Analyzing Comorbidity Patterns in Patients With Thyroid Disease Using Large-Scale Electronic Medical Records: Network-Based Retrospective Observational Study.

BACKGROUND: Thyroid disease (TD) is a prominent endocrine disorder that raises global health concerns; however, its comorbidity patterns remain unclear. OBJECTIVE: This study aims to apply a network-based method to comprehensively analyze the comorbidity patterns of TD using large-scale real-world health data. METHODS: In this retrospective observational study, we extracted the comorbidities of adult patients with TD from both private and public data sets. All comorbidities were identified using ICD-10 (International Classification of Diseases, 10th Revision) codes at the 3-digit level, and those with a prevalence greater than 2% were analyzed. Patients were categorized into several subgroups based on sex, age, and disease type. A phenotypic comorbidity network (PCN) was constructed, where comorbidities served as nodes and their significant correlations were represented as edges, encompassing all patients with TD and various subgroups. The associations and differences in comorbidities within the PCN of each subgroup were analyzed and compared. The PageRank algorithm was used to identify key comorbidities. RESULTS: The final cohorts included 18,311 and 50,242 patients with TD in the private and public data sets, respectively. Patients with TD demonstrated complex comorbidity patterns, with coexistence relationships differing by sex, age, and type of TD. The number of comorbidities increased with age. The most prevalent TDs were nontoxic goiter, hypothyroidism, hyperthyroidism, and thyroid cancer, while hypertension, diabetes, and lipoprotein metabolism disorders had the highest prevalence and PageRank values among comorbidities. Males and patients with benign TD exhibited a greater number of comorbidities, increased disease diversity, and stronger comorbidity associations compared with females and patients with thyroid cancer. CONCLUSIONS: Patients with TD exhibited complex comorbidity patterns, particularly with cardiocerebrovascular diseases and diabetes. The associations among comorbidities varied across different TD subgroups. This study aims to enhance the understanding of comorbidity patterns in patients with TD and improve the integrated management of these individuals.

Interleukin signaling in the regulation of natural killer cells biology in breast cancer.

In the field of breast cancer treatment, the immunotherapy involving natural killer (NK) cells is increasingly highlighting its distinct potential and significance. Members of the interleukin (IL) family play pivotal regulatory roles in the growth, differentiation, survival, and apoptosis of NK cells, and are central to their anti-tumor activity. These cytokines enhance the ability of NK cells to recognize and eliminate tumor cells by binding to specific receptors and activating downstream signaling pathways. Furthermore, interleukins do not function in isolation; the synergistic or antagonistic interactions between different interleukins can drive NK cells toward various functional pathways, ultimately leading to diverse outcomes for breast cancer patients. This paper reviews the intricate relationship between NK cells and interleukins, particularly within the breast cancer tumor microenvironment. Additionally, we summarize the latest clinical studies and advancements in NK cell therapy for breast cancer, along with the potential applications of interleukin signaling in these therapies. In conclusion, this article underscores the critical role of NK cells and interleukin signaling in breast cancer treatment, providing valuable insights and a significant reference for future research and clinical practice.

Effect of D-limonene on volatile fatty acidsproduction from anaerobic fermentation of waste activated sludge under pH regulation: Performance and mechanisms.

D-limonene extracted from citrus peels possesses an inhibitory effect on methanogenic archaea. This study is aimed to bridge the research gap on the influence of D-limonene on volatile fatty acids (VFA) production from waste activated sludge (WAS) and to address the low VFA yield in standalone anaerobic fermentation of WAS. When the initial pH was not controlled, 1.00 g/g TSS D-limonene resulted in a VFA accumulation of 1175.45 ± 101.36 mg/L (174.45 ± 8.13 mgCOD/gVS). When the initial pH was controlled at 10 and the D-limonene concentration was 0.50 g/g TSS, the VFA accumulation reached 2707.44 ± 183.65 mg/L (445.51 ± 17.10 mgCOD/gVS). The pH-regulated D-limonene treatment enhanced solubilization and acidification, slightly inhibited hydrolysis, and significantly suppressed methanogenesis. D-limonene under alkaline conditions can increase the relative abundance of Clostridium_sensu_stricto, significantly enhancing acidification. Moreover, it markedly inhibited methanogenesis by particularly reducing the relative abundance of Methanothrix that was responsible for acetate consumption, thus favoring the accumulation of VFA. The research reveals the potential mechanism of pH regulation and D-limonene on anaerobic fermentation acid production, providing a theoretical basis for improving the acid production performance of the anaerobic fermentation of WAS.

