Policy makers and researchers are interested in the factors driving gentrification, a process often associated with the displacement of lower-income residents by higher-income, more educated households. This study examines one potential factor: the influx of same-sex couples into a community. Anecdotal evidence suggests there is a connection between the presence of same-sex couples and gentrification. We empirically investigate this relationship using both least-squares methods and an instrumental variables strategy. In our instrumental variables approach, we use voting results for the state-level equivalent of the Defense of Marriage Act in Ohio as an instrument for changes in the number of same-sex couples. Our findings consistently demonstrate that areas experiencing a larger increase in same-sex couples are more likely to undergo gentrification. Furthermore, semiparametric analysis reveals a tipping point beyond which gentrification becomes significantly more likely, suggesting that the clustering of same-sex couples contributes to neighborhood change.
Systemic lupus erythematosus (SLE) is known to be clinically heterogeneous. Previous efforts to characterize subsets of SLE patients based on gene expression analysis have not been reproduced because of small sample sizes or technical problems. The aim of this study was to develop a robust patient stratification system using gene expression profiling to characterize individual lupus patients. We employed gene set variation analysis (GSVA) of informative gene modules to identify molecular endotypes of SLE patients, machine learning (ML) to classify individual patients into molecular subsets, and logistic regression to develop a composite metric estimating the scope of immunologic perturbations. SHapley Additive ExPlanations (SHAP) revealed the impact of specific features on patient sub-setting. Using five datasets comprising 2183 patients, eight SLE endotypes were identified. Expanded analysis of 3166 samples in 17 datasets revealed that each endotype had unique gene enrichment patterns, but not all endotypes were observed in all datasets. ML algorithms trained on 2183 patients and tested on 983 patients not used to develop the model demonstrated effective classification into one of eight endotypes. SHAP indicated a unique array of features influential in sorting individual samples into each of the endotypes. A composite molecular score was calculated for each patient and significantly correlated with standard laboratory measures. Significant differences in clinical characteristics were associated with different endotypes, with those with the least perturbed transcriptional profile manifesting lower disease severity. The more abnormal endotypes were significantly more likely to experience a severe flare over the subsequent 52 weeks while on standard-of-care medication and specific endotypes were more likely to be clinical responders to the investigational product tested in one clinical trial analyzed (tabalumab). Transcriptomic profiling and ML reproducibly separated lupus patients into molecular endotypes with significant differences in clinical features, outcomes, and responsiveness to therapy. Our classification approach using a composite scoring system based on underlying molecular abnormalities has both staging and prognostic relevance.
Biomarkers of potential harm (BoPH) are indicators of biological perturbations which may contribute to the pathophysiology of disease. In this review, we critically assessed the published data on lung-related BoPH in human lung disease for potential use in evaluating the effects of tobacco and nicotine products. A Scopus literature search was conducted on lung disease biomarkers used in a clinical setting over the last 10 years. We identified 1171 papers which were further screened using commercial software (Sciome SWIFT-Active Screener) giving 68 publications that met our inclusion criteria (data on the association of the biomarker with cigarette smoking, the impact of smoking cessation on the biomarker, and differences between smokers and non-smokers), the majority of which investigated chronic obstructive pulmonary disease. Several physiological and biochemical measures were identified that are potentially relevant for evaluating the impact of tobacco products on lung health. Promising new candidates included blood biomarkers, such as surfactant protein D (SP-D), soluble receptor for advanced glycation end products (sRAGE), skin autofluorescence (SAF), and imaging techniques. These biomarkers may provide insights into lung disease development and progression; however, all require further research and validation to confirm their role in the context of tobacco and nicotine exposure, their time course of development and ability to measure or predict disease progression.
Manufacturing of high temperature materials is an energy expensive process, and conventional techniques require long periods of homogenization to ensure uniform chemical composition in large batches of material. Several covalent ceramics, and high temperature materials can be manufactured by utilizing the exothermicity of the reactants. Some of the salient features of combustion synthesis, approaches to densify the combustion synthesized product, and other processes that utilize exothermic reactions for the synthesis of materials have been discussed.
