Regression-based heterogeneity analysis to identify overlapping subgroup structure in high-dimensional data

Heterogeneity is a hallmark of complex diseases. Regression-based heterogeneity analysis, which is directly concerned with outcome-feature relationships, has led to a deeper understanding of disease biology. Such an analysis identifies the underlying subgroup structure and estimates the subgroup-specific regression coefficients. However, most of the existing regression-based heterogeneity analyses can only address disjoint subgroups; that is, each sample is assigned to only one subgroup. In reality, some samples have multiple labels, for example, many genes have several biological functions, and some cells of pure cell types transition into other types over time, which suggest that their outcome-feature relationships (regression coefficients) can be a mixture of relationships in more than one subgroups, and as a result, the disjoint subgrouping results can be unsatisfactory. To this end, we develop a novel approach to regression-based heterogeneity analysis, which takes into account possible overlaps between subgroups and high data dimensions. A subgroup membership vector is introduced for each sample, which is combined with a loss function. Considering the lack of information arising from small sample sizes, an $l_2$ norm penalty is developed for each membership vector to encourage similarity in its elements. A sparse penalization is also applied for regularized estimation and feature selection. Extensive simulations demonstrate its superiority over direct competitors. The analysis of Cancer Cell Line Encyclopedia data and lung cancer data from The Cancer Genome Atlas shows that the proposed approach can identify an overlapping subgroup structure with favorable performance in prediction and stability.Comment: 33 pages, 16 figure

Formation of the synaptonemal complex in a gynogenetic allodiploid hybrid fish

Introduction: The correct pairing and separation of homologous chromosomes during meiosis is crucial to ensure both genetic stability and genetic diversity within species. In allodiploid organisms, synapsis often fails, leading to sterility. However, a gynogenetic allodiploid hybrid clone line (GDH), derived by crossing red crucian carp (Carassius auratus ♀) and common carp (Cyprinus carpio ♂), stably produces diploid eggs. Because the GDH line carries 100 chromosomes with 50 chromosomes from the red crucian carp (RCC; ♀, 2n = 2x = 100) and 50 chromosomes from the common carp (CC; C. carpio L., ♂, 2n = 2x = 100), it is interesting to study the mechanisms of homologous chromosome pairing during meiosis in GDH individuals.Methods: By using fluorescence in situ hybridization (FISH) with a probe specific to the red crucian carp to label homologous chromosomes, we identified the synaptonemal complex via immunofluorescence assay of synaptonemal complex protein 3 (SCP3).Results: FISH results indicated that, during early ovarian development, the GDH oogonium had two sets of chromosomes with only one set from Carassius auratus, leading to the failure formation of normal bivalents and the subsequently blocking of meiosis. This inhibition lasted at least 5 months. After this long period of inhibition, pairs of germ cells fused, doubling the chromosomes such that the oocyte contained two sets of chromosomes from each parent. After chromosome doubling at 10 months old, homologous chromosomes and the synaptonemal complex were identified.Discussion: Causally, meiosis proceeded normally and eventually formed diploid germ cells. These results further clarify the mechanisms by which meiosis proceeds in hybrids

Associations of polysocial risk score with incident rosacea: a prospective cohort study of government employees in China

BackgroundThe associations between single risk factors and incident rosacea have been reported, but the effects of social risk factors from multiple domains coupled remain less studied.ObjectivesTo quantify the influence of social determinants on rosacea comprehensively and investigate associations between the polysocial risk score (PsRS) with the risks of incident rosacea.MethodsThis was a prospective cohort study of government employees undertaken from January 2018 to December 2021 among participants aged >20 from five cities in Hunan province of China. At baseline, information was collected by a questionnaire and participants were involved in an examination of the skin. Dermatologists with certification confirmed the diagnosis of rosacea. The skin health status of participants was reassessed every year since the enrolment of study during the follow-up period. The PsRS was determined using the nine social determinants of health from three social risk domains (namely socioeconomic status, psychosocial factors, and living environment). Incident rosacea was estimated using binary logistic regression models adjusted for possible confounding variables.ResultsAmong the 3,773 participants who completed at least two consecutive skin examinations, there were 2,993 participants included in the primary analyses. With 7,457 person-years of total follow-up, we detected 69 incident rosacea cases. After adjustment for major confounders, participants in the group with high social risk had significantly raised risks of incident rosacea with the adjusted odds ratio (aOR) being 2.42 (95% CI 1.06, 5.55), compared to those in low social risk group.ConclusionOur findings suggest that a higher PsRS was associated with an elevated risk of incident rosacea in our study population

