Established Model Research Articles

A Determination Method for the Biomass Lower-Calorific-Value-Based Blending Ratio in Co-Firing Power Plants

The accurate measurement of the biomass lower-calorific-value-based blending ratio (BLBR) is the premise and key to scale development of the power generation technology from co-firing of biomass and coal. However, existing determination methods of BLBR are less accurate, which has impeded the scale utilization of the co-firing technology of biomass and coal. To accurately determine the BLBR, this research proposed a flue gas sampling system suitable for determining BLBR through 14C quantitative analysis and established a medium-scale experimental platform for BLBR determination. In addition, predictive models for the lower calorific values of coal and biomass fuels were built and the BLBR calculation model for co-firing power plants was deduced. Furthermore, experimental verification of the BLBR determination was also conducted. The results show that the lower calorific values of coal and biomass fuels are highly linearly positively correlated with the carbon content. The relative errors of the established prediction models for lower calorific values of biomass and coal are separately lower than 5% and 8%. The results indicate that the proposed BLBR determination method is accurate.

The pointing uncertainty Region's analysis of inter-satellite laser alignment systems under multiple uncertainties

Laser inter-satellite links (LISLs) are crucial for enabling rapid information exchange within low earth orbit (LEO) constellations. Previous research on laser link establishment often relied on the idealized probability density function (PDF) of the pointing uncertainty regions (PURs) when designing scan-and-capture methods, neglecting the impact of various practical uncertainties. To address the above issues, there is an urgent need to analyze the practical uncertainties to enhance the stability and reliability of LISL. This paper first provides a comprehensive introduction to the alignment process in LISL and mathematically formulates typical sources of uncertainty errors. Subsequently, a generalized system model (GSM) for laser link establishment was established at three stages: Orbit, Attitude, and Laser terminal. Finally, we integrate uncertainty models into the GSM and propose a PUR generating method to obtain precise PDFs. The research method proposed in this study can consider the influence of multiple uncertainties and generate the PDF for the PURs. Compared with traditional research, the PDF obtained by fitting the oscillation range considering dynamics is more detailed. By analyzing the PDF generated by typical uncertainty errors, it is observed that the orbit initial uncertainty may be the predominant factor influencing the PUR.

Electrical discharge reproduction in rod-barrier-plane system

The present paper deals with new modeling to reproduce the electric discharge in the rod-plane air gap system with rubber insulating barrier under AC and impulse voltage. This model considers the randomness character of discharge evolution which is governed by the electric field. The discharges shape obtained by this model are compared with ones given by experimental tests. The established model reproduces correctly the forms of discharges obtained by experimental tests under AC voltage. It is found that the behavior of the electrical discharge depends not only on the dimension (thickness and width) of the insulating barriers but on its positions in the air gap as well. It is to highlight that the mode of applied voltage is of key importance barrier. Experimental investigation shows that the developed arc can evolve on 1 to 4 channels. The generated discharges in AC voltage distinguish by the formation of a multiple-channel arc. Whereas, the discharge under lightning impulse voltage found to progress in a single channel whatever the barrier position and dimensions. The model confirms that electric field is the most important factor in the behavior of the rod-insulating barrier-plane system submitted to high voltage.

A theoretical model for roller-shape design of three-roller continuous and synchronous calibration process of ovality and straightness for large thin-walled pipes

Large thin-walled pipes are particularly suitable for oil and gas transport and auxiliary pipes in cold areas or deep sea-beds. At present, the rounding and straightening processes are completed independently, and the theoretical model for roller-shape design is analyzed only in a single direction. To solve this problem, a theoretical model for roller-shape design of three-roller continuous and synchronous adjusting straightness and ovality process for large thin-walled pipes is established. The established model is verified by numerical simulation and experimental research using 304 stainless steel pipes. The results show that the three roller-shape design schemes, including three-section, four-section and five-section, proposed based on the theoretical model, can obtain qualified formed pipes. Based on the model, the residual ovality, residual straightness and maximum residual stress of the three roller-shape schemes are discussed. The residual straightness can reach within 0.2%, and the residual ovality can reach within 1%. It verifies the applicability of the model and the feasibility of the process. The model can provide a theoretical basis for presetting the process parameters and optimizing the roller-shape.

