A poly(3,3-diethyl-3,4-dihydro-2H-thieno-3,4-b1,4dioxepine) (PProDOT-Et
2) counter electrode prepared by electrochemical polymerization on a fluorine-doped tin oxide (FTO) glass substrate was ...incorporated in a platinum-free dye-sensitized solar cell (DSSC). The surface roughness and I
−/I
3
− redox reaction behaviors based on PProDOT-Et
2, poly(3,4-propylenedioxythiophene) (PProDOT), poly(3,4-ethylenedioxythiophene) (PEDOT), and sputtered-Pt electrodes were characterized, and their performances as counter electrodes in DSSCs were compared. Cells fabricated with a PProDOT-Et
2 counter electrode showed a higher conversion efficiency of 7.88% compared to cells fabricated with PEDOT (3.93%), PProDOT (7.08%), and sputtered-Pt (7.77%) electrodes. This enhancement was attributed to increases in the effective surface area and good catalytic properties for I
3
− reduction. In terms of the film thickness effect, the fill factor was strongly dependent on the deposition charge capacity of the PProDOT-Et
2 layer, but the aggregation of PProDOT-Et
2 in thicker layers (>80
mC
cm
−2) resulted in decreases in
J
SC and the cell conversion efficiency. The charge transfer resistances (
R
ct1) of the PProDOT-Et
2 counter electrodes had the lowest value of ∼18
Ω at a deposition charge capacity of 40
mC
cm
−2. These results indicate that films with high conductivity, high active surface area, and good catalytic properties for I
3
− reduction can potentially be used as the counter electrode in a high-performance DSSC.
Air pollution is at the center of pollution-control discussion due to the significant adverse health effects on individuals and the environment. Research has shown the association between unsafe ...environments and different sizes of particulate matter (PM), highlighting the importance of pollutant monitoring to mitigate its detrimental effect. By monitoring air quality with low-cost monitoring devices that collect massive observations, such as Air Box, a comprehensive collection of ground-level PM concentration is plausible due to the simplicity and low-cost, propelling applications in agriculture, aquaculture, and air quality, water resources, and disaster prevention. This paper aims to view IoT-based systems with low-cost microsensors at the sensor, network, and application levels, along with machine learning algorithms that improve sensor networks’ precision, providing better resolution. From the analysis at the three levels, we analyze current PM monitoring methods, including the use of sensors when collecting PM concentrations, demonstrate the use of IoT-based systems in PM monitoring and its challenges, and finally present the integration of AI and IoT (AIoT) in PM monitoring, indoor air quality control, and future directions. In addition, the inclusion of Taiwan as a site analysis was illustrated to show an example of AIoT in PM-control policy-making potential directions.
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•Recent advances of AIoT technology related to PM monitoring & control are reviewed.•PM monitoring techniques, including sensors for collecting PM data, are presented.•Knowledge and challenges of IoT-based devices on PM monitoring are provided.•AIoT in PM monitoring, air quality control & their future directions are addressed.
One major task in the post-genome era is to reconstruct proteomic and genomic interacting networks using high-throughput experiment data. To identify essential nodes/hubs in these interactomes is a ...way to decipher the critical keys inside biochemical pathways or complex networks. These essential nodes/hubs may serve as potential drug-targets for developing novel therapy of human diseases, such as cancer or infectious disease caused by emerging pathogens. Hub Objects Analyzer (Hubba) is a web-based service for exploring important nodes in an interactome network generated from specific small- or large-scale experimental methods based on graph theory. Two characteristic analysis algorithms, Maximum Neighborhood Component (MNC) and Density of Maximum Neighborhood Component (DMNC) are developed for exploring and identifying hubs/essential nodes from interactome networks. Users can submit their own interaction data in PSI format (Proteomics Standards Initiative, version 2.5 and 1.0), tab format and tab with weight values. User will get an email notification of the calculation complete in minutes or hours, depending on the size of submitted dataset. Hubba result includes a rank given by a composite index, a manifest graph of network to show the relationship amid these hubs, and links for retrieving output files. This proposed method (DMNC || MNC) can be applied to discover some unrecognized hubs from previous dataset. For example, most of the Hubba high-ranked hubs (80% in top 10 hub list, and >70% in top 40 hub list) from the yeast protein interactome data (Y2H experiment) are reported as essential proteins. Since the analysis methods of Hubba are based on topology, it can also be used on other kinds of networks to explore the essential nodes, like networks in yeast, rat, mouse and human. The website of Hubba is freely available at http://hub.iis.sinica.edu.tw/Hubba.
Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions ...affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.
