Clinical Significance and Prognostic Implications of Discontinuous Growth Pattern in Esophageal Adenocarcinoma: A Multi-Institutional Study.

The significance of discontinuous growth (DG) of the tumor to include tumor deposits and intramural metastasis in esophageal adenocarcinoma (EAC) is unclear. Esophagectomy specimens from 151 treatment-naïve and 121 treated patients with EAC were reviewed. DG was defined as discrete (≥2 mm away) tumor foci identified at the periphery of the main tumor in the submucosa, muscularis propria, and/or periadventitial tissue. Patients' demographics, clinicopathologic parameters, and oncologic outcomes were compared between tumors with DG versus without DG. DGs were identified in 16% of treatment-naïve and 29% of treated cases ( P =0.01). Age, gender, and tumor location were comparable in DG+ and DG- groups. For the treatment-naïve group, DG+ tumors were larger with higher tumor grade and stage and more frequent extranodal extension, lymphovascular/perineural invasion, and positive margin. Patients with treated tumors presented at higher disease stages with higher rates of recurrence and metastasis compared with treatment-naïve patients. In this group, DG was also associated with TNM stage and more frequent lymphovascular/perineural spread and positive margin, but not with tumor size, grade, or extranodal extension. In multivariate analysis, in all patients adjusted for tumor size, lymphovascular involvement, margin, T and N stage, metastasis, neoadjuvant therapy status, treatment year, and DG, DG was found to be an independent adverse predictor of survival outcomes in EAC. DG in EAC is associated with adverse clinicopathologic features and worse patient outcomes. DG should be considered throughout the entire clinicopathologic evaluation of treatment-naïve and treated tumors as well as in future staging systems.

Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions.

With the development of data collection techniques, analysis with a survival response and high-dimensional covariates has become routine. Here we consider an interaction model, which includes a set of low-dimensional covariates, a set of high-dimensional covariates, and their interactions. This model has been motivated by gene-environment (G-E) interaction analysis, where the E variables have a low dimension, and the G variables have a high dimension. For such a model, there has been extensive research on estimation and variable selection. Comparatively, inference studies with a valid false discovery rate (FDR) control have been very limited. The existing high-dimensional inference tools cannot be directly applied to interaction models, as interactions and main effects are not "equal". In this article, for high-dimensional survival analysis with interactions, we model survival using the Accelerated Failure Time (AFT) model and adopt a "weighted least squares + debiased Lasso" approach for estimation and selection. A hierarchical FDR control approach is developed for inference and respect of the "main effects, interactions" hierarchy. The asymptotic distribution properties of the debiased Lasso estimators are rigorously established. Simulation demonstrates the satisfactory performance of the proposed approach, and the analysis of a breast cancer dataset further establishes its practical utility.

HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.

This study has been motivated by cancer research, in which heterogeneity analysis plays an important role and can be roughly classified as unsupervised or supervised. In supervised heterogeneity analysis, the finite mixture of regression (FMR) technique is used extensively, under which the covariates affect the response differently in subgroups. High-dimensional molecular and, very recently, histopathological imaging features have been analyzed separately and shown to be effective for heterogeneity analysis. For simpler analysis, they have been shown to contain overlapping, but also independent information. In this article, our goal is to conduct the first and more effective FMR-based cancer heterogeneity analysis by integrating high-dimensional molecular and histopathological imaging features. A penalization approach is developed to regularize estimation, select relevant variables, and, equally importantly, promote the identification of independent information. Consistency properties are rigorously established. An effective computational algorithm is developed. A simulation and an analysis of The Cancer Genome Atlas (TCGA) lung cancer data demonstrate the practical effectiveness of the proposed approach. Overall, this study provides a practical and useful new way of conducting supervised cancer heterogeneity analysis.

FunctanSNP: an R package for functional analysis of dense SNP data (with interactions).

SUMMARY: Densely measured SNP data are routinely analyzed but face challenges due to its high dimensionality, especially when gene-environment interactions are incorporated. In recent literature, a functional analysis strategy has been developed, which treats dense SNP measurements as a realization of a genetic function and can 'bypass' the dimensionality challenge. However, there is a lack of portable and friendly software, which hinders practical utilization of these functional methods. We fill this knowledge gap and develop the R package FunctanSNP. This comprehensive package encompasses estimation, identification, and visualization tools and has undergone extensive testing using both simulated and real data, confirming its reliability. FunctanSNP can serve as a convenient and reliable tool for analyzing SNP and other densely measured data. AVAILABILITY AND IMPLEMENTATION: The package is available at https://CRAN.R-project.org/package=FunctanSNP.

