness of our algorithmic approach, we conducted an in-depth case study centered around a contemporary high-speed railway project located in the southwestern region of China. The extracted models’ integrity was verified using BIMvision, while semantic syntax verification was performed using Express Engine and IfcObjectCounter. The results demonstrate that our algorithm not only accurately extracts the desired model segments from the IFC file, but also significantly improves model loading efficiency, minimizes memory usage, achieves model miniaturization, and shows promising performance and application prospects.

Background Colitis caused by checkpoint inhibitors (CPI) is frequent and is treated with empiric steroids, but CPI colitis mechanisms in steroid-experienced or refractory disease are unclear.Methods Using colon biopsies and blood from predominantly steroid-experienced CPI colitis patients, we performed multiplexed single-cell transcriptomics and proteomics to nominate contributing populations.Results CPI colitis biopsies showed enrichment of CD4+resident memory (RM) T cells in addition to CD8+ RM and cytotoxic CD8+ T cells. Matching T cell receptor (TCR) clonotypes suggested that both RMs are progenitors that yield cytotoxic effectors. Activated, CD38+ HLA-DR+ CD4+ RM and cytotoxic CD8+ T cells were enriched in steroid-experienced and a validation data set of steroid-naïve CPI colitis, underscoring their pathogenic potential across steroid exposure. Distinct from ulcerative colitis, CPI colitis exhibited perturbed stromal metabolism (NAD+, tryptophan) impacting epithelial survival and inflammation. Endothelial cells in CPI colitis after anti-TNF and anti-cytotoxic T-lymphocyte-associated antigen 4 (anti-CTLA-4) upregulated the integrin α4β7 ligand molecular vascular addressin cell adhesion molecule 1 (MAdCAM-1), which may preferentially respond to vedolizumab (anti-α4β7).Conclusions These findings nominate CD4+ RM and MAdCAM-1+ endothelial cells for targeting in specific subsets of CPI colitis patients.

BackgroundOsteoclast stimulatory transmembrane protein (OC-STAMP) is involved in silicosis fibrosis induced by silicon oxide (SiO2) exposure. Its role in silicosis fibrosis by inducing ferroptosis of alveolar type II epithelial cells and its related mechanism remain unclear. ObjectiveTo explore the effect and possible mechanism of OC-STAMP on ferroptosis of alveolar type II epithelial cells and silicosis fibrosis in rats under SiO2 exposure. MethodsTwenty male Wistar rats of SPF grade were randomly divided into two groups: control (Sham) group and SiO2 group, 15 rats in each group. Rats in the SiO2 group were given 1 mL of 50 mg·L−1 SiO2 suspension at one time through the non-exposed intratracheal instillation method to establish an animal model of silicosis, and rats in the Sham group were give 1 mL of 0.9% sodium chloride solution in the same way. Rats were sacrificed after 8 weeks. Samples of lung tissue were fixed in glutaraldehyde or paraformaldehyde for observing ultrastructure of mitochondria by transmission electron microscopy; HE, Masson, VG, and Prussian blue were used to observe changes in lung tissue structure and iron deposition. The expression level of OC-STAMP and the degree of lung fibrosis were evaluated by immunohistochemistry and immunofluorescence. The expression level of OC-STAMP in rat lung tissue was detected and the transfection effect of OC-STAMP was verified by real-time fluorescence quantitative polymerase chain reaction (RT-PCR). Overexpression (OCS group) and inhibition expression (SI-OC group) models were constructed by OC-STAMP plasmid and OC-STAMP small interfering RNA (siRNA) transfection to cultured MLE-12 cells, respectively. The relative expression levels of glutathione peroxidase 4 (GPX4), solute carrier family 7 member 11 (SLC7A11), and other proteins in lung tissue and MLE-12 were detected by Western blotting. ResultsThe results of HE, Masson, and VG staining showed that the silicosis modeling was successful after 8 weeks of SiO2 exposure. The immunofluorescence results showed that OC-STAMP and ATP binding cassette subfamily A member 3 (ABCA3) co-localized in alveolar type II epithelium. The immunohistochemical results showed that the levels of OC-STAMP and collagen I in the SiO2 group were significantly higher than those in the Sham group (P

