Who Identifies as “Latinx”? The Generational Politics of Ethnoracial Labels

Social Forces ◽  
2021 ◽  
Author(s):  
G Cristina Mora ◽  
Reuben Perez ◽  
Nicholas Vargas
Keyword(s):  
Political Ideology ◽  
Early Stage ◽  
Strongly Correlated ◽  
Data Set ◽  
Progressive Politics ◽  
Generation Z ◽  
Early Adoption ◽  
Generational Politics ◽  
Survey Results ◽  
Hispanic And Latino

Abstract Over the past 5 years, the “Latinx” label has become increasingly popular within academia, politics, and social media. Yet, little is known about who has adopted the term at this relatively early stage and how it might be interpreted. Drawing on a unique data set of US-born Californians, as well as Latina/o/x Studies insight, this paper provides the first academic survey results on “Latinx” identification. We find that close to 25 percent of respondents identify with the term regularly, and that its early adoption varies across groups. Specifically, political ideology, especially progressive politics, is strongly correlated with use of the label. We also find that both immigrant generation and birth cohort matter considerably, with second generation immigrants and people born in Generation Z (since 1995) most likely to identify with the term. Finally, our findings show that “Latinx” is largely understood as complementary to, not mutually exclusive of, other panethnic labels like “Hispanic” and “Latino.” Taken together, our results suggest that generational politics matter for understanding early label diffusion and that Latinos adopt a variety of panethnic labels that they likely use in different contexts. We discuss these findings in terms of our broader understanding of Latinidad, racialization, and the symbolic power of ethnoracial labels.

Dalton and Putnam: Teaching Political Polarization to Generation Z Students

2021 ◽  
pp. 1-5
Author(s):  
Mark K. McBeth ◽  
Jules Belyea ◽  
Andrew Perry
Keyword(s):  
Political Science ◽  
Cognitive Biases ◽  
Political Polarization ◽  
Science Classrooms ◽  
Robert Putnam ◽  
Generation Z ◽  
Generational Politics ◽  
Survey Results ◽  
Approach To Teaching ◽  
Micro Level

ABSTRACT Political polarization and generational politics are important topics in contemporary political science classrooms. This article presents an approach to teaching political polarization in an introduction to politics course. Coauthored by two Generation Z students from the course and their Boomer Generation professor, the article provides conflicting views of young people and politics as found in the work of Robert Putnam and Russell Dalton. The article presents survey data on affective and issue political polarization from the course, including discussion by the two student coauthors of the survey results interpreting their generation’s political polarization. The course approaches the introductory politics course using cognitive psychology concepts including confirmative bias, motivated reasoning, and other cognitive biases. Teaching from this micro-level perspective helps students to reflect on their own political biases. The article provides concepts and readings for political science professors to use in replicating the course.

Identification of Coronary Artery Disease using Artificial Neural Network and Case Based Reasoning

2020 ◽  
Vol 13 ◽  
Author(s):  
Varun Sapra ◽  
M.L Saini ◽  
Luxmi Verma
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Early Stage ◽  
Second Phase ◽  
Case Based Reasoning ◽  
Data Set ◽  
Artery Disease ◽  
Severity Of The Disease ◽  
Case Based

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

scholarly journals Stigma, Discrimination, and Hate Crimes in Chinese-Speaking World amid Covid-19 Pandemic

2021 ◽  
Author(s):  
Jianhua Xu ◽  
Guyu Sun ◽  
Wei Cao ◽  
Wenyuan Fan ◽  
Zhihao Pan ◽  
...  
Keyword(s):  
Data Mining ◽  
Hong Kong ◽  
Political Ideology ◽  
Chinese Population ◽  
Early Stage ◽  
Chinese Language ◽  
Hate Crimes ◽  
Overseas Chinese ◽  
Mainland Chinese ◽  
Media Reports

AbstractThe Covid-19 pandemic has given rise to stigma, discrimination, and even hate crimes against various populations in the Chinese language–speaking world. Using interview data with victims, online observation, and the data mining of media reports, this paper investigated the changing targets of stigma from the outbreak of Covid-19 to early April 2020 when China had largely contained the first wave of Covid-19 within its border. We found that at the early stage of the pandemic, stigma was inflicted by some non-Hubei Chinese population onto Wuhan and Hubei residents, by some Hong Kong and Taiwan residents onto mainland Chinese, and by some Westerners towards overseas Chinese. With the number of cases outside China surpassing that in China, stigmatization was imposed by some Chinese onto Africans in China. We further explore how various factors, such as the fear of infection, food and mask culture, political ideology, and racism, affected the stigmatization of different victim groups. This study not only improved our understanding of how stigmatization happened in the Chinese-speaking world amid Covid-19 but also contributes to the literature of how sociopolitical factors may affect the production of hate crimes.

