Research on Construction of Female Image in the Media and Entertainment Industry Women—The Past, the Present and the Future

The development of the Internet does not necessarily promote the harmonious development of gender in society. The focus and gaze of the Internet media on women’s bodies constructs the image of “being seen” on the Internet, which is a derogatory and distorted image of women. In the unequal relationship of “seeing and being seen”, the female body is an important carrier. The silence chosen by online audiences has further led to the online media’s eagerness to portray “being seen” women. News media and film and literature have not done enough to reflect and represent women in a fair, balanced, and consistent manner, and the number, perspective, and content of their works are not commensurate with the actual role women play in social production. This paper focuses on several images of women constructed by the news media and film and literature, analyzes their underlying causes and their impact on women, and tries to suggest possibilities for changing this phenomenon

Significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer

BackgroundThe poor clinical accuracy to predict the survival of colon cancer patients is associated with a high incidence rate and a poor 3-year survival rate. This study aimed to identify the poor prognostic biomarkers of colon cancer from natural killer cell-mediated cytotoxic pathway (NKCP), and establish a logistical regression scoring model to predict its prognosis.MethodsBased on the expressions and methylations of NKCP-related genes (NRGs) and the clinical information, dimensionality reduction screening was performed to establish a logistic regression scoring model to predict survival and prognosis. Risk score, clinical stage, and ULBP2 were used to establish a logistic regression scoring model to classify the 3-year survival period and compare with each other. Comparison of survival, tumor mutation burden (TMB), estimation of immune invasion, and prediction of chemotherapeutic drug IC50 were performed between low- and high-risk score groups.ResultsThis study found that ULBP2 was significantly overexpressed in colon cancer tissues and colon cancer cell lines. The logistic regression scoring model was established to include six statistically significant features: S = 1.70 × stage – 9.32 × cg06543087 + 6.19 × cg25848557 + 1.29 × IFNA1 + 0.048 × age + 4.37 × cg21370856 − 8.93, which was used to calculate risk score of each sample. The risk scores, clinical stage, and ULBP2 were classified into three-year survival, the 3-year prediction accuracy based on 10-fold cross-validation was 80.17%, 67.24, and 59.48%, respectively. The survival time of low-risk score group was better than that of the high-risk score group. Moreover, compared to high-risk score group, low-risk score group had lower TMB [2.20/MB (log10) vs. 2.34/MB (log10)], higher infiltration score of M0 macrophages (0.17 vs. 0.14), and lower mean IC50 value of oxaliplatin (3.65 vs 3.78) (p < 0.05).ConclusionsThe significantly upregulated ULBP2 was a poor prognostic biomarker of colon cancer. The risk score based on the six-feature logistic regression model can effectively predict the 3-year survival time. High-risk score group demonstrated a poorer prognosis, higher TMB, lower M0 macrophage infiltration score, and higher IC50 value of oxaliplatin. The six-feature logistic scoring model has certain clinical significance in colon cancer

Regulator of G Protein Signaling Protein 12 (Rgs12) Controls Mouse Osteoblast Differentiation via Calcium Channel/Oscillation and Gαi-ERK Signaling

Bone homeostasis intimately relies on the balance between osteoblasts (OBs) and osteoclasts (OCs). Our previous studies have revealed that regulator of G protein signaling protein 12 (Rgs12), the largest protein in the Rgs super family, is essential for osteoclastogenesis from hematopoietic cells and OC precursors. However, how Rgs12 regulates OB differentiation and function is still unknown. To understand that, we generated an OB-targeted Rgs12 conditional knockout (CKO) mice model by crossing Rgs12 fl/fl mice with Osterix (Osx)-Cre transgenic mice. We found that Rgs12 was highly expressed in both OB precursor cells (OPCs) and OBs of wild-type (WT) mice, and gradually increased during OB differentiation, whereas Rgs12-CKO mice (Osx Cre/+ ; Rgs12 fl/fl ) exhibited a dramatic decrease in both trabecular and cortical bone mass, with reduced numbers of OBs and increased apoptotic cell population. Loss of Rgs12 in OPCs in vitro significantly inhibited OB differentiation and the expression of OB marker genes, resulting in suppression of OB maturation and mineralization. Further mechanism study showed that deletion of Rgs12 in OPCs significantly inhibited guanosine triphosphatase (GTPase) activity and cyclic adenosine monophosphate (cAMP) level, and impaired Calcium (Ca 2+ ) oscillations via restraints of major Ca 2+ entry sources (extracellular Ca 2+ influx and intracellular Ca 2+ release from endoplasmic reticulum), partially contributed by the blockage of L-type Ca 2+ channel mediated Ca 2+ influx. Downstream mediator extracellular signal-related protein kinase (ERK) was found inactive in OBs of Osx Cre/+ ; Rgs12 fl/fl mice and in OPCs after Rgs12 deletion, whereas application of pertussis toxin (PTX) or overexpression of Rgs12 could rescue the defective OB differentiation via restoration of ERK phosphorylation. Our findings reveal that Rgs12 is an important regulator during osteogenesis and highlight Rgs12 as a potential therapeutic target for bone disorders. © 2018 American Society for Bone and Mineral Research. © 2018 American Society for Bone and Mineral Researc