Event-triggered adaptive vibration control for a flexible satellite with time-varying actuator faults

In this study, vibration control is studied for the flexible satellite through wireless communication, and given the fact that the actuators are prone to unknown faults, the input signal event-triggering and time-varying actuator faults are considered for the flexible satellite simultaneously. The satellite system is composed of two symmetrical flexible panels attached to a centrebody, which is a distributed parameter system and the established model is represented by partial differential equations (PDEs). Based on the PDEs model of the flexible satellite, the input event-triggered mechanism using the relative threshold strategy is designed firstly to lower the communication burden, and then the event-triggered-based adaptive fault-tolerant control laws are developed with the aid of the designed auxiliary signals for the system with time-varying actuator faults. With the proposed control scheme, the vibration of the system is controlled to the small neighborhood of zero, and the angle of the panels is regulated to the desired position. Finally, numerical simulation results are given to further show the effectiveness of the designed control scheme.

Empowering Graph Neural Networks with Block-Based Dual Adaptive Deep Adjustment for Drug Resistance-Related NcRNA Discovery.

Drug resistance to chemotherapeutic agents remains a formidable challenge in cancer treatment, significantly impacting treatment efficacy. Extensive research has exposed the intimate involvement of noncoding RNAs (ncRNAs) in conferring resistance to cancer drugs. Understanding the intricate associations between ncRNAs and drug resistance is of pivotal importance in advancing clinical interventions and expediting drug development. However, traditional biological experimental methods are hampered by limitations, such as labor intensiveness, time consumption, and constraints in scalability. Addressing these challenges necessitates the development of efficient computational methods for the accurate prediction of potential ncRNA-drug resistance associations (NDRA). However, most existing predictive models primarily focus on known ncRNA-drug resistance associations, often neglecting the critical aspect of similarity information between ncRNAs and drug resistance. This oversight may hinder the accuracy of characterizing these associations. To overcome the limitations of existing computational models, we proposed B-NDRA, a computational framework designed for the discovery of drug resistance-related ncRNA. Initially, we constructed a heterogeneous graph that integrates ncRNA-drug resistance pairs, leveraging both known associations and similarity fusion information between ncRNAs and drug resistance. Subsequently, we employed an attention mechanism to aggregate local features of graph nodes following a dimensionality reduction of node features. Further, a graph neural network (GNN) facilitated the learning of global node embeddings. Notably, the integration of dual adaptive deep adjustment architectures, encompassing intrablock and interblock methodologies, enabled efficient extraction of global features while balancing local and global features. Finally, B-NDRA employed a multilayer perceptron to predict associations between ncRNAs and drug resistance. Through rigorous 5-fold cross-validation, B-NDRA achieved average AUC, AUPR, Accuracy, Precision, Recall, and F1-score values of 92.2%, 91.9%, 84.88%, 86.9%, 82.37%, and 84.44%, respectively. Furthermore, comparative evaluations were conducted on established models, namely, GAEMDA, GRPAMDA, and LRGCPND. The results, obtained through three distinct 5-fold cross-validation strategies, demonstrated a notable performance improvement across almost all metrics for our B-NDRA. Specific case studies targeting Doxorubicin and Imatinib further validated the practicality of our B-NDRA in discovering potential NDRA. These results confirm the potential of our B-NDRA as a valuable tool in advancing cancer research and therapeutic development. The source code and data set of B-NDRA can be found at https://github.com/XuanLi1145/B-NDRA.

