Time-dependent Analysis Research Articles

Timing of cesarean delivery for fetal heart rate abnormalities in hypertensive pregnancies induced with oral misoprostol or Foley catheter: Secondary analysis of a randomized clinical trial.

The study aims to assess how oral misoprostol for cervical ripening affects the time of cesarean delivery (CD) for fetal heart rate (FHR) abnormalities in pre-eclampsia patients. Secondary goals include determining the role of uterine hyperstimulation, comparing misoprostol with Foley catheter, and identifying risk factors for FHR abnormalities associated with CD. A previously published randomized clinical trial was subjected to a secondary analysis (NCT01801410). We conducted a time-dependent analysis, stratifying the population based on the final mode of induction used (low-dose oral misoprostol vs Foley catheter). There was no CD for FHR abnormalities within 2 h of starting misoprostol. At 5 h, the cumulative incidence of CD for FHR abnormalities in the misoprostol group was 2.10%, while it was 1.00% in the Foley group (P = 0.565). After 25 h, the CD risk for FHR abnormalities remained constant in both groups at 21.00% (95% confidence interval [CI] 15.00%-28.00%). Within 5 h of misoprostol induction, the risk of uterine hyperstimulation was similar in both groups (0.33% in misoprostol vs 0.34% in Foley group, P = 0.161). The risk of CD for FHR abnormalities was unaffected by newborn weight centiles. There was no significant difference in CD risk for FHR abnormalities between misoprostol and Foley catheter induction. Nonetheless, the cumulative incidence of CD for FHR abnormalities increased faster in the misoprostol group, indicating that FHR monitoring timing should be tailored to the induction method.

Three-dimensional creep calculation model for reliability analysis of concrete-filled steel tubular (CFST) structure

For long-span concrete-filled steel tube (CFST) structure, it is essential to implement efficient and realistic prediction models for the long-term processes of core concrete creep. In order to systematically study the main influential factors in CFST structure creep effects, a probabilistic analysis can be performed. The objective of this paper is to analyse the long-term response of CFST structure by using time-dependent reliability method. To address the problem of creep calculation, a novel and comprehensive presentation was developed. By converting the integral-type creep law to a differential-type form with internal variables, based on the Kelvin chain model, an elastic structural analysis with generally orthotropic elastic moduli and eigenstrains was established. We have compiled a three-dimensional (3-D) finite-element analysis programme that considers the creep Poisson effect of core concrete to analyse the long-term performance of the CFST structure. This is achieved by developing a commercial finite element code, such as Usermat in ANSYS. As calculation examples, several time-dependent analyses of a CFST stub column and long-span CFST arch bridge were performed. The calculated results of long-term behaviour agree well with experimental and measured data. Considering the randomness of various input parameters, such as physical dimensions and materials of the CFST structure, we have analysed the uncertainty using the Latin-hypercube-sampling (LHS) stochastic finite-element method. The results of creep sensitivity analysis revealed that coefficient of creep model uncertainty, steel and concrete elastic modulus were the three most influential parameters for CFST stub columns’ creep, and load, coefficient of creep model uncertainty, relative humidity, steel and concrete elastic modulus were the five for CFST arch bridges’ long-term deflection. In addition, the use of Monte Carlo (MC) and response surface (RS) methods for structural stochastic reliability analysis demonstrates the ability of these approaches to assess the reliability index of long-span CFST arch bridges. This provides a valuable reference for the design and performance evaluation of CFST structures.

Construction of an ER stress-related prognostic signature for predicting prognosis and screening the effective anti-tumor drug in osteosarcoma

