The increasing threat of climate change has created a pressing need for cities to lower their carbon footprints. Urban laboratories are emerging in numerous cities around the world as a strategy for local governments to partner with public and private property owners to reduce carbon emissions, while simultaneously stimulating economic growth. In this article, we use insights from laboratory studies to analyse the notion of urban laboratories as they relate to experimental governance, the carbonization agenda and the transition to low‐carbon economies. We present a case study of the O xford Road corridor in M anchester in the UK that is emerging as a low‐carbon urban laboratory, with important policy implications for the city's future. The corridor is a bounded space where a public‐private partnership comprised of the City Council, two universities and other large property owners is redeveloping the physical infrastructure and installing monitoring equipment to create a recursive feedback loop intended to facilitate adaptive learning. This low‐carbon urban laboratory represents a classic sustainable development formula for coupling environmental protection with economic growth, using innovation and partnership as principal drivers. However, it also has significant implications in reworking the interplay of knowledge production and local governance, while reinforcing spatial differentiation and uneven participation in urban development.
Objectives: To describe key characteristics (laboratory quality, test volumes, and complexity) of clinical laboratories in Kampala, Uganda (population similar to 1.7 million). Methods: Cross-sectional survey using a standard questionnaire to document laboratory type and quality, as well as test menus and volumes. Quality was based on the World Health Organization Africa Region checklist. Results: Of the 954 laboratories identified (a density of one laboratory per 1,781 persons), 779 (82%) performed only simple kit tests or light microscope examinations. The 95% (907/954) of laboratories for whom volumes were obtained performed an average aggregate of 13,189 tests daily, for a test utilization rate of around 2 tests per individual per year. Laboratories could be segregated into eight groups based on quality, test volume, and complexity. However, 90% of the testing was performed by just two groups: (I) low-volume (100 tests daily), high-quality laboratories. Each of these two groups did 45% of the daily testing volume (90% combined). Conclusions: Clinical laboratory density in Kampala (1/1,781 persons) is high, approaching that in the United States (1/1,347 persons). Low-volume/low-quality and high-volume/high-quality laboratories do 90% of the daily aggregate testing. Quality improvement (QI) schemes for Africa must be appropriate to low-volume laboratories as well as to the large laboratories that have been the focus of previous QI efforts.
This study investigated the effects of the use of augmented reality (AR) technologies in science laboratories on university students' laboratory skills and attitudes towards laboratories. A quasi-experimental pre-test/post-test control group design was employed. The participants were 76 first-year university students, aged 18–20 years old. They were assigned to either an experimental or a control group. Qualitative and quantitative data collection tools were used. The experimental results obtained following the 5-week application revealed that the AR technology significantly enhanced the development of the university students' laboratory skills. AR technology both improved the students’ laboratory skills and helped them to build positive attitudes towards physics laboratories. The statements of the students and the instructor regarding other effects of AR technology on science laboratories, both negative and positive, are also discussed.
Aims To critically evaluate the clinical implications of the use of non-fasting rather than fasting lipid profiles and to provide guidance for the laboratory reporting of abnormal non-fasting or fasting lipid profiles. Methods and results Extensive observational data, in which random non-fasting lipid profiles have been compared with those determined under fasting conditions, indicate that the maximal mean changes at 1-6 h after habitual meals are not clinically significant [+0.3 mmol/L (26 mg/dL) for triglycerides; -0.2 mmol/L (8 mg/dL) for total cholesterol; -0.2 mmol/L (8 mg/dL) for LDL cholesterol; +0.2 mmol/L (8 mg/dL) for calculated remnant cholesterol; -0.2 mmol/L (8 mg/dL) for calculated non-HDL cholesterol]; concentrations of HDL cholesterol, apolipoprotein A1, apolipoprotein B, and lipoprotein( a) are not affected by fasting/non-fasting status. In addition, non-fasting and fasting concentrations vary similarly over time and are comparable in the prediction of cardiovascular disease. To improve patient compliance with lipid testing, we therefore recommend the routine use of non-fasting lipid profiles, while fasting sampling may be considered when non-fasting triglycerides >5 mmol/L (440 mg/dL). For non-fasting samples, laboratory reports should flag abnormal concentrations as triglycerides >= 2 mmol/L (175 mg/dL), total cholesterol >= 5 mmol/L (190 mg/dL), LDL cholesterol >= 3 mmol/L (115 mg/dL), calculated remnant cholesterol >= 0.9 mmol/L (35 mg/dL), calculated non-HDL cholesterol >= 3.9 mmol/L (150 mg/dL), HDL cholesterol = 1.0 g/L (100 mg/dL), and lipoprotein(a) >= 50 mg/dL (80th percentile); for fasting samples, abnormal concentrations correspond to triglycerides >= 1.7 mmol/L (150 mg/dL). Life-threatening concentrations require separate referral when triglycerides >10 mmol/L (880 mg/dL) for the risk of pancreatitis, LDL cholesterol >13 mmol/L (500 mg/dL) for homozygous familial hypercholesterolaemia, LDL cholesterol >5 mmol/L (190 mg/dL) for heterozygous familial hypercholesterolaemia, and lipoprotein(a) >150 mg/dL (99th percentile) for very high cardiovascular risk. Conclusion We recommend that non-fasting blood samples be routinely used for the assessment of plasma lipid profiles. Laboratory reports should flag abnormal values on the basis of desirable concentration cut-points. Non-fasting and fasting measurements should be complementary but not mutually exclusive.
The notion of the ‘urban laboratory’ is increasingly striking a chord with actors involved in urban change. Is this term simply a metaphor for urban development or does it suggest urbanization by substantially different means? To answer this question, we review the work of science and technology studies ( STS ) scholars who have empirically investigated laboratories and practices of experimentation over the past three decades to understand the significance of these spaces of experimentation in urban contexts. Based on this overview of laboratory studies, we argue that urban laboratories and experimentation involve three key achievements — situatedness, change‐orientation and contingency — that are useful for evaluating and critiquing those practices that claim to be urban laboratories. We conclude by considering some future directions of research on urban laboratories.
Remote laboratories have been introduced during the last few decades into engineering education processes as well as integrated within e-learning frameworks offered to engineering and science students. Remote laboratories are also being used to support life-long learning and student's autonomous learning activities. In this paper, after a brief overview of state-of-the-art technologies in the development of remote laboratories and presentation of recent and interesting examples of remote laboratories in several areas related with industrial electronics education, some current trends and challenges are also identified and discussed.
While biochemical markers of diseases of the heart have undergone substantial evolution since the first report in 1954 of the utility of aspartate aminotransferase in patients with myocardial infarction, the utility of creatinine as an indicator of renal impairment has remained essentially unchanged for well over 100 years. Author Contributions: All authors confirmed they have contributed to the intellectual content ofthis paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition ofdata, or analysis and interpretation ofdata; (b) drafting or revising the article for intellectual content; and (c) final approval ofthe published article.