896 research outputs found

    Environmental consequences of population, affluence and technological progress for European countries : A Malthusian view

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    This paper examines the short-run and long-run effects of economic, sociological and energy factors on environmental degradation in 28 European countries. In so doing, we employ Panel Vector Autoregressive (PVAR) and Fully Modified OLS (FMOLS) approaches on data from 1990 to 2014 in a STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework. Key empirical results indicate that these factors may contribute to environmental improvement in the short run; however, there are adverse implications in the long-run. Specifically, economic factors including economic growth, trade openness and foreign direct investment cause environmental degradation in the under-analysis economies. The sociological factors as measured by the population growth and the level of urbanization also show a negative impact on the environmental degradation in the short-run but in the long run, both population size and urbanization increase environmental degradation. These findings are in line with the concerns raised by Thomas Robert Malthus in his Essay on the Principle of Population. With regards to the energy factors, it indicates that the renewable energies help the European environment by reducing the level of carbon dioxide emissions whereas the higher energy intensity is an ecological threat. Our results remain robust in the EKC framework

    Mechanography assessment of fall risk in older adults: the Vietnam Osteoporosis Study

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    Background Jumping mechanography is a technology for quantitatively assessing muscular function and balance in older adults. This study sought to define the association between jumping mechanography parameters and fall risk in Vietnamese individuals. Methods The study involved 375 women and 244 men aged 50 years and older, who were recruited from the general population in Ho Chi Minh City (Vietnam). The individuals had been followed for 2 years. At baseline, Esslinger Fitness index (EFI), jumping power, force, velocity of lower limbs, and the ability to maintain balance were measured by a Leonardo Mechanograph Ground Reaction Force system (Novotec Medical, Pforxheim, Germany). The incidence of falls during the follow-up period was ascertained from self-report. Logistic regression analysis was used to analyse the association between jumping mechanography parameters and fall risk. Results The average age of participants at baseline was 56.7 years (SD 5.85). During the 2 year follow-up, 92 falls were reported, making the incidence of fall at ~15% [95% confidence interval (CI), 12.1 to 18.2]. The incidence of fall increased with advancing age, and women had a higher incidence than men (17.6% vs. 10.7%; P = 0.024). In univariate analysis, maximal velocity [odds ratio (OR) 0.65; 95% CI, 0.52 to 0.82], maximal force (OR 0.83; 95% CI, 0.65 to 1.04), and maximal power (OR 0.68; 95% CI, 0.52 to 0.88) were each significantly associated with fall risk. EFI was not significantly associated with fall risk (OR 1.09; 95% CI, 0.86 to 1.39). However, in a multiple logistic regression model, greater maximum velocity was associated with lower odds of fall (OR 0.38; 95% CI, 0.16 to 0.92). Conclusions These data suggest that jumping mechanography is a useful tool for assessing fall risk in older adults of Vietnamese background

    Association of serum leptin and ghrelin with depressive symptoms in a Japanese working population: a cross-sectional study

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    Leptin and ghrelin have been implicated in the pathogenesis of major depression. However, evidence is lacking among apparently healthy people. This study examined the relationship of these appetite hormones to depressive symptoms in a Japanese working population

    Access route selection for percutaneous coronary intervention among Vietnamese patients: Implications for in-hospital costs and outcomes

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    Background:Little is known about rates of access site (transradial (TRI) or transfemoral (TFI)) preference for percutaneous coronary intervention (PCI) and in-hospital costs of patients undergoing these procedures in lower-and middle-countries. Here, we report on access site use, in-hospital costs and outcomes of patients undergoing PCI in Vietnam.Methods:Information from 868 patients were included in the cohort of 1022 patients recruited into the first PCI registry in Vietnam. The total hospital costs and in-hospital outcomes of patients undergoing TRI and TFI were compared. Hospital costs were obtained from the hospital admission system, and major adverse cardiac events, major bleeding events and length of stay were identified through review of medical records.Findings:TRI was the dominant access site for interventionists (694/868 patients). The TFI group reported more lesions of the left main artery, more previous coronary artery bypass grafts and previous PCI in comparison with the TRI group (all p Interpretations:Among patients undergoing PCI, TRI was associated with lower costs and favourable clinical outcomes relative to TFI

    Turning defence into offence? Intrusion of cladoceran brood chambers by a green alga leads to reproductive failure

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    We observed a novel anti-grazer strategy in the green alga Chlorella vulgaris, where the cells entered the brood chambers of two grazers, Daphnia magna and Simocephalus sp, densely colonized the eggs, and significantly reduced the grazers' reproductive success

    Robust Automated Tumour Segmentation on Histological and Immunohistochemical Tissue Images

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    Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification

    Risk prediction model for knee pain in the Nottingham Community: a Bayesian modeling approach

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    Background: 25% of the British population over the age of 50 experience knee pain. It can limit physical ability, cause distress and bears significant socioeconomic costs. Knee pain, not knee osteoarthritis (KOA) is the all to common malady. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiaitve (OAI) Cohort. Methods: 1822 participants at risk for knee pain from the Nottingham community were followed up for 12 years. Of this cohort, 2/3 (n=1203) were used to develop the risk prediction model and 1/3 (n=619) were used to validate the model. Incident knee pain was defined as pain on most days for at least one month in the past 12 months. Predictors were age, gender, body mass index (BMI), pain elsewhere, prior knee injury and knee alignment. Bayesian logistic regression model was used to determine the probability of an odds ratio >1. The Hosmer-Lemeshow x2 statistic (HLS) was used for calibration and receiver operator characteristics (ROC) was used for discrimination. The OAI cohort was used to examine the performance of the model in a secondary care population. Results: A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration with HLS of 7.17 (p=0.52) and moderate discriminative abilities (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p<0.01) and poor discriminative ability (ROC 0.54) in the OAI secondary care dataset. Conclusion: This is the first risk prediction model for knee pain, irrespective of underlying structural changes of KOA, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in a hospital derived cohort and may provide a convenient tool for primary care to predict the risk of knee pain in the general population
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