BackgroundOsteosarcoma is the most common malignant primary bone tumor in infants and adolescents. The lack of understanding of the molecular mechanisms underlying osteosarcoma progression and metastasis has contributed to a plateau in the development of current therapies. Endoplasmic reticulum (ER) stress has emerged as a significant contributor to the malignant progression of tumors, but its potential regulatory mechanisms in osteosarcoma progression remain unknown.MethodsIn this study, we collected RNA sequencing and clinical data of osteosarcoma from The TCGA, GSE21257, and GSE33382 cohorts. Differentially expressed analysis and the least absolute shrinkage and selection operator regression analysis were conducted to identify prognostic genes and construct an ER stress-related prognostic signature (ERSRPS). Survival analysis and time dependent ROC analysis were performed to evaluate the predictive performance of the constructed prognostic signature. The “ESTIMATE” package and ssGSEA algorithm were utilized to evaluate the differences in immune cells infiltration between the groups. Cell-based assays, including CCK-8, colony formation, and transwell assays and co-culture system were performed to assess the effects of the target gene and small molecular drug in osteosarcoma. Animal models were employed to assess the anti-osteosarcoma effects of small molecular drug.ResultsFive genes (BLC2, MAGEA3, MAP3K5, STC2, TXNDC12) were identified to construct an ERSRPS. The ER stress-related gene Stanniocalcin 2 (STC2) was identified as a risk gene in this signature. Additionally, STC2 knockdown significantly inhibited osteosarcoma cell proliferation, migration, and invasion. Furthermore, the ER stress-related gene STC2 was found to downregulate the expression of MHC-I molecules in osteosarcoma cells, and mediate immune responses through influencing the infiltration and modulating the function of CD8+ T cells. Patients categorized by risk scores showed distinct immune status, and immunotherapy response. ISOX was subsequently identified and validated as an effective anti-osteosarcoma drug through a combination of CMap database screening and in vitro and in vivo experiments.ConclusionThe ERSRPS may guide personalized treatment decisions for osteosarcoma, and ISOX holds promise for repurposing in osteosarcoma treatment.

Exploring the utility of a latent variable as comprehensive inflammatory prognostic index in critically ill patients with cerebral infarction.

We introduce the comprehensive inflammatory prognostic index (CIPI), a novel prognostic tool for critically ill cerebral infarction patients, designed to meet the urgent need for timely and convenient clinical decision-making in this high-risk patient group. Using exploratory factor analysis on selected indices-neutrophil to lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and systemic immune inflammation index (SIII)-we derived CIPI, a latent variable capturing their combined predictive power. Data from 1,022 patients in the Medical Information Mart for Intensive Care (MIMIC)-IV database were used to develop CIPI-based survival models, with the robustness and area under the receiver operating characteristic curve (AUC) performance of CIPI validated against an independent dataset of 326 patients from the MIMIC-III CareVue subset. The CIPI's predictive power for in-hospital and intensive care unit (ICU) mortality was assessed through Kaplan-Meier analysis, univariate and multivariate Cox regression models, and time-dependent AUC analysis. Linearity, subgroup sensitivity analyses and interaction effects with CIPI were also evaluated. CIPI was an independent prognostic factor, demonstrating a statistically significant association with in-hospital and ICU mortality, when assessed as a continuous and a categorical variable. It showed a linear relationship with mortality rates and demonstrated stability across most subgroups, with no significant interactions observed. Its predictive capabilities for in-hospital and ICU mortality among critically ill cerebral infarction patients matched those of established prognostic indices in the MIMIC database. Our study indicates that CIPI is a reliable and effective prognostic tool for critically ill cerebral infarction patients in predicting in-hospital and ICU mortality. Its straightforward calculation, rooted in routine blood tests, enhances its practicality, promising significant utility in clinical settings.

Time-dependent seismic fragility analysis of reinforced concrete columns subjected to chloride-induced corrosion

This paper presents a stochastic methodology to perform time-dependent seismic fragility analysis for reinforced concrete (RC) columns subjected to chloride attacks. Numerical modelling methods considering the increasing variability of reinforcement mechanical properties were proposed based on the stochastic constitutive model for corroded steel bars. Depending on the finite element model validated by test data for uncorroded and corroded RC columns, two novel stochastic cases and a traditional deterministic case were introduced and then applied to pushover analysis, incremental dynamic analysis and seismic fragility analysis. The results of damage limit states (DLSs) determined by stochastic pushover curves indicated that the seismic capacity/DLSs for columns along the service life were time-dependent and followed lognormal distributions. The incremental dynamic analysis curves and seismic fragility curves implied that chloride-induced corrosion typically led to a substantial increase in the failure probability of columns. In comparison with the correlated stochastic method, considerable underestimations of component level fragility across different damage limit states were obtained using the deterministic approach, which highlighted the importance of accounting for the variability of mechanical properties of corroded steel bars in the time-dependent seismic fragility analysis.