Histology Shift in Esophageal Cancer Between Biopsies and Resections After Neoadjuvant Therapy: A Pilot Study.

Preoperative neoadjuvant therapy followed by resection is the mainstay treatment for locally advanced esophageal adenocarcinoma. We recently observed the histology shift from predominant esophageal adenocarcinoma in the biopsy to neuroendocrine neoplasm with or without adenocarcinoma in the post-treatment resection. The underlying mechanism of this finding is uncertain, and there is limited information in the literature. A total of 11 patients were identified: 10 patients received presurgical chemoradiation and 1 with chemotherapy. All biopsies were diagnosed with adenocarcinoma. When neuroendocrine immunomarkers were retrospectively performed on 5 biopsies, 2 showed focal positivity, although the classic neuroendocrine morphology was not readily appreciated. All resections contained neuroendocrine neoplasm, including 8 of well-differentiated type and 3 of neuroendocrine carcinomas. Two post-treatment esophagectomies consisted of neuroendocrine neoplasm only without residual adenocarcinoma. Upon follow-up, 8 patients died of the disease (median survival = 26 months), and 3 patients were alive after a median follow-up of 14 months. The overall median survival time was better than the reported esophageal neuroendocrine carcinoma (15 months). The 5-year observed survival rate was 11.3%, which was lower than the Surveillance, Epidemiology, and End Results 5-year survival rate of adenocarcinoma (21.8%). We reported a small series of esophageal adenocarcinoma that showed histology shift between biopsy and esophagectomy after neoadjuvant therapy. Our limited data suggest that prognosis of this group is different than the conventional adenocarcinoma. Awareness of this morphological change reminds pathologists to examine the biopsy specimens thoroughly, because recognition of neuroendocrine neoplasm, especially high-grade neuroendocrine component, might potentially affect pre- and post-surgical regimens.

Study on the Water-Sensitivity Passivation Effect and Mechanism of PA-ES Composite Materials.

The water-sensitive effect of expansive soil (ES) poses a serious challenge to the safety and durability of infrastructure. To reduce the effect of water sensitivity on expansive soil, a new powder soil passivator with polyacrylic (PA) as the main component was proposed. In this paper, a series of macroscopic and microscopic tests were conducted to evaluate the water-sensitive passivation effect and mechanism of PA-ES composites. The results showed that PA significantly attenuated the water sensitivity of ES. With the increase in PA content in the PA-ES composites, the water sensitivity of the composites decreased, swelling and shrinkage deformation decreased, and the strength of the composites increased significantly. In addition, when the content of PA in the PA-ES composite is 6%, it can significantly alleviate the deformation of the composite and improve the saturated shear strength of the composite, meeting the requirements of ES engineering disposal. Finally, the results show that the mechanism of PA passivation of ES water-sensitive effect mainly includes adsorption, binding, and filling. The study shows that PA has a broad engineering application prospect as an ES passivator.

Pathological imaging-assisted cancer gene-environment interaction analysis.

Gene-environment (G-E) interactions have important implications for cancer outcomes and phenotypes beyond the main G and E effects. Compared to main-effect-only analysis, G-E interaction analysis more seriously suffers from a lack of information caused by higher dimensionality, weaker signals, and other factors. It is also uniquely challenged by the "main effects, interactions" variable selection hierarchy. Effort has been made to bring in additional information to assist cancer G-E interaction analysis. In this study, we take a strategy different from the existing literature and borrow information from pathological imaging data. Such data are a "byproduct" of biopsy, enjoys broad availability and low cost, and has been shown as informative for modeling prognosis and other cancer outcomes/phenotypes in recent studies. Building on penalization, we develop an assisted estimation and variable selection approach for G-E interaction analysis. The approach is intuitive, can be effectively realized, and has competitive performance in simulation. We further analyze The Cancer Genome Atlas (TCGA) data on lung adenocarcinoma (LUAD). The outcome of interest is overall survival, and for G variables, we analyze gene expressions. Assisted by pathological imaging data, our G-E interaction analysis leads to different findings with competitive prediction performance and stability.

Does the presence of capsule influence prognosis in poorly differentiated thyroid carcinoma?