Droplet Impedance Feedback-Enabled Microsampling Microfluidic Device for Precise Chemical Information Monitoring.

Microelectrodes have transformed our understanding of spatiotemporal responses to electrical stimulation. However, biological signals are often molecular, complicating the capture of intricate chemical signals. The microfluidic chip developed in this paper accurately measures droplet volume by using impedance analysis. The utilization of droplet volume as a feedback signal for precise microsampling pressure control ensures that microsampling remains unaffected by droplet volume influence. Once the microsampling is complete, chemiluminescence detection enables high temporal resolution and continuous and sensitive monitoring of chemical information within the droplets. Experimental verification shows that the chip can avoid volume influence through impedance feedback, achieving consistent and stable microampling at the nanoliter level (0-3 nL). In just 0.3 s, it can perform sensitive chemiluminescence detection of H2O2 and glucose within droplets. The linear detection ranges for these analytes are 10-50,000 and 20-600 µM, respectively, with the limit of detection being 0.648 and 0.334 µM. The significance of this chip lies in its ability to reveal changes in both electrical and chemical signals during transient biological processes. Its potential applications are numerous, encompassing a wide range of emerging areas such as single-cell analysis, cell communication, and cellular immunity.

An AI Agent for Fully Automated Multi-Omic Analyses.

With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle bioinformatics analysis continues to grow. In response to this need, Automated Bioinformatics Analysis (AutoBA) is introduced, an autonomous AI agent designed explicitly for fully automated multi-omic analyses based on large language models (LLMs). AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. AutoBA's unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA offers multiple LLM backends, with options for both online and local usage, prioritizing data security and user privacy. In comparison to ChatGPT and open-source LLMs, an automated code repair (ACR) mechanism in AutoBA is designed to improve its stability in automated end-to-end bioinformatics analysis tasks. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents an advanced and convenient tool, offering robustness and adaptability for conventional multi-omic analyses.

Loss of Carbamoyl Phosphate Synthetase 1 Potentiates Hepatocellular Carcinoma Metastasis by Reducing Aspartate Level.

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide. Numerous studies have shown that metabolic reprogramming is crucial for the development of HCC. Carbamoyl phosphate synthase 1 (CPS1), a rate-limiting enzyme in urea cycle, is an abundant protein in normal hepatocytes, however, lacking systemic research in HCC. It is found that CPS1 is low-expressed in HCC tissues and circulating tumor cells, negatively correlated with HCC stage and prognosis. Further study reveals that CPS1 is a double-edged sword. On the one hand, it inhibits the activity of phosphatidylcholine-specific phospholipase C to block the biosynthesis of diacylglycerol (DAG), leading to the downregulation of the DAG/protein kinase C pathway to inhibit invasion and metastasis of cancer cells. On the other hand, CPS1 promotes cell proliferation by increasing intracellular S-adenosylmethionin to enhance the m6A modification of solute carrier family 1 member 3 mRNA, a key transporter for aspartate intake. Finally, CPS1 overexpressing adeno-associated virus can dampen HCC progression. Collectively, this results uncovered that CPS1 is a switch between HCC proliferation and metastasis by increasing intracellular aspartate level.

Comprehensive transcriptome and scRNA-seq analyses uncover the expression and underlying mechanism of SYNJ2 in papillary thyroid carcinoma.

Synaptojanin 2 (SYNJ2) has crucial role in various tumors, but its role in papillary thyroid carcinoma (PTC) remains unexplored. This study first detected SYNJ2 protein expression in PTC using immunohistochemistry method and further assessed SYNJ2 mRNA expression through mRNA chip and RNA sequencing data and its association with clinical characteristics. Additionally, KEGG, GSVA, and GSEA analyses were conducted to investigate potential biological functions, while single-cell RNA sequencing data were used to explore SYNJ2's underlying mechanisms in PTC. Meanwhile, immune infiltration status in different SYNJ2 expression groups were analyzed. Besides, we investigated the immune checkpoint gene expression and implemented drug sensitivity analysis. Results indicated that SYNJ2 is highly expressed in PTC (SMD = 0.66 [95% CI: 0.17-1.15]) and could distinguish between PTC and non-PTC tissues (AUC = 0.74 [0.70-0.78]). Furthermore, the study identified 134 intersecting genes of DEGs and CEGs, mainly enriched in the angiogenesis and epithelial-mesenchymal transition (EMT) pathways. Subsequent analysis showed the above pathways were activated in PTC epithelial cells. PTC patients with high SYNJ2 expression showed higher sensitivity to the six common drugs. Summarily, SYNJ2 may promote PTC progression through angiogenesis and EMT pathways. High SYNJ2 expression is associated with better response to immunotherapy and chemotherapy.

