Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth.

The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7-30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth. PRACTITIONER POINTS: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.

Stromal Rigidity Stress Accelerates Pancreatic Intraepithelial Neoplasia Progression and Chromosomal Instability via Nuclear Protein Tyrosine Kinase 2 Localization.

Because the mechanotransduction by stromal stiffness stimulates the rupture and repair of the nuclear envelope in pancreatic progenitor cells, accumulated genomic aberrations are under selection in the tumor microenvironment. Analysis of cell growth, micronuclei, and phosphorylated Ser-139 residue of the histone variant H2AX (γH2AX) foci linked to mechanotransduction pressure in vivo during serial orthotopic passages of mouse KrasLSL-G12D/+;Trp53flox/flox;Pdx1-Cre (KPC) cancer cells in the tumor and in migrating through the size-restricted 3-µm micropores. To search for pancreatic cancer cell-of-origin, analysis of single-cell data sets revealed that the extracellular matrix shaped an alternate route of acinar-ductal transdifferentiation of acinar cells into topoisomerase II α (TOP2A)-overexpressing cancer cells and derived subclusters with copy number amplifications in MYC-PTK2 (protein tyrosine kinase 2) locus and PIK3CA. High-PTK2 expression is associated with 171 differentially methylated CpG loci, 319 differentially expressed genes, and poor overall survival in The Cancer Genome Atlas-Pancreatic Adenocarcinoma cohort. Abolished RGD-integrin signaling by disintegrin KG blocked the PTK2 phosphorylation, increased cancer apoptosis, decreased vav guanine nucleotide exchange factor 1 (VAV1) expression, and prolonged overall survival in the KPC mice. Reduction of α-smooth muscle actin deposition in the CD248 knockout KPC mice remodeled the tissue stroma and down-regulated TOP2A expression in the epithelium. In summary, stromal stiffness induced the onset of cancer cells-of-origin by ectopic TOP2A expression, and the genomic amplification of MYC-PTK2 locus via alternative transdifferentiation of pancreatic progenitor cells is the vulnerability useful for disintegrin KG treatment.

Loss of fragile WWOX gene leads to senescence escape and genome instability.

Induction of DNA damage response (DDR) to ensure accurate duplication of genetic information is crucial for maintaining genome integrity during DNA replication. Cellular senescence is a DDR mechanism that prevents the proliferation of cells with damaged DNA to avoid mitotic anomalies and inheritance of the damage over cell generations. Human WWOX gene resides within a common fragile site FRA16D that is preferentially prone to form breaks on metaphase chromosome upon replication stress. We report here that primary Wwox knockout (Wwox-/-) mouse embryonic fibroblasts (MEFs) and WWOX-knockdown human dermal fibroblasts failed to undergo replication-induced cellular senescence after multiple passages in vitro. Strikingly, by greater than 20 passages, accelerated cell cycle progression and increased apoptosis occurred in these late-passage Wwox-/- MEFs. These cells exhibited γH2AX upregulation and microsatellite instability, indicating massive accumulation of nuclear DNA lesions. Ultraviolet radiation-induced premature senescence was also blocked by WWOX knockdown in human HEK293T cells. Mechanistically, overproduction of cytosolic reactive oxygen species caused p16Ink4a promoter hypermethylation, aberrant p53/p21Cip1/Waf1 signaling axis and accelerated p27Kip1 protein degradation, thereby leading to the failure of senescence induction in Wwox-deficient cells after serial passage in culture. We determined that significantly reduced protein stability or loss-of-function A135P/V213G mutations in the DNA-binding domain of p53 caused defective induction of p21Cip1/Waf1 in late-passage Wwox-/- MEFs. Treatment of N-acetyl-L-cysteine prevented downregulation of cyclin-dependent kinase inhibitors and induced senescence in Wwox-/- MEFs. Our findings support an important role for fragile WWOX gene in inducing cellular senescence for maintaining genome integrity during DDR through alleviating oxidative stress.

HLA-Ehigh /HLA-Ghigh /HLA-IIlow Human iPSC-Derived Cardiomyocytes Exhibit Low Immunogenicity for Heart Regeneration.

