Economic and Socio-Psychological Analyses of Social Housing Policies in the U.K.

Whilst access to housing is a fundamental part of the United Nation’s Universal Declaration of Human Rights, it remains an unfulfilled objective in the U.K. On the contrary, the U.K. housing crisis has continued to worsen, with housing affordability deteriorating significantly since the 1980s due to the increased financialisation of housing. The crisis is particularly reflected in the social housing sector, where contemporary discussions on potential drivers have focused on structural ‘supply’ and other issues that can be easily materialised or quantified. However, issues beyond supply have often been overlooked in quantitative housing studies. Therefore, I aim to bridge the research gap by discussing social housing issues beyond ‘bricks and mortar’. This paper contributes to two further research gaps. First, there remains limited attempts in bringing Bourdieusian social theories into social housing studies and policy making. Second, incorporating computational modelling into social housing studies remains an under-explored area. The analysis is predominantly based on a case study of London, utilising Zoopla rental listings and granular neighbourhood data. The main research methods involve a range of econometric techniques including hedonic modelling, spatial analysis and panel data regression. Furthermore, I apply computational simulation methods including agent-based modelling and Monte-Carlo simulations. The findings draw the following key insights. First, residents and relocators make housing choices to maximise both material and objective benefits, as well as immaterial and subjective benefits. Second, distinct habitus exists between family and non-family households, between different socio-economic statuses, and between suburban and Central London locations. In addition, migrants carry their habitus into their newly migrated country, which may be conveyed in their benefit claiming behaviour. The research findings suggest that a multi-agency partnership is required to establish a sustainable social housing policy framework. Moreover, there is a need to critically reassess the fundamental philosophy of the current social housing policies

Tilings of the sphere by congruent quadrilaterals II: edge combination a3ba^3 b with rational angles

Edge-to-edge tilings of the sphere by congruent quadrilaterals are completely classified in a series of three papers. This second one applies the powerful tool of trigonometric Diophantine equations to classify the case of $a^3b$-quadrilaterals with all angles being rational degrees. There are $12$ sporadic and $3$ infinite sequences of quadrilaterals admitting the $2$-layer earth map tilings together with their modifications, and $3$ sporadic quadrilaterals admitting $4$ exceptional tilings. Among them only $3$ quadrilaterals are convex. New interesting non-edge-to-edge triangular tilings are obtained as a byproduct.Comment: 36 pages, 36 figures, 10 table

Tilings of the sphere by congruent quadrilaterals III: edge combination a3ba^3b with general angles

Edge-to-edge tilings of the sphere by congruent quadrilaterals are completely classified in a series of three papers. This last one classifies the case of $a^3b$-quadrilaterals with some irrational angle: there are a sequence of $1$-parameter families of quadrilaterals admitting $2$-layer earth map tilings together with their basic flip modifications under extra condition, and $5$ sporadic quadrilaterals each admitting a special tiling. A summary of the full classification is presented in the end.Comment: 29 pages, 22 figures, 12 tabl

Tilings of the sphere by congruent quadrilaterals I: edge combination a2bca^2bc

The edge-to-edge tilings of the sphere by congruent quadrilaterals of Type $a^2bc$ are classified as $3$ classes: a sequence of two-parameter families of $2$-layer earth map tilings with $2n$ $(n\ge3)$ tiles, a one-parameter family of quadrilateral subdivisions of the octahedron with $24$ tiles together with a flip modification for a special parameter, and a sequence of $3$-layer earth map tilings with $8n$ $(n\ge2)$ tiles together with two flip modifications for odd $n$. We also describe the moduli and calculate the geometric data.Comment: Final version: 44 pages, 42 figures, 2 tables. Thank two junior students Fangbin Chen and Nan Zhang for showing us how to draw Fig. 2 using GeoGebra. Thank one referee for the long and detailed suggestions so much, which essentially improved the writing

An Integrated Air Quality Improvement Path of Energy-Environment Policies in the Guangdong-Hong Kong-Macao Greater Bay Area