Biomechanics on Ultra-Sensitivity of Venus Flytrap's Micronewton Trigger Hairs.

Numerous plants evolve ingeniously microcantilever-based hairs to ultra-sensitively detect out-of-plane quasi-static tactile loads, providing a natural blueprint for upgrading the industrial static mode microcantilever sensors, but how do the biological sensory hairs work mechanically? Here, the action potential-producing trigger hairs of carnivorous Venus flytraps (Dionaea muscipula) are investigated in detail from biomechanical perspective. Under tiny mechanical stimulation, the deformable trigger hair, composed of distal stiff lever and proximal flexible podium, will lead to rapid trap closure and prey capture. The multiple features determining the sensitivity such as conical morphology, multi-scale functional structures, kidney-shaped sensory cells, and combined deformation under tiny mechanical stimulation are comprehensively researched. Based on materials mechanics, finite element simulation, and bio-inspired original artificial sensors, it is verified that the omnidirectional ultra-sensitivity of trigger hair is attributed to the stiff-flexible coupling of material, the double stress concentration, the circular distribution of sensory cells, and the positive local buckling. Also, the balance strategy of slender hair between sensitivity and structural stability (i.e., avoiding disastrous collapse) is detailed revealed. The unique basic biomechanical mechanism underlying trigger hairs is essential for significantly enhancing the performance of the traditional industrial static mode microcantilever sensors, and ensure the stability of arbitrary load perception.

Clinical characteristics and risk factors for acute abdomen in patients with abdominal lymphatic malformations.

OBJECTIVE: The diagnosis of abdominal lymphatic malformations (ALMs) is often overlooked in clinical practice. However, reports in the literature about ALMs are limited to case reports/series with small sample sizes. This study aimed to review our currently available data to describe the clinical characteristics of ALMs and evaluate the risk factors for acute abdomen caused by ALMs. METHODS: We reviewed the records of patients with ALMs who were diagnosed between December 2008 and January 2023 in our institution. The associations between acute abdomen and ALMs were analyzed based on single-factor and multivariate logistic regression analyses. RESULTS: This study included 345 patients with pathologically confirmed ALMs, with a slight female predominance of 1:1.4. Approximately 39.1% (135/345) of patients were asymptomatic, and 24.6% (85/345) presented with acute abdomen. Among the ALMs in the cohort, 42.6% (147/345) were retroperitoneal lymphatic malformations (LMs). The maximal lesion dimensions in patients with acute abdomen and nonacute abdomen were 10.0 cm and 7.8 cm, respectively, with no significant difference based on multivariate analyses. Children were more likely to develop acute abdomen than adults were (P=0.002; odds ratio [OR], 5.128; 95% confidence interval [CI], 1.835-14.326). ALMs accompanying acute abdomen were more common for lesions involving the small intestinal mesentery (P=0.023; OR, 2.926; 95% CI, 1.157-7.400). CONCLUSION: ALMs are rare with insidious onset, and retroperitoneal LMs are the most common ALMs, followed by jejunal MLMs. Our retrospective analysis suggested that young age and small intestinal mesenteric lymphatic malformation are independent risk factors for acute abdomen with ALMs.

Causal effects between personality and psychiatric traits and lung cancer: a bidirectional two-sample Mendelian randomization and bibliometric study.

Introduction: The causality between personality and psychiatric traits and lung cancer (LC) remains unclear. Therefore, we aimed to elucidate the causality between these traits and LC. Methods: Bidirectional two-sample Mendelian randomization (MR) and bibliometric approaches were conducted to estimate the causality between personality (neuroticism, extraversion, agreeableness, conscientiousness, and openness) and psychiatric (schizophrenia, attention-deficit/hyperactivity disorder [ADHD], major depressive disorder, autism spectrum disorder, bipolar disorder, insomnia, and anxiety) traits and LC and its subtypes (lung squamous cell carcinoma, lung adenocarcinoma, and small cell LC). Summary data of these traits were extracted from large datasets (17,375-462,341 participants). Inverse variance weighting was used as the primary MR analysis, with supplementary models, including MR-Egger and weighted medians. Sensitivity analyses were conducted to detect pleiotropy. Bibliometric data were retrieved from the Web of Science Core Collection, Scopus, and PubMed. The main mapping techniques adopted were co-word, collaboration, and citation analyses. Results: Schizophrenia was associated with an increased risk of LC (odds ratio [OR] = 1.077, 95% confidence interval [CI] = 1.030-1.126, P = 0.001). Moreover, LC increased the risk of ADHD (OR = 1.221, 95% CI = 1.096-1.362, P < 0.001). No significant bidirectional associations were observed between other mental traits and LC and its subtypes. Causality, psychiatry, and psychiatric comorbidity are emerging keywords. Research dynamics and landscapes were revealed. Conclusion: This study suggests that schizophrenia is a risk factor for LC and that LC is a risk factor for ADHD. Furthermore, causality, psychiatry, and psychiatric comorbidity have become emerging research trends in related fields.