A three-dimensional dynamic heat transfer mathematical model of the process when cored wire feed in molten iron is established based on finite volume method (FVM). The calculation area is meshed with triangles and quadrilaterals to determine nodes and control volumes, and implicit time integration method is used to ensure the stability of calculating process. For exposing the dynamic heat transfer behavior, the variation of temperature field and explosion characteristics of cored wires are studied. In addition, the melt loss rate of the top end of cored wire and the correlation among melt explosion depth, molten iron temperature and feeding speed of cored wires are theoretically calculated. More importantly, the influence of different structures of cored wires on the absorption rate of magnesium are considered. The calculation results are in good agreement with the experimental data, which indicate that the existing theoretical model has good validity and can provide theoretical guidance for spheroidization process in molten iron.

Abstract Although hyperspectral data, especially spaceborne images, are rich in spectral information, their spatial resolution is usually low due to the limitation of sensor design and other factors. Therefore, for the application of hyperspectral images, unmixing technology is a key processing technology, such as linear mixing model and its derived algorithms have made a certain progress. However, a real scene often contains both pure and mixed pixels. The existing methods usually ignore the consideration and analysis of this situation in the process of model design and simulation experiment. In this context, this paper proposes a robust linear unmixing model with the enhanced constraint of classification for hyperspectral image. In general, it designs a framework combining unmixing and classification. In the task for real scene data, endmembers are extracted first, and then the hard classification term constructed after the expansion of endmembers (training samples) based on similarity is introduced to provide the sparsity constraint of the overall model, so as to realize relatively complete adjustment and effective image unmixing under complex conditions. Considering the scene with different distributions, the simulation experiment designs several groups of data tests, including different proportions of pure and mixing pixels. The unmixing results of three simulated datasets and two real datasets show that the unmixing results of this method are better than those of the other six comparison methods. This model improves the accuracy of unmixing and realizes effective unmixing.

Objective. Deficiencies in klotho are implicated in various kidney dysfunctions including diabetic nephropathy (DN) related to inflammatory responses. Klotho is closely related to inflammatory responses and is a potential target for ameliorating kidney failure. Pyroptosis, an inflammatory form of programmed cell death, is reported to take part in DN pathogenesis recently. This study is aimed at exploring whether and how klotho inhibited podocyte pyroptosis and whether astragaloside IV (AS-IV) protect podocyte through the regulation of klotho. Materials and Methods. SD rat model of DN and conditionally immortalized mouse podocytes exposed to high glucose were treated with AS-IV. Biochemical assays and morphological examination, cell viability assay, cell transfection, phalloidin staining, ELISA, LDH release assay, SOD and MDA detection, MMP assay, ROS level detection, flow cytometry analysis, TUNEL staining assay, PI/Hoechst 33342 staining, immunofluorescence assay, and western blot were performed to elucidate podocyte pyroptosis and to observe the renal morphology. Results. The treatment of AS-IV can improve renal function and protect podocytes exposed to high glucose. Klotho was decreased, and AS-IV increased klotho levels in serum and kidney tissue of DN rats as well as podocytes exposed to high glucose. AS-IV can inhibit DN glomeruli pyroptosis in vivo. In vitro, overexpressed klotho and treatment with AS-IV inhibited pyroptosis of podocytes cultured in high glucose. Klotho knockdown promoted podocyte pyroptosis, and treatment with AS-IV reversed this effect. Furthermore, the overexpression of klotho and AS-IV reduces oxidative stress levels and inhibited NF-κB activation and NLRP3-mediated podocytes’ pyroptosis which was abolished by klotho knockdown. In addition, both the ROS inhibitor NAC and the NF-κB pathway inhibitor PDTC can inhibit NLRP3 inflammasome activation. NLRP3 inhibitor MCC950 can inhibit pyroptosis of podocytes exposed to high glucose. Conclusion. Altogether, our results demonstrate that the protective effect of AS-IV in upregulating klotho expression in diabetes-induced podocyte injury is associated with the inhibition of NLRP3-mediated pyroptosis via the NF-κB signaling pathway.