Energy-related clean air measures in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) can yield substantial air quality improvement benefits and promote energy structure optimization. Here, we first evaluate the reduction effect of the stringent energy-related clean air measures in the GBA during the 13th Five-Year Plan period. First, a reduction of 19.3% emission in air pollutant equivalent was measured in 2020 compared to 2015. Second, we compare the energy structure development and air quality benefits of energy-environment policy scenarios by 2025 (SBAU, SA, SO) geared towards proposing integrated energy-environment development paths of air quality improvement. Under SBUA, SA and SO, the annual average PM2.5 concentration will be 21.7, 19.9 and 18.1 μg/m3, respectively, and the total energy demand would be controlled within 318.9, 300.6 and 282.3 Mtce in the GBA in 2025, reaching 7.5%, 8.4% and 9.4% of SO2, 23.5%, 29.3% and 35.4% of NOX, 18.2%, 19.6% and 22.7% of primary PM2.5, and 25.1%, 29.9% and 34.7% of VOCs emission reductions compared to 2020, respectively. Our study proposes that it is necessary for the GBA to jointly set up regional air quality improvement targets and issue integrated regional energy-environment policies in the process of building an “Air Quality Improvement Pioneering Demonstration Area”

Analysis of clinical features and identification of risk factors in patients with non-alcoholic fatty liver disease based on FibroTouch

Abstract Our aim was to explore the correlation between ultrasound attenuation parameter (UAP) and liver stiffness measurement (LSM) based on FibroTouch (China) and clinical features in patients with non-alcoholic fatty liver disease (NAFLD), so as to provide a certain basis for the clinical application of FibroTouch in NAFLD. Hepatic steatosis and fibrosis in patients with NAFLD were graded according to FibroTouch, and the relationship between steatosis and fibrosis levels and clinical characteristics was retrospectively analyzed. Hepatic steatosis was positively related with weight, BMI, waist, hyperlipidemia, hyperuricemia, FBG, UA, TG, ALT, AST, GGT, LSM and hepatic fibrosis grading, and was negatively related with gender (male), age and AST/ALT ratio. Hepatic fibrosis was positively related with age, BMI, waist, hypertension, FBG, ALT, AST, GGT, NFS, APRI, FIB-4, UAP and hepatic steatosis grading, and was negatively related with blood platelet (PLT) counts. Moreover, BMI, waist, TG, ALT and LSM were independent risk factors of hepatic steatosis, while decreased PLT counts, AST and UAP were independent risk factors of hepatic fibrosis. Body mass parameters, metabolic risk factors and liver function indicators increase the risk of hepatic steatosis and fibrosis in patients with NAFLD, and UAP and LSM can interact with each other

Table_4_Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus.xlsx

IntroductionSystemic lupus erythematosus (SLE) is a chronic autoimmune disease for which there is no cure. Effective diagnosis and precise assessment of disease exacerbation remains a major challenge.MethodsWe performed peripheral blood mononuclear cell (PBMC) proteomics of a discovery cohort, including patients with active SLE and inactive SLE, patients with rheumatoid arthritis (RA), and healthy controls (HC). Then, we performed a machine learning pipeline to identify biomarker combinations. The biomarker combinations were further validated using enzyme-linked immunosorbent assays (ELISAs) in another cohort. Single-cell RNA sequencing (scRNA-seq) data from active SLE, inactive SLE, and HC PBMC samples further elucidated the potential immune cellular sources of each of these PBMC biomarkers.ResultsScreening of the PBMC proteome identified 1023, 168, and 124 proteins that were significantly different between SLE vs. HC, SLE vs. RA, and active SLE vs. inactive SLE, respectively. The machine learning pipeline identified two biomarker combinations that accurately distinguished patients with SLE from controls and discriminated between active and inactive SLE. The validated results of ELISAs for two biomarker combinations were in line with the discovery cohort results. Among them, the six-protein combination (IFIT3, MX1, TOMM40, STAT1, STAT2, and OAS3) exhibited good performance for SLE disease diagnosis, with AUC of 0.723 and 0.815 for distinguishing SLE from HC and RA, respectively. Nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1, and STAT2) showed a robust performance in assessing disease exacerbation (AUC=0.990). Further, the potential immune cellular sources of nine PBMC biomarkers, which had the consistent changes with the proteomics data, were elucidated by PBMC scRNAseq.DiscussionUnbiased proteomic quantification and experimental validation of PBMC samples from two cohorts of patients with SLE were identified as biomarker combinations for diagnosis and activity monitoring. Furthermore, the immune cell subtype origin of the biomarkers in the transcript expression level was determined using PBMC scRNAseq. These findings present valuable PBMC biomarkers associated with SLE and may reveal potential therapeutic targets.</p