scholarly journals Social connectedness and political behavior

2017 ◽  
Vol 4 (3) ◽  
pp. 205316801771917 ◽  
Author(s):  
Jack Lyons Reilly
Keyword(s):  
Social Networks ◽  
Political Behavior ◽  
Political Ideology ◽  
Social Connectedness ◽  
Local Environment ◽  
Social Environments ◽  
Data Set ◽  
Individual Level ◽  
Nationally Representative ◽  
Socially Isolated

One of the focal points of social networks research has been the process by which individuals utilize information and cues from their social networks and communities to form political attitudes and make decisions about how and when to participate in politics. Not all individuals, however, have large social networks or are strongly connected to their local social environments. Furthermore, despite concerns about rising social isolation in American society, the role that relatively socially disconnected individuals play in politics is not well understood. Using a nationally representative data set with information about communities, social networks, and individual-level variables, this paper examines social connectedness and political behavior. Those who are more socially isolated, it is found, are neither more conservative nor liberal on any particular political issues, but clearly participate in politics less than individuals who are well connected to those around them. Finally, while individual political ideology is not correlated with isolation, the contextual influence of the local environment on individual preferences is correlated with social connectedness. When compared with well connected citizens, individuals who are more isolated are less likely to have their vote choices influenced by those around them. Individual social connectedness conditions the effect of contextual social influence.

Racial disparities in stage-specific gastric cancer: analysis of results from the Surveillance Epidemiology and End Results (SEER) program database

2017 ◽  
Vol 65 (6) ◽  
pp. 991-998 ◽  
Author(s):  
Gang Zhang ◽  
Xing Zhao ◽  
Jie Li ◽  
Yu Yuan ◽  
Ming Wen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Radiation Treatment ◽  
Early Stage ◽  
Ethnic Disparities ◽  
Incidence Rates ◽  
Pacific Islanders ◽  
Seer Program ◽  
Second Primary ◽  
Data Set ◽  
End Results

The incidence of gastric cancer is declining in western countries but continues to represent a serious health problem worldwide, especially in Asia and among Asian Americans. This study aimed to investigate ethnic disparities in stage-specific gastric cancer, including differences in incidence, treatment and survival. The cohort study was analyzed using the data set of patients with gastric cancer registered in the Surveillance, Epidemiology, and End Results (SEER) program from 2004 to 2013. Among 54,165 patients with gastric cancer, 38,308 were whites (70.7%), 7546 were blacks (13.9%), 494 were American Indian/Alaskan Natives (0.9%) and 7817 were Asians/Pacific Islanders (14.4%). Variables were patient demographics, disease characteristics, surgery/radiation treatment, overall survival (OS) and cause specific survival (CSS). Asians/Pacific Islanders demonstrated the highest incidence rates for gastric cancer compared with other groups and had the greatest decline in incidence during the study period (13.03 to 9.28 per 100,000/year), as well as the highest percentage of patients with American Joint Committee on Cancer (AJCC) early stage gastric cancer. There were significant differences between groups in treatment across stages I–IV (all p<0.001); Asians/Pacific Islanders had the highest rate of surgery plus radiation (45.1%). Significant differences were found in OS and CSS between groups (p<0.001); OS was highest among Asians/Pacific Islanders. Multivariate analysis revealed that age, race, grade, stage, location, and second primary cancer were valid prognostic factors for survival. Marked ethnic disparities exist in age-adjusted incidence of primary gastric cancer, with significant differences between races in age, gender, histological type, grade, AJCC stage, location, second cancer, treatment and survival.

Computer-assisted Diagnosis for Early Stage Pleural Mesothelioma

2007 ◽  
Vol 46 (03) ◽  
pp. 324-331 ◽  
Author(s):  
P. Jäger ◽  
S. Vogel ◽  
A. Knepper ◽  
T. Kraus ◽  
T. Aach ◽  
...  
Keyword(s):  
Early Stage ◽  
Computation Time ◽  
Hounsfield Unit ◽  
Computer Assisted ◽  
Clinical Test ◽  
Pleural Mesothelioma ◽  
Data Sets ◽  
Data Set ◽  
Average Computation Time ◽  
Ct Data

