Currently, high‐efficiency perovskite solar cells are mainly fabricated by the spin‐coating process, which limits the possibility of commercial mass‐production of perovskite solar cells. In this ...work, the slot‐die coating process is combined with near‐infrared irradiation heating to quickly manufacture perovskite solar cells in air. The composition of the perovskite precursor solution is tuned by adding n‐butanol, with its low boiling point and low surface tension, to increase the near‐infrared energy absorption, facilitate the evaporation of the solvent system and film formation, and accelerate the crystallization of perovskite. High‐quality uniform perovskite film can be prepared within 18 s. Moreover, the all slot‐die coating process is demonstrated to prepare over an area of 12 cm × 12 cm, four layers of uniform film overlay on top of each other for the devices except electrode in ambient air. A power conversion efficiency of ≈11% is achieved when this all slot‐die coated film is used to fabricate device. This facile process can greatly reduce the cost, time and bypass post‐annealing to fabricate high‐efficiency large‐area perovskite solar cells in ambient air.
An industry compatible slot‐die coating process combined with near‐infrared irradiation heating enables rapid manufacture of large‐area and uniform perovskite solar cells in air. The highest power conversion efficiency for a device, which is fabricated using the slot‐die coated four layer, is nearly 11%.
The new classification announced by the World Health Organization in 2016 recognized five molecular subtypes of diffuse gliomas based on isocitrate dehydrogenase (IDH) and 1p/19q genotypes in ...addition to histologic phenotypes. We aim to determine whether clinical MRI can stratify these molecular subtypes to benefit the diagnosis and monitoring of gliomas.
The data from 456 subjects with gliomas were obtained from The Cancer Imaging Archive. Overall, 214 subjects, including 106 cases of glioblastomas and 108 cases of lower grade gliomas with preoperative MRI, survival data, histology, IDH, and 1p/19q status were included. We proposed a three-level machine-learning model based on multimodal MR radiomics to classify glioma subtypes. An independent dataset with 70 glioma subjects was further collected to verify the model performance.
The IDH and 1p/19q status of gliomas can be classified by radiomics and machine-learning approaches, with areas under ROC curves between 0.922 and 0.975 and accuracies between 87.7% and 96.1% estimated on the training dataset. The test on the validation dataset showed a comparable model performance with that on the training dataset, suggesting the efficacy of the trained classifiers. The classification of 5 molecular subtypes solely based on the MR phenotypes achieved an 81.8% accuracy, and a higher accuracy of 89.2% could be achieved if the histology diagnosis is available.
The MR radiomics-based method provides a reliable alternative to determine the histology and molecular subtypes of gliomas.
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Purpose Medulloblastoma (MB) is a highly malignant pediatric brain tumor. In the latest classification, medulloblastoma is divided into four distinct groups: wingless (WNT), sonic hedgehog (SHH), ...Group 3, and Group 4. We analyzed the magnetic resonance imaging radiomics features to find the imaging surrogates of the 4 molecular subgroups of MB. Material and methods Frozen tissue, imaging data, and clinical data of 38 patients with medulloblastoma were included from Taipei Medical University Hospital and Taipei Veterans General Hospital. Molecular clustering was performed based on the gene expression level of 22 subgroup-specific signature genes. A total 253 magnetic resonance imaging radiomic features were generated from each subject for comparison between different molecular subgroups. Results Our cohort consisted of 7 (18.4%) patients with WNT medulloblastoma, 12 (31.6%) with SHH tumor, 8 (21.1%) with Group 3 tumor, and 11 (28.9%) with Group 4 tumor. 8 radiomics gray-level co-occurrence matrix texture (GLCM) features were significantly different between 4 molecular subgroups of MB. In addition, for tumors with higher values in a gray-level run length matrix feature-Short Run Low Gray-Level Emphasis, patients have shorter survival times than patients with low values of this feature (p = 0.04). The receiver operating characteristic analysis revealed optimal performance of the preliminary prediction model based on GLCM features for predicting WNT, Group 3, and Group 4 MB (area under the curve = 0.82, 0.72, and 0.78, respectively). Conclusion The preliminary result revealed that 8 contrast-enhanced T1-weighted imaging texture features were significantly different between 4 molecular subgroups of MB. Together with the prediction models, the radiomics features may provide suggestions for stratifying patients with MB into different risk groups.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Oral squamous cell carcinoma (OSCC) is the fifth common cause of cancer mortality in Taiwan with high incidence and recurrence and needs new therapeutic strategies. In this study, ursolic acid (UA), ...a triterpenoid, was examined the antitumor potency in OSCC cells. Our results showed that UA inhibited the proliferation of OSCC cells in a dose‐ and time‐dependent manner in both Ca922 and SCC2095 oral cancer cells. UA induced caspase‐dependent apoptosis accompanied with the modulation of various biological biomarkers including downregulating Akt/mTOR/NF‐κB signaling, ERK, and p38. In addition, UA inhibited angiogenesis as evidenced by abrogation of migration/invasion and blocking MMP‐2 secretion in Ca922 cells. Interestingly, UA induced autophagy in OSCC cells, as manifested by LC3B‐II conversion and increased p62 expression and accumulation of autophagosomes. Inhibition by autophagy inhibitor enhanced UA‐mediated apoptosis in Ca922 cells. The experiment provides a rationale for using triterpenoid in the treatment of OSCC.