Coupled DEM-FDM numerical model for EPB shield tunnelling simulation with foam conditioning

It is widely recognized that an appropriate application of soil conditioning significantly improves the tunnelling performance of earth pressure balance (EPB) shield tunnel boring machines (TBMs). This study presents a numerical approach that simulates an EPB shield tunnelling, considering the injection of foam into the soil. To enhance computational efficiency and reduce the number of particles simulated in the ground formation, the numerical study coupled the discrete element method (DEM) and the finite difference method (FDM). The properties of in-situ soil and foam-conditioned soil were incorporated into the DEM domain by determining the contact parameters of particles using two calibration models: the triaxial compression test and the pressurized vane shear test models. With the established model, tunnelling analyses were conducted under specific TBM operation conditions, such as the penetration rate, and rotational speed of the cutter head and the screw conveyor. The effect of foam injection on EPB shield TBM operation was evaluated by adjusting the contact parameters of DEM elements. The numerical simulations revealed that foam conditioning reduced the torque, thrust force, and forces on cutting tools. These simulations provided valuable insights into the applicability of the numerical approach in assessing foam conditioning for EPB shield TBMs.

PalScDiff: A diffusion-based framework with progressive augmentation learning and semantic consistency for breast ultrasound tumor segmentation

Background: Breast cancer diagnosis relies on accurate lesion segmentation in medical images. Automated computer-aided diagnosis reduces clinician workload and improves efficiency, but existing image segmentation methods face challenges in model performance and generalization. Objective: This study aims to develop a generative framework using a denoising diffusion model for efficient and accurate breast cancer lesion segmentation in medical images. Methods: We design a novel generative framework, PalScDiff, that leverages a denoising diffusion probabilistic model to reconstruct the label distribution for medical images, thereby enabling the sampling of diverse, plausible segmentation outcomes. Specifically, with the condition of the corresponding image, PalScDiff learns to estimate the masses region probability through denoising step by step. Furthermore, we design a Progressive Augmentation Learning strategy to incrementally handle segmentation challenges of irregular and blurred tumors. Moreover, multi-round sampling is employed to achieve robust breast mass segmentation. Results: Our experimental results show that PalScDiff outperforms established models such as U-Net and transformer-based alternatives, achieving an accuracy of 95.15%, precision of 79.74%, Dice coefficient of 77.61%, and Intersection over Union (IOU) of 81.51% . Conclusion: The proposed model demonstrates promising capabilities for accurate and efficient computer-aided segmentation of breast cancer.

Abstract 5002: Next-generation mRNA-LNP vaccine prototype achieves tumor clearance in a GBM mouse model

Abstract Glioblastoma (GBM) is the most prevalent and aggressive primary brain tumor type with an abysmal prognosis, featuring an immunologically dampened tumor microenvironment and limited neoantigen production. Novel therapeutic strategies are urgently needed. A well-characterized GBM-associated mutation that can elicit an anti-tumor immune response is EGFRvIII mutation. The cancer-driver mutation is detected in approximately 30% of patients at the time of diagnosis and plays a pivotal role in the emergence of GBM. EGFRvIII is an in-frame epidermal growth factor receptor (EGFR) deletion generating a constitutively active oncogenic protein. Its highly immunogenic nature represents an ideal focal point for cutting-edge mRNA-LNP candidate therapeutics. To evaluate proprietary lipid nanoparticle (LNP) compositions for their capacity to induce anti-GBM immunity, we developed a murine transplantable GBM tumor model by neonatal electroporation. The somatically engineered glioblastomas harbor two targeted CRISPR knockouts and stably overexpress EGFRvIII to yield transplantable cell lines for orthotopic tumor initiation. One week after intracranial tumor cell implantation of 6-8 week-old female mice (day 0; n=10), half the animals were administered either buffer (control cohort) or our novel anti-EGFRvIII mRNA-LNP vaccine (experimental cohort). The regimen involved four intramuscular injections before monitoring tumor progression by MRI on day 30. The imaging revealed a high tumor burden in all control group animals, compared to no detectable tumor cells in vaccine-protected mice. All control group animals were humanely euthanized by day 38 due to symptoms associated with the growing brain tumor mass. The asymptomatic experimental animals received a fifth anti-EGFRvIII vaccination before MRI-based examinations on day 43 and day 78 confirmed that no tumor cells could be detected in vaccine-protected mice. The results highlight the potency and potential of our proprietary anti-EGFRvIII mRNA-LNP vaccine as a novel anticancer therapeutic. In addition, the established tumor model represents a valuable tool to test future mRNA-LNP designs. Outstanding cellular and molecular investigations will reveal vaccine-mediated mechanistic insights, allowing for the co-advancement of the tumor model and next-generation multi-target mRNA-LNP vaccine candidates. Citation Format: Robert Nechanitzky, Shannon Snelling Snelling, Kristofor Ellestad, Xueqing Lun, Kirstin Olsen, Yu Wu, Yury Karpov, Jun Liu, Matthew Gold, Haritha Menon, Rajesh Krishnan, Robert Georgantas, Pamela S. Ohashi, Douglas J. Mahoney, Jennifer A. Chan, Natalia Martin-Orozco. Next-generation mRNA-LNP vaccine prototype achieves tumor clearance in a GBM mouse model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5002.