We collected all cases of poorly differentiated thyroid carcinoma at our institution diagnosed between 2007 and 2022 to investigate the role of tumor capsule in these neoplasms along with other histologic factors that may lead to adverse patient outcomes. After the exclusion of those meeting criteria for differentiated high-grade thyroid carcinoma or anaplastic carcinoma, we were left with 65 cases with a poorly differentiated component. Four of those cases (6.2%) were entirely encapsulated with no invasion of the tumor capsule. Unencapsulated tumors showed significantly greater rates of extrathyroidal extension (75.0% versus 41.5%) and death from disease (45.5% versus 12.5%) than encapsulated tumors, regardless of capsular invasion, with no differences in sex, tumor size, angioinvasion, local recurrence, or metastasis. Compared with encapsulated tumors with invasion, encapsulated tumors without capsular invasion showed a strong male predominance (100% versus 38.8%). No encapsulated tumors without capsular invasion demonstrated local recurrence, metastasis, or death from disease. No differences in the percentage of poorly differentiated components were noted among the 3 groups, although there was a trend for encapsulated tumors to have a higher percentage of poorly differentiated components than unencapsulated tumors. We conclude that invasive tumors lacking a capsule demonstrate greater rates of disease-related death despite showing similar adverse histologic features to invasive encapsulated tumors. Moreover, we confirm that encapsulated tumors without capsular invasion have excellent long-term outcomes in terms of recurrences, metastases, and survival.

Multivariate functional data clustering using adaptive density peak detection.

Clustering for multivariate functional data is a challenging problem since the data are represented by a set of curves and functions belonging to an infinite-dimensional space. In this article, we propose a novel clustering method for multivariate functional data using an adaptive density peak detection technique. It is a quick cluster center identification algorithm based on the two measures of each functional data observation: the functional density estimate and the distance to the closest observation with a higher functional density. We suggest two types of functional density estimators for multivariate functional data. The first one is a functional k $$ k $$ -nearest neighbor density estimator based on (a) an L2 distance between raw functional curves, or (b) a semimetric of multivariate functional principal components. The second one is a k $$ k $$ -nearest neighbor density estimator based on multivariate functional principal scores. Our clustering method is computationally fast since it does not need an iterative process. The flexibility and advantages of the method are examined by comparing it with other existing clustering methods in simulation studies. A user-friendly R package FADPclust is developed for public use. Finally, our method is applied to a real case study in lung cancer research.

Gene-environment interaction analysis via deep learning.

Gene-environment (G-E) interaction analysis plays an important role in studying complex diseases. Extensive methodological research has been conducted on G-E interaction analysis, and the existing methods are mostly based on regression techniques. In many fields including biomedicine and omics, it has been increasingly recognized that deep learning may outperform regression with its unique flexibility (e.g., in accommodating unspecified nonlinear effects) and superior prediction performance. However, there has been a lack of development in deep learning for G-E interaction analysis. In this article, we fill this important knowledge gap and develop a new analysis approach based on deep neural network in conjunction with penalization. The proposed approach can simultaneously conduct model estimation and selection (of important main G effects and G-E interactions), while uniquely respecting the "main effects, interactions" variable selection hierarchy. Simulation shows that it has superior prediction and feature selection performance. The analysis of data on lung adenocarcinoma and skin cutaneous melanoma overall survival further establishes its practical utility. Overall, this study can advance G-E interaction analysis by delivering a powerful new analysis approach based on modern deep learning.

Study on Shear Creep Characteristics of the Discontinuities with Different 3D Morphologies.

The rheological phenomenon of rock mass affects the long-term safety of rock mass engineering. In this study, gneiss samples with different 3D morphologies are prepared by splitting tests and are tested through multi-step creep tests. The long-term strength of rock discontinuities is determined by using several methods. The test results show that as the 3D morphological parameter increases, the creep deformation, creep rate, and the duration of failure all decrease. The long-term strength of rock discontinuities is linearly related to the 3D morphological parameter. Based on the principle of damage mechanics for rock mass, a damage variable is introduced in the creep model, and an improved non-linear Burgers model is established. Research results are of great theoretical significance and practical value for the design, construction, and long-term safety of rock mass engineering.

Physical Model Experiments on Failure Mechanism on Slopes of Weathered Basalt Soils during Heavy Rainfall Events.

Basalt platforms are widely distributed in many areas of China, where landslides occur frequently. It is well recognized that landslide hazards seriously threaten engineering constructions and property safety. It is, therefore, of great significance to understand deformation and failure behaviors and their mechanisms in basalt slopes to reduce the loss caused by landslides. In this work, the Pengshan Landslide in Zhejiang Province is taken as a prototype and slope model tests are carried out. During the tests, real-time monitoring of pore pressure, earth pressure and slope deformation is conducted. Based on the experimental data, the influence of rainfall intensity and the thickness of a weak interlayer on the slope stability are obtained. It is demonstrated that the rainfall and weak interlayer are the most important factors causing the slope instability of a basalt platform. Furthermore, damage from a basalt platform slope usually starts from local failure, and the slope foot is the most likely sliding part. Moreover, when the rainfall intensity is doubled, the initial deformation time of the slope is reduced by about half and the final failure time is advanced by one-third. In addition, when the thickness of the weak interlayer is doubled, the initial deformation time of slope is shortened by about half and the final failure time is advanced by one-quarter.