A mannose-rich exopolysaccharide-1 isolated from Bifidobacterium breve mitigates ovalbumin-induced intestinal damage in mice by modulation CD4 + T cell differentiation and inhibiting NF-κB signaling pathway.

Ovalbumin (OVA)-induced intestinal injury is a recurrent and potentially fatal condition. Previous studies have highlighted the roles of exopolysaccharides, particularly a mannose-rich (89.59 %) exopolysaccharide-1 (EPS-1) with a molecular weight of 39.9 kDa, isolated from Bifidobacterium breve H4-2, in repairing intestinal barriers and regulating immune responses. In this study, a mouse model of OVA-induced intestinal injury was used to investigate the effects of EPS-1 on intestinal barrier restoration. The results demonstrated that EPS-1 treatment (400 mg/kg. d) significantly reduced the allergic index (3.25 ± 0.43) in OVA-challenged mice (p < 0.05), improved the physical integrity of the intestinal barrier by increasing mucin content and goblet cell number in the ileum (p < 0.05). EPS-1 treatment (400 mg/kg. d) also maintained immune barrier integrity by restoring imbalanced CD4 + T/CD8 + T ratios from 0.86 ± 0.02 to 1.04 ± 0.06, regulating Th1/Th2 and Th17/Treg cells balance, as well as inhibited the NF-κB signaling pathway. Furthermore, EPS-1 maintained microbiota homeostasis by increasing the abundances of Ruminococcus, Butyricicoccus, and Muribaculaceae, while reducing Streptococcus and Candidatus arthromitus. This microbiota modulation enhanced the levels of metabolites such as tyrosine, methionine, tryptophan, triglycerides, and salidroside. In conclusion, EPS-1 shows promise as a functional polysaccharide for therapeutic use.

Diagnostic Value of Serum Apolipoprotein B100 Combined With Hippocampal Volume in Alzheimer's Disease.

PURPOSE: To explore the diagnostic value of serum apolipoprotein B100 (Apo B100) combined with hippocampal volume in Alzheimer's disease (AD). METHODS: A total of 59 AD patients and 59 healthy subjects were selected. The Mini-Mental State Examination (MMSE) was used for neuropsychological assessment. Blood glucose and serum lipid levels were detected by biochemical analyzer. Polymerase chain reaction (PCR) was used to detect apolipoprotein E (Apo E) ε3/ε4 genotypes in the plasma. Hippocampal volume was calculated using Slicer software. Independent-sample t test or Mann-Whitney U test were used to compare the levels of various indicators between the two groups. Spearman's correlation analysis was used to analyze the correlation between each level. The receiver operating characteristic curve (ROC) was plotted, and the area under the curve (AUC) was calculated to compare the diagnostic efficacy of individual and combined detection of serum Apo B100 levels and hippocampal volume in AD. RESULTS: Compared with the healthy control group, the levels of serum total cholesterol (TC), low-density lipoprotein (LDL), Apo B100, and plasma Apo E ε3/ε4 were higher in the AD group, and serum high-density lipoprotein (HDL) level was lower in the AD group (both p < 0.05). The hippocampal volume in the AD group was lower than in the control group (p < 0.01). The serum Apo B100 level was negatively correlated with MMSE score (r = -0.646), whereas hippocampal volume was positively correlated with MMSE score (r = 0.630). ROC curve analysis showed that the AUC of the combined serum Apo B100 level and hippocampal volume for AD was higher than that of either alone (AUC = 0.821, p < 0.01). CONCLUSION: Serum Apo B100 level is elevated, and the hippocampal volume is reduced in AD patients. The combined detection of the two has a higher diagnostic efficiency for AD than other alone and has the potential to become an important indicator for the diagnosis of AD in the future.

