Picturing Poverty: Developing Best Practices in Poverty Research and Map Making

This study investigates the dynamics of poverty by examining the historical construction of poverty definitions and measures in the United States. In addition, the paper explores how the definitions work to influence the measurements through an analysis of past and contemporary research on poverty and impoverished groups. The study also investigates how the definitions and measurements affect decision making through maps as communication tools by considering the recent Los Angeles General Plan Elements. Subsequently, the research breaks down the design of maps to quantify the gaps in information by using both Monmonier’s and Tufte’s guidelines in graphic design and map making best practice. The paper further examines the toolkits that are available to planners to improve definitions, measurements, and maps of poverty. The study concludes by offering planning recommendations to advance best practices for studying and displaying information about poverty

Investigation of pharmaceutical metabolites in environmental waters by LC-MS/MS

Pharmaceuticals, once ingested, are commonly metabolized in the body into more polar and soluble forms. These compounds might not be completely removed in the wastewater treatment plants and consequently being discharged into the aquatic ecosystem. In this work, a multi-class sensitive method for the analysis of 21 compounds, including 7 widely consumed pharmaceuticals and 14 relevant metabolites, has been developed based on the use of UHPLC-MS/MS in selected reaction monitoring (SRM) mode. The method was validated in six surface waters (SW) and six effluent wastewaters (EWW) at realistic concentration levels that can be found in waters. The optimized method was applied to the analysis of different types of water samples (rivers, lakes and effluent wastewater), detecting nearly all the parent compounds and metabolites investigated in this work. This fact illustrates that not only pharmaceuticals but also their metabolites are commonly present in these types of waters. Analytical research and monitoring programs should be directed not only towards parent pharmaceuticals but also towards relevant metabolites to have a realistic overview of the impact of pharmaceuticals in the aquatic environment

CHARMS and PROBAST at your fingertips:a template for data extraction and risk of bias assessment in systematic reviews of predictive models

Background: Systematic reviews of studies of clinical prediction models are becoming increasingly abundant in the literature. Data extraction and risk of bias assessment are critical steps in any systematic review. CHARMS and PROBAST are the standard tools used for these steps in these reviews of clinical prediction models. Results: We developed an Excel template for data extraction and risk of bias assessment of clinical prediction models including both recommended tools. The template makes it easier for reviewers to extract data, to assess the risk of bias and applicability, and to produce results tables and figures ready for publication. Conclusion: We hope this template will simplify and standardize the process of conducting a systematic review of prediction models, and promote a better and more comprehensive reporting of these systematic reviews

Identification of new omeprazole metabolites in wastewaters and surface waters

Omeprazole is one of the world-wide most consumed pharmaceuticals for treatment of gastric diseases. As opposed to other frequently used pharmaceuticals, omeprazole is scarcely detected in urban wastewaters and environmental waters. This was corroborated in a previous research, where parent omeprazole was not detected while four transformation products (TPs), mainly resulting from hydrolysis, were found in effluent wastewaters and surface waters. However, the low abundance of omeprazole TPs in the water samples together with the fact that omeprazole suffers an extensive metabolism, with a wide range of excretion rates (between 0.01 and 30%), suggests that human urinary metabolites should be investigated in the water environment. In this work, the results obtained in excretion tests after administration of a 40 mg omeprazole dose in three healthy volunteers are reported. Analysis by liquid chromatography coupled to hybrid quadrupole time-of-flight mass spectrometry (LC-QTOF MS) reported low concentrations of omeprazole in urine. Up to twenty-four omeprazole metabolites (OMs) were detected and tentatively elucidated. The most relevant OM was an omeprazole isomer, which obviously presented the same exact mass (m/z 346.1225), but also shared a major common fragment at m/z 198.0589. Subsequent analyses of surface water and effluent wastewater samples by both LC-QTOF MS and LC-MS/MS with triple quadrupole revealed that this metabolite (named as OM10) was the compound most frequently detected in water samples, followed by OM14a and OM14b. Up to our knowledge, OM10 had not been used before as urinary biomarker of omeprazole in waters. On the contrary, parent omeprazole was never detected in any of the water samples. After this research, it seems clear that monitoring the presence of omeprazole in the aquatic environment should be focused on the OMs suggested in this article instead of the parent compound

Highly concentrated and stable few-layers graphene suspensions in pure and volatile organic solvents

Highly stable graphene suspensions in pure organic solvents, including volatile solvents such as ethanol, tetrahydrofurane, chloroform, acetone or toluene have been prepared by re-dispersion of a graphene-powder. Such re-dispersable solid is produced by precipitation or solvent elimination from graphene suspensions obtained by sonication of graphite in several organic solvent-water mixtures. Re-dispersion is feasible in a wide range of pure organic solvents, obtaining high quality few-layers graphene flakes stable in suspension for months. As a proof-of-concept, on-glass spray deposition of some of these suspensions, e.g. ethanol or tetrahydrofuran, results on electrically conductive transparent coatings. These results suggest industrial potential use of the scalable technology here developed to fabricate low-cost devices with many different potential applicationsThis research was financially supported by Abengoa Co., the Spanish Ministry of Economy and Competitiveness (MAT2013-46753-C2-1-P and RYC2012-09864) and Comunidad de Madrid (CAM 09-S2009_MAT-1467

Predicting major complications in patients undergoing laparoscopic and open hysterectomy for benign indications