Neuroendocrine Neoplasms of the Pancreas: Diagnostic Challenges and Practical Approach.

Most pancreatic neuroendocrine neoplasms are slow-growing, and the patients may survive for many years, even after distant metastasis. The tumors usually display characteristic organoid growth patterns with typical neuroendocrine morphology. A smaller portion of the tumors follows a more precipitous clinical course. The classification has evolved from morphologic patterns to the current World Health Organization classification, with better-defined grading and prognostic criteria. Recent advances in molecular pathology have further improved our understanding of the pathogenesis of these tumors. Various issues and challenges remain, including the correct recognition of a neuroendocrine neoplasm, accurate classification and grading of the tumor, and differentiation from mimickers. This review focuses on the practical aspects during the workup of pancreatic neuroendocrine neoplasms and attempts to provide a general framework to help achieve an accurate diagnosis, classification, and grading.

Replicability in cancer omics data analysis: measures and empirical explorations.

In biomedical research, the replicability of findings across studies is highly desired. In this study, we focus on cancer omics data, for which the examination of replicability has been mostly focused on important omics variables identified in different studies. In published literature, although there have been extensive attention and ad hoc discussions, there is insufficient quantitative research looking into replicability measures and their properties. The goal of this study is to fill this important knowledge gap. In particular, we consider three sensible replicability measures, for which we examine distributional properties and develop a way of making inference. Applying them to three The Cancer Genome Atlas (TCGA) datasets reveals in general low replicability and significant across-data variations. To further comprehend such findings, we resort to simulation, which confirms the validity of the findings with the TCGA data and further informs the dependence of replicability on signal level (or equivalently sample size). Overall, this study can advance our understanding of replicability for cancer omics and other studies that have identification as a key goal.

TLR Signaling Pathway Gene Polymorphisms, Gene-Gene and Gene-Environment Interactions in Allergic Rhinitis.

Background: Allergic rhinitis (AR) is a nasal inflammatory disease resulting from a complex interplay between genetic and environmental factors. The association between Toll-like receptor (TLR) signaling pathway and environmental factors in AR pathogenesis remains to be explored. This study aims to assess the genetic association of AR with single nucleotide polymorphisms (SNPs) in TLR signaling pathway, and investigate the roles of gene-gene and gene-environment interactions in AR. Methods: A total of 452 AR patients and 495 healthy controls from eastern China were enrolled in this hospital-based case-control study. We evaluated putatively functional genetic polymorphisms in TLR2, TLR4 and CD14 genes for their association with susceptibility to AR and related clinical phenotypes. Interactions between environmental factors (such as traffic pollution, residence, pet keeping) and polymorphisms with AR were examined using logistic regression. Models were stratified by genotype and interaction terms, and tested for the significance of gene-gene and gene-environment interactions. Results: In the single-locus analysis, two SNPs in CD14, rs2563298 (A/C) and rs2569191 (C/T) were associated with a significantly decreased risk of AR. Compared with the GG genotype, the GT and GT/TT genotypes of TLR2 rs7656411 (G/T) were associated with a significantly increased risk of AR. Gene-gene interactions (eg, TLR2 rs7656411, TLR4 rs1927914, and CD14 rs2563298) was associated with AR. Gene-environment interactions (eg, TLR4 or CD14 polymorphisms and certain environmental exposures) were found in AR cases, but they were not significant after Bonferroni correction. Conclusion: The genetic polymorphisms of TLR2 and CD14 and gene-gene interactions in TLR signaling pathway were associated with susceptibility to AR in this Han Chinese population. However, the present results were limited to support the association between gene-environment interactions and AR.

Structured sparse support vector machine with ordered features.

In the application of high-dimensional data classification, several attempts have been made to achieve variable selection by replacing the ℓ 2 -penalty with other penalties for the support vector machine (SVM). However, these high-dimensional SVM methods usually do not take into account the special structure among covariates (features). In this article, we consider a classification problem, where the covariates are ordered in some meaningful way, and the number of covariates p can be much larger than the sample size n. We propose a structured sparse SVM to tackle this type of problems, which combines the non-convex penalty and cubic spline estimation procedure (i.e. penalizing second-order derivatives of the coefficients) to the SVM. From a theoretical point of view, the proposed method satisfies the local oracle property. Simulations show that the method works effectively both in feature selection and classification accuracy. A real application is conducted to illustrate the benefits of the method.

iSFun: an R package for integrative dimension reduction analysis.