Self-Adaptive Teacher-Student framework for colon polyp segmentation from unannotated private data with public annotated datasets.

Colon polyps have become a focal point of research due to their heightened potential to develop into appendiceal cancer, which has the highest mortality rate globally. Although numerous colon polyp segmentation methods have been developed using public polyp datasets, they tend to underperform on private datasets due to inconsistencies in data distribution and the difficulty of fine-tuning without annotations. In this paper, we propose a Self-Adaptive Teacher-Student (SATS) framework to segment colon polyps from unannotated private data by utilizing multiple publicly annotated datasets. The SATS trains multiple teacher networks on public datasets and then generates pseudo-labels on private data to assist in training a student network. To enhance the reliability of the pseudo-labels from the teacher networks, the SATS includes a newly proposed Uncertainty and Distance Fusion (UDFusion) strategy. UDFusion dynamically adjusts the pseudo-label weights based on a novel reconstruction similarity measure, innovatively bridging the gap between private and public data distributions. To ensure accurate identification and segmentation of colon polyps, the SATS also incorporates a Granular Attention Network (GANet) architecture for both teacher and student networks. GANet first identifies polyps roughly from a global perspective by encoding long-range anatomical dependencies and then refines this identification to remove false-positive areas through multi-scale background-foreground attention. The SATS framework was validated using three public datasets and one private dataset, achieving 76.30% on IoU, 86.00% on Recall, and 7.01 pixels on HD. These results outperform the existing five methods, indicating the effectiveness of this approach for colon polyp segmentation.

BR-ChromNet: Banding resolution localization of chromosome structural abnormality with conditional random field.

Detecting chromosome structural abnormalities in medical genetics is essential for diagnosing genetic disorders and understanding their implications for an individual's health. However, existing computational methods are formulated as a binary-class classification problem trained only on representations of positive/negative chromosome pairs. This paper introduces an innovative framework for detecting chromosome abnormalities with banding resolution, capable of precisely identifying and masking the specific abnormal regions. We highlight a pixel-level abnormal mapping strategy guided by banding features. This approach integrates data from both the original image and banding characteristics, enhancing the interpretability of prediction results for cytogeneticists. Furthermore, we have implemented an ensemble approach that pairs a discriminator with a conditional random field heatmap generator. This combination significantly reduces the false positive rate in abnormality screening. We benchmarked our proposed framework with state-of-the-art (SOTA) methods in abnormal screening and structural abnormal region segmentation. Our results show cutting-edge effectiveness and greatly reduce the high false positive rate. It also shows superior performance in sensitivity and segmentation accuracy. Being able to identify abnormal regions consistently shows that our model has demonstrated significant clinical utility with high model interpretability. BRChromNet is open-sourced and available at https://github.com/frankchen121212/BR-ChromNet.

Putative Adverse Outcome Pathway for Parkinson's Disease-like Symptoms Induced by Silicon Quantum Dots based on In Vivo/Vitro Approaches.

Given the commercial proliferation of silicon quantum dots (SiQDs) and their inevitable environmental dispersal, this study critically examines their biological and public health implications, specifically regarding Parkinson's disease. The study investigated the toxicological impact of SiQDs on the onset and development of PD-like symptoms through the induction of ferroptosis, utilizing both in vivo [Caenorhabditis elegans (C. elegans)] and in vitro (SH-SY5Y neuroblastoma cell line) models. Our findings demonstrated that SiQDs, characterized by their stable and water-soluble physicochemical properties, tended to accumulate in neuronal tissues. This accumulation precipitated dopaminergic neurodegeneration, manifested as diminished dopamine-dependent behaviors, and escalated the expression of PD-specific genes in C. elegans. Importantly, the results revealed that SiQDs induced ferritinophagy, a selective autophagy pathway that triggered ferroptosis and resulted in PD-like symptoms, even exacerbating disease progression in biological models. These insights were incorporated into a putatively qualitative and quantitative adverse outcome pathway framework, highlighting the serious neurodegenerative risks posed by SiQDs through ferroptosis pathways. This study provides a multidisciplinary analysis critical for informing policy on the regulation of SiQDs exposure to safeguard susceptible populations and guiding the responsible development of nanotechnologies impacting environmental and public health.