Time-dependent earthquake-fire coupling fragility analysis under limited prior knowledge: A perspective from type-2 fuzzy probability

Earthquake-triggered fire domino scenarios (E-FDSs) arise frequently from the interaction between earthquakes and chemical installations, resulting in catastrophic multi-hazard coupling events. The complicated mutually amplified phenomena between natural disasters and chemical accidents significantly aggravates the escalation of domino accidents, which has posed great challenges for modeling and preventing E-FDSs. Under this impetus, this work proposes an advanced type-2 fuzzy probabilistic methodology to obtain the time-dependent failure probability of steel cylindrical tanks (SCTs) subjected to the earthquake-fire sequence. To cope with the limited prior knowledge on E-FDSs, a basic universal is established to describe the fire resistance attenuation caused by the seismic damage. The coupling failure criterion of SCTs is formulated by a type-2 fuzzy time-dependent limit state equation. A credibility-based stochastic simulation algorithm is developed for the hybrid uncertainty analysis (combining ambiguity and stochasticity). The proposed methodology is validated by case studies of a 5000 m3 fixed roof tank. Compared to the existing accident probability model, the proposed methodology can not only capture the fire resistance attenuation caused by the seismic damage but also provide a dynamic estimation of tank failure probability with respect to the fire exposure time. The proposed methodology can effectively and dynamically capture the accident evolution process, which in turn helps mitigate and prevent the spatiotemporal propagation of domino effects.

Consumption of foods and beverages rich in added sugar associated with incident metabolic syndrome: the Coronary Artery Risk Development in Young Adults (CARDIA) study.

Numerous studies report positive associations between total carbohydrate (CHO) intake and incident metabolic syndrome (MetS), but few differentiate quality or type of CHO relative to MetS. We examined source of CHO intake, including added sugar (AS), AS-rich CHO foods, and sugar-sweetened beverages (SSBs) associated with incident MetS in adults enrolled in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Among 3154 Black American and White American women and men aged 18-30 years at baseline, dietary intake was assessed by diet history three times over 20 years. Sources of AS-rich CHO foods and beverages include sugar-rich refined grain products, candy, sugar products, and SSBs. Incident MetS was created according to standard criteria. Time-dependent Cox proportional hazards regression analysis evaluated the associations of incident MetS across quintiles of cumulative intakes of AS-rich CHO foods and beverages, AS, and SSBs adjusted for potential confounding factors over 30 years of follow-up. The associations of AS-rich CHO foods and beverages, AS, and SSB intakes with incident MetS were consistent. Compared with the lowest intake, the greatest intakes of AS-rich CHOs, AS, and SSBs were associated with 59% (Ptrend < 0.001), 44% (Ptrend = 0.01), and 34% (Ptrend = 0.03) higher risk of developing MetS, respectively. As expected, diet quality was lower across increasing quintiles of AS-rich CHO foods and beverages, AS, and SSBs (all Ptrend < 0.001). Our study findings are consistent with an elevated risk of developing MetS with greater consumption of AS, AS-rich CHO foods, and SSBs, which support consuming fewer AS-rich CHO foods and SSBs.

A novel active learning Gaussian process modeling-based method for time-dependent reliability analysis considering mixed variables