SUMMARY: In the analysis of high-dimensional omics data, dimension reduction techniques-including principal component analysis (PCA), partial least squares (PLS) and canonical correlation analysis (CCA)-have been extensively used. When there are multiple datasets generated by independent studies with compatible designs, integrative analysis has been developed and shown to outperform meta-analysis, other multidatasets analysis, and individual-data analysis. To facilitate integrative dimension reduction analysis in daily practice, we develop the R package iSFun, which can comprehensively conduct integrative sparse PCA, PLS and CCA, as well as meta-analysis and stacked analysis. The package can conduct analysis under the homogeneity and heterogeneity models and with the magnitude- and sign-based contrasted penalties. As a 'byproduct', this article is the first to develop integrative analysis built on the CCA technique, further expanding the scope of integrative analysis. AVAILABILITY AND IMPLEMENTATION: The package is available at https://CRAN.R-project.org/package=iSFun. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Experimental Investigation on the Breaching Process of Landslide Dams with Differing Materials under Different Inflow Conditions.

Landslide dams are dangerous because the outburst floods produced by dam failures seriously threaten life and property downstream. In this study, a series of physical flume tests were conducted to investigate the breaching process of landslide dams with fine-grained, well graded, and coarse-grained material under different inflow conditions. The effects of dam material and inflow discharge on the breach development, outflow discharge and erosion characteristics were studied. The erosion resistance of materials and lateral collapses were also discussed. Experimental results reveal that the whole breaching process is determined by the water-sediment interaction. For the fine-grained dams, a general constant downstream slope angle is maintained during the breaching process. For the well-graded dams, a step-pool structure is generated due to the scarp erosion. For the coarse-grained dams, they can remain stable under normal circumstances but fail by overtopping in a short duration under the extreme inflow condition. The final breach of the dam with higher fine content or larger inflow discharge is deeper and narrower. In addition, many fluctuations are observed in the changing curve of the erosion rates along the flow direction for the well-graded and coarse-grained dams. The erosion resistance of materials increases along the flow direction, which needs to be further considered in physically based breach models. Furthermore, the lateral collapse is affected by the dam material instead of inflow discharge. The lower fine content causes more lateral collapses with smaller volumes.

A tree-based gene-environment interaction analysis with rare features.

Gene-environment (G-E) interaction analysis plays a critical role in understanding and modeling complex diseases. Compared to main-effect-only analysis, it is more seriously challenged by higher dimensionality, weaker signals, and the unique "main effects, interactions" variable selection hierarchy. In joint G-E interaction analysis under which a large number of G factors are analysed in a single model, effort tailored to rare features (e.g., SNPs with low minor allele frequencies) has been limited. Existing investigations on rare features have been mostly focused on marginal analysis, where various data aggregation techniques have been developed, and hypothesis testings have been conducted to identify significant aggregated features. However, such techniques cannot be extended to joint G-E interaction analysis. In this study, building on a very recent tree-based data aggregation technique, which has been developed for main-effect-only analysis, we develop a new G-E interaction analysis approach tailored to rare features. The adopted data aggregation technique allows for more efficient information borrowing from neighboring rare features. Similar to some existing state-of-the-art ones, the proposed approach adopts penalization for variable selection, regularized estimation, and respect of the variable selection hierarchy. Simulation shows that it has more accurate identification of important interactions and main effects than several competing alternatives. In the analysis of NFBC1966 study, the proposed approach leads to findings different from the alternatives and with satisfactory prediction and stability performance.

Information-incorporated Gaussian graphical model for gene expression data.

In the analysis of gene expression data, network approaches take a system perspective and have played an irreplaceably important role. Gaussian graphical models (GGMs) have been popular in the network analysis of gene expression data. They investigate the conditional dependence between genes and "transform" the problem of estimating network structures into a sparse estimation of precision matrices. When there is a moderate to large number of genes, the number of parameters to be estimated may overwhelm the limited sample size, leading to unreliable estimation and selection. In this article, we propose incorporating information from previous studies (for example, those deposited at PubMed) to assist estimating the network structure in the present data. It is recognized that such information can be partial, biased, or even wrong. A penalization-based estimation approach is developed, shown to have consistency properties, and realized using an effective computational algorithm. Simulation demonstrates its competitive performance under various information accuracy scenarios. The analysis of TCGA lung cancer prognostic genes leads to network structures different from the alternatives.