Diabetic nephropathy (DN) is a microvascular complication of diabetes mellitus (DM) and the most common cause of death in diabetic patients. DN progression is associated with podocyte damage due to reduced autophagy caused by mTORC1 activation. Tangshenning (TSN) has been shown to reduce proteinuria, protect renal function, and reduce podocyte damage. Still, the effect of TSN on the autophagic activity of podocytes remains unclear. Herein, in vitro experiments using a high glucose-induced podocyte injury model were performed. Results showed that TSN treatment enhanced the weakened nephrin expression and autophagic activity of podocytes and inhibited the mTORC1 pathway (p-mTOR, mTOR, p-p70S6K, p70S6K, ULK1, and 4EBP1) under high glucose conditions. Furthermore, the mTORC1 activator (siRNA-TSC2) partially inhibited the above beneficial effects of TSN, suggesting that mTORC1 was the target of TSN to regulate autophagy. In summary, TSN reduces podocyte damage induced by high glucose via inhibiting mTORC1 pathway and downstream targets and restoring podocyte autophagy.

Knowledge graphs are collections of factual triples. Link prediction aims to predict lost factual triples in knowledge graphs. In this paper, we present a novel capsule network method for link prediction taking advantages of quaternion. More specifically, we explore two methods, including a relational rotation model called QuaR and a deep capsule neural model called CapS-QuaR to encode semantics of factual triples. QuaR model defines each relation as a rotation from the head entity to the tail entity in the hyper-complex vector space, which could be used to infer and model diverse relation patterns, including: symmetry/anti-symmetry, reversal and combination. Based on these characteristics of quaternions, we use the embeddings of entities and relations trained from QuaR as the input to CapS-QuaR model. Experimental results on multiple benchmark knowledge graphs show that the proposed method is not only scalable, but also able to predict the correctness of triples in knowledge graphs and significantly outperform the existing state-of-the-art models for link prediction. Finally, the evaluation of a real dataset for search personalization task is conducted to prove the effectiveness of our model.

We develop a finite volume method for the simulation of the liquid iron cored wire feeding spheroidization technology, which can realize the visualization of the spheroidization process and provide guidance for parameter optimization. In this work, we first define a two-dimensional unsteady heat-transfer mathematical model for single-core structure cored wire melting in liquid iron and then discretize the governing equation numerically. The present solver is validated by comparing it with the data obtained from experimental studies. Interestingly, in the numerical results we found that the absorption velocity of the magnesium element can be improved to the greatest extent to get the most ideal spheroidizing effect. To further show the melting and boiling characteristics of spheroidization, we also explore the relationship between melting and boiling depth, wire feeding speed, and temperature of liquid iron in the feeding process, and the wire feeding speed–depth function was also obtained.

In this paper, we proposed a dynamic stress–strength model for coherent system. It is supposed that the system consists of n components with initial random strength and each component is subjected to random stresses. The stresses, applied repeatedly at random cycle times, will cause the degradation of strength. In addition, the number of cycles in an interval is assumed to follow a Poisson distribution. In the case of the strength and stress random variables following exponential distributions, the expression for the reliability of the proposed dynamic stress–strength model is derived based on survival signature. The reliability is estimated by using the best linear unbiased estimation (BLUE). Considering the Type-II censored failure times, the best linear unbiased predictors (BLUP) for the unobserved coherent system failure times are developed based on the observed failure times. Monte Carlo simulations are performed to compare the BLUE of parameters with different values and compute the BLUP. A real data set is also analysed for an illustration of the findings.

In this paper, we consider the statistical analysis for the dependent competing risks model in the constant stress accelerated life testing with Type-II progressive censoring. It is focused on two competing risks from Lomax distribution. The maximum likelihood estimators of the unknown parameters, the acceleration coefficients and the reliability of unit are obtained by using the Bivariate Pareto Copula function and the measure of dependence known as Kendall's tau. In addition, the 95% confidence intervals as well as the coverage percentages are obtained by using Bootstrap-p and Bootstrap-t method. Then, a simulation study is carried out by the Monte Carlo method for different measures of Kendall's tau and different testing schemes. Finally, a real competing risks data is analysed for illustrative purposes. The results indicate that using copula function to deal with the dependent competing risks problems is effective and feasible.

