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Anker Thomson posted an update 6 months, 2 weeks ago
Results showed that the present method, with a small amount of computation and low memory requirement, had higher location-estimation accuracy than that of traditional methods under deformation conditions.Timber treated with the anti-fungal chemical copper chrome arsenate is used extensively in the New Zealand building industry. While illegal, the burning of treated timber is commonplace in New Zealand and presents a health risk. Outdoor ambient monitoring of arsenic in airborne particulate matter in New Zealand has identified levels that exceed the maximum standards of 5.5 ng m-3 (annual average) at some urban locations. In this study, two-week-old beard hair samples were collected during the winter months to establish individual exposure to arsenic using Inductively Coupled Plasma-Mass Spectrometry. read more These results were then compared with questionnaire data about wood burner use for the two weeks prior to sampling, and spatial trends in arsenic from ambient monitoring. Results suggest that the burning of construction timber that may contain arsenic is associated with a higher level of arsenic in hair than those who burn logs or coal exclusively. There is no association between the area-level density of wood burners and arsenic levels but a significant correlation with individual household choice of fuel as well as the smell of wood smoke in the community, suggesting very localised influences. Strategies are needed to raise awareness of the risks of burning treated timber and to provide economically-viable alternatives.The COVID-19 pandemic is progressing worldwide with an alarming death toll. There is an urgent need for novel therapeutic strategies to combat potentially fatal complications. Distinctive clinical features of severe COVID-19 include acute respiratory distress syndrome, neutrophilia, and cytokine storm, along with severe inflammatory response syndrome or sepsis. Here, we propose the putative role of enhanced neutrophil infiltration and the release of neutrophil extracellular traps, complement activation and vascular thrombosis during necroinflammation in COVID-19. Furthermore, we discuss how neutrophilic inflammation contributes to the higher mortality of COVID-19 in patients with underlying co-morbidities such as diabetes and cardiovascular diseases. This perspective highlights neutrophils as a putative target for the immunopathologic complications of severely ill COVID-19 patients. Development of the novel therapeutic strategies targeting neutrophils may help reduce the overall disease fatality rate of COVID-19.Herein, we describe a simple and cost-effective design for the fabrication of a novel ternary RGO/BiOCl/TiO2 nanocomposites through a simple hydrothermal process. The prepared nanocomposites were characterized by Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), X-ray diffraction (XRD), UV-vis diffuse reflectance spectroscopy (UV-vis DRS) and N2 adsorption-desorption analysis. Organic contaminants-such as methylene blue (MB), methyl orange (MO), rhodamine B (RhB) and amido black-10B (AB-10B)-were employed as the target pollutants to evaluate the adsorption capacity and photocatalytic activity of RGO/BiOCl/TiO2 nanocomposites. From experimental data, it was also found that the amount of TiO2 impressed the photocatalytic performance, and the nanocomposites with 10% of TiO2 showed the best photocatalytic activity. The improved photocatalytic performance may be mainly due to the narrow band gap, and the charge separation and migration of RGO. Moreover, good recyclability was obtained from RGO/BiOCl/TiO2 nanocomposites, and scavenger tests indicated that photogenerated holes were the main active species in the reaction system. Therefore, the prepared RGO/BiOCl/TiO2 nanocomposites have broad applications foreground in pollutants purification.This study proposes a novel multi-network architecture consisting of a multi-scale convolution neural network (MSCNN) with fully connected graph convolution network (GCN), named MSCNN-GCN, for the detection of musculoskeletal abnormalities via musculoskeletal radiographs. To obtain both detailed and contextual information for a better description of the characteristics of the radiographs, the designed MSCNN contains three subnetwork sequences (three different scales). It maintains high resolution in each sub-network, while fusing features with different resolutions. A GCN structure was employed to demonstrate global structure information of the images. Furthermore, both the outputs of MSCNN and GCN were fused through the concat of the two feature vectors from them, thus making the novel framework more discriminative. The effectiveness of this model was verified by comparing the performance of radiologists and three popular CNN models (DenseNet169, CapsNet, and MSCNN) with three evaluation metrics (Accuracy, F1 score, and Kappa score) using the MURA dataset (a large dataset of bone X-rays). Experimental results showed that the proposed framework not only reached the highest accuracy, but also demonstrated top scores on both F1 metric and kappa metric. This indicates that the proposed model achieves high accuracy and strong robustness in musculoskeletal radiographs, which presents strong potential for a feasible scheme with intelligent medical cases.Rotating shift work is associated with risk factors for cardiovascular disease (CVD). We have studied the effect of 17 min high-intensity training three times a week over eight weeks on CVD risk factors among shift workers. Sixty-five shift workers from two plants were recruited. They were all deemed healthy at the initial health screening and in 100% work. From plant A, 42 workers, and plant B, 23 workers participated. After the intervention, 56 workers were retested. The intervention group consisted of 19 participants from plant A who had participated in at least 10 sessions. Twenty workers from plant B and 17 workers from plant A that not had taken part in the training were included in the control group. All workers reported physical activity (PA) by questionnaires before and after the training intervention. We measured blood pressure, heart rate, lipids, glycated hemoglobin (HbA1c), and C-reactive protein (CRP) and arterial stiffness. Maximal oxygen uptake (V̇O2max) was assessed by bicycle ergometry. The intervention group favorably differed significantly from the control group in improvement of systolic and diastolic blood pressure and glycated hemoglobin (HbA1c).
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