The purpose of time-dependent reliability analysis is evaluating the reliability of a system or component within a specified timeframe. Several approaches have been suggested for addressing time-dependent reliability challenges, including analytical methods and methods based on response surrogate models. As a representative of Machine Learning methods, the Active Learning Gaussian process model has gained more and more popularity in time-dependent reliability analysis. In practical applications, certain uncertainties may not be adequately represented by random variables following deterministic distributions. In contrast, these uncertainties can be easily described by interval variables. This article introduces an innovative method based on Active Learning Gaussian process modeling for time-dependent reliability analysis, encompassing both random and interval variables. The initial step involves transforming stochastic processes into random parameters through spectral decomposition. Moreover, a new sampling strategy to manage mixed uncertainties is proposed, where the candidate samples of mixed variables with positive maximum extreme responses and negative minimum extreme responses are identified. These samples are utilized to construct and renew an extreme Gaussian process model. Lastly, we present an improved confidence-based learning function to accelerate the learning process, which is designed to locate the optimal random input and interval input for updating the extreme Gaussian process model. One mathematical example and two engineering examples are conducted to verify the performance of the proposed method. Results illustrate that our method offers high accuracy while reducing the evaluations of true performance functions in addressing time-dependent reliability issues under mixed uncertainties.

Risk of severe infection associated with immunoglobulin deficiency under rituximab therapy in immune-mediated inflammatory disease

ObjectivesWe evaluated the risk of severe infection in patients with immune-mediated inflammatory disease (IMID) treated with RTX and with Ig deficiency.MethodsThis was an observational, retrospective single-centre study of patients undergoing...

Assessing the quality of antimicrobial prescribing in solid organ transplant recipients: a new frontier in antimicrobial stewardship.

Post-transplant infections remain a leading cause of morbidity and mortality in solid organ transplant recipients (SOTRs) and local standardized antimicrobial treatment guidelines may contribute to improved clinical outcomes. Our study assessed the rate of therapeutic compliance with local standard guidelines in the treatment of common infections in SOTR, and their associated outcomes. Consecutive adult SOTRs admitted to the transplant floor from January-September 2020 and were treated for an infectious syndrome were followed until discharge or for 30 days following the date of diagnosis, whichever was shorter. Data was extracted from electronic medical records. Guideline compliance was characterized as either appropriate, effective but unnecessary, undertreatment, or inappropriate. Nine hundred and thirty-six SOTR were admitted to the transplant ward, of which 328 patients (35%) received treatment for infectious syndromes. Guidelines were applicable to 252 patients, constituting 275 syndromes: 86 pneumonias; 82 urinary tract infections; 40 intra-abdominal infections; 38 bloodstream infections; and 29 C. difficile infections. 200/246 (81%) of infectious syndromes received appropriate or effective but unnecessary empiric treatment. In addition, appropriate tailoring of antimicrobials resulted in a significant difference in 30-day all-cause mortality (adjusted OR of 0.07, 95% CI 0.01-0.38; P = .002). Lastly, we found that guideline-compliant empiric therapy was found to prevent the development of multi-drug resistance in a time-dependent analysis (adjusted HR of 0.21, 95% CI 0.08-0.52; P = .001). Our data show that adherence to locally developed guidelines was associated with reduced mortality and resistant-organism development in our cohort of SOTR.

Combined toxicity and adverse outcome pathways of common pesticides on Chlorella pyrenoidosa.

Pesticides due to their extensive use have entered the soil and water environment through various pathways, causing great harm to the environment. Herbicides and insecticides are common pesticides with long-term biological toxicity and bioaccumulation, which can harm the human body. The concept of the adverse outcome pathway (AOP) involves systematically analyzing the response levels of chemical mixtures to health-related indicators at the molecular and cellular levels. The AOP correlates the structures of chemical pollutants, toxic molecular initiation events and adverse outcomes of biological toxicity, providing a new model for toxicity testing, prediction, and evaluation of pollutants. Therefore, typical pesticides including diquat (DIQ), cyanazine (CYA), dipterex (DIP), propoxur (PRO), and oxamyl (OXA) were selected as research objects to explore the combined toxicity of typical pesticides on Chlorella pyrenoidosa (C. pyrenoidosa) and their adverse outcome pathways (AOPs). The mixture systems of pesticides were designed by the direct equipartition ray (EquRay) method and uniform design ray (UD-Ray) method. The toxic effects of single pesticides and their mixtures were systematically investigated by the time-dependent microplate toxicity analysis (t-MTA) method. The interactions of their mixtures were analyzed by the concentration addition model (CA) and the deviation from the CA model (dCA). The toxicity data showed a good concentration-effect relationship; the toxicities of five pesticides were different and the order was CYA > DIQ > OXA > PRO > DIP. Binary, ternary and quaternary mixture systems exhibited antagonism, while quinary mixture systems exhibited an additive effect. The AOP of pesticides showed that an excessive accumulation of peroxide in green algae cells led to a decline in stress resistance, inhibition of the synthesis of chlorophyll and protein in algal cells, destruction of the cellular structure, and eventually led to algal cell death.