Aim
To understand whether the sleep quality of the caregivers of elderly inpatients is associated with their own characteristics and with the characteristics or sleep quality of the elderly ...inpatients.
Design
A cross‐sectional study design that recruited participants from September to December 2020 was adopted, in which 106 pairs of elderly inpatients and caregivers were recruited.
Methods
Data collected from the elderly inpatients included demographic characteristics as well as the numerical rating scale (NRS) score, Charlson Comorbidity Index (CCI), Geriatric Depression Scale Short Form (GDS‐SF) score, and Pittsburgh Sleep Quality Index (PSQI). Caregiver data included demographic characteristics and PSQI.
Results
In the regression analysis of caregiver characteristics and caregiver sleep quality, only caregiver age and the relationship between caregiver and inpatient (other vs. spouse) were correlated with caregiver sleep quality. In the regression analysis of elderly inpatient characteristics, caregiver characteristics, and caregiver sleep quality, only the PSQI of elderly inpatients and the relationship between caregiver and inpatient (other vs. spouse) were correlated with caregiver sleep quality.
Patient or Public Contribution
Poor caregiver sleep quality was more likely to manifest when the elderly inpatients had poor sleep quality, when the caregivers themselves were older, and when the caregiver was the inpatient's spouse.
Background
Whether the prevalence of frailty and its clinical significance are relevant to treatment outcomes in younger (aged < 65 years) cancer patients remains uncertain. This study aimed to ...evaluate the impact of frailty on treatment outcomes in younger cancer patients with head and neck and esophageal malignancy.
Material and methods
This multicenter prospective study recruited 502 patients with locally advanced head and neck and esophageal cancer during 2016–2017 in Taiwan, aged 20–64 years who received curative-intent concurrent chemoradiotherapy (CCRT) as first-line antitumor treatment. Baseline frailty assessment using geriatric assessment (GA) was performed for each patient within 7 days before CCRT initiation.
Results
Frailty was observed in 169 (33.7%) of 502 middle-aged patients. Frail patients had significantly higher incidences of chemotherapy incompletion (16.6% versus 3.3%, P < .001) and radiotherapy incompletion (16.6% versus 3.6%, P < .001) than fit patients. During CCRT, frail patients had a significantly higher percentage of hospitalizations (42.0% versus 24.6%, P < .001) and a trend toward a higher percentage of emergency room visits (37.9% versus 30.0%, P = .08) than fit patients. Frail patients more likely had a significantly higher incidence of grade ≥ 3 adverse events than fit patients during CCRT. The 1-year survival rate was 68.7% and 85.2% (hazard ratio 2.56, 95% confidence interval 1.80–3.63, P < .001) for frail and fit patients, respectively.
Conclusions
This study demonstrated the significance of pretreatment frailty on treatment tolerance, treatment-related toxicity, and survival outcome in younger patients with head and neck and esophageal cancer undergoing CCRT. While GA is commonly targeted toward the older population, frailty assessment by GA may also be utilized in younger patients for decision-making guidance and prognosis prediction.
The aim of this study was to determine whether the oncogenic microRNA family members miR-196a and miR-196b can be circulating biomarkers for the early detection of oral cancer.
To determine the ...stability of circulating miRNA, the blood sample was aliquot and stored at different temperature conditions for analysis. To assess the diagnostic efficacy, we determined the levels of miR-196s in plasma samples, including 53 from healthy individuals, 16 from pre-cancer patients, and 90 from oral cancer patients.
In general, circulating miRNA was very stable when storing plasma samples at -20°C or below. In clinical study, both circulating miR-196a and miR-196b were substantially up-regulated in patients with oral pre-cancer lesions (5.9- and 14.8-fold, respectively; P<0.01), as well as in oral cancer patients (9.3- and 17.0-fold, respectively; P<0.01). These results show prominent discrimination between normal and pre-cancer patients (AUC=0.764 or 0.840, miR-196a or miR-196b, respectively), and between normal and cancer patients (AUC=0.864 or 0.960, miR-196a or miR-196b, respectively). The combined determination of miR-196a and miR-196b levels produces excellent sensitivity and specificity in the diagnosis of patients with oral pre-cancer (AUC=0.845) or oral cancer (AUC=0.963), as well as in the prediction of potential malignancy (AUC=0.950, sensitivity=91%, specificity=85%).
Combined determination of circulating miR-196a and miR-196b levels may serve as panel plasma biomarkers for the early detection of oral cancer.
