Forward and Backward Continuation Ratio Models for Ordinal Response Variables

There are different types of continuation ratio (CR) models for ordinal response variables. The different model equations, corresponding parameterizations, and nonequivalent results are confusing. The purpose of this study is to introduce different types of forward and backward CR models, demonstrate how to implement these models using Stata, and compare the results using data from the Educational Longitudinal Study of 2002 (ELS:2002)

Measuring and assessing HIV/AIDS stigma and discrimination among migrant workers in Zhejiang, China

Abstract Background The aim of this study was to develop a Chinese HIV/AIDS Stigma Scale (C-HSS) and test its reliability and validity among migrant workers in eastern China. Methods Nine hundred sixty four migrant workers completed the C-HSS questionnaire in Zhejiang province. The Split-half reliability coefficient (R) and Cronbach’s alpha coefficient (a) for internal consistency of the scale were used. Factor analysis was applied for construct validity. Scores of total and subscales were compared among migrants. Correlation between scores and knowledge of HIV/AIDS was analyzed. Results The 24-items scale and the four subscales of C-HSS had good internal consistency (R overall was 0.877, subscales ranged from 0.693 to 0.862; Cronbach’s alpha overall was 0.845, subscales ranged from 0.709 to 0.810). Correlation coefficients between each domain and total score were significant (p < 0.01). The cumulative contribution rate was 54.17 % by five public factors based on exploratory factor analysis. Except for the thirteenth item and twentieth item, four public factors were in accordance with the basic conceived concept. The confirmatory factor analysis indicated a good fit to the data for the four-domain structure. Negative correlation existed between the level of HIV/AIDS knowledge and stigma. Conclusion The results suggest that the C-HSS is a reliable and valid measure for HIV/AIDS stigma in migrant workers

Sequential Wnt Agonist then Antagonist Treatment Accelerates Tissue Repair and Minimizes Fibrosis

Tissue fibrosis compromises organ function and occurs as a potential long-term outcome in response to acute tissue injuries. Currently, lack of mechanistic understanding prevents effective prevention and treatment of the progression from acute injury to fibrosis. Here, we combined quantitative experimental studies with a mouse kidney injury model and a computational approach to determine how the physiological consequences are determined by the severity of ischemia injury, and to identify how to manipulate Wnt signaling to accelerate repair of ischemic tissue damage while minimizing fibrosis. The study reveals that Wnt-mediated memory of prior injury contributes to fibrosis progression, and ischemic preconditioning reduces the risk of death but increases the risk of fibrosis. Furthermore, we validated the prediction that sequential combination therapy of initial treatment with a Wnt agonist followed by treatment with a Wnt antagonist can reduce both the risk of death and fibrosis in response to acute injuries

Overexpression of an isoform of AML1 in acute leukemia and its potential role in leukemogenesis

AML1/RUNX1 is a critical transcription factor in hematopoietic cell differentiation and proliferation. From the _AML1_ gene, at least three isoforms, _AML1a_, _AML1b_ and _AML1c_, are produced through alternative splicing. AML1a interferes with the function of AML1b/1c, which are often called AML1. In the current study, we found a higher expression level of _AML1a_ in ALL patients in comparison to the controls. Additionally, AML1a represses transcription from promotor of macrophage-colony simulating factor receptor (M-CSFR) mediated by AML1b, indicating that AML1a antagonized the effect of AML1b. In order to investigate the role of _AML1a_ in hematopoiesis and leukemogenesis _in vivo_, bone marrow mononuclear cells (BMMNCs) from mice were transduced with AML1a and transplanted into lethally irradiated mice, which develop lymphoblastic leukemia after transplantation. Taken together, these results indicate that overexpression of AML1a may be an important contributing factor to leukemogenesis

Weekend Effect in African Stock Markets

This study investigates daily stock market anomalies in the African stock markets, using two most representative stock index ETFs, each over at least eleven-year time period spanning from pre-financial crisis era to ten years into the financial crisis. This research attempts to test the presence of the weekend effect on stock returns in the African stock exchanges during the financial crisis. The results indicate a significant negative effect on Mondays. Our results shed some light on the degree of market efficiency in one of the major emerging capital markets in the world. Keywords: Monday Effect, Weekend Effect, African stock marke

Cross-Resolution Land Cover Classification Using Outdated Products and Transformers

Large-scale high-resolution land cover classification is a prerequisite for constructing Earth system models and addressing ecological and resource issues. Advancements in satellite sensor technology have led to an improvement in spatial resolution and wider coverage areas. Nevertheless, the lack of high-resolution labeled data is still a challenge, hindering the largescale application of land cover classification methods. In this paper, we propose a Transformerbased weakly supervised method for cross-resolution land cover classification using outdated data. First, to capture long-range dependencies without missing the fine-grained details of objects, we propose a U-Net-like Transformer based on a reverse difference mechanism (RDM) using dynamic sparse attention. Second, we propose an anti-noise loss calculation (ANLC) module based on optimal transport (OT). Anti-noise loss calculation identifies confident areas (CA) and vague areas (VA) based on the OT matrix, which relieves the impact of noises in outdated land cover products. By introducing a weakly supervised loss with weights and employing unsupervised loss, the RDM-based U-Net-like Transformer was trained. Remote sensing images with 1 m resolution and the corresponding ground-truths of six states in the United States were employed to validate the performance of the proposed method. The experiments utilized outdated land cover products with 30 m resolution from 2013 as training labels, and produced land cover maps with 1 m resolution from 2017. The results show the superiority of the proposed method compared to state-of-the-art methods. The code is available at https://github.com/yu-ni1989/ANLC-Former