Preoperative Serum Cystatin C as an Independent Prognostic Factor for Survival in Patients with Renal Cell Carcinoma.

Purpose: This study aims to evaluate the prognostic significance of preoperative serum cystatin C (Cys-C) in patients with renal cell carcinoma (RCC). Methods: We analyzed clinicopathological data and follow-up information of 624 RCC patients who underwent partial or radical nephrectomy at our institution. The optimal cutoff value of Cys-C was determined using X-tile software. Survival outcomes, including overall survival (OS) and cancer-specific survival (CSS), were evaluated using the Kaplan-Meier method and log-rank test. To avoid overfitting and collinearity, we used LASSO-based multivariable Cox regression analysis to identify independent predictors of OS and CSS. The predictive accuracy of the established model, including preoperative serum Cys-C, was evaluated using the time-dependent receiver operating characteristic (ROC) curves and the area under the curve (AUC). Results: The median follow-up period was 40 months. The optimal cutoff value of preoperative serum Cys-C levels was 0.95 mg/L. Compared with the low Cys-C group, patients in the high Cys-C group had significantly shorter OS and CSS. Multivariable Cox regression analysis indicated that elevated preoperative serum Cys-C level was an independent adverse predictor for RCC patients post-nephrectomy. After adjusting for all covariates, high preoperative serum Cys-C level was associated with worse OS (hazard ratio [HR]: 2.254; 95% confidence interval [CI]: 1.144, 4.439; P = 0.019) and CSS (HR: 3.621; 95% CI: 1.386, 9.456; P = 0.009). Time-dependent ROC analysis demonstrated that our model, including preoperative serum Cys-C, performed well in predicting accuracy of survival. Conclusions: Preoperative serum Cys-C level is an effective prognostic indicator for OS and CSS in RCC patients undergoing nephrectomy.

A Synthesis-Component-Based GO-FLOW Modeling Paradigm for Automated Time-Dependent and Phased-Mission Reliability Model Generation and Analysis

A Synthesis-Component-Based GO-FLOW Modeling Paradigm for Automated Time-Dependent and Phased-Mission Reliability Model Generation and Analysis

A Nomogram-Based Prognostic Model for Lymphoma Patients Initially Presenting with Fever of Unknown Origin.

Patients with lymphoma who present with fever of unknown origin (FUO) as an initial symptom lack specific clinical feature analysis, prognostic factor analysis, and existing prognostic models. We aim to create a prognostic model for these patients to improve prognosis and risk assessment. A total of 555 lymphoma patients with FUO as initial symptom studied at Huadong Hospital affiliated with Fudan University. Univariable Cox regression identified outcome predictors, analyzed by LASSO Cox. Multifactorial Cox on screened coefficients determined independent prognostic factors and nomogram model. The validity of the nomogram was evaluated through bootstrap sampling, calibration curves for model calibration, time-dependent ROC curve analysis for discrimination assessment, and decision curve analysis for evaluating clinical usefulness. Further validation involved utilizing Kaplan-Meier curves and Log rank tests. Lastly, X-tile software determined the optimal cutoff point for the nomogram score by comparing it with the traditional International Prognostic Index (IPI) scoring system. The entire cohort was divided into a training cohort (n=388) and a validation cohort (n=167). These risk factors (cell pathologic type, performance status score, Ann Arbor staging, thrombocytopenia, and raised direct bilirubin) were used to construct a web-based dynamic survival rate calculator for lymphoma patients initially presenting with FUO. The lymphoma-specific nomogram demonstrated good consistency and efficacy in predicting the model's risk stratification. Compared to the IPI scoring system, the nomogram model had higher AUC values for different clinical endpoints. The new nomogram prognostic model showed better differentiation of risk groups compared to traditional IPI scoring. Our study developed and validated a prognostic nomogram for lymphoma patients initially presenting with FUO, demonstrating robust predictive efficacy and risk stratification ability. Furthermore, we have successfully implemented this model into a web-based dynamic survival rate calculator.

