Predicting Humans’ Identity and Mental Load from EEG: Performed by AI

EEG-based brain machine/computer interfaces (BMIs/BCIs) have a wide range of clinical and non-clinical applications. Mental workload (MW) classification, emotion recognition, motor imagery, seizure detection, and sleep stage scoring are among the active BCI research areas. One of the relatively new BCI area is EEG-based human subject recognition (i.e., EEG biometric). There still exist several challenges that need to be addressed to design a successful EEG-based biometric model applicable for real-world environments. First, there is a need for a protocol that can elicit the individual dependent EEG responses in a short period of time. A classification algorithm with high generalization power is also required to deal with the EEG signals classification task. The latter is a common challenge for all EEG-based BCI paradigms; given the non-stationary nature of the EEG signals and the small size of the EEG datasets. In addition, to building a stable EEG biometric model, the effects of human mental states (e.g., emotion, mental load) on the model performance needs to be carefully examined. In this thesis, a new protocol for the area of the EEG biometric has been proposed. The proposed protocol called “(the) N-back task” is based on the human working memory and the experimental results obtained in this thesis prove that the EEG signals elicited by the N-back task contain subject specific features, even for very short time intervals. It has also been shown that three load levels of the typical N-back task are all capable of evoking subject specific EEG features. As a result, the N-back task can be used as a protocol having more than one mode (i.e, cancelable protocol) that comes with added security benefits. The EEG signals evoked by the N-back task have been used to train a compact convolutional neural network called the EEGNet. A configuration of the EEGNet having 16 temporal and 2 spatial filters has reached an identification accuracy of approximately 97% using data instances as short as 1.1s for a pool of 26 subjects. To further improve the accuracy, a novel ensemble classifier has been designed in this thesis. The principle underlying the proposed ensemble is the “division and exclusion” of the EEG channels guided by scalp locations. The ensemble classifier has (statistically significantly) improved the subject recognition rate from 97% to 99%. Performance of the proposed ensemble model has also been assessed in the EEG-based MW classification paradigm. The ensemble classifier outperformed the single EEGNet as well as a state-of-the-art classifier called WLnet in the challenging scenario of the subject-independent (cross-subject) MW classification. The results suggest that the ensemble structure proposed in this thesis can generalize to different BCI paradigms. Finally, effects of the mental workload on the performance of the EEG-based subject authentication models have been thoroughly explored in this thesis. The obtained results affirm that MW of the genuine and impostor subjects at the train and test phases have significant effects on both false negative rate (FNR) and false positive rate (FPR) of an authentication system. Different subjects have also shown different clusters of authentication behaviors when affected by the MW changes. This finding establishes the importance of the human’s mental load in the design of real-world EEG authentication systems and introduces a new investigation line for the EEG biometric community

Characterization of Indicators for Adaptive Human-Swarm Teaming

Swarm systems consist of large numbers of agents that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from urban search and rescue situations to cyber defence. However, the successful deployment of the swarm in such applications is conditioned by the effective coupling between human and swarm. While adaptive autonomy promises to provide enhanced performance in