Summary Objectives: Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. Foritsearly stage, pleurectomy with perioperative treatment can reduce morbidity and mortality. The diagnosis is based on a visual investigation of CT images, which is a time-consuming and subjective procedure. Our aim is to develop an automatic image processing approach to detect and quantitatively assess pleural thickenings. Methods: We first segment the lung areas, and identify the pleural contours. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura. Results: Tests were carried out on 14 data sets from three patients. In all cases, pleural contours were reliably identified, and pleural thickenings detected. PC-based Computation times were 85 min for a data set of 716 slices, 35 min for 401 slices, and 4 min for 75 slices, resulting in an average computation time of about 5.2 s per slice. Visualizations of pleurae and detected thickeningswere provided. Conclusion: Results obtained so far indicate that our approach is able to assist physicians in the tedious task of finding and quantifying pleural thickenings in CT data. In the next step, our system will undergo an evaluation in a clinical test setting using routine CT data to quantifyits performance.

scholarly journals An Immune Signature Robustly Predicts Clinical Deterioration for Hepatitis C Virus-Related Early-Stage Cirrhosis Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Cheng Guo ◽  
Chenglai Dong ◽  
Junjie Zhang ◽  
Rui Wang ◽  
Zhe Wang ◽  
...  
Keyword(s):  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Early Stage ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Calibration Plot ◽  
Prognostic Signature ◽  
Data Set ◽  
Immune Infiltration ◽  
Immune Related Genes

Hepatitis C virus (HCV)-related cirrhosis leads to a heavy global burden of disease. Clinical risk stratification in HCV-related compensated cirrhosis remains a major challenge. Here, we aim to develop a signature comprised of immune-related genes to identify patients at high risk of progression and systematically analyze immune infiltration in HCV-related early-stage cirrhosis patients. Bioinformatics analysis was applied to identify immune-related genes and construct a prognostic signature in microarray data set. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted with the “clusterProfiler” R package. Besides, the single sample gene set enrichment analysis (ssGSEA) was used to quantify immune-related risk term abundance. The nomogram and calibrate were set up via the integration of the risk score and clinicopathological characteristics to assess the effectiveness of the prognostic signature. Finally, three genes were identified and were adopted to build an immune-related prognostic signature for HCV-related cirrhosis patients. The signature was proved to be an independent risk element for HCV-related cirrhosis patients. In addition, according to the time-dependent receiver operating characteristic (ROC) curves, nomogram, and calibration plot, the prognostic model could precisely forecast the survival rate at the first, fifth, and tenth year. Notably, functional enrichment analyses indicated that cytokine activity, chemokine activity, leukocyte migration and chemotaxis, chemokine signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved in HCV-related cirrhosis progression. Moreover, ssGSEA analyses revealed fierce immune-inflammatory response mechanisms in HCV progress. Generally, our work developed a robust prognostic signature that can accurately predict the overall survival, Child-Pugh class progression, hepatic decompensation, and hepatocellular carcinoma (HCC) for HCV-related early-stage cirrhosis patients. Functional enrichment and further immune infiltration analyses systematically elucidated potential immune response mechanisms.

scholarly journals CYSTM1: A Novel Biomarker for Hepatocellular Carcinoma Prognosis

2021 ◽  
Author(s):  
Jun Du ◽  
Jinguo Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Early Stage ◽  
Human Tumor ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Cox Proportional Hazard Regression ◽  
The Relationship

Abstract Background: The expression and molecular mechanism of cysteine rich transmembrane module containing 1 (CYSTM1) in human tumor cells remains unclear. The aim of this study was to determine whether CYSTM1 could be used as a potential prognostic biomarker for hepatocellular carcinoma (HCC).Methods: We first demonstrated the relationship between CYSTM1 expression and HCC in various public databases. Secondly, Kaplan–Meier analysis and Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CYSTM1 and the survival of HCC patients which data was downloaded in the cancer genome atlas (TCGA) database. Finally, we used the expression data of CYSTM1 in TCGA database to predict CYSTM1-related signaling pathways through bioinformatics analysis.Results: The expression level of CYSTM1 in HCC tissues was significantly correlated with T stage (p = 0.039). In addition, Kaplan–Meier analysis showed that the expression of CYSTM1 was significantly associated with poor prognosis in patients with early-stage HCC (p = 0.003). Multivariate analysis indicated that CYSTM1 is a potential predictor of poor prognosis in HCC patients (p = 0.036). The results of biosynthesis analysis demonstrated that the data set of CYSTM1 high expression was mainly enriched in neurodegeneration and oxidative phosphorylation pathways.Conclusion: CYSTM1 is an effective biomarker for the prognosis of patients with early-stage HCC and may play a key role in the occurrence and progression of HCC.

scholarly journals Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7821 ◽  
Author(s):  
Xiaoming Zhang ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
Zhengguo He ◽  
Cun Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Value ◽  
Early Stage ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

Sign in / Sign up

Export Citation Format

Share Document