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Nuclear power plants produce the largest amount of tritium. Tritium is a by-product of nuclear reactors. Currently, it can only be placed in sealed storage tanks. Radioactive waste can be used as a source of energy for radioisotope battery. Since isotope energy is continuously converted into electrical energy for a long period of time, it is considered an ideal choice for remote applications. The mechanism of current generation of nuclear batteries is similar to that of solar cells. The only difference is that the electron holes are generated from radiation, not from light. The novel technologies of betavoltaic batteries would make use of the synthetic strategy for TiO2 semiconductors and the radioactive wastes radiation sources (H-3) stored in INER (Institute of Nuclear Energy Research, Taiwan). The radioactive waste can become a source of energy for radioisotope generators. At first, this study evaluates the radiation shielding safety distance required for radioactivity used in a betavoltaic tritium battery. The technological innovation includes the experimental synthesis of TiO2 nanotube in a complete array and the use of TiO2 nanotube arrays (TNTAs) as the semiconductor inside radioisotope batteries. In this work, combining the technology of TiO2 synthesizing TiO2 nanotube arrays with liquid waste radiation sources (tritium, H-3), the betavoltaic tritium battery (Ni/H-3/TNTAs/Ti) have successfully created and analyzing the characteristics of betavoltaic battery. Durability was assessed using analytical parameters of betavoltaic batteries compared to lithium-ion batteries. Such betavoltaic batteries have practical advantages in tritium handling applications and long-term battery performance. [ABSTRACT FROM AUTHOR]

Background The genetic background of patients with hemoglobin (Hb) H disease in Taiwan has been investigated; however, the clinical features and treatment outcomes were not reported. Objective To analyze the clinical features and genotypes of patients with HbH who reside in Taiwan. Methods We conducted a retrospective analysis of the clinical and molecular characteristics of 38 patients with HbH disease who were undergoing treatment at Kaohsiung Medical University Hospital, Taiwan. Results Initial Hb levels were lower and the numbers of patients requiring iron-chelation therapy were higher in the nondeletional HbH group than in the deletional HbH group (P <.05). Compared with the healthy population, the patients with HbH disease exhibited short body length, low body weight, and low body mass index (BMI). Conclusions Patients with nondeletional HbH disease had lower Hb levels and a higher requirement for splenectomy and iron-chelation therapy than did those with deletional HbH disease. Also, growth status was compromised in patients with HbH disease. [ABSTRACT FROM AUTHOR]

This study utilized a long-term satellite-based vegetation index, and considered culture-specific emission sources (temples and Chinese restaurants) with Land-use Regression (LUR) modelling to estimate the spatial-temporal variability of PM2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average PM2.5 concentrations from 2006 to 2012 of 17 air quality monitoring stations established by Environmental Protection Administration of Taiwan were used for model development. PM2.5 measurements from 2013 were used for external data verification. Monthly Normalized Difference Vegetation Index (NDVI) images coupled with buffer analysis were used to assess the spatial-temporal variations of greenness surrounding the monitoring sites. The distribution of temples and Chinese restaurants were included to represent the emission contributions from incense and joss money burning, and gas cooking, respectively. Spearman correlation coefficient and stepwise regression were used for LUR model development, and 10-fold cross-validation and external data verification were applied to verify the model reliability. The results showed a strongly negative correlation (r: -0.71 to -0.77) between NDVI and PM2.5 while temples (r: 0.52 to 0.66) and Chinese restaurants (r: 0.31 to 0.44) were positively correlated to PM2.5 concentrations. With the adjusted model R2 of 0.89, a cross-validated adj-R2 of 0.90, and external validated R2 of 0.83, the high explanatory power of the resultant model was confirmed. Moreover, the averaged NDVI within a 1750 m circular buffer (p < 0.01), the number of Chinese restaurants within a 1750 m buffer (p < 0.01), and the number of temples within a 750 m buffer (p = 0.06) were selected as important predictors during the stepwise selection procedures. According to the partial R2, NDVI explained 66% of PM2.5 variation and was the dominant variable in the developed model. We suggest future studies consider these three factors when establishing LUR models for estimating PM2.5 in other Asian cities. [ABSTRACT FROM AUTHOR]