Although human pluripotent stem cells (hPSCs)-derived cardiomyocytes (hPSC-CMs) can remuscularize infarcted hearts and restore post-infarct cardiac function, post-transplant rejection resulting from human leukocyte antigen (HLA) mismatching is an enormous obstacle. It is crucial to identify hypoimmunogenic hPSCs for allogeneic cell therapy. This study is conducted to demonstrate the immune privilege of HLA-Ehigh /HLA-Ghigh /HLA-IIlow human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (hiPSC-CMs). Ischemia-reperfusion surgery is done to create transmural myocardial infarction in rats. At post-infarct 4 days, hPSC-CMs (1.0×107 cells per kg), including human embryonic stem cell-derived cardiomyocytes (hESC-CMs), HLA-Elow/HLA-Glow/HLA-IIhigh hiPSC-CMs, and HLA-Ehigh /HLA-Ghigh /HLA-IIlow hiPSC-CMs, are injected into the infarcted myocardium. Under the treatment of very low dose cyclosporine A (CsA), only HLA-Ehigh /HLA-Ghigh /HLA-IIlow hiPSC-CMs survive in vivo and improved post-infarct cardiac function with infarct size reduction. HLA-Ehigh /HLA-Ghigh /HLA-IIlow hiPSC-CMs activate the SHP-1 signaling pathway of natural killer (NK) cells and cytotoxic T cells to evade attack by NK cells and cytotoxic T cells. Herein, it is demonstrated that using a clinically relevant CsA dose, HLA-Ehigh /HLA-Ghigh /HLA-IIlow hiPSC-CMs repair the infarcted myocardium and restore the post-infarct heart function. HLA-Ehigh /HLA-Ghigh /HLA-IIlow hiPSCs are less immunogenic and may serve as platforms for regeneration medicine.

Enrichment of mRNA and Bisulfite-mRNA Library Preparation for Next-Generation Sequencing.

RNA post-transcriptional modifications in various types of RNA transcripts are associated with diverse RNA regulation in eukaryotic cells. Aberrant RNA 5-methylcytosine modifications and the dysregulated expression of RNA methyltransferases have been shown to be associated with various diseases, including cancers. Transcriptome-wide bisulfite-sequencing was developed to characterize the positions and the quantitative cytosine methylation levels in the bisulfite-converted RNA at the base-pair resolution. Herein, this protocol presents the procedures of two rounds of poly(A) RNA purification, three cycles of bisulfite reaction, and library preparation in detail to allow the transcriptome-wide mapping of mRNA 5-methylcytosine modification sites. The assessment of RNA quantity and quality after the main reaction is essential to monitor RNA integrity and is a critical step for ensuring high-quality sequencing libraries. Ideally, the procedures can be completed within three days. With this protocol, using high-quality total RNA as the input can practically build up robust bisulfite-mRNA libraries for next-generation sequencing from the sample of interest.

Accelerating item factor analysis on GPU with Python package xifa.

Item parameter estimation is a crucial step when conducting item factor analysis (IFA). From the view of frequentist estimation, marginal maximum likelihood (MML) seems to be the gold standard. However, fitting a high-dimensional IFA model by MML is still a challenging task. The current study demonstrates that with the help of a GPU (graphics processing unit) and carefully designed vectorization, the computational time of MML could be largely reduced for large-scale IFA applications. In particular, a Python package called xifa (accelerated item factor analysis) is developed, which implements a vectorized Metropolis-Hastings Robbins-Monro (VMHRM) algorithm. Our numerical experiments show that the VMHRM on a GPU may run 33 times faster than its CPU version. When the number of factors is at least five, VMHRM (on GPU) is much faster than the Bock-Aitkin expectation maximization, MHRM implemented by mirt (on CPU), and the importance-weighted autoencoder (on GPU). The GPU-implemented VMHRM is most appropriate for high-dimensional IFA with large data sets. We believe that GPU computing will play a central role in large-scale psychometric modeling in the near future.

Usability and effectiveness of adherence monitoring of a mobile app designed to monitor and improve adherence to event-driven and daily HIV pre-exposure prophylaxis among men who have sex with men in Taiwan.