Magnetic Fields of the Starless Core L 1512

We present JCMT POL-2 850 μm dust polarization observations and Mimir H-band stellar polarization observations toward the starless core L 1512. We detect the highly ordered core-scale magnetic field traced by the POL-2 data, of which the field orientation is consistent with the parsec-scale magnetic fields traced by Planck data, suggesting the large-scale fields thread from the low-density region to the dense core region in this cloud. The surrounding magnetic field traced by the Mimir data shows a wider variation in the field orientation, suggesting there could be a transition of magnetic field morphology at the envelope-scale. L 1512 was suggested to be presumably older than 1.4 Myr in a previous study via time-dependent chemical analysis, hinting that the magnetic field could be strong enough to slow the collapse of L 1512. In this study, we use the Davis–Chandrasekhar–Fermi method to derive a plane-of-sky magnetic field strength (B pos) of 18 ± 7 μG and an observed mass-to-flux ratio (λ obs) of 3.5 ± 2.4, suggesting that L 1512 is magnetically supercritical. However, the absence of significant infall motion and the presence of an oscillating envelope are inconsistent with the magnetically supercritical condition. Using a virial analysis, we suggest the presence of a hitherto hidden line-of-sight magnetic field strength of ∼27 μG with a mass-to-flux ratio (λ tot) of ∼1.6, in which case both magnetic and kinetic pressures are important in supporting the L 1512 core. On the other hand, L 1512 may have just reached supercriticality and will collapse at any time.

On efficient time-dependent reliability analysis method through most probable point-oriented Kriging model combined with importance sampling

On efficient time-dependent reliability analysis method through most probable point-oriented Kriging model combined with importance sampling

Reassigning the role of a mesophilic xylan hydrolysing family GH43 β-xylosidase from Bacteroides ovatus, BoExXyl43A as exo-β-1,4-xylosidase

The recombinant 40 kDa BoExXyl43A glycoside hydrolase family 43 (GH43) from bacterium Bacteroides ovatus exhibited highest specific activity (U/mg) against corn cob xylan (136.8), followed by Beechwood xylan (81.1), Carbosynth xylan (69.3), 4-O-D-methylglucuronoxylan (61.4) and Birchwood xylan (59.9). BoExXyl43A demonstrated optimal performance at 37 °C and pH 7.6 with Vmax and Km of 141.8 U/mg and 4.0 mg/mL as well as 64.1 U/mg and 6.0 mg/mL against corn cob and Birchwood xylan, respectively. The activity of BoExXyl43A increased by 48 % by addition of 10 mM Ca2+ ions, while 1 mM EDTA or 1 mM EGTA decreased its activity by 100 % or 42.5 %, respectively, highlighting its calcium-ion dependence. Thin-layer chromatography (TLC) analysis of BoExXyl43A hydrolysates of Birchwood and Beechwood xylan as well as that of various xylooligosaccharides (DP2-DP9) from corn cob xylan showed the release of D-xylose, identifying it as an exo-β-1,4-xylosidase/exo-β-1,4-xylanase (EC 3.2.1.-/3.2.1.37). Moreover, the time-dependent TLC analysis of xylobiose hydrolysis showed release of D-xylose units, confirming its β-xylosidase activity. BoExXyl43A also exhibited exo-1,4-β-xylosidase activity on Larchwood and Carbosynth xylans. Notably, it released D-xylose from α-L-Araf2-xylotriose demonstrating its activity against decorated xylooligosaccharides. BoExXyl43A's exo-1,4-β-xylosidase and residual β-xylosidase activity on xylan and xylobiose, respectively, could potentially enhance xylan saccharification efficiency in bioethanol-based refineries. The molecular modeling showed that BoExXyl43A has 5-bladed β-propeller structure with a very shallow active-site having −1, +1 and + 2 subsites, which could accommodate three D-xylose units of longer xylan like xylododecaose thus supporting its exoxylosidase activity.

