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This Portuguese project compares the classification of AI risks and vulnerabilities performed by humans and performed by the computing algorithms.

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Carlos Baquero on whether using artificial intelligence provides an unfair advantage to writers.

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There are lot of interactive environments that encourage the practice of computer programming. Despite their usefulness, these systems are naturally disconnected from the schools' educational environments, forcing teachers and students to alternate between tools. This work integrates Agni - a computer programming learning playground - with Moodle Learning Management System (LMS). Unlike the plugins' strategy to link applications to specific LMS, this integration uses a broader approach, through the IMS LTI standard which defines a set of specifications that allows the connection of any LTI-compliant LMS to external tools.

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Deep brain stimulation of the Anterior Nucleus of the Thalamus (ANT-DBS) is an effective therapy in epilepsy. Poorer surgical outcomes are related to deviations of the lead from the ANT-target. The target identification relies on the visualization of anatomical structures by medical imaging, which presents some disadvantages. This study aims to research whether ANT-LFPs recorded with the Percept (TM) PC neurostimulator can be an asset in the identification of the DBS-target. For this purpose, 17 features were extracted from LFPs recorded from a single patient, who stayed at an Epilepsy Monitoring Unit for a 5-day period. Features were then integrated into two machine learning (ML)-based methodologies, according to different LFP bipolar montages: Pass1 (nonadjacent channels) and Pass2 (adjacent channels). We obtained an accuracy of 76.6% for the Pass1-classifier and 83.33% for the Pass2-classifier in distinguishing locations completely inserted in the target and completely outside. Then, both classifiers were used to predict the target percentage of all combinations, and we found that contacts 3 (left hemisphere) and 2 and 3 (right hemisphere) presented higher signatures of the ANT-target, which agreed with the medical images. This result opens a new window of opportunity for the use of LFPs in the guidance of DBS target identification.

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Background: The gap between offer and need for a kidney transplant (KT) has been increasing. The Kidney Donor Profile Index (KDPI) is a measure of organ quality and allows estimation of graft survival, but could not apply to all populations. Knowledge of our kidney donor and recipient population is vital to adjust transplant strategies. Methods: We performed a retrospective evaluation of donors and recipients of KT regarding two kidney transplant units: Centro Hospitalar Universitario de Coimbra, CHUC (Coimbra, Portugal) and Centro Hospitalar Universitario de Sao Joao, CHUSJ (Porto, Portugal), between 2013 and 2018. We then did statistical analysis and modeling, correlating these KT outcomes with donor and recipient characteristics, including KDPI. Artificial intelligence methods were performed to determine the best predictors of graft survival. Results: We analyzed a total of 808 kidney donors and 829 recipients of KT. The association between KDPI and graft dysfunction was only moderate. The decision tree machine learning algorithm proved to be better at predicting graft failure than artificial neural networks. Multinomial logistic regression revealed recipient age as an important prognostic factor for graft loss. Conclusions: In this Portuguese cohort, KDPI was not a good measure of KT survival, although it correlated with GFR 1 year post-transplant. The decision tree proved to be the best algorithm to predict graft failure. Age of the recipient was the most important predictor of graft dysfunction.