Objective: The UPrEPU mobile app is a self-monitoring system to enable men who have sex with men to optimize their pre-exposure prophylaxis adherence for HIV prevention. The app was designed to accommodate a rather complicated event-driven dosing schedule. We aim to evaluate the usability of the UPrEPU app and its effectiveness in improving adherence monitoring. Methods: From May to October 2020, 35 participants were enrolled for the usability study and followed up for 4 months. Blood samples for the drug concentration in the dried blood spots were obtained once during the second to fourth follow-up visits. The effectiveness of adherence monitoring was analyzed using Cohen's kappa statistic to calculate the concordance between the average number of pills taken and drug concentration in the dried blood spots. Results: Overall retention was 91.4% (32 participants) at the end of the study. Participants used the app for a mean of 29 days and made 2565 data entries in total, with an average of 76 data entries. The average systematic usability scale score for the app was 71.5, indicating acceptable usability. Slight agreement was reached between the dried blood spots measurement and the number of pills taken and recorded in the app (weighted kappa: 0.21). Conclusions: Our user-centered UPrEPU app demonstrated that it could accommodate both daily and event-driven dosing schedules for men who have sex with men clients with acceptable usability scores. We confirmed that complex behaviors such as different drug-dosing regimens that are contingent on sexual behaviors could be incorporated into the design of a mobile app.

RNA bisulfite sequencing reveals NSUN2-mediated suppression of epithelial differentiation in pancreatic cancer.

Posttranscriptional modifications in RNA have been considered to contribute to disease pathogenesis and tumor progression. NOL1/NOP2/Sun domain family member 2 (NSUN2) is an RNA methyltransferase that promotes tumor progression in several cancers. Pancreatic cancer relapse inevitably occurs even in cases where primary tumors have been successfully treated. Associations of cancer progression due to reprogramming of the cancer methyl-metabolome and the cancer genome have been noted, but the effect of base modifications, namely 5-methylcytosine (m5C), in the transcriptome remains unclear. Aberrant regulation of 5-methylcytosine turnover in cancer may affect posttranscriptional modifications in coding and noncoding RNAs in disease pathogenesis. Mutations in NSUN2 have been reported as drivers of neurodevelopmental disorders in mice, and upregulated expression of NSUN2 in tumors of the breast, bladder, and pancreas has been reported. In this study, we conducted mRNA whole transcriptomic bisulfite sequencing to categorize NSUN2 target sites in the mRNA of human pancreatic cancer cells. We identified a total of 2829 frequent m5C sites in mRNA from pancreatic cancer cells. A total of 90.9% (2572/2829) of these m5C sites were mapped to annotated genes in autosomes and sex chromosomes X and Y. Immunohistochemistry staining confirmed that the NSUN2 expression was significantly upregulated in cancer lesions in the LSL-KrasG12D/+;Trp53fl/fl;Pdx1-Cre (KPC) spontaneous pancreatic cancer mouse model induced by Pdx1-driven Cre/lox system expressing mutant KrasG12D and p53 deletion. The in vitro phenotypic analysis of NSUN2 knockdown showed mild effects on pancreatic cancer cell 2D/3D growth, morphology and gemcitabine sensitivity in the early phase of tumorigenesis, but cumulative changes after multiple cell doubling passages over time were required for these mutations to accumulate. Syngeneic transplantation of NSUN2-knockdown KPC cells via subcutaneous injection showed decreased stromal fibrosis and restored differentiation of ductal epithelium in vivo. SIGNIFICANCE: Transcriptome-wide mRNA bisulfite sequencing identified candidate m5C sites of mRNAs in human pancreatic cancer cells. NSUN2-mediated m5C mRNA metabolism was observed in a mouse model of pancreatic cancer. NSUN2 regulates cancer progression and epithelial differentiation via mRNA methylation.

Penalized Least Squares for Structural Equation Modeling with Ordinal Responses.