•miR-196a/b are elevated in the plasma of oral cancer and pre-cancer patients.•The elevations of miR-196a/b in oral cancer patients are associated with T stage.•miR-196a provides excellent specificity for the diagnosis of oral malignancy.•miR-196b provides super sensitivity for the screening of oral malignancy.•Panel miR-196s serve as excellent biomarkers for early detection of oral cancer.
Patients with Parkinson's disease (PD) suffer from motor and non-motor symptoms; 40% would develop dementia (PD-D). Impaired face and emotion processing in PD has been reported; however, the deficits ...of face processing in PD-D remain unclear. We investigated three essential aspects of face processing capacity in PD-D, and the associations between cognitive, neuropsychiatric assessments and task performances. Twenty-four PD-D patients (mean age: 74.0 ± 5.55) and eighteen age-matched healthy controls (HC) (mean age: 71.0 ± 6.20) received three computerized tasks, morphing-face discrimination, dynamic facial emotion recognition, and expression imitation. Compared to HC, PD-D patients had lower sensitivity (d') and greater neural internal noises in discriminating faces; responded slower and had difficulties with negative emotions; imitated some expressions but with lower strength. Correlation analyses revealed that patients with advancing age, slow mentation, and poor cognition (but not motor symptoms) showed stronger deterioration in face perception. Importantly, these correlations were absent in the age-matched HC. The present study is among the first few examined face processing in patients with PD-D, and found consistent deficits correlated with advancing age and slow mentation. We propose that face discrimination task could be included as a potential test for the early detection of dementia in PD.
Antibiotic resistance has become a global concern. Vancomycin is known as the last line of antibiotics, but its treatment index is narrow. Therefore, clinical dosing decisions must be made with the ...utmost care; such decisions are said to be "suitable" only when both "efficacy" and "safety" are considered. This study presents a model, namely the "ensemble strategy model," to predict the suitability of vancomycin regimens. The experimental data consisted of 2141 "suitable" and "unsuitable" patients tagged with a vancomycin regimen, including six diagnostic input attributes (sex, age, weight, serum creatinine, dosing interval, and total daily dose), and the dataset was normalized into a training dataset, a validation dataset, and a test dataset. AdaBoost.M1, Bagging, fastAdaboost, Neyman-Pearson, and Stacking were used for model training. The "ensemble strategy concept" was then used to arrive at the final decision by voting to build a model for predicting the suitability of vancomycin treatment regimens.
The results of the tenfold cross-validation showed that the average accuracy of the proposed "ensemble strategy model" was 86.51% with a standard deviation of 0.006, and it was robust. In addition, the experimental results of the test dataset revealed that the accuracy, sensitivity, and specificity of the proposed method were 87.54%, 89.25%, and 85.19%, respectively. The accuracy of the five algorithms ranged from 81 to 86%, the sensitivity from 81 to 92%, and the specificity from 77 to 88%. Thus, the experimental results suggest that the model proposed in this study has high accuracy, high sensitivity, and high specificity.
The "ensemble strategy model" can be used as a reference for the determination of vancomycin doses in clinical treatment.
Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control ...measures can be cost-effective, and epidemic incidents can be controlled before devastated consequence occurs. However, timing relations between a measurable signal and the onset of the pandemic are complex to be discovered, and the typical lag period regression is difficult to capture in these complex relations. This study investigates the dynamic diffusion pattern of the disease in terms of a probability distribution. We estimate the parameters of an epidemic compartment model with the cross-infection of patients and mosquitoes in various infection cycles. We comprehensively study the incorporated meteorological and mosquito factors that may affect the epidemic of dengue fever to predict dengue fever epidemics. We develop a dual-parameter estimation algorithm for a composite model of the partial differential equations for vector-susceptible-infectious-recovered with exogeneity compartment model, Markov chain Montel Carlo method, and boundary element method to evaluate the epidemic periodicity under the effect of environmental factors of dengue fever, given the time series data of 2000-2016 from three cities with a population of 4.7 million. The established computer model of "energy accumulation-delayed diffusion-epidemics" is proven to be effective to predict the future trend of reported and unreported infected incidents. Our artificial intelligent algorithm can inform the authority to cease the larvae at the highest vector infection time. We find that the estimated dengue report rate is about 20%, which is close to the number of official announcements, and the percentage of infected vectors increases exponentially yearly. We suggest that the executive authorities should seriously consider the accumulated effect among infected populations. This established epidemic prediction model of dengue fever can be used to simulate and evaluate the best time to prevent and control dengue fever. Given our developed model, government epidemic prevention teams can apply this platform before they physically carry out the prevention work. The optimal suggestions from these models can be promptly accommodated when real-time data have been continuously corrected from clinics and related agents.
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