Tremella fuciformis Berk (TF) is a common edible and medicinal mushroom, and has long been used in food and in Chinese medicine. It possesses anticancer, anti-inflammation, anti-oxidative, and neuroprotective abilities. Since their cultivation is a problem, TFs in Taiwan are primarily imported from China, which has a problem with pesticide residues. Thus, the question of whether the Taiwan cultivated TFs, T1, and T6 showed similar or even better results than TFs from China (CH) was assessed in the present study. The results of the physicochemical tests of these TFs showed that T1 extracted by hot water (T1H) has the highest concentration of polysaccharide; meanwhile, T6 extracted by cold water (T6C) showed the highest amount of protein. Regarding the immune modulatory effects of these TFs, hot water extracts of these TFs augmented significantly the inducible nitric oxide synthetase (iNOS), interleukin (IL)-6, and tumor necrosis factor (TNF)- mRNA expression than those of cold water extracts. On the other hand, the cold water extracts of TFs, especially of T1C, obviously suppressed cancer cell survival better than those of hot water extracts. Interestingly, we found that hot water extracts of TFs may augment necrotic cell death, whereas, cold water extracts of TFs induce apoptosis. Furthermore, we also showed that these TFs activate caspase-3 cleavage, up regulate the Bax/Bcl-2 ratio, and decrease MMP-9 expressions in PC-3 cells. Taken together, our results indicated that T1 and T6 strains of TFs showed the similar immune modulatory and anticancer abilities were better than the CH strain of TFs. [ABSTRACT FROM AUTHOR]

Ambient fine particulate matter (PM 2.5) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM 2.5 spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM 2.5. Daily average PM 2.5 data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM 2.5 variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM 2.5 exposures. [Display omitted] • Estimating long-term daily PM 2.5 concentration with machine learning models. • Land-use patterns were included in machine learning models by using land-use regression. • Explanatory power of daily PM 2.5 concentration was increased from 0.58 to 0.94. • XGboost outperformed random forest and deep neural network algorithms. [ABSTRACT FROM AUTHOR]

Kriging interpolation and land use regression (LUR) have characterized the spatial variability of long-term nitrogen dioxide (NO 2), but there has been little research on combining these two methods to capture small-scale spatial variation. Furthermore, studies predicting NO 2 exposure are almost exclusively based on traffic-related variables, which may not be transferable to Taiwan, a typical Asian country with diverse local emission sources, where densely distributed temples and restaurants may be important for NO 2 levels. To advance the exposure estimates in Taiwan, a hybrid kriging/LUR model incorporates culture-specific sources as potential predictors. Based on 14-year NO 2 observations from 73 monitoring stations across Taiwan, a set of interpolated NO 2 values were generated through a leave-one-out ordinary kriging algorithm, and this was included as an explanatory variable in the stepwise LUR procedures. Kriging interpolated NO 2 and culture-specific predictors were entered in the final models, which captured 90% and 87% of NO 2 variation in annual and monthly resolution, respectively. Results from 10-fold cross-validation and external data verification demonstrate robust performance of the developed models. This study demonstrates the value of incorporating the kriging-interpolated estimates and culture-specific emission sources into the traditional LUR model structure for predicting NO 2 , which can be particularly useful for Asian countries. Image 1 • A hybrid Kriging/LUR model was applied to assess NO 2 variability. • High prediction power (R2 = 0.90) was obtained regardless the model resolutions. • A 3–15% increase of R2 was obtained from the proposed hybrid methodology. • Kriging-based NO 2 estimates played the most important role in the monthly model. The proposed hybrid kriging/LUR model incorporated culture-specific sources provide accurate estimations of NO2 variations of Asia. [ABSTRACT FROM AUTHOR]

This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial-temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms-random forest (RF) and extreme gradient boosting (XGBoost)-for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.