Self-organization of PIP3 waves is controlled by the topology and curvature of cell membranes

Phosphatidylinositol (3,4,5)-trisphosphate (PIP3) is a signaling lipid on the plasma membrane that plays a fundamental role in cell signaling with a strong impact on cell physiology and diseases. It is responsible for the protruding edge formation, cell polarization, macropinocytosis, and other membrane remodeling dynamics in cells. It has been shown that the membrane confinement and curvature affects the wave formation of PIP3 and F-actin. But, even in the absence of F-actin, a complex self-organization of the spatiotemporal PIP3 waves is observed. In recent findings, we have shown that these waves can be guided and pinned on strongly bended Dictyostelium membranes caused by molecular crowding and curvature-limited diffusion. Based on these experimental findings, we investigate the spatiotemporal PIP3 wave dynamics on realistic three-dimensional cell-like membranes to explore the effect of curvature-limited diffusion, as observed experimentally. We use an established stochastic reaction-diffusion model with enzymatic Michaelis-Menten-type reactions that mimics the dynamics of Dictyostelium cells. As these cells mimic the three-dimensional shape and size observed experimentally, we found that the PIP3 wave directionality can be explained by a Hopf-like and a reverse periodic-doubling bifurcation for uniform diffusion and curvature-limited diffusion properties. Finally, we compare the results with recent experimental findings and discuss the discrepancy between the biological and numerical results.

Multiaxial fatigue life prediction using an improved Smith‐Watson‐Topper model

AbstractIn this paper, the SWT model was improved by incorporating the Walker equation into the strain–life curve. The established model takes into account the material's sensitivity to mean stress by introducing the Walker exponent, w. Under uniaxial loading condition, the proposed model can reduce to the SWT model, Manson‐Coffin equation, and Walker model for w values of 0.5, 1, and 0, respectively. Under multiaxial symmetric loading, when w = 0.5, the proposed model can be simplified as another SWT correction model (CXH) proposed by Chen et al. The prediction accuracy of the established model was validated using about 200 data points collected from literature. These data points were obtained from tests conducted on eight different kinds of metals under various multiaxial loading paths. The verification results indicate that 96.8% and 97.9% of the data points fall within the factor‐of‐three boundary for the loading paths without and with mean stresses, respectively.

Melatonin facts: Melatonin lacks immuno-inflammation boosting capacities at the molecular and cellular levels

Among the properties melatonin is claimed to possess, are the immuno-inflammation inductive capacities that would be responsible of some of the paramount of activities melatonin is reported to have in most of the human pathological conditions. In the present paper, we measured the effect of melatonin on established cellular models of immuno-inflammation, and found none. The discrepancies are discussed, especially because those properties are reported at pharmacological concentration (1 μM and beyond) at which the melatonin receptors are desensitized by internalization, leading to putative non-receptor-dependent mechanism of action.

