Search

Liposarcoma (LPS) is a rare soft tissue sarcoma that develops from the differentiation of fat cells, typically occurring in the lower extremities and retroperitoneal space. Depending on its histological morphology and molecular changes, LPS can be divided into various subtypes, each exhibiting distinct biological behaviors. During treatment, especially for LPS arising in the retroperitoneum, the extent and quality of the initial surgery are critically important. Treatment strategies must be tailored to the specific type of LPS. Over the past few decades, the treatment of LPS has undergone numerous advancements, with new therapeutic approaches such as targeted drugs and immunotherapies continually emerging. This paper reviews the biological characteristics, molecular alterations, as well as surgical and pharmacological treatments of various LPS subtypes, with the aim of enhancing clinicians' understanding and emphasizing the importance of individualized precision therapy. With a deeper understanding of the biological characteristics and molecular alterations of LPS, future treatment trends are likely to focus more on developing personalized treatment plans to better address the various types of LPS.

Abstract Plant-specific WRKY transcription factors play important roles in regulating the expression of defense-responsive genes against pathogen attack. A multiple stress-responsive WRKY gene, ZmWRKY65, was identified in maize by screening salicylic acid (SA)-induced de novo transcriptomic sequences. The ZmWRKY65 protein was localized in the nucleus of mesophyll protoplasts. The analysis of the ZmWRKY65 promoter sequence indicated that it contains several stress-related transcriptional regulatory elements. Many environmental factors affecting the transcription of ZmWRKY65 gene, such as drought, salinity, high temperature and low temperature stress. Moreover, the transcription of ZmWRKY65 gene was also affected by the induction of defense related plant hormones such as SA and exogenous ABA. The results of seed germination and stomatal aperture assays indicated that transgenic Arabidopsis plants exhibit enhanced sensitivity to ABA and high concentrations of SA. Overexpression of ZmWRKY65 improved tolerance to both pathogen attack and abiotic stress in transgenic Arabidopsis plants and activated several stress-related genes such as RD29A, ERD10, and STZ as well as pathogenesis-related (PR) genes such as PR1, PR2 and PR5; these genes are involved in resistance to abiotic and biotic stresses in Arabidopsis. Together, this evidence implies that the ZmWRKY65 gene is involved in multiple stress signal transduction pathways.

Adult Chrysoperla sinica Tjeder is a common pollen feeder in maize fields. They are thus directly exposed to insecticidal proteins by consumption of genetically engineered maize pollen containing Bacillus thuringiensis (Bt) proteins. Here we assessed the potential effects of Cry1Ab/2Aj- or Cry1Ac-containing Bt maize pollen on the fitness of adult C. sinica via a dietary-exposure assay under laboratory conditions. Survival, pre-oviposition, fecundity and adult dry weight did not differ between adult C. sinica consuming Bt or the corresponding non-Bt maize pollen. The stability of the Cry protein in the food sources and uptake of the Cry protein by adult C. sinica during the feeding experiment were confirmed by ELISA. These results demonstrate that adult C. sinica are not affected by the consumption of Cry1Ab/2Aj- or Cry1Ac-containing maize pollen, suggesting that production of Bt maize expressing cry1Ab/2Aj or cry1Ac genes will pose a negligible risk to adult C. sinica.

Dehydration-responsive element-binding (DREB) transcription factors regulate diverse processes during plant development. Here, a 2-year field study was conducted to assess the potential effects of DREB-genetically modified maize (GM1) on arthropod species and ecological communities. Arthropod abundance, diversity, and community composition in GM1 and its non-transformed counterpart maize variety, Chang 7-2, were compared using whole plant inspection, pitfall trap, and suction sampler methods. Based on Shannon–Wiener diversity, Simpson’s diversity, Pielou’s indexes, number of species, and total number of individuals, GM1 had a negligible effect on arthropod abundance and diversity. Redundancy analysis indicated that the composition of arthropod community was not associated with maize type in the three investigation methods, while it exhibited significant correlation with year and sampling time in whole plant inspection and suction sample methods, and distinctly correlated with sampling time in the pitfall trap method. Nonmetric multidimensional scaling analysis of variable factors in the three investigation methods showed that sampling time, rather than maize type or year, was closely related to the composition of arthropod community in the field. Our results provide direct evidence to support that DREB-GM maize had negligible effects on arthropods in the Jilin Province under natural conditions.

To evaluate the effect of Bt maize expressing Cry1Ie protein on non-target soil Collembola, a two-year field study was conducted in Northeast China. Bt maize line IE09S034 and its near isoline Zong 31 were selected as experimental crops; we investigated the collembolan community using both taxonomic and trait-based approaches, and elucidated the relationship between environmental variables and the collembolan community using redundancy analysis (RDA).The ANOVA results showed that maize variety neither had significant effect on the parameters based on taxonomic approach (abundance, species richness, Shannon–Wiener index, Pielou’s evenness index), nor on the parameters based on trait-based approach (ocelli number, body length, pigmentation level, and furcula development) in either year. The results of RDA also showed that maize variety did not affect collembolan community significantly. These results suggest that two years cultivation of cry1Ie maize does not affect collembolan community in Northeast China.