In this paper, we investigate parameter inference for four-unit hybrid systems based on Type-II progressively hybrid censored and masked data from constant-stress partially accelerated life test. It is assumed that the unit lifetime in systems has exponential hazard rate. The maximum likelihood estimations (MLEs) for unknown parameters and acceleration factor are obtained. The observed Fisher information matrix, as well as the asymptotic variance-covariance matrix of the MLEs are derived. Approximate confidence intervals (CIs) and studentized-t bootstrap CIs for the parameters are presented based on normal approximation to the asymptotic distribution of MLEs and bootstrap method, respectively. A Monte Carlo simulation study is carried out to investigate the performance of the MLEs and to compare the effects of two different CIs in terms of interval length and coverage percentage for the parameters.

This paper investigates the statistical inference of inverse power Lomax distribution parameters under progressive first-failure censored samples. The maximum likelihood estimates (MLEs) and the asymptotic confidence intervals are derived based on the iterative procedure and asymptotic normality theory of MLEs, respectively. Bayesian estimates of the parameters under squared error loss and generalized entropy loss function are obtained using independent gamma priors. For Bayesian computation, Tierney–Kadane’s approximation method is used. In addition, the highest posterior credible intervals of the parameters are constructed based on the importance sampling procedure. A Monte Carlo simulation study is carried out to compare the behavior of various estimates developed in this paper. Finally, a real data set is analyzed for illustration purposes.

Entropy measures the uncertainty associated with a random variable. It has important applications in cybernetics, probability theory, astrophysics, life sciences and other fields. Recently, many authors focused on the estimation of entropy with different life distributions. However, the estimation of entropy for the generalized Bilal (GB) distribution has not yet been involved. In this paper, we consider the estimation of the entropy and the parameters with GB distribution based on adaptive Type-II progressive hybrid censored data. Maximum likelihood estimation of the entropy and the parameters are obtained using the Newton–Raphson iteration method. Bayesian estimations under different loss functions are provided with the help of Lindley’s approximation. The approximate confidence interval and the Bayesian credible interval of the parameters and entropy are obtained by using the delta and Markov chain Monte Carlo (MCMC) methods, respectively. Monte Carlo simulation studies are carried out to observe the performances of the different point and interval estimations. Finally, a real data set has been analyzed for illustrative purposes.

In this paper, the evidential estimation method for the parameters of the mixed exponential distribution is considered when a sample is obtained from Type-II progressively censored data. Different from the traditional statistical inference methods for censored data from mixture models, here we consider a very general form where there is some uncertain information about the sub-class labels of units. The partially specified label information, as well as the censored data are represented in a united frame by mass functions within the theory of belief functions. Following that, the evidential likelihood function is derived based on the completely observed failures and the uncertain information included in the data. Then, the optimization method using the evidential expectation maximization algorithm (E2M) is introduced. A general form of the maximal likelihood estimates (MLEs) in the sense of the evidential likelihood, named maximal evidential likelihood estimates (MELEs), can be obtained. Finally, some Monte Carlo simulations are conducted. The results show that the proposed estimation method can incorporate more information than traditional EM algorithms, and this confirms the interest in using uncertain labels for the censored data from finite mixture models.

Quantitative real-time PCR (qRT-PCR) has been a widely used accurate technique for gene expression analysis in various species. However, its results require data normalization by reliable reference genes. Despite the horticultural importance of Narcissus pseudonarcissus, and genome sequence has become available for the species, no gene expression study based on the stability of reference genes in qRT-PCR has been conducted. To boost the use of qRT-PCR in N. pseudonarcissus, we uncovered eight commonly used candidate reference genes for their stability. The expression levels of the eight genes were detected for the normalization in five different organs (bulbs, scapes, leaves, perianths and coronas) of three N. pseudonarcissus cultivars (‘Marieke’, ‘Pinza’ and ‘Slim Whitman’) by qRT-PCR. Subsequently, three commonly used computational programs were applied for evaluating the stability of the candidate reference gene's expressions. It turned out that for all the samples and most subgroups, ACT and GAPDH were the most suitable reference genes for normalization. However, the best reference genes were found not always the same one across diverse samples by different computational programs. Our study was the first reference gene evaluation in N. pseudonarcissus and will promote future studies on gene expression levels of N. pseudonarcissus.