In this paper, we have proposed a novel 6-DOF magnetic levitation (maglev) system to improve the robustness and upgrade positioning precision. The design concept attempts to keep the good performance ...in the whole journey of moving rather than the point-to-point positioning precision. Furthermore, we endeavor to develop this system with an expectable large moving range. Based on these concepts, we built the force model that considers the variation from the displacement to the magnetic forces first, and avoids the constraint of the attractive levitation in replacing the repulsive levitation. Finally, we adopt the concept of relative place to build the measuring system. All of the performance of the improved framework is demonstrated in the experimental results.
The fluorescence intensity of inorganic CsPbBr3 (CPB) perovskite nanocrystals (NCs) decreases in the presence of O2. In this study, we synthesized CPB NCs with various shapes and sizes for use as ...optical gas sensing materials. We fabricated O2 gas sensors from the various CPB NCs on several porous and nonporous substrates and examined the effects of the NC shapes and aggregate sizes and the substrate pore size on the device response. Our sensor fabricated from CPB nanocrystals on a porous substrate exhibited the highest response; the porous substrate allowed the rapid diffusion of O2 such that the NC surface was exposed effectively to the gas. Thus, the interfacial interaction between NC surfaces and substrates is a critical factor for consideration when preparing gas sensors with a high response.
The non-uniform sampling scheme and economic statistical design approaches have been successfully applied to determine three parameters of x-bar control charts to monitor a manufacturing process with ...increasing hazard functions for the last three decades. Nevertheless, a primary assumption for these cost models is that measurements within a sample are independent. However, the conventional supposition may significantly underestimate the type I error probability for the x-bar control chart. Hence, we develop a cost model that combines different researches with the multivariate normal distribution model that given the maximum probability of type I error and the minimum value of power. The optimal parameters of non-uniform sampling interval x-bar control charts are used for the measurements within a sample being correlated. In addition, an industrial example is applied to indicate the solution procedure. Sensitivity analysis is accompanied with input parameters including correlated coefficients as well as process and cost parameters of the model are performed. The genetic algorithm is adopted to reveal the optimal solution of the economic design. The method proposed can be used on related industries to achieve the ability of production monitoring and cost reducing. The human resource consuming and the amount of scraps will be avoided toward the conclusive goals of economic benefit, environmental benefit and social benefit.
Erinacine A, derived from the mycelia of Hericium erinaceus, has attracted much attention due to its neuroprotective properties. However, very few studies have been conducted on the bioavailability, ...tissue distribution, and protein binding of erinacine A. This study aimed to investigate the bioavailability, tissue distribution, and protein binding of erinacine A in Sprague-Dawley rats. After oral administration (po) and intravenous administration (iv) of 2.381 g/kg BW of the H. erinaceus mycelia extract (equivalent to 50 mg/kg BW of erinacine A) and 5 mg/kg BW of erinacine A, respectively, the absolute bioavailability of erinacine A was estimated as 24.39%. Erinacine A was detected in brain at 1 h after oral dosing and reached the peak at 8 h. Protein binding assay showed unbound erinacine A fractions in brain to blood ratio is close to unity, supporting passive diffusion as the dominating transport. Feces was the major route for the elimination of erinacine A. This study is the first to show that erinacine A can penetrate the blood-brain barrier of rats by the means of passive diffusion and thus support the development of H. erinaceus mycelia for the improvement of neurohealth.
The different profiles of e-cigarette users in different age groups have seldom been investigated, particularly in populations facing a high prevalence of cigarette smoking. This study aims to ...examine the prevalence and correlates of e-cigarette use separately for adolescents and adults in nationally representative samples in Taiwan.
Among 17,837 participants in the 2014 National Survey of Substance Use in Taiwan, 4445 were aged 12 to 17 years and 13,392 were aged 18 to 64 years. Individuals' lifetime tobacco use was divided into four groups: non-use, exclusive e-cigarette use, exclusive cigarette use, and dual use. Questions on sociodemographic features, use and problematic use of tobacco, alcohol, and other drugs, and psychosocial distress, among others, were administered using a computer-assisted self-interview on tablet computers.
Among lifetime users of e-cigarette (2.2% for adults and 0.8% for adolescents), 4.5% for adults and 36.6% for adolescents were exclusive e-cigarette users. From use of exclusive e-cigarettes to use of exclusive cigarettes to dual use, those usage groups were related to an increasing trend of adjusted odds ratios for use of other psychoactive substances, particularly problematic use of alcohol or drugs, and with more depressive symptoms. Two correlates were specific to e-cigarette use: alcohol use had stronger relationships with e-cigarette use among adolescents, and younger adults (18-34) were more likely to try e-cigarettes compared to older adults.
These results provide essential information regarding e-cigarette use in the general population, and future prevention strategies should account for its specific correlates in young people.
The credit scoring has been regarded as a critical topic and its related departments make efforts to collect huge amount of data to avoid wrong decision. An effective classificatory model will ...objectively help managers instead of intuitive experience. This study proposes four approaches combining with the SVM (support vector machine) classifier for features selection that retains sufficient information for classification purpose. Different credit scoring models are constructed by selecting attributes with four approaches. Two UCI (University of California, Irvine) data sets are chosen to evaluate the accuracy of various hybrid-SVM models. SVM classifier combines with conventional statistical LDA, Decision tree, Rough sets and F-score approaches as features pre-processing step to optimize feature space by removing both irrelevant and redundant features. In this paper, the procedure of the proposed approaches will be described and then evaluated by their performances. The results are compared in combination with SVM classifier and nonparametric Wilcoxon signed rank test will be held to show if there is any significant difference between these models. The result in this study suggests that hybrid credit scoring approach is mostly robust and effective in finding optimal subsets and is a promising method to the fields of data mining.
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