Development and internal validation of a prediction model for patients with hematologic diseases of fall risk: a cohort study

ABSTRACT Background To develop and internally validate a prediction model for identifying patients with hematologic diseases of fall risk. Research design and methods This is a prospective cohort study from a prospective collection of data for 6 months. We recruited 412 patients with hematologic diseases in medical institutions and home environment of China. The outcome of the prediction model was fall or not. These variables were filtered via univariable logistic analysis, LASSO, and multivariable logistic analysis. We adopt an internal validation method of K-fold cross validation. The area under the ROC curve and the H-L test were used to evaluate the discrimination and calibration of the model. Results Five influencing factors were identified multivariable logistic regression analysis. The established model equation is as follows: the H-L goodness-of-fit test of the model p > 0.05. The area under the ROC curve of train is 0.957 (95% CI: 0.936 ~ 0.978), and the area under the ROC curve of test is 0.962 (95% CI: 0.884 ~ 1), so the model calibration and discriminant validity are good. Conclusion Our equation has good sensitivity and specificity in predicting the fall risk of patients with hematologic diseases, and has certain positive significance for clinical assessment of their fall risk. Trial registration number ChiCTR2200063940

DO PEOPLE SAVE ENOUGH FOR TOMORROW? THE COMPARATIVE STUDY ON WESTERN BALKANS

Due to the increase in life expectancy and the decline in the birth rate, which has an impact on the increase in the proportion of the older population, there is a need to develop a rational pension strategy. This is a behavioral economic analysis of voluntary pension funds in the Western Balkans. The main goal of the research is to determine whether fund users behave according to the well-known Richard Thaler model or have a specific pattern of behavior. According to Thaler’s model, people make financial decisions with limited rationality. They are impatient and short-sighted, which is why they focus on the present rather than the future. Younger employees mainly opt for actions that give them immediate gratification rather than planning for the future. More money flows into pension funds when people are approaching retirement, not when they are in their best working and financial position. Another peculiarity of the Western Balkans is that they have gone through a long period of transition, a period of instability, and that long-term savings were not on the agenda for the average family. The paper is based on a hypothesis that should determine whether users of voluntary pension funds in Western Balkans countries (Serbia, North Macedonia, Montenegro) increase a portion of their salary savings as they approach the retirement period. Analysing the content of the statistical reports collected in the pension funds was used as a method of data collection. A regression analysis was performed to examine the established model. Model creation and calculations were performed in IBM Amos 25 and SPSS 25. A significance level was set to α=0.05 (Hair et al., 2019). The results for Serbia, showed significant values for all three R coefficients. R square values were taken into account. Based on the results, the predictor variable (number of users by age) has significantly explained the variability of “amounts of accumulated funds by age” in all three countries of the Western Balkans. The fund users in all three countries behave similarly, paying significantly higher amounts into the fund after the age of forty. Beneficiaries aged between 20 and 39, members of a very productive labour cohort, pay lower, often symbolic amounts into the pension funds. We are talking about relatively young funds in their infancy, which are unstable or insufficiently stable. A more stable and efficient structure can be observed in the case of Serbian voluntary pension funds.

Riboflavin Attenuates Fluoride-Induced Testicular Injury via Interleukin 17A-Mediated Classical Pyroptosis.

Male reproductive toxicity of fluoride is of great concern worldwide, yet the underlying mechanism is unclear. Pyroptosis is a novel mode of inflammatory cell death, and riboflavin with anti-inflammatory properties has the potential to protect against fluoride damage. However, it is unknown whether pyroptosis is involved in fluoride-induced testicular injury and riboflavin intervention. Here, we first found that riboflavin could alleviate fluoride-caused lower sperm quality and damaged testicular morphology by reducing pyroptosis based on a model of ICR mice treated with NaF (100 mg/L) and/or riboflavin supplementation (40 mg/L) via drinking water for 13 weeks. And then, together with the results of in vitro Leydig cell modelsm it was confirmed that the pyroptosis occurs predominantly through classical NLRP3/Caspase-1/GSDMD pathway. Furthermore, our results reveal that interleukin-17A mediates the process of pyroptosis in testes induced by fluoride and riboflavin attenuation according to the results of our established models of riboflavin- and/or fluoride-treated IL-17A knockout mice. The results not only declare a new mechanism by which fluoride induces testicular injury via interleukin 17A-mediated classical pyroptosis but also provide evidence for the potential clinical application of riboflavin as an effective therapy for fluoride toxicity.