Topic Modeling for Hiking Trail Online Reviews: Analysis of the Mutianyu Great Wall

Hiking is now one of the most popular activities amongst adventure travelers. Although recent studies have highlighted the differences between Chinese adventure tourists and their international counterparts, few studies have comprehensively explored the differences in hikers’ interests and concerns for experience elements between these two groups. Topic modeling is adopted for an analysis of the online reviews of the Mutianyu Great Wall to identify attributes influencing hikers’ experiences and behavior. Using a large-scale review dataset, the latent Dirichlet allocation (LDA) technique was applied to construct a comprehensive list of the topics posted by hikers. The findings revealed that Chinese and non-Chinese hikers have common concerns regarding the degree of challenges, scenery, tour services and crowding during hiking. Nevertheless, their perceptions of cultural resources are presented in a different way. These findings are beneficial for understanding the similarities and differences between Chinese and non-Chinese hikers’ perceptions, in addition to improving domestic and international markets’ management and marketing strategies

A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data

In high-throughput profiling studies, extensive efforts have been devoted to searching for the biomarkers associated with the development and progression of complex diseases. The heterogeneity of covariate effects associated with the outcomes across subjects has been noted in the literature. In this paper, we consider a scenario where the effects of covariates change smoothly across subjects, which are ordered by a known auxiliary variable. To this end, we develop a penalization-based approach, which applies a penalization technique to simultaneously select important covariates and estimate their unique effects on the outcome variables of each subject. We demonstrate that, under the appropriate conditions, our method shows selection and estimation consistency. Additional simulations demonstrate its superiority compared to several competing methods. Furthermore, applying the proposed approach to two The Cancer Genome Atlas datasets leads to better prediction performance and higher selection stability

A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data

In high-throughput profiling studies, extensive efforts have been devoted to searching for the biomarkers associated with the development and progression of complex diseases. The heterogeneity of covariate effects associated with the outcomes across subjects has been noted in the literature. In this paper, we consider a scenario where the effects of covariates change smoothly across subjects, which are ordered by a known auxiliary variable. To this end, we develop a penalization-based approach, which applies a penalization technique to simultaneously select important covariates and estimate their unique effects on the outcome variables of each subject. We demonstrate that, under the appropriate conditions, our method shows selection and estimation consistency. Additional simulations demonstrate its superiority compared to several competing methods. Furthermore, applying the proposed approach to two The Cancer Genome Atlas datasets leads to better prediction performance and higher selection stability

ACPA-Net: Atrous Channel Pyramid Attention Network for Segmentation of Leakage in Rail Tunnel Linings

The automatic segmentation of leakage in rail tunnel linings is a useful and challenging task. Unlike other scenarios, the complex environment inside the tunnels makes it difficult to obtain accurate results for the segmentation of leakages. Some deep learning-based methods have been used to automatically segment tunnel leakage, but these methods ignore the interdependencies between feature channels, which are very important for extracting robust leakage feature representations. In this work, we propose an atrous channel pyramid attention network (ACPA-Net) for rail tunnel lining leakage segmentation. In ACPA-Net, the proposed atrous channel pyramid attention (ACPA) module is added into a U-shaped segmentation network. The ACPA module can effectively strengthen the representation ability of ACPA-Net by explicitly modeling the dependencies between feature channels. In addition, a deep supervision strategy that helps ACPA-Net improve its discrimination ability has also been introduced into ACPA-Net. A rail tunnel leakage image dataset consisting of 1370 images with manual annotation maps is built to verify the effectiveness of ACPA-Net. The final experiment shows that ACPA-Net achieves state-of-the-art performance on the Crack500 dataset and our rail tunnel leakage image dataset, and our method has the least number of parameters of all the methods

Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion

Heterogeneity is a hallmark of many complex diseases. There are multiple ways of defining heterogeneity, among which the heterogeneity in genetic regulations, for example, gene expressions (GEs) by copy number variations (CNVs), and methylation, has been suggested but little investigated. Heterogeneity in genetic regulations can be linked with disease severity, progression, and other traits and is biologically important. However, the analysis can be very challenging with the high dimensionality of both sides of regulation as well as sparse and weak signals. In this article, we consider the scenario where subjects form unknown subgroups, and each subgroup has unique genetic regulation relationships. Further, such heterogeneity is “guided” by a known biomarker. We develop a multivariate sparse fusion (MSF) approach, which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and regulation relationships within each subgroup. An effective computational algorithm is developed, and extensive simulations are conducted. The analysis of heterogeneity in the GE-CNV regulations in melanoma and GE-methylation regulations in stomach cancer using the TCGA data leads to interesting findings

Associations between Street-View Perceptions and Housing Prices: Subjective vs. Objective Measures Using Computer Vision and Machine Learning Techniques

This study investigated the extent to which subjectively and objectively measured street-level perceptions complement or conflict with each other in explaining property value. Street-scene perceptions can be subjectively assessed from self-reported survey questions, or objectively quantified from land use data or pixel ratios of physical features extracted from street-view imagery. Prior studies mainly relied on objective indicators to describe perceptions and found that a better street environment is associated with a price premium. While very few studies have addressed the impact of subjectively-assessed perceptions. We hypothesized that human perceptions have a subtle relationship to physical features that cannot be comprehensively captured with objective indicators. Subjective measures could be more effective to describe human perceptions, thus might explain more housing price variations. To test the hypothesis, we both subjectively and objectively measured six pairwise eye-level perceptions (i.e., Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity). We then investigated their coherence and divergence for each perception respectively. Moreover, we revealed their similar or opposite effects in explaining house prices in Shanghai using the hedonic price model (HPM). Our intention was not to make causal statements. Instead, we set to address the coherent and conflicting effects of the two measures in explaining people’s behaviors and preferences. Our method is high-throughput by extending classical urban design measurement protocols with current artificial intelligence (AI) frameworks for urban-scene understanding. First, we found the percentage increases in housing prices attributable to street-view perceptions were significant for both subjective and objective measures. While subjective scores explained more variance over objective scores. Second, the two measures exhibited opposite signs in explaining house prices for Greenness and Imageability perceptions. Our results indicated that objective measures which simply extract or recombine individual streetscape pixels cannot fully capture human perceptions. For perceptual qualities that were not familiar to the average person (e.g., Imageability), a subjective framework exhibits better performance. Conversely, for perceptions whose connotation are self-evident (e.g., Greenness), objective measures could outperform the subjective counterparts. This study demonstrates a more holistic understanding for street-scene perceptions and their relations to property values. It also sheds light on future studies where the coherence and divergence of the two measures could be further stressed

Self-Adaptive Switch Enabling Complete Charge Separation in Molecular-Based Optoelectronic Conversion

Achieving high charge recombination probability has been the major challenge for the practical utilization of molecule-based solar harvesting. Molecular switches were introduced to stabilize the charge separation state in donor–acceptor systems, but it is difficult to seamlessly incorporate the ON/OFF switching actions into the optoelectronic conversion cycle. Here we present a self-adaptive system in which the donor and acceptor are bridged by a switchable moiety that enables a complete charge separation repeatedly. Calculations are presented for a platinum­(II) terpyridyl complex with an azobenzene bridge. The charge transfer induced by light extracts electrons from the azobenzene group, automatically triggering a <i>trans</i> → <i>cis</i> isomerization. The resulting conformation suppresses charge recombination. Energized charges are trapped in the acceptor, ready for charge collection by electrodes. The bridge then goes through inverse isomerization to restore the conjugation and conductance. This self-adaptive design provides a novel way to improve the performance of optoelectronic conversion and realize practical solar-harvesting applications in organic molecular systems