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When entering an educational institution, the student lacksknowledge and information about that. Misinformation requires thestudent to have a direct dialogue with students, alumni, and faculty.This paper presents the development of the INGRESSI mobileapplication. The project aims to facilitate the entry of students intoan educational institution through games and activities to completeobjectives. The methodology and the results were strengthenedfrom applied research with 26 students of a technical course incomputer science for the internet. The development was carried outusing JavaScript language, React Native framework and NoSQLMongoDB database. The mobile application is intended forelementary school II students and students entering technicaleducation. As a result, the application presents information aboutthe institution, courses, and news and is expected to expand itsfunctionality.

Alcohol use is a leading risk factor for substantial health loss, disability, and death. Thus, there is a general interest in developing computational tools to classify electroencephalographic (EEG) signals in alcoholism, but there are a limited number of studies on convolutional neural network (CNN) classification of alcoholism using topographic EEG signals. We produced an original dataset recorded from Brazilian subjects performing a language recognition task. Then, we transformed the Event-Related Potentials (ERPs) into topographic maps by using the ERP’s statistical parameters across time, and used a CNN network to classify the topographic dataset. We tested the effect of the size of the dataset in the accuracy of the CNNs and proposed a data augmentation approach to increase the size of the topographic dataset to improve the accuracies. Our results encourage the use of CNNs to classify abnormal topographic EEG patterns associated with alcohol abuse.

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Most patients with KRASG12C–mutant non–small cell lung cancer (NSCLC) experience clinical benefit from selective KRASG12C inhibition, whereas patients with colorectal cancer bearing the same mutation rarely respond. To investigate the cause of the limited efficacy of KRASG12C inhibitors in colorectal cancer, we examined the effects of AMG510 in KRASG12C colorectal cancer cell lines. Unlike NSCLC cell lines, KRASG12C colorectal cancer models have high basal receptor tyrosine kinase (RTK) activation and are responsive to growth factor stimulation. In colorectal cancer lines, KRASG12C inhibition induces higher phospho-ERK rebound than in NSCLC cells. Although upstream activation of several RTKs interferes with KRASG12C blockade, we identify EGFR signaling as the dominant mechanism of colorectal cancer resistance to KRASG12C inhibitors. The combinatorial targeting of EGFR and KRASG12C is highly effective in colorectal cancer cells and patient-derived organoids and xenografts, suggesting a novel therapeutic strategy to treat patients with KRASG12C colorectal cancer.Significance:The efficacy of KRASG12C inhibitors in NSCLC and colorectal cancer is lineage-specific. RTK dependency and signaling rebound kinetics are responsible for sensitivity or resistance to KRASG12C inhibition in colorectal cancer. EGFR and KRASG12C should be concomitantly inhibited to overcome resistance to KRASG12C blockade in colorectal tumors.See related commentary by Koleilat and Kwong, p. 1094.This article is highlighted in the In This Issue feature, p. 1079

Supplementary Figure from Targeting the DNA Damage Response Pathways and Replication Stress in Colorectal Cancer

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Supplementary Table from Targeting the DNA Damage Response Pathways and Replication Stress in Colorectal Cancer

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Purpose:Genomic instability is a hallmark of cancer and targeting DNA damage response (DDR) is emerging as a promising therapeutic strategy in different solid tumors. The effectiveness of targeting DDR in colorectal cancer has not been extensively explored.Experimental Design:We challenged 112 cell models recapitulating the genomic landscape of metastatic colorectal cancer with ATM, ATR, CHK1, WEE1, and DNA-PK inhibitors, in parallel with chemotherapeutic agents. We focused then on ATR inhibitors (ATRi) and, to identify putative biomarkers of response and resistance, we analyzed at multiple levels colorectal cancer models highly sensitive or resistant to these drugs.Results:We found that around 30% of colorectal cancers, including those carrying KRAS and BRAF mutations and unresponsive to targeted agents, are sensitive to at least one DDR inhibitor. By investigating potential biomarkers of response to ATRi, we found that ATRi-sensitive cells displayed reduced phospho-RPA32 foci at basal level, while ATRi-resistant cells showed increased RAD51 foci formation in response to replication stress. Lack of ATM and RAD51C expression was associated with ATRi sensitivity. Analysis of mutational signatures and HRDetect score identified a subgroup of ATRi-sensitive models. Organoids derived from patients with metastatic colorectal cancer recapitulated findings obtained in cell lines.Conclusions:In conclusion, a subset of colorectal cancers refractory to current therapies could benefit from inhibitors of DDR pathways and replication stress. A composite biomarker involving phospho-RPA32 and RAD51 foci, lack of ATM and RAD51C expression, as well as analysis of mutational signatures could be used to identify colorectal cancers likely to respond to ATRi.