Impaired neutrophil extracellular trap-forming capacity contributes to susceptibility to chronic vaginitis in a mouse model of vulvovaginal candidiasis.

Vulvovaginal candidiasis (VVC), caused by Candida albicans, is characterized by aberrant inflammation by polymorphonuclear neutrophils (PMNs) in the vaginal lumen. Data from the established murine model shows that despite potent antifungal properties, PMNs fail to clear C. albicans due to local heparan sulfate that inhibits the interaction between PMNs and C. albicans, resulting in chronic vaginal immunopathology. To understand the role of neutrophil extracellular traps (NETs) in defense against C. albicans at the vaginal mucosa, we investigated the NET-forming capacity of PMNs in chronic VVC-susceptible (CVVC-S/C3H) and -resistant (CVVC-R/CD-1) mouse strains. Immunofluorescence revealed the formation of NETs (release of DNA with PMN-derived antimicrobial proteins) in PMN-C. albicans cocultures using vaginal conditioned medium (VCM) generated from CVVC-R/CD-1 mice, similar to NET-inducing positive controls. Under these NETotic conditions, PMNs released high levels of double-stranded DNA bound with NET-associated proteins, concomitant with substantial C. albicans killing activity. In contrast, PMN-C. albicans cocultures in VCM from CVVC-S/C3H mice lacked NET formation together with reduced antifungal activity. Similar results were observed in vivo: active NET-C. albicans interaction followed by fungal clearance in inoculated CVVC-R/CD-1 mice, and sustained high vaginal fungal burden and no evidence of NETs in inoculated CVVC-S/C3H mice. Furthermore, the level of Ki67 expression, a putative NETotic PMN marker, was significantly reduced in vaginal lavage fluid from CVVC-S/C3H mice compared to CVVC-R/CD-1 mice. Finally, scanning electron microscopy revealed that PMNs in CVVC-R, but not CVVC-S, conditions exhibited NETs in direct contact with C. albicans hyphae in vitro and in vivo. These results suggest that VVC-associated immunopathology includes impaired NET-mediated antifungal activity.

Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

This study provides insights into the challenges involved in predicting the Reid vapor pressure (RVP) of gasoline-oxygenate blends (GOB), which is an important indicator of fuel quality and compliance with environmental and performance standards. Given the enormous variety of gasoline compositions and ratios available, there is a significant demand for a fast, straightforward, and cost-effective technique to predict RVP without relying on costly instruments or complicated spectral measurements that involve numerous input variables. A comparative performance analysis has been performed for different regression modelling strategies for predicting RVP in GOB, which is valuable for researchers and practitioners in the petroleum industry for saving time and money. Parametric and nonparametric approaches were compared using partial least squares regression (PLSR), nonlinear regression (NLR), and nonparametric regression (NPR) models. Locally weighted scatterplot smoothing (LOWESS) approach was applied to the NPR model. The gasoline’s physical characteristics (distillation curves and density) formed the basis for the analysis of these models’ performances. Acceptable error metrics have been reached for root mean square error of calibration and prediction (RMSEC and RMSEP) values, for the PLSR, NLR, and NPR models, which are 4.790, 6.235, 4.739, 6.149, 3.968, and 6.029, respectively, which are close for those reported in literature. The NPR model eliminates parametric constraints and allows for a different kind of data structure to emerge. The established models here demonstrate a sound ability to overcome barriers by omitting the use of inconvenient spectral measurements to save expense and simplify data calibration, making them a promising approach for RVP detection of GOB. This finding aids in the development of more accurate RVP prediction models and contributes to the optimization of fuel formulations.

The Dysregulated IL-23/TH17 Axis in Endometriosis Pathophysiology.