Statistical modeling with sparsity has become an active research topic in the fields of statistics and machine learning. Because the true sparsity pattern of a model is generally unknown aforehand, it is often explored by a sparse estimation procedure, like least absolute shrinkage and selection operator (lasso). In this study, a penalized least squares (PLS) method for structural equation modeling (SEM) with ordinal data is developed. PLS describes data generation by an underlying response approach, and uses a least squares (LS) fitting function to construct a penalized estimation criterion. A numerical simulation was used to compare PLS with existing penalized likelihood (PL) in terms of averaged mean square error, absolute bias, and the correctness of the model. Based on these empirical findings, a hybrid PLS was also proposed to improve both PL and PLS. The hybrid PLS first chooses an optimal sparsity pattern by PL, then estimates model parameters by an unpenalized LS under the model selected by PL. We also extended PLS to cases of mixed type data and multi-group analysis. All proposed methods could be realized in the R package lslx.

Integrating domain knowledge with machine learning to detect obstructive sleep apnea: Snore as a significant bio-feature.

Our study's main purpose is to emphasise the significance of medical knowledge of pathophysiology before machine learning. We investigated whether combining domain knowledge with machine learning results might increase accuracy and minimise the number of bio-features used to detect obstructive sleep apnea (OSA). The present study analysed data on 36 self-reported symptoms and 24 clinical features obtained from 3,495 patients receiving polysomnography at a regional hospital and a medical centre. The area under the receiver operating characteristic (AUC) curve was used to evaluate patients with and without moderate or severe OSA using three prediction models on the basis of various estimation methods: the multiple logistic regression (MLR), support vector machine (SVM), and neural network (NN) methods. Odds ratios stratified by gender and age were also measured to account for clinicians' common sense. We discovered that adding the self-reported snoring item improved the AUC by 0.01-0.10 and helped us to rapidly achieve the optimum level. The performance of four items (gender, age, body mass index [BMI], and snoring) was comparable with that of adding two or more items (neck and waist circumference) for predicting moderate to severe OSA (Apnea-Hypopnea Index ≥15 events/hr) in all three prediction models, demonstrating the medical knowledge value of pathophysiology. The four-item test sample AUCs were 0.83, 0.84, and 0.83 for MLR, SVM, and NN, respectively. Participants with regular snoring and a BMI of ≥25 kg/m2 had a greater chance of moderate to severe OSA according to the stratified adjusted odds ratios. Combining domain knowledge into machine learning could increase efficiency and enable primary care physicians to refer for an OSA diagnosis earlier.

Condomless Anal Sex Associated With Heterogeneous Profiles Of HIV Pre-Exposure Prophylaxis Use and Sexual Activities Among Men Who Have Sex With Men: A Latent Class Analysis Using Sex Diary Data on a Mobile App.

BACKGROUND: New innovative technologies, such as mobile apps, have been developed to increase pre-exposure prophylaxis (PrEP) adherence and the use of log sex diaries. The contiguity of mobile apps reduces the recall bias that generally affects reported condom and PrEP use. However, none of the currently used mobile apps were designed for event-driven PrEP users, and few studies have demonstrated the potential usage of sex diary data to facilitate the understanding of the different HIV risks among heterogeneous profiles of sex diaries and PrEP use. OBJECTIVE: We aim to discriminate the heterogeneous profiles of sex events and PrEP use and examine the risk of condomless anal sex among different types of sex events. METHODS: We recruited 35 adult men who have sex with men from two medical centers in Taiwan since May 2020 and followed up for four months. Participants were on PrEP or willing to take PrEP. They were asked to log their sex events, PrEP use, and dosing regimens on a mobile app to improve their PrEP adherence. Latent class analysis was used to distinguish profiles of sex events and PrEP use. Indicators included correct intake of PrEP for each sex event, participants' sexual positioning, partner's HIV status, and age. RESULTS: A total of 551 sex events were classified into three classes by latent class analysis: PrEP nonadherent flip-flopping (234/551, 42%), PrEP imperfect-adherent power bottoming (284/551, 52%), and PrEP adherent serodiscordant topping (33/551, 6%). "PrEP nonadherent flip-flopping" sex events were more likely to involve condomless anal sex than "PrEP imperfect-adherent power bottoming" (OR 1.83, 95% CI 1.03-3.25) after considering random intercepts for individuals, and this class needed to increase their PrEP adherence and use of condoms. "PrEP imperfect-adherent power bottoming" realized their own risk and packaged PrEP with condoms to protect themselves. Up to 99% (32/33) of sex events in "PrEP adherent serodiscordant topping" were protected by PrEP, but all of the sex events in this group were condomless. CONCLUSIONS: Using the sex diary data could advance the capacity to identify high-risk groups. HIV prevention strategy should be more flexible and combine PrEP with condom use for future HIV prevention.