Mito-LND and (E)-Akt inhibitor-IV: novel compounds inducing endoplasmic reticulum stress and ROS accumulation against hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. Although multi-kinase inhibitors can prolong the overall survival of late-stage HCC patients, the emergence of drug resistance diminishes these benefits, ultimately resulting in treatment failure. Therefore, there is an urgent need for novel and effective drugs to impede the progression of liver cancer. METHODS: This study employed a concentration gradient increment method to establish acquired sorafenib or regorafenib-resistant SNU-449 cells. Cell viability was assessed using the cell counting kit-8 assay. A library of 793 bioactive small molecules related to metabolism screened compounds targeting both parental and drug-resistant cells. The screened compounds will be added to both the HCC parental cells and the drug-resistant cells, followed by a comprehensive assessment. Intracellular adenosine triphosphate (ATP) levels were quantified using kits. Flow cytometry was applied to assess cell apoptosis and reactive oxygen species (ROS). Real-time quantitative PCR studied relative gene expression, and western blot analysis assessed protein expression changes in HCC parental and drug-resistant cells. A xenograft model in vivo evaluated Mito-LND and (E)-Akt inhibitor-IV effects on liver tumors, with hematoxylin and eosin staining for tissue structure and immunohistochemistry staining for endoplasmic reticulum stress protein expression. RESULTS: From the compound library, we screened out two novel compounds, Mito-LND and (E)-Akt inhibitor-IV, which could potently kill both parental cells and drug-resistant cells. Mito-LND could significantly suppress proliferation and induce apoptosis in HCC parental and drug-resistant cells by upregulating glycolytic intermediates and downregulating those of the tricarboxylic acid (TCA) cycle, thereby decreasing ATP production and increasing ROS. (E)-Akt inhibitor-IV achieved comparable results by reducing glycolytic intermediates, increasing TCA cycle intermediates, and decreasing ATP synthesis and ROS levels. Both compounds trigger apoptosis in HCC cells through the interplay of the AMPK/MAPK pathway and the endoplasmic reticulum stress response. In vivo assays also showed that these two compounds could significantly inhibit the growth of HCC cells and induce endoplasmic reticulum stress. CONCLUSION: Through high throughput screening, we identified that Mito-LND and (E)-Akt inhibitor-IV are two novel compounds against both parental and drug-resistant HCC cells, which could offer new strategies for HCC patients.

Electric Resistance and Curing Temperature Development of Carbon Fiber-Reinforced Conductive Concrete: A Comparative Study.

The development of electric resistance is a key factor affecting the performance of conductive concrete, especially the electrical-thermal performance. In this work, the effects of different influencing factors (including the water-to-binder ratio, coarse aggregate content and carbon fiber (CF) content) on the electric resistance of conductive concrete were systematically investigated. At the same time, ohmic heating (OH) curing was applied to fabricate CF-reinforced conductive concrete (CFRCC) under a negative temperature environment at -20 °C. The effects of different factors on the electrothermal properties (curing temperature and conductive stability) of the samples were studied. The mechanical strengths of the CFRCC cured by different curing conditions were also tested, and the feasibility of OH curing for preparing CFRCC in a negative-temperature environment was verified at various electric powers. This work aims to give new insights into the effects of multiple factors on the performance of CFRCC for improved concrete construction in winter.

Modeling construct change over time amidst potential changes in construct measurement: A longitudinal moderated factor analysis approach.

In analyzing longitudinal data with growth curve models, a critical assumption is that changes in the observed measures reflect construct changes and not changes in the manifestation of the construct over time. However, growth curve models are often fit to a repeated measure constructed as a sum or mean of scale items, making an implicit assumption of constancy of measurement. This practice risks confounding actual construct change with changes in measurement (i.e., differential item functioning [DIF]), threatening the validity of conclusions. An improved method that avoids such confounding is the second-order growth curve (SGC) model. It specifies a measurement model at each occasion of measurement that can be evaluated for invariance over time. The applicability of the SGC model is hindered by key limitations: (a) the SGC model treats time as continuous when modeling construct growth but as discrete when modeling measurement, reducing interpretability and parsimony; (b) the evaluation of DIF becomes increasingly error-prone given multiple timepoints and groups; (c) DIF associated with continuous covariates is difficult to incorporate. Drawing on moderated nonlinear factor analysis, we propose an alternative approach that provides a parsimonious framework for including many time points and DIF from different types of covariates. We implement this model through Bayesian estimation, allowing for incorporation of regularizing priors to facilitate efficient evaluation of DIF. We demonstrate a two-step workflow of measurement evaluation and growth modeling, with an empirical example examining changes in adolescent delinquency over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).