Purpose:Defects in the homologous recombination (HR) repair pathway are of clinical interest due to sensitivity of HR-deficient cells to PARP inhibitors. We were interested in defining PARP vulnerability in patients with metastatic colorectal cancer (mCRC) carrying KRAS and BRAF mutations who display poor prognosis, have limited therapeutic options, and represent an unmet clinical need.Experimental Design:We tested colorectal cancer cell lines, patient-derived organoids (PDO), and patient-derived xenografts (PDX) enriched for KRAS and BRAF mutations for sensitivity to the PARP inhibitor olaparib, and the chemotherapeutic agents oxaliplatin and 5-fluorouracil (5-FU). Genomic profiles and DNA repair proficiency of colorectal cancer models were compared with pharmacologic response.Results:Thirteen of 99 (around 13%) colorectal cancer cell lines were highly sensitive to clinically active concentrations of olaparib and displayed functional deficiency in HR. Response to PARP blockade was positively correlated with sensitivity to oxaliplatin in colorectal cancer cell lines as well as patient-derived organoids. Treatment of PDXs with olaparib impaired tumor growth and maintenance therapy with PARP blockade after initial oxaliplatin response delayed disease progression in mice.Conclusions:These results indicate that a colorectal cancer subset characterized by poor prognosis and limited therapeutic options is vulnerable to PARP inhibition and suggest that PDO-based drug-screening assays can be used to identify patients with colorectal cancer likely to benefit from olaparib. As patients with mCRC almost invariably receive therapies based on oxaliplatin, “maintenance” treatment with PARP inhibitors warrants further clinical investigation.

To identify the main barriers to dental care access for patients with inherited bleeding (IBD) and hemoglobin disorders (HbD).Patients with IBD and HbD were invited to participate in this study between August 2019 and March 2020. Data were collected through a questionnaire consisting of socioeconomic and demographic items and questions about access to dental services and history of dental treatment. Univariate and multiple Poisson regression model was used to determine associations between professional refusal of dental care and other co-variables (p .05).The participants (29.1%) have already had professional refusal of dental care and participants with IBD (53.2%) did not feel confident with their local dentist due to their bleeding tendency. Most (64.6%) felt apprehensive about visiting the local dentist and high prevalence of refusal to provide dental care was associated with age (prevalence ratio [PR] = 1.021; 95% confidence interval [CI] = 1.010-1.032). Individuals with low bleeding risk were less likely to be denied dental care by a professional compared to those with high bleeding risk (PR = 0.536; 95%CI = 0.291-0.990).Professional refusal of dental care was high among patients with IBD, particularly older adults and with an increased risk of bleeding.

The integration of wind power with traditional electricity grids is challenging because of the volatile and intermittent nature of wind speed. Consequently, wind speed forecasting is an essential tool in the energy system because it supports economic, regulation, and operational efficiency issues decisions. The development of systems for accurate wind speed forecasting is a difficult task because it presents linear and nonlinear temporal patterns, and is differently influenced by climate and environmental variables. In this paper, we propose a method to search for the best combination of linear, nonlinear forecasts, and exogenous variables , aiming to increase the accuracy of wind speed forecasting. The proposed method performs: (i) the choice of the linear statistical model to forecast the time series; (ii) the error series modeling using a Long Short-Term Memory model; and (iii) the evolutionary search for the most suitable nonlinear combination of linear and nonlinear forecasts with exogenous variables. The proposed method is assessed using hourly and monthly time series of three stations located in northeast Brazil. The evaluation of the results using two traditional measures shows that the proposed method has a better performance than statistical techniques, Machine Learning models, and state-of-the-art hybrid systems in most series.

As research in Lithium-ion batteries field has extended, the need for better management systems also increases. An important part of them is the proper estimation of battery status over time with indirect metrics such as State-of-Charge (SoC). In the machine learning environment, different simple techniques have been tested showing good performance and being surpassed by hybrid systems.In this study, a static selection model is proposed to choose the best non-linear predictor to work with and ARIMA model and combination function for a specific database considering how the perform in a validation set. This architecture allows to consider linear and non-linear components of the time series using residual forecasting and a selection step to reduce the chances of choosing the wrong combination in each specific database.SoC values for five different databases where forecasted, allowing to compare the results of six models relevant in literature to the proposed one.The results showed a superior performance for the proposed model in four out of the five databases, with gains of 5.27%, 13.51%, 56.67%, and 38.71%. There was only one database where the proposed model scored in second place. To determine whether the obtained results were statistically significant enough to make a conclusion, a Nemenyi test was conducted using MSE and MAE values to rank the performance of all models in all databases. The critical distance and rank achieved by the proposed model allowed to conclude that this, in fact, delivered the best performance amongst all models tested.