BACKGROUND: Hysterectomy, the most common gynecological operation, requires surgeons to counsel women about their operative risks. We aimed to develop and validate multivariable logistic regression models to predict major complications of laparoscopic or abdominal hysterectomy for benign conditions. METHODS: We obtained routinely collected health administrative data from the English National Health Service (NHS) from 2011 to 2018. We defined major complications based on core outcomes for postoperative complications including ureteric, gastrointestinal and vascular injury, and wound complications. We specified 11 predictors a priori. We used internal–external cross-validation to evaluate discrimination and calibration across 7 NHS regions in the development cohort. We validated the final models using data from an additional NHS region. RESULTS: We found that major complications occurred in 4.4% (3037/68 599) of laparoscopic and 4.9% (6201/125 971) of abdominal hysterectomies. Our models showed consistent discrimination in the development cohort (laparoscopic, C-statistic 0.61, 95% confidence interval [CI] 0.60 to 0.62; abdominal, C-statistic 0.67, 95% CI 0.64 to 0.70) and similar or better discrimination in the validation cohort (laparoscopic, C-statistic 0.67, 95% CI 0.65 to 0.69; abdominal, C-statistic 0.67, 95% CI 0.65 to 0.69). Adhesions were most predictive of complications in both models (laparoscopic, odds ratio [OR] 1.92, 95% CI 1.73 to 2.13; abdominal, OR 2.46, 95% CI 2.27 to 2.66). Other factors predictive of complications included adenomyosis in the laparoscopic model, and Asian ethnicity and diabetes in the abdominal model. Protective factors included age and diagnoses of menstrual disorders or benign adnexal mass in both models and diagnosis of fibroids in the abdominal model. INTERPRETATION: Personalized risk estimates from these models, which showed moderate discrimination, can inform clinical decision-making for people with benign conditions who may require hysterectomy

Tunable Graphene Electronics with Local Ultrahigh Pressure

We achieve fine tuning of graphene effective doping by applying ultrahigh pressures (> 10 GPa) using Atomic Force Microscopy (AFM) diamond tips. Specific areas in graphene flakes are irreversibly flattened against a SiO2 substrate. Our work represents the first demonstration of local creation of very stable effective p-doped graphene regions with nanometer precision, as unambiguously verified by a battery of techniques. Importantly, the doping strength depends monotonically on the applied pressure, allowing a controlled tuning of graphene electronics. Through this doping effect, ultrahigh pressure modifications include the possibility of selectively modifying graphene areas to improve their electrical contact with metal electrodes, as shown by Conductive AFM. Density Functional Theory calculations and experimental data suggest that this pressure level induces the onset of covalent bonding between graphene and the underlying SiO2 substrate. Our work opens a convenient avenue to tuning the electronics of 2D materials and van der Waals heterostructures through pressure with nanometer resolution

Recent progress in 2D group-VA semiconductors: from theory to experiment

This review provides recent theoretical and experimental progress in the fundamental properties, electronic modulations, fabrications and applications of 2D group-VA materials.</p

Development, validation and clinical utility of a risk prediction model for adverse pregnancy outcomes in women with gestational diabetes:The PeRSonal GDM model

BACKGROUND: The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with gestational diabetes mellitus (GDM) would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. We aimed to develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM. METHODS: A prediction model development and validation study was conducted on data from a observational cohort. Participants included all women with GDM from three metropolitan tertiary teaching hospitals in Melbourne, Australia. The development cohort comprised those who delivered between 1 July 2017 to 30 June 2018 and the validation cohort those who delivered between 1 July 2018 to 31 December 2018. The main outcome was a composite of critically important maternal and perinatal complications (hypertensive disorders of pregnancy, large-for-gestational age neonate, neonatal hypoglycaemia requiring intravenous therapy, shoulder dystocia, perinatal death, neonatal bone fracture and nerve palsy). Model performance was measured in terms of discrimination and calibration and clinical utility evaluated using decision curve analysis. FINDINGS: The final PeRSonal (Prediction for Risk Stratified care for women with GDM) model included body mass index, maternal age, fasting and 1-hour glucose values (75-g oral glucose tolerance test), gestational age at GDM diagnosis, Southern and Central Asian ethnicity, East Asian ethnicity, nulliparity, past delivery of an large-for-gestational age neonate, past pre-eclampsia, GWG until GDM diagnosis, and family history of diabetes. The composite adverse pregnancy outcome occurred in 27% (476/1747) of women in the development (1747 women) and in 26% (244/955) in the validation (955 women) cohorts. The model showed excellent calibration with slope of 0.99 (95% CI 0.75 to 1.23) and acceptable discrimination (c-statistic 0.68; 95% CI 0.64 to 0.72) when temporally validated. Decision curve analysis demonstrated that the model was useful across a range of predicted probability thresholds between 0.15 and 0.85 for adverse pregnancy outcomes compared to the alternatives of managing all women with GDM as if they will or will not have an adverse pregnancy outcome. INTERPRETATION: The PeRSonal GDM model comprising of routinely available clinical data shows compelling performance, is transportable across time, and has clinical utility across a range of predicted probabilities. Further external validation of the model to a more disparate population is now needed to assess the generalisability to different centres, community based care and low resource settings, other healthcare systems and to different GDM diagnostic criteria. FUNDING: This work is supported by the Mothers and Gestational Diabetes in Australia 2 NHMRC funded project #1170847