Exergy intensity and environmental consequences of the medical face masks curtailing the COVID-19 pandemic: Malign bodyguard?

On January 30, 2020, the World Health Organization identified SARS-CoV-2 as a public health emergency of global concern. Accordingly, the demand for personal protective equipment (PPE), including medical face masks, has sharply risen compared with 2019. The new situation has led to a sharp increase in energy demand and the environmental impacts associated with these product systems. Hence, the pandemic's effects on the environmental consequences of various PPE types, such as medical face masks, should be assessed. In light of that, the current study aimed to identify the environmental hot-spots of medical face mask production and consumption by using life cycle assessment (LCA) and tried to provide solutions to mitigate the adverse impacts. Based on the results obtained, in 2020, medical face masks production using fossil-based plastics causes the loss of 2.03 × 103 disability-adjusted life years (DALYs); 1.63 × 108 PDF*m2*yr damage to ecosystem quality; the climate-damaging release of 2.13 × 109 kg CO2eq; and 5.65 × 1010 MJ damage to resources. Besides, annual medical face mask production results in 5.88 × 104 TJ demand for exergy. On the other hand, if used makes are not appropriately handled, they can lead to 4.99 × 105 Pt/yr additional damage to the environment in 2020 as determined by the EDIP 2003. Replacement of fossil-based plastics with bio-based plastics, at rates ranging from 10 to 100%, could mitigate the product's total yearly environmental damage by 4–43%, respectively. Our study calls attention to the environmental sustainability of PPE used to prevent virus transmission in the current and future pandemics

Childhood secondhand smoke exposure and pregnancy loss in never smokers: The Guangzhou Biobank Cohort Study

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Numerical simulations for a typical train fire in China

Author name used in this publication: W. K ChowAuthor name used in this publication: N. K. Fong2010-2011 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree

In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identify significant die filling process variables. FNT resembles feedforward neural network, which creates a tree-like structure by using genetic programming. To improve accuracy, FNT parameters were optimized by using differential evolution algorithm. The performance of the FNT-based CI model was evaluated and compared with other CI techniques: multilayer perceptron, Gaussian process regression, and reduced error pruning tree. The accuracy of the CI model was evaluated experimentally using die filling as a case study. The die filling experiments were performed using a model shoe system and three different grades of microcrystalline cellulose (MCC) powders (MCC PH 101, MCC PH 102, and MCC DG). The feed powders were roll-compacted and milled into granules. The granules were then sieved into samples of various size classes. The mass of granules deposited into the die at different shoe speeds was measured. From these experiments, a dataset consisting true density, mean diameter (d50), granule size, and shoe speed as the inputs and the deposited mass as the output was generated. Cross-validation (CV) methods such as 10FCV and 5x2FCV were applied to develop and to validate the predictive models. It was found that the FNT-based CI model (for both CV methods) performed much better than other CI models. Additionally, it was observed that process variables such as the granule size and the shoe speed had a higher impact on the predictability than that of the powder property such as d50. Furthermore, validation of model prediction with experimental data showed that the die filling behavior of coarse granules could be better predicted than that of fine granules

Changes in BMI before and during economic development and subsequent risk of cardiovascular disease and total mortality: A 35-year follow-up study in China

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Spin-valley phase diagram of the two-dimensional metal-insulator transition

Using symmetry breaking strain to tune the valley occupation of a two-dimensional (2D) electron system in an AlAs quantum well, together with an applied in-plane magnetic field to tune the spin polarization, we independently control the system's valley and spin degrees of freedom and map out a spin-valley phase diagram for the 2D metal-insulator transition. The insulating phase occurs in the quadrant where the system is both spin- and valley-polarized. This observation establishes the equivalent roles of spin and valley degrees of freedom in the 2D metal-insulator transition.Comment: 4 pages, 2 figure

Dynamic magnetic resonance imaging in assessing lung function in adolescent idiopathic scoliosis: a pilot study of comparison before and after posterior spinal fusion

<p>Abstract</p> <p>Background</p> <p>Restrictive impairment is the commonest reported pulmonary deficit in AIS, which improves following surgical operation. However, exact mechanism of how improvement is brought about is unknown. Dynamic fast breath-hold (BH)-MR imaging is a recent advance which provides direct quantitative visual assessment of pulmonary function. By using above technique, change in lung volume, chest wall and diaphragmatic motion in AIS patients before and six months after posterior spinal fusion surgery were measured.</p> <p>Methods</p> <p>16 patients with severe right-sided predominant thoracic scoliosis (standing Cobb's angle 50° -82°, mean 60°) received posterior spinal fusion without thoracoplasty were recruited into this study. BH-MR sequences were used to obtain coronal images of the whole chest during full inspiration and expiration. The following measurements were assessed: (1) inspiratory, expiratory and change in lung volume; (2) change in anteroposterior (AP) and transverse (TS) diameter of the chest wall at two levels: carina and apex (3) change in diaphragmatic heights. The changes in parameters before and after operation were compared using Wilcoxon signed ranks test. Patients were also asked to score their breathing effort before and after operation using a scale of 1–9 with ascending order of effort. The degree of spinal surgical correction at three planes was also assessed by reformatted MR images and correction rate of Cobb's angle was calculated.</p> <p>Results</p> <p>The individual or total inspiratory and expiratory volume showed slight but insignificant increase after operation. There was significantly increase in bilateral TS chest wall movement at carina level and increase in bilateral diaphragmatic movements between inspiration and expiration. The AP chest wall movements, however, did not significantly change.</p> <p>The median breathing effort after operation was lower than that before operation (p < 0.05).</p> <p>There was significant reduction in coronal Cobb's angle after operation but the change in sagittal and axial angle at scoliosis apex was not significant.</p> <p>Conclusion</p> <p>There is improvement of lateral chest wall and diaphragmatic motions in AIS patients six months after posterior spinal fusion, associated with subjective symptomatic improvement. Lung volumes however, do not significantly change after operation. BH-MR is novel non-invasive method for long term post operative assessment of pulmonary function in AIS patients.</p

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Exome-wide association analysis reveals novel coding sequence variants associated with lipid traits in Chinese

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Biofilter aquaponic system for nutrients removal from fresh market wastewater

Aquaponics is a significant wastewater treatment system which refers to the combination of conventional aquaculture (raising aquatic organism) with hydroponics (cultivating plants in water) in a symbiotic environment. This system has a high ability in removing nutrients compared to conventional methods because it is a natural and environmentally friendly system (aquaponics). The current chapter aimed to review the possible application of aquaponics system to treat fresh market wastewater with the intention to highlight the mechanism of phytoremediation occurs in aquaponic system. The literature revealed that aquaponic system was able to remove nutrients in terms of nitrogen and phosphorus