The plant-special SHI-RELATED SEQUENCE (SRS) family plays vital roles in various biological processes. However, the genome-wide analysis and abiotic stress-related functions of this family were less reported in soybean. In this work, 21 members of soybean SRS family were identified, which were divided into three groups (Group I, II, and III). The chromosome location and gene structure were analyzed, which indicated that the members in the same group may have similar functions. The analysis of stress-related cis-elements showed that the SRS family may be involved in abiotic stress signaling pathway. The analysis of expression patterns in various tissues demonstrated that SRS family may play crucial roles in special tissue-dependent regulatory networks. The data based on soybean RNA sequencing (RNA-seq) and quantitative Real-Time PCR (qRT-PCR) proved that SRS genes were induced by drought, NaCl, and exogenous abscisic acid (ABA). GmSRS18 significantly induced by drought and NaCl was selected for further functional verification. GmSRS18, encoding a cell nuclear protein, could negatively regulate drought and salt resistance in transgenic Arabidopsis. It can affect stress-related physiological index, including chlorophyll, proline, and relative electrolyte leakage. Additionally, it inhibited the expression levels of stress-related marker genes. Taken together, these results provide valuable information for understanding the classification of soybean SRS transcription factors and indicates that SRS plays important roles in abiotic stress responses.

This article considers a Bayesian approach for analyzing a longitudinal 2-level nonlinear structural equation model with covariates, and mixed continuous and ordered categorical variables. The first-level model is formulated for measures taken at each time point nested within individuals for investigating their characteristics that are dynamically changed over time. The second level is defined for individuals for assessing their characteristics that are invariant with time. The proposed longitudinal structural equation model also accommodates missing data. A Bayesian approach is developed for estimation of parameters and model comparison. The newly developed methodologies are applied to a longitudinal study concerning cocaine use. [ABSTRACT FROM AUTHOR]

Structural equation models have wide applications. One of the most important issues in analyzing structural equation models is model comparison. This article proposes a Bayesian model comparison statistic, namely the Lν-measure for both semiparametric and parametric structural equation models. For illustration purposes, we consider a Bayesian semiparametric approach for estimation and model comparison in the context of structural equation models with fixed covariates. A finite dimensional Dirichlet process is used to model the crucial latent variables, and a blocked Gibbs sampler is implemented for estimation. Empirical performance of the Lν-measure is evaluated through a simulation study. Results obtained indicate that the Lν-measure, which additionally requires very minor computational effort, gives satisfactory performance. Moreover, the methodologies are demonstrated through an example with a real data set on kidney disease. Finally, the application of the Lν-measure to Bayesian semiparametric nonlinear structural equation models is outlined. [ABSTRACT FROM AUTHOR]

Analysis of ordered binary and unordered binary data has received considerable attention in social and psychological research. This article introduces a Bayesian approach, which has several nice features in practical applications, for analyzing nonlinear structural equation models with dichotomous data. We demonstrate how to use the software WinBUGS and R2WinBUGS to obtain Bayesian estimates of the unknown parameters, estimates of latent variables, and the Deviance Information Criterion for model comparison. An illustrative example with an artificial data set is provided. Finally, simulation studies are conducted, not only to reveal the empirical performance of the Bayesian approach, but also to show that incorrectly treating binary data as ordinal, and vice versa, would produce misleading results. [ABSTRACT FROM AUTHOR]

In longitudinal studies, investigators often measure multiple variables at multiple time points and are interested in investigating individual differences in patterns of change on those variables. Furthermore, in behavioral, social, psychological, and medical research, investigators often deal with latent variables that cannot be observed directly and should be measured by 2 or more manifest variables. Longitudinal latent variables occur when the corresponding manifest variables are measured at multiple time points. Our primary interests are in studying the dynamic change of longitudinal latent variables and exploring the possible interactive effect among the latent variables. Much of the existing research in longitudinal studies focuses on studying change in a single observed variable at different time points. In this article, we propose a novel latent curve model (LCM) for studying the dynamic change of multivariate manifest and latent variables and their linear and interaction relationships. The proposed LCM has the following useful features: First, it can handle multivariate variables for exploring the dynamic change of their relationships, whereas conventional LCMs usually consider change in a univariate variable. Second, it accommodates both first- and second-order latent variables and their interactions to explore how changes in latent attributes interact to produce a joint effect on the growth of an outcome variable. Third, it accommodates both continuous and ordered categorical data, and missing data. [ABSTRACT FROM AUTHOR]

Structural equation models are widely appreciated in behavioral, social, and psychological research to model relations between latent constructs and manifest variables, and to control for measurement errors. Most applications of structural equation models are based on fully observed data that are independently distributed. However, hierarchical data with a correlated structure are common in behavioral research, and very often, missing data are encountered. In this article, we propose a 2-level structural equation model for analyzing hierarchical data with missing entries, and describe a Bayesian approach for estimation and model comparison. We show how to use WinBUGS software to get the solution conveniently. The proposed methodologies are illustrated through a simulation study, and a real application in relation to organizational and management research concerning the study of the interrelationships of the latent constructs about job satisfaction, job responsibility, and life satisfaction for citizens in 43 countries. [ABSTRACT FROM AUTHOR]

This article proposes an intuitive approach for predictive discriminant analysis with mixed continuous, dichotomous, and ordered categorical variables that are defined via an underlying multivariate normal distribution with a threshold specification. The classification rule is based on the comparison of the observed data logarithm probability density functions. To reduce the computational burden, the analysis is conducted in the context of a confirmatory factor analysis model with independent error measurements. Identification of the dichotomous and ordered categorical variables is discussed. Results are obtained by implementations of a Monte Carlo expectation maximization (MCEM)algorithm and a path sampling procedure. Probabilities of misclassification are estimated via the idea of the "jackknife" method. A real example is given to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

The analysis of interaction among latent variables has received much attention. This article introduces a Bayesian approach to analyze a general structural equation model that accommodates the general nonlinear terms of latent variables and covariates. This approach produces a Bayesian estimate that has the same statistical optimal properties as a maximum likelihood estimate. Other advantages over the traditional approaches are discussed. More important, we demonstrate through examples how to use the freely available software WinBUGS to obtain Bayesian results for estimation and model comparison. Simulation studies are conducted to assess the empirical performances of the approach for situations with various sample sizes and prior inputs. [ABSTRACT FROM AUTHOR]