Mobile App (UPrEPU) to Monitor Adherence to Pre-exposure Prophylaxis in Men Who Have Sex With Men: Protocol for a User-Centered Approach to Mobile App Design and Development.

BACKGROUND: Daily and on-demand pre-exposure prophylaxis (PrEP) has been well demonstrated to effectively prevent HIV acquisition for men who have sex with men (MSM). More than half of the MSM PrEP users in Taiwan prefer on-demand PrEP; however, on-demand PrEP involves a complicated dosing regimen because it requires precoital and postcoital dosing and sex events are hard to anticipate. Although there are a growing number of mobile apps designed to improve access to HIV prevention services and HIV medication adherence, few mobile apps focus on adherence to PrEP or are designed to accommodate a complicated, on-demand PrEP dosing schedule. OBJECTIVE: The aim of this project is to evaluate the usability of a newly developed mobile app (UPrEPU) to assist MSM PrEP users to self-monitor their adherence to either daily or on-demand PrEP using a user-centered scheme. METHODS: This research will be conducted in 2 phases: app development and usability study. In the app development phase, we will first conduct formative research with end users and stakeholders through in-depth interviews; the results will provide PrEP users' and PrEP navigators' personas as material used in the app conceptualization stage. PrEP navigators are individuals in the health care system that help HIV-negative individuals who need assistance in accessing PrEP care. A low-fidelity prototype of the app feature will be formatted by applying a participatory design approach to engage PrEP users, designers, and app developers in the design process of the app. Then, a high-fidelity prototype of the app will be developed for the usability study and refined iteratively by the multidisciplinary team and new internal testers. Internal testers include the research team consisting of experts in public health, infectious disease, and industrial design and a close network of the research team that is taking PrEP. In the usability study phase, we will enroll 70 MSM PrEP users and follow them up for 4 months. Usability, feasibility, and effectiveness of adherence monitoring will be evaluated. RESULTS: Refinement of the UPrEPU app is currently ongoing. The usability study commenced in May 2020. CONCLUSIONS: The UPrEPU app is one of the first apps designed to help MSM PrEP users to self-manage their PrEP schedule better regardless of dosing modes. With a design-thinking approach and adapting to the cultural context in Taiwan's MSM population, this novel app will have substantial potential to be acceptable and feasible and contribute to the reduction of new HIV infections. TRIAL REGISTRATION: ClinicalTrials.gov NCT04248790; https://clinicaltrials.gov/ct2/show/NCT04248790. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/20360.

Epigenetic silencing of AATK in acinar to ductal metaplasia in murine model of pancreatic cancer.

BACKGROUND: Cancer subtype switching, which involves unclear cancer cell origin, cell fate decision, and transdifferentiation of cells within a confined tumor microenvironment, remains a major problem in pancreatic cancer (PDA). RESULTS: By analyzing PDA subtypes in The Cancer Genome Atlas, we identified that epigenetic silencing of apoptosis-associated tyrosine kinase (AATK) inversely was correlated with mRNA expression and was enriched in the quasi-mesenchymal cancer subtype. By comparing early mouse pancreatic lesions, the non-invasive regions showed AATK co-expression in cells with acinar-to-ductal metaplasia, nuclear VAV1 localization, and cell cycle suppression; but the invasive lesions conversely revealed diminished AATK expression in those with poorly differentiated histology, cytosolic VAV1 localization, and co-expression of p63 and HNF1α. Transiently activated AATK initiates acinar differentiation into a ductal cell fate to establish apical-basal polarization in acinar-to-ductal metaplasia. Silenced AATK and ectopically expressed p63 and HNF1α allow the proliferation of ductal PanINs in mice. CONCLUSION: Epigenetic silencing of AATK regulates the cellular transdifferentiation, proliferation, and cell cycle progression in converting PDA-subtypes.

Postselection Inference in Structural Equation Modeling.