Application of area scaling analysis to identify natural killer cell and monocyte involvement in the GranToxiLux antibody dependent cell-mediated cytotoxicity assay

Several different assay methodologies have been described for the evaluation of HIV or SIV-specific antibody-dependent cell-mediated cytotoxicity (ADCC). Commonly used assays measure ADCC by evaluating effector cell functions, or by detecting elimination of target cells. Signaling through Fc receptors, cellular activation, cytotoxic granule exocytosis, or accumulation of cytolytic and immune signaling factors have been used to evaluate ADCC at the level of the effector cells. Alternatively, assays that measure killing or loss of target cells provide a direct assessment of the specific killing activity of antibodies capable of ADCC. Thus, each of these two distinct types of assays provides information on only one of the critical components of an ADCC event; either the effector cells involved, or the resulting effect on the target cell. We have developed a simple modification of our previously described high-throughput ADCC GranToxiLux (GTL) assay that uses area scaling analysis (ASA) to facilitate simultaneous quantification of ADCC activity at the target cell level, and assessment of the contribution of natural killer cells and monocytes to the total observed ADCC activity when whole human peripheral blood mononuclear cells are used as a source of effector cells. The modified analysis method requires no additional reagents and can, therefore, be easily included in prospective studies. Moreover, ASA can also often be applied to pre-existing ADCC-GTL datasets. Thus, incorporation of ASA to the ADCC-GTL assay provides an ancillary assessment of the ability of natural and vaccine-induced antibodies to recruit natural killer cells as well as monocytes against HIV or SIV; or to any other field of research for which this assay is applied

A comparison of hemagglutination inhibition and neutralization assays for characterizing immunity to seasonal influenza A

SummaryBackgroundSerum antibody to influenza can be used to identify past exposure and measure current immune status. The two most common methods for measuring this are the hemagglutination inhibition assay (HI) and the viral neutralization assay (NT), which have not been systematically compared for a large number of influenza viruses.Methods151 study participants from near Guangzhou, China were enrolled in 2009 and provided serum. HI and NT assays were performed for 12 historic and recently circulating strains of seasonal influenza A. We compared titers using Spearman correlation and fit models to predict NT using HI results.ResultsWe observed high positive mean correlation between HI and NT assays (Spearman's rank correlation, rho=0.86) across all strains. Correlation was highest within subtypes and within close proximity in time. Overall, an HI=20 corresponded to NT=10, and HI=40 corresponded to NT=20. Linear regression of log(NT) on log(HI) was statistically significant, with age modifying this relationship. Strain-specific area under a curve (AUC) indicated good accuracy (>80%) for predicting NT with HI.ConclusionsWhile we found high overall correspondence of titers between NT and HI assays for seasonal influenza A, no exact equivalence between assays could be determined. This was further complicated by correspondence between titers changing with age. These findings support generalized comparison of results between assays and give further support for use of the hemagglutination inhibition assay over the more resource intensive viral neutralization assay for seasonal influenza A, though attention should be given to the effect of age on these assays