Kriging interpolation and land use regression (LUR) have characterized the spatial variability of long-term nitrogen dioxide (NO 2 ), but there has been little research on combining these two methods to capture small-scale spatial variation. Furthermore, studies predicting NO 2 exposure are almost exclusively based on traffic-related variables, which may not be transferable to Taiwan, a typical Asian country with diverse local emission sources, where densely distributed temples and restaurants may be important for NO 2 levels. To advance the exposure estimates in Taiwan, a hybrid kriging/LUR model incorporates culture-specific sources as potential predictors. Based on 14-year NO 2 observations from 73 monitoring stations across Taiwan, a set of interpolated NO 2 values were generated through a leave-one-out ordinary kriging algorithm, and this was included as an explanatory variable in the stepwise LUR procedures. Kriging interpolated NO 2 and culture-specific predictors were entered in the final models, which captured 90% and 87% of NO 2 variation in annual and monthly resolution, respectively. Results from 10-fold cross-validation and external data verification demonstrate robust performance of the developed models. This study demonstrates the value of incorporating the kriging-interpolated estimates and culture-specific emission sources into the traditional LUR model structure for predicting NO 2 , which can be particularly useful for Asian countries., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Aim and Objectives: To investigate factors related to self-management and predictors of self-management in older adult patients with type 2 diabetic nephropathy., Background: Diabetic patients suffer many comorbidities during their lifetime, and the process of self-management is complex. Self-management and an integrated care experience are extremely important for older adults with diabetic nephropathy., Design: A cross-sectional correlation design was adopted., Methods: A total of 123 older patients were enrolled in the study from June 2016 to November 2017. Data collection involved a demographic questionnaire, the Patients' Experience of Integrated Care Questionnaire and the Partners in Health Scale to measure the integrated care experience and self-management. Data analysis included descriptive statistics, independent t tests, Pearson product-moment correlation and multiple linear regression. The methods are consistent with the STROBE criteria (Data S1)., Results: The results showed that the majority of the sample was female (56.9%). The average age was 77 years old. Stepwise regression analysis showed that re-admission during the past year (p < .001), physical function (p < .001) and integrated care experience (p < .001) are predictors of self-management in older adult patients with type 2 diabetic nephropathy and explained 42.8% of the variation in self-management behaviour., Conclusion: The results can be used to enhance the awareness of clinicians of the importance of an integrated care experience and self-management among older patients with type 2 diabetic nephropathy. Clinicians also should pay attention to physical function and the integrated care experience to promote self-management., Relevance to Clinical Practice: Studies on the integrated care experience and self-management of diabetic neuropathy in older adults are limited in Taiwan. The results of this study provide valuable information to support the importance of integrated care among this specific population., (© 2019 John Wiley & Sons Ltd.)

Background/aims: Hepatitis C virus (HCV) prevails in patients with thalassemia. We aimed to investigate the efficacy, safety, and impact on red blood cells (RBC) transfusion demand of pegylated interferon (Peg-IFN)/ribavirin therapy in thalassemic patients with HCV., Methods: This retrospective study included 18 thalassemic patients (16 with HCV-1b, one HCV-1b/2b, and one HCV-2b) and 54 consecutive sex- and genotype-matched controls. Patients with HCV-2, or HCV-1 or mixed HCV-1/2 with lower viral loads plus rapid virological response (RVR) received 24-week Peg-IFN/ribavirin; whereas HCV-1 or mixed HCV-1/2 with higher viral loads or without RVR received 48-week regimens., Results: The rates of RVR, complete early virological response, and sustained virological response (SVR) in thalassemic patients were 72.2% (13/18), 94.1% (16/17), and 77.8% (14/18), which resembled those of controls (63.0%, 94.4%, and 81.5%, respectively). RVR was the only significant factor associated with SVR in thalassemic group, and was the strongest predictor for SVR among both groups (OR/95% CI = 14.7/2.20-98.6), followed by male gender and lower viral loads. More proportion of interleukin-28B-TT carriage were observed among thalassemic patients with SVR versus non-SVR (78.6% vs. 50.0%). Thalassemic patients experienced significantly less 80/80/80 adherence, more ribavirin reduction and serious adverse events than controls. Notably, there was a decreased post-treatment RBC transfusion demand versus baseline in thalassemic patients with SVR (5.21 vs. 5.64 units/month, p = 0.05), but not in those without SVR (6.33 vs. 6.56 units/month, p = 0.54)., Conclusion: Peg-IFN/ribavirin was effective and tolerable for thalassemic HCV patients. Successful antiviral therapy might have extra benefit of reducing the post-treatment transfusion demand., (Copyright © 2017. Published by Elsevier B.V.)