Most statistical inference methods were established under the assumption that the fitted model is known in advance. In practice, however, researchers often obtain their final model by some data-driven selection process. The selection process makes the finally fitted model random, and it also influences the sampling distribution of the estimator. Therefore, implementing naive inference methods may result in wrong conclusions-which is probably a prime source of the reproducibility crisis in psychological science. The present study accommodates three valid state-of-the-art postselection inference methods for structural equation modeling (SEM) from the statistical literature: data splitting (DS), postselection inference (PoSI), and the polyhedral (PH) method. A simulation is conducted to compare the three methods with the commonly used naive procedure under selection events made by L1-penalized SEM. The results show that the naive method often yields incorrect inference, and that the valid methods control the coverage rate in most cases with their own pros and cons. Real world data examples show the practical use of the valid inference methods.

Pancreatic stellate cells activated by mutant KRAS-mediated PAI-1 upregulation foster pancreatic cancer progression via IL-8.

Background: The dense fibrotic stroma enveloping pancreatic tumors is a major cause of drug resistance. Pancreatic stellate cells (PSCs) in the stroma can be activated to induce intra-tumor fibrosis and worsen patient survival; however, the molecular basics for the regulation of PSC activation remains unclear. Methods: The in vitro coculture system was used to study cancer cell-PSC interactions. Atomic force microscopy was used to measure the stiffness of tumor tissues and coculture gels. Cytokine arrays, qPCR, and Western blotting were performed to identify the potential factors involved in PSC activation and to elucidate underlying pathways. Results: PSC activation characterized by α-SMA expression was associated with increased pancreatic tumor stiffness and poor prognosis. Coculture with cancer cells induced PSC activation, which increased organotypic coculture gel stiffness and cancer cell invasion. Cancer cells-derived PAI-1 identified from coculture medium could activate PSCs, consistent with pancreatic cancer tissue microarray analysis showing a strong positive correlation between PAI-1 and α-SMA expression. Suppression by knocking down PAI-1 in cancer cells demonstrated the requirement of PAI-1 for coculture-induced PSC activation and gel stiffness. PAI-1 could be upregulated by KRAS in pancreatic cancer cells through ERK. In PSCs, inhibition of LRP-1, ERK, and c-JUN neutralized the effect of PAI-1, suggesting the contribution of LRP-1/ERK/c-JUN signaling. Furthermore, activated PSCs might exacerbate malignant behavior of cancer cells via IL-8 because suppression of IL-8 signaling reduced pancreatic tumor growth and fibrosis in vivo. Conclusions: KRAS-mutant pancreatic cancer cells can activate PSCs through PAI-1/LRP-1 signaling to promote fibrosis and cancer progression.

A penalized likelihood method for multi-group structural equation modelling.

In the past two decades, statistical modelling with sparsity has become an active research topic in the fields of statistics and machine learning. Recently, Huang, Chen and Weng (2017, Psychometrika, 82, 329) and Jacobucci, Grimm, and McArdle (2016, Structural Equation Modeling: A Multidisciplinary Journal, 23, 555) both proposed sparse estimation methods for structural equation modelling (SEM). These methods, however, are restricted to performing single-group analysis. The aim of the present work is to establish a penalized likelihood (PL) method for multi-group SEM. Our proposed method decomposes each group model parameter into a common reference component and a group-specific increment component. By penalizing the increment components, the heterogeneity of parameter values across the population can be explored since the null group-specific effects are expected to diminish. We developed an expectation-conditional maximization algorithm to optimize the PL criteria. A numerical experiment and a real data example are presented to demonstrate the potential utility of the proposed method.

A Penalized Likelihood Method for Structural Equation Modeling.

A penalized likelihood (PL) method for structural equation modeling (SEM) was proposed as a methodology for exploring the underlying relations among both observed and latent variables. Compared to the usual likelihood method, PL includes a penalty term to control the complexity of the hypothesized model. When the penalty level is appropriately chosen, the PL can yield an SEM model that balances the model goodness-of-fit and model complexity. In addition, the PL results in a sparse estimate that enhances the interpretability of the final model. The proposed method is especially useful when limited substantive knowledge is available for model specifications. The PL method can be also understood as a methodology that links the traditional SEM to the exploratory SEM (Asparouhov & Muthén in Struct Equ Model Multidiscipl J 16:397-438, 2009). An expectation-conditional maximization algorithm was developed to maximize the PL criterion. The asymptotic properties of the proposed PL were also derived. The performance of PL was evaluated through a numerical experiment, and two real data illustrations were presented to demonstrate its utility in psychological research.