Screening of PDSS1 as a Potential Biomarker for Hepatocellular Carcinoma Based on a Copper-Related Prognostic Signature through Bulk and Single-cell RNA-sequencing Analysis.

Background: Currently, there is few literature comprehensively analyzing landscape of cuproptosis-related genes (CRGs) in liver hepatocellular carcinoma (LIHC) with multiple omics approaches. Aims: Using comprehensive analysis, we aim to find out how CRGs works on LIHC. Method: With data from The Cancer Genome Atlas (TCGA) database, we constructed a prognostic prediction model for CGRs using LASSO regression analysis and performed immune infiltration analysis using the same dataset. To validate findings, we utilized RNA expression data from the International Cancer Genome Consortium (ICGC). Furthermore, we analyzed the enrichment and features of CRGs in epithelial cells using single-cell RNA sequencing (scRNA-seq) data. To validate the reliability of findings, we performed several experiments including RT-PCR, cloning formation assay, scratch assay, and Transwell assay. Result: We have constructed a high-precision risk scoring model composed of CRGs for predicting prognosis in TCGA-LIHC. Reliability of the risk prognosis model was confirmed through Kaplan-Meier curve analysis, time-dependent ROC analysis, and multivariate regression analysis. Furthermore, we found knocking down PDSS1 increased sensitivity of LIHC cells to copper ions, and both proliferation and migration abilities were significantly reduced. Finally, we comprehensively characterized the features of CRGs in LIHC through scRNA-seq. Conclusion: In this study, we introduce PDSS1 as a novel CRG in HCC. Utilizing scRNA-seq, we provide a comprehensive landscape of cuproptosis across various cell subtypes within the HCC tumor microenvironment. Furthermore, we detailed the characteristics of high PDSS1-expressing tumor cells, including their distinctive transcription factors, metabolic profiles, and interactions with different subtypes within the tumor microenvironment. This work not only elucidated the role of PDSS1 in HCC but also enhanced our understanding of cuproptosis dynamics during tumor progression.

Synergizing ANSYS Simulations and Machine Learning for Transient Thermal Analysis in Aluminium Alloys

Time-dependent thermal analysis plays a pivotal role in the manufacturing industry as it greatly influences the overall performance of the final product. This study delves into transient thermal analysis of an aluminum alloy concerning both temperature and time. Employing ANSYS, a finite element-based software, an axisymmetric model is constructed. This model encompasses a mold made of sand and a pattern filled with aluminum alloy. The analysis focuses on temperatures ranging from 650 to 1050°C, examining the temperature changes in the mold and pattern after 1500 seconds, primarily due to the convection process. Parameters like heat flux and directional heat flux are also determined. Subsequently, machine learning models are utilized to interpret the data acquired from ANSYS, enabling the extension of the results to a broader temperature range of 1150 to 1550°C. This study is instrumental in facilitating the effective design of transient thermal analysis for various alloys at different temperatures.

Исследование особенностей медленной динамики и эффектов старения в трехпленочных наноструктурах с намагниченностью перпендикулярной плоскости плёнок

The results of a Monte Carlo study of trilayer nanostructure behavior with magnetization perpendicular to plane of films with thickness = 3 ML = 21 ML are given. At the calculated value of the critical temperature, as well as for temperatures &lt; investigation of nonequilibrium behavior of this structure is carried out with calculation and analysis of time dependence for magnetization, autocorrelation function and dynamic susceptibility with evolution from different initial states. Aging properties are revealed. The value of the asymptotical fluctuation ratio 𝑋∞(𝑁 = 3, 𝑐) = 0.329(3), 𝑋∞(𝑁 = 21, 𝑐) = 0.324(3),𝑋∞(𝑁 = 3, 0.5𝑇 𝑐) = 0.9208(24), 𝑋∞(𝑁 = 21, 0.5𝑇 𝑐) = 0.9528(21) is obtained with demonstration of violation for the fluctuation-dissipation theorem in nonequilibrium behavior both at the critical temperature and in the low-temperature phase of trilayer nanostructure.