Table_2_Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus.xlsx

IntroductionSystemic lupus erythematosus (SLE) is a chronic autoimmune disease for which there is no cure. Effective diagnosis and precise assessment of disease exacerbation remains a major challenge.MethodsWe performed peripheral blood mononuclear cell (PBMC) proteomics of a discovery cohort, including patients with active SLE and inactive SLE, patients with rheumatoid arthritis (RA), and healthy controls (HC). Then, we performed a machine learning pipeline to identify biomarker combinations. The biomarker combinations were further validated using enzyme-linked immunosorbent assays (ELISAs) in another cohort. Single-cell RNA sequencing (scRNA-seq) data from active SLE, inactive SLE, and HC PBMC samples further elucidated the potential immune cellular sources of each of these PBMC biomarkers.ResultsScreening of the PBMC proteome identified 1023, 168, and 124 proteins that were significantly different between SLE vs. HC, SLE vs. RA, and active SLE vs. inactive SLE, respectively. The machine learning pipeline identified two biomarker combinations that accurately distinguished patients with SLE from controls and discriminated between active and inactive SLE. The validated results of ELISAs for two biomarker combinations were in line with the discovery cohort results. Among them, the six-protein combination (IFIT3, MX1, TOMM40, STAT1, STAT2, and OAS3) exhibited good performance for SLE disease diagnosis, with AUC of 0.723 and 0.815 for distinguishing SLE from HC and RA, respectively. Nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1, and STAT2) showed a robust performance in assessing disease exacerbation (AUC=0.990). Further, the potential immune cellular sources of nine PBMC biomarkers, which had the consistent changes with the proteomics data, were elucidated by PBMC scRNAseq.DiscussionUnbiased proteomic quantification and experimental validation of PBMC samples from two cohorts of patients with SLE were identified as biomarker combinations for diagnosis and activity monitoring. Furthermore, the immune cell subtype origin of the biomarkers in the transcript expression level was determined using PBMC scRNAseq. These findings present valuable PBMC biomarkers associated with SLE and may reveal potential therapeutic targets.</p

DataSheet_1_Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus.docx

IntroductionSystemic lupus erythematosus (SLE) is a chronic autoimmune disease for which there is no cure. Effective diagnosis and precise assessment of disease exacerbation remains a major challenge.MethodsWe performed peripheral blood mononuclear cell (PBMC) proteomics of a discovery cohort, including patients with active SLE and inactive SLE, patients with rheumatoid arthritis (RA), and healthy controls (HC). Then, we performed a machine learning pipeline to identify biomarker combinations. The biomarker combinations were further validated using enzyme-linked immunosorbent assays (ELISAs) in another cohort. Single-cell RNA sequencing (scRNA-seq) data from active SLE, inactive SLE, and HC PBMC samples further elucidated the potential immune cellular sources of each of these PBMC biomarkers.ResultsScreening of the PBMC proteome identified 1023, 168, and 124 proteins that were significantly different between SLE vs. HC, SLE vs. RA, and active SLE vs. inactive SLE, respectively. The machine learning pipeline identified two biomarker combinations that accurately distinguished patients with SLE from controls and discriminated between active and inactive SLE. The validated results of ELISAs for two biomarker combinations were in line with the discovery cohort results. Among them, the six-protein combination (IFIT3, MX1, TOMM40, STAT1, STAT2, and OAS3) exhibited good performance for SLE disease diagnosis, with AUC of 0.723 and 0.815 for distinguishing SLE from HC and RA, respectively. Nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1, and STAT2) showed a robust performance in assessing disease exacerbation (AUC=0.990). Further, the potential immune cellular sources of nine PBMC biomarkers, which had the consistent changes with the proteomics data, were elucidated by PBMC scRNAseq.DiscussionUnbiased proteomic quantification and experimental validation of PBMC samples from two cohorts of patients with SLE were identified as biomarker combinations for diagnosis and activity monitoring. Furthermore, the immune cell subtype origin of the biomarkers in the transcript expression level was determined using PBMC scRNAseq. These findings present valuable PBMC biomarkers associated with SLE and may reveal potential therapeutic targets.</p