Metodologias alternativas no ensino de física

Screening a compound library of quinolinone derivatives identified compound 11a as a new P2X7 receptor antagonist. To optimize its activity, we assessed structure-activity relationships (SAR) at three different positions, R_1, R_2 and R_3, of the quinolinone scaffold. SAR analysis suggested that a carboxylic acid ethyl ester group at the R_1 position, an adamantyl carboxamide group at R_2 and a 4-methoxy substitution at the R_3 position are the best substituents for the antagonism of P2X7R activity. However, because most of the quinolinone derivatives showed low inhibitory effects in an IL-1β ELISA assay, the core structure was further modified to a quinoline skeleton with chloride or substituted phenyl groups. The optimized antagonists with the quinoline scaffold included 2-chloro-5-adamantyl-quinoline derivative (16c) and 2-(4-hydroxymethylphenyl)-5-adamantyl-quinoline derivative (17k), with IC_(50) values of 4 and 3 nM, respectively. In contrast to the quinolinone derivatives, the antagonistic effects of the quinoline compounds (16c and 17k) were paralleled by their ability to inhibit the release of the pro-inflammatory cytokine, IL-1β, from LPS/IFN-γ/BzATP-stimulated THP-1 cells (IC_(50) of 7 and 12 nM, respectively). In addition, potent P2X7R antagonists significantly inhibited the sphere size of TS15-88 glioblastoma cells

Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques

Dementia is the most prevalent degenerative disease in seniors in which progression can be prevented or delayed by early diagnosis. In this study, we proposed a two-layer model inspired by the method used in dementia support centers for the early diagnosis of dementia and using machine learning techniques. Data were collected from patients who received dementia screening from 2008 to 2013 at the Gangbuk-Gu center for dementia in the Republic of Korea. The data consisted of the patient’s gender, age, education, the Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet (MMSE-KC) for dementia screening test, and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K) for the dementia precise test. In the proposed model, MMSE-KC data are initially classified into normal and abnormal. In the second stage, CERAD-K data are used to classify dementia and mild cognitive impairment. The performance of each algorithm is compared with that of Naive Bayes, Bayes Network, Begging, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) using Precision, Recall and F-measure. Comparing the F-measure values of normal, mild cognitive impairment (MCI), and dementia, the MLP was the highest in the F-measure values of normal with 0.97, while the SVM appear to be the highest in MCI and dementia with 0.739. Using the proposed early diagnosis model for dementia reduces the time and economic burden and can help simplify the diagnosis method for dementia

Return on Advertising Spend Prediction with Task Decomposition-Based LSTM Model

Return on advertising spend (ROAS) refers to the ratio of revenue generated by advertising projects to its expense. It is used to assess the effectiveness of advertising marketing. Several simulation-based controlled experiments, such as geo experiments, have been proposed recently. This refers to calculating ROAS by dividing a geographic region into a control group and a treatment group and comparing the ROAS generated in each group. However, the data collected through these experiments can only be used to analyze previously constructed data, making it difficult to use in an inductive process that predicts future profits or costs. Furthermore, to obtain ROAS for each advertising group, data must be collected under a new experimental setting each time, suggesting that there is a limitation in using previously collected data. Considering these, we present a method for predicting ROAS that does not require controlled experiments in data acquisition and validates its effectiveness through comparative experiments. Specifically, we propose a task deposition method that divides the end-to-end prediction task into the two-stage process: occurrence prediction and occurred ROAS regression. Through comparative experiments, we reveal that these approaches can effectively deal with the advertising data, in which the label is mainly set to zero-label

Inverted perovskite solar cells with low-loss hole-selective contact on textured substrates

Inverted perovskite solar cells (PSCs) promise enhanced operating stability compared to their normal-structure counterparts. To improve efficiency further, it is crucial to combine effective light management with low interfacial losses. Here we develop a conformal self-assembled monolayer as the hole-selective contact on light-managing textured substrates. Molecular dynamics simulations indicate cluster formation during phosphonic acid adsorption leads to incomplete SAM coverage. We devise a co-adsorbent strategy that disassembles high-order clusters, thus homogenizing the distribution of phosphonic acid molecules, thereby minimizing interfacial recombination and improving electronic structures. We report a lab-measured power-conversion efficiency (PCE) of 25.3% and a certified quasi-steady-state PCE of 24.8% for inverted PSCs, with a photocurrent approaching 95% of the Shockley-Queisser maximum. An encapsulated device having a PCE of 24.6% at room temperature retains 95% of its peak performance when stressed at 65°C and 50% relative humidity following > 1000 hours of maximum power point tracking under 1-sun illumination

Low-loss contacts on textured substrates for inverted perovskite solar cells

<p>Inverted perovskite solar cells (PSCs) promise enhanced operating stability compared to their normal-structure counterparts. To improve efficiency further, it is crucial to combine effective light management with low interfacial losses. Here we develop a conformal self-assembled monolayer as the hole-selective contact on light-managing textured substrates. Molecular dynamics simulations indicate cluster formation during phosphonic acid adsorption leads to incomplete SAM coverage. We devise a co-adsorbent strategy that disassembles high-order clusters, thus homogenizing the distribution of phosphonic acid molecules, thereby minimizing interfacial recombination and improving electronic structures. We report a lab-measured power-conversion efficiency (PCE) of 25.3% and a certified quasi-steady-state PCE of 24.8% for inverted PSCs, with a photocurrent approaching 95% of the Shockley-Queisser maximum. An encapsulated device having a PCE of 24.6% at room temperature retains 95% of its peak performance when stressed at 65°C and 50% relative humidity following > 1000 hours of maximum power point tracking under 1-sun illumination. </p&gt