In this paper, we investigate a step-stress partially accelerated lifetime test for the four-component hybrid systems with Type-II progressive hybrid censoring scheme while the life time of system component follows exponential failure rate. In many cases, the exact component causing the system failure cannot be identified and the cause of failure is masked. Based on Type-II progressively hybrid censored and masked data, the maximum likelihood estimations for unknown parameters and acceleration factor are obtained. In addition, approximate confidence interval and bootstrap confidence interval are presented by using the asymptotic distributions of the maximum likelihood estimations for unknown parameters and bootstrap method, respectively. Finally, the proposed method is illustrated through the simulation studies.

Narcissus pseudonarcissus is an important bulbous plant with white or yellow perianths and light yellow to orange-red coronas, but little is known regarding the biochemical and molecular basis related to flower color polymorphisms. To investigate the mechanism of color formation, RNA-Seq of flower of two widely cultured cultivars (‘Slim Whitman’ and ‘Pinza’) with different flower color was performed. A total of 84,463 unigenes were generated from the perianths and coronas. By parallel metabolomic and transcriptomic analyses, we provide an overview of carotenoid biosynthesis, degradation, and accumulation in N. pseudonarcissus. The results showed that the content of carotenoids in the corona was higher than that in the perianth in both cultivars. Accordingly, phytoene synthase (PSY) transcripts have a higher abundance in the coronas than that in perianths. While the expression levels of carotenoid biosynthetic genes, like GGPPS, PSY, and LCY-e, were not significantly different between two cultivars. In contrast, the carotenoid degradation gene NpCCD4 was highly expressed in white-perianth cultivars, but was hardly detected in yellow-perianth cultivars. Silencing of NpCCD4 resulted in a significant increase in carotenoid accumulation, especially in all-trans-β-carotene. Therefore, we presume that NpCCD4 is a crucial factor that causes the low carotenoid content and color fading phenomenon of ‘Slim Whitman’ by mediating carotenoid turnover. Our findings provide mass RNA-seq data and new insights into carotenoid metabolism in N. pseudonarcissus.

Narcissus is widely used for cut flowers and potted plants, and is one of the most important commercial bulbous flowers in the floricultural industry. In this study, ten carotenoid and eighteen flavonoid compounds from the perianths and coronas of fifteen narcissus cultivars were measured by HPLC-APCI-MS/MS and UPLC-Q-TOF-MS/MS. Among these, six carotenoids, a total of seventeen flavonols and chlorogenic acid were identified in narcissus for the first time. A multivariate analysis was used to explore the relationship between flower color and pigment composition. We found that all-trans-violaxanthin and total carotenoid content were the main factors that affected flower color. These investigations could provide a global view of flower color formation and a theoretical basis for hybridization breeding in narcissus.

Astrocytomas are common malignant intracranial tumors that comprise the majority of adult primary central nervous system tumors. MicroRNAs (miRNAs) are small, non-coding RNAs (20-24 nucleotides) that post-transcriptionally modulate gene expression by negatively regulating the stability or translational efficiency of their target mRNAs. In our previous studies, we found that the downregulation of miR-106a-5p in astrocytomas is associated with poor prognosis. However, its specific gene target(s) and underlying functional mechanism(s) in astrocytomas remain unclear. In this study, we used mRNA microarray experiments to measure global mRNA expression in the presence of increased or decreased miR-106a-5p levels. We then performed bioinformatics analysis based on multiple target prediction algorithms to obtain candidate target genes that were further validated by computational predictions, western blot analysis, quantitative real-time PCR, and the luciferase reporter assay. Fas-activated serine/threonine kinase (FASTK) was identified as a direct target of miR-106a-5p. In human astrocytomas, miR-106a-5p is downregulated and negatively associated with clinical staging, whereas FASTK is upregulated and positively associated with advanced clinical stages, at both the protein and mRNA levels. Furthermore, Kaplan-Meier analysis revealed that the reduced expression of miR-106a-5p or the increased expression of FASTK is significantly associated with poor survival outcome. These results further supported the finding that FASTK is a direct target gene of miR-106a-5p. Next, we explored the function of miR-106a-5p and FASTK during astrocytoma progression. Through gain-of-function and loss-of-function studies, we demonstrated that miR-106a-5p can significantly inhibit cell proliferation and migration and can promote cell apoptosis in vitro. The knockdown of FASTK induced similar effects on astrocytoma cells as those induced by the overexpression of miR-106a-5p. These observations suggest that miR-106a-5p functions as a tumor suppressor during the development of astrocytomas by targeting FASTK.