Endometriosis is a chronic inflammatory disease in which endometrial-like tissue grows ectopically, resulting in pelvic pain and infertility. IL-23 is a key contributor in the development and differentiation of TH17 cells, driving TH17 cells toward a pathogenic profile. In a variety of inflammatory and autoimmune disorders, TH17 cells secrete proinflammatory cytokines, including IL-17, contributing to disease pathophysiology. Our studies and others have implicated IL-17 and TH17 cell dysregulation in endometriosis, which is associated with disease severity. In this article, we address whether IL-23-driven TH17 cells contribute to cardinal features of lesion proliferation, vascularization, and inflammation in endometriosis using patient samples, representative cell lines, and our established mouse model of endometriosis. The results indicated dysregulated expression of key genes in the IL-23/TH17 axis in patient ectopic and eutopic endometrial samples and increased IL-23 protein in patient plasma compared with controls. In vitro studies using primary human TH cells determined that rIL-23 mixture treatment increased pathogenic TH17 cell frequency. Similarly, rIL-23 treatment of cell lines (12Z cells, EECCs, HUVECs, and hESCs) representative of the endometriotic lesion microenvironment increased cytokines and growth factors, which play a role in lesion establishment and maintenance. In a syngeneic mouse model of endometriosis, rIL-23 treatment altered numbers of myeloid and T cell subsets in peritoneal fluid and increased giant cells within the lesion. Lesions from rIL-23-treated mice did not reveal significant alterations in proliferation/vascularization, although trends of increased proliferation and vascularization were observed. Collectively, these findings provide insights into the impact of the IL-23/TH17 axis on local immune dysfunction and broadly on endometriosis pathophysiology.

A novel machine learning-derived four-gene signature predicts STEMI and post-STEMI heart failure.

High mortality and morbidity rates associated with ST-elevation myocardial infarction (STEMI) and post-STEMI heart failure (HF) necessitate proper risk stratification for coronary artery disease (CAD). A prediction model that combines specificity and convenience is highly required. This study aimed to design a monocyte-based gene assay for predicting STEMI and post-STEMI HF. A total of 1,956 monocyte expression profiles and corresponding clinical data were integrated from multiple sources. Meta-results were obtained through the weighted gene co-expression network analysis (WGCNA) and differential analysis to identify characteristic genes for STEMI. Machine learning models based on the decision tree (DT), support vector machine (SVM), and random forest (RF) algorithms were trained and validated. Five genes overlapped and were subjected to the model proposal. The discriminative performance of the DT model outperformed the other two methods. The established four-gene panel (HLA-J, CFP, STX11, and NFYC) could discriminate STEMI and HF with an area under the curve (AUC) of 0.86 or above. In the gene set enrichment analysis (GSEA), several cardiac pathogenesis pathways and cardiovascular disorder signatures showed statistically significant, concordant differences between subjects with high and low expression levels of the four-gene panel, affirming the validity of the established model. In conclusion, we have developed and validated a model that offers the hope for accurately predicting the risk of STEMI and HF, leading to optimal risk stratification and personalized management of CAD, thereby improving individual outcomes.

Modeling and improvement for the thermal stability of ring-shaped workpieces with shape boundary constraints

Ring-shaped workpieces are commonly utilized in high-precision measuring instruments, and their thermal deformation affects the instruments’ measuring accuracy. A novel method for establishing the thermal deformation model of ring-shaped workpieces is proposed in this article. The mechanism of thermal deformation caused by shape boundaries is investigated using the principle of molecular dynamics. A mathematical model between deformation degree and height, diameter ratio, or temperature is created using a large-scale atomic/molecular massively parallel simulator. The established model is verified by measuring the thermal deformation of ring-shaped workpieces. The model is used to optimize laser collimation systems, and stability experiments for laser collimation systems in different sizes are performed. The stability of optimized systems can be improved by 50%, 50%, and 48% with temperature increases of 10 °C, 20 °C, and 30 °C, respectively. The experimental results indicate the obtained model can be utilized to improve the stability of instruments.