Asymptotics of AIC, BIC, and RMSEA for Model Selection in Structural Equation Modeling.

Model selection is a popular strategy in structural equation modeling (SEM). To select an "optimal" model, many selection criteria have been proposed. In this study, we derive the asymptotics of several popular selection procedures in SEM, including AIC, BIC, the RMSEA, and a two-stage rule for the RMSEA (RMSEA-2S). All of the results are derived under weak distributional assumptions and can be applied to a wide class of discrepancy functions. The results show that both AIC and BIC asymptotically select a model with the smallest population minimum discrepancy function (MDF) value regardless of nested or non-nested selection, but only BIC could consistently choose the most parsimonious one under nested model selection. When there are many non-nested models attaining the smallest MDF value, the consistency of BIC for the most parsimonious one fails. On the other hand, the RMSEA asymptotically selects a model that attains the smallest population RMSEA value, and the RESEA-2S chooses the most parsimonious model from all models with the population RMSEA smaller than the pre-specified cutoff. The empirical behavior of the considered criteria is also illustrated via four numerical examples.

Pharmacological strategies to target oncogenic KRAS signaling in pancreatic cancer.

The clear importance of mutated KRAS as a therapeutic target has driven the investigation of multiple approaches to inhibit oncogenic KRAS signaling at different molecular levels. However, no KRAS-targeted therapy has reached the clinic to date, which underlies the intrinsic difficulty in developing effective, direct inhibitors of KRAS. Thus, this article provides an overview of the history and recent progress in the development of pharmacological strategies to target oncogenic KRAS with small molecule agents. Mechanistically, these KRAS-targeted agents can be classified into the following four categories. (1) Small-molecule RAS-binding ligands that prevent RAS activation by binding within or outside the nucleotide-binding motif. (2) Inhibitors of KRAS membrane anchorage. (3) Inhibitors that bind to RAS-binding domains of RAS-effector proteins. (4) Inhibitors of KRAS expression. The advantage and limitation of each type of these anti-KRAS agents are discussed.

Suppression of Tumor Growth and Muscle Wasting in a Transgenic Mouse Model of Pancreatic Cancer by the Novel Histone Deacetylase Inhibitor AR-42.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States. This study was aimed at evaluating the efficacy of AR-42 (formerly OSU-HDAC42), a novel histone deacetylase (HDAC) inhibitor currently in clinical trials, in suppressing tumor growth and/or cancer-induced muscle wasting in murine models of PDAC. EXPERIMENTAL DESIGN: The in vitro antiproliferative activity of AR-42 was evaluated in six human pancreatic cancer cell lines (AsPC-1, COLO-357, PANC-1, MiaPaCa-2, BxPC-3, SW1990). AsPC-1 subcutaneous xenograft and transgenic KPfl/flC (LSL-KrasG12D;Trp53flox/flox;Pdx-1-Cre) mouse models of pancreatic cancer were used to evaluate the in vivo efficacy of AR-42 in suppressing tumor growth and/or muscle wasting. RESULTS: Growth suppression in AR-42-treated cells was observed in all six human pancreatic cancer cell lines with dose-dependent modulation of proliferation and apoptotic markers, which was associated with the hallmark features of HDAC inhibition, including p21 upregulation and histone H3 hyperacetylation. Oral administration of AR-42 at 50 mg/kg every other day resulted in suppression of tumor burden in the AsPC-1 xenograft and KPfl/flC models by 78% and 55%, respectively, at the end of treatment. Tumor suppression was associated with HDAC inhibition, increased apoptosis, and inhibition of proliferation. Additionally, AR-42 as a single agent preserved muscle size and increased grip strength in KPfl/flC mice. Finally, the combination of AR-42 and gemcitabine in transgenic mice demonstrated a significant increase in survival than either agent alone. CONCLUSIONS: These results suggest that AR-42 represents a therapeutically promising strategy for the treatment of pancreatic cancer.