Identifying hypermethylated CpG islands using a quantile regression model

<p>Abstract</p> <p>Background</p> <p>DNA methylation has been shown to play an important role in the silencing of tumor suppressor genes in various tumor types. In order to have a system-wide understanding of the methylation changes that occur in tumors, we have developed a differential methylation hybridization (DMH) protocol that can simultaneously assay the methylation status of all known CpG islands (CGIs) using microarray technologies. A large percentage of signals obtained from microarrays can be attributed to various measurable and unmeasurable confounding factors unrelated to the biological question at hand. In order to correct the bias due to noise, we first implemented a quantile regression model, with a quantile level equal to 75%, to identify hypermethylated CGIs in an earlier work. As a proof of concept, we applied this model to methylation microarray data generated from breast cancer cell lines. However, we were unsure whether 75% was the best quantile level for identifying hypermethylated CGIs. In this paper, we attempt to determine which quantile level should be used to identify hypermethylated CGIs and their associated genes.</p> <p>Results</p> <p>We introduce three statistical measurements to compare the performance of the proposed quantile regression model at different quantile levels (95%, 90%, 85%, 80%, 75%, 70%, 65%, 60%), using known methylated genes and unmethylated housekeeping genes reported in breast cancer cell lines and ovarian cancer patients. Our results show that the quantile levels ranging from 80% to 90% are better at identifying known methylated and unmethylated genes.</p> <p>Conclusions</p> <p>In this paper, we propose to use a quantile regression model to identify hypermethylated CGIs by incorporating probe effects to account for noise due to unmeasurable factors. Our model can efficiently identify hypermethylated CGIs in both breast and ovarian cancer data.</p

Preprocessing differential methylation hybridization microarray data

<p>Abstract</p> <p>Background</p> <p>DNA methylation plays a very important role in the silencing of tumor suppressor genes in various tumor types. In order to gain a genome-wide understanding of how changes in methylation affect tumor growth, the differential methylation hybridization (DMH) protocol has been developed and large amounts of DMH microarray data have been generated. However, it is still unclear how to preprocess this type of microarray data and how different background correction and normalization methods used for two-color gene expression arrays perform for the methylation microarray data. In this paper, we demonstrate our discovery of a set of internal control probes that have log ratios (M) theoretically equal to zero according to this DMH protocol. With the aid of this set of control probes, we propose two LOESS (or LOWESS, locally weighted scatter-plot smoothing) normalization methods that are novel and unique for DMH microarray data. Combining with other normalization methods (global LOESS and no normalization), we compare four normalization methods. In addition, we compare five different background correction methods.</p> <p>Results</p> <p>We study 20 different preprocessing methods, which are the combination of five background correction methods and four normalization methods. In order to compare these 20 methods, we evaluate their performance of identifying known methylated and un-methylated housekeeping genes based on two statistics. Comparison details are illustrated using breast cancer cell line and ovarian cancer patient methylation microarray data. Our comparison results show that different background correction methods perform similarly; however, four normalization methods perform very differently. In particular, all three different LOESS normalization methods perform better than the one without any normalization.</p> <p>Conclusions</p> <p>It is necessary to do within-array normalization, and the two LOESS normalization methods based on specific DMH internal control probes produce more stable and relatively better results than the global LOESS normalization method.</p

Endocrine therapy resistant ESR1 variants revealed by genomic characterization of breast cancer derived xenografts

To characterize patient-derived xenografts (PDXs) for functional studies, we made whole-genome comparisons with originating breast cancers representative of the major intrinsic subtypes. Structural and copy number aberrations were found to be retained with high fidelity. However, at the single-nucleotide level, variable numbers of PDX-specific somatic events were documented, although they were only rarely functionally significant. Variant allele frequencies were often preserved in the PDXs, demonstrating that clonal representation can be transplantable. Estrogen-receptor-positive PDXs were associated with ESR1 ligand-binding-domain mutations, gene amplification, or an ESR1/YAP1 translocation. These events produced different endocrine-therapy-response phenotypes in human, cell line, and PDX endocrine-response studies. Hence, deeply sequenced PDX models are an important resource for the search for genome-forward treatment options and capture endocrine-drug-resistance etiologies that are not observed in standard cell lines. The originating tumor genome provides a benchmark for assessing genetic drift and clonal representation after transplantation

Endocrine-Therapy-Resistant ESR1 Variants Revealed by Genomic Characterization of Breast-Cancer-Derived Xenografts

To characterize patient-derived xenografts (PDXs) for functional studies, we made whole-genome comparisons with originating breast cancers representative of the major intrinsic subtypes. Structural and copy number aberrations were found to be retained with high fidelity. However, at the single-nucleotide level, variable numbers of PDX-specific somatic events were documented, although they were only rarely functionally significant. Variant allele frequencies were often preserved in the PDXs, demonstrating that clonal representation can be transplantable. Estrogen-receptor-positive PDXs were associated with ESR1 ligand-binding-domain mutations, gene amplification, or an ESR1/YAP1 translocation. These events produced different endocrine-therapy-response phenotypes in human, cell line, and PDX endocrine-response studies. Hence, deeply sequenced PDX models are an important resource for the search for genome-forward treatment options and capture endocrine-drug-resistance etiologies that are not observed in standard cell lines. The originating tumor genome provides a benchmark for assessing genetic drift and clonal representation after transplantation