21 research outputs found
Measuring urban social sustainability:Scale development and validation
Despite the significant role of social sustainability in the sustainable development agenda, there is a lack of research to clearly define and fully operationalise the concept of urban social sustainability. The aim of this study is to contribute to the existing literature by developing a comprehensive measurement scale to assess urban social sustainability at the neighbourhood level. We argue that urban social sustainability is a multidimensional concept that incorporates six main dimensions of social interaction, sense of place, social participation, safety, social equity and neighbourhood satisfaction. Failure to consider each of these dimensions may lead to an incomplete picture of social sustainability. Validity, reliability and dimensionality of the urban social sustainability scale are examined using factor analysis. We also illustrate the application of the urban social sustainability scale by investigating the influence of quality of design, as one of the least studied factors of urban form, on different dimensions of social sustainability. The paper uses data collected from the household questionnaire survey in a sample of 251 respondents from five case study neighbourhoods of Dunedin city, New Zealand. This study provides new evidence on the significance of improving neighbourhood quality of design and its positive and significant relationship with different dimensions of social sustainability and the overall social sustainability
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers
The physiological strain index does not reliably identify individuals at risk of reaching a thermal tolerance limit
Purpose
The physiological strain index (PSI) was developed to assess individuals' heat strain, yet evidence supporting its use to identify individuals at potential risk of reaching a thermal tolerance limit (TTL) is limited. The aim of this study was to assess whether PSI can identify individuals at risk of reaching a TTL.
Methods
Fifteen females and 21 males undertook a total of 136 trials, each consisting of two 40-60 minute periods of treadmill walking separated by ~ 15 minutes rest, wearing permeable or impermeable clothing, in a range of climatic conditions. Heart rate (HR), skin temperature (T sk), rectal temperature (T re), temperature sensation (TS) and thermal comfort (TC) were measured throughout. Various forms of the PSI-index were assessed including the original PSI, PSI fixed , adaptive-PSI (aPSI) and a version comprised of a measure of heat storage (PSI HS). Final physiological and PSI values and their rate of change (ROC) over a trial and in the last 10 minutes of a trial were compared between trials completed (C, 101 trials) and those terminated prematurely (TTL, 35 trials).
Results
Final PSI original , PSI fixed , aPSI, PSI HS did not differ between TTL and C (p > 0.05). However, differences between TTL and C occurred in final T sk , T re-T sk , TS, TC and ROC in PSI fixed , T re , T sk and HR (p < 0.05).
Conclusion
These results suggest the PSI, in the various forms, does not reliably identify individuals at imminent risk of reaching their TTL and its validity as a physiological safety index is therefore questionable. However, a physiological-perceptual strain index may provide a more valid measure
Novel loci associated with increased risk of sudden cardiac death in the context of coronary artery disease.
BACKGROUND: Recent genome-wide association studies (GWAS) have identified novel loci associated with sudden cardiac death (SCD). Despite this progress, identified DNA variants account for a relatively small portion of overall SCD risk, suggesting that additional loci contributing to SCD susceptibility await discovery. The objective of this study was to identify novel DNA variation associated with SCD in the context of coronary artery disease (CAD). METHODS AND FINDINGS: Using the MetaboChip custom array we conducted a case-control association analysis of 119,117 SNPs in 948 SCD cases (with underlying CAD) from the Oregon Sudden Unexpected Death Study (Oregon-SUDS) and 3,050 controls with CAD from the Wellcome Trust Case-Control Consortium (WTCCC). Two newly identified loci were significantly associated with increased risk of SCD after correction for multiple comparisons at: rs6730157 in the RAB3GAP1 gene on chromosome 2 (P = 4.93×10(-12), OR = 1.60) and rs2077316 in the ZNF365 gene on chromosome 10 (P = 3.64×10(-8), OR = 2.41). CONCLUSIONS: Our findings suggest that RAB3GAP1 and ZNF365 are relevant candidate genes for SCD and will contribute to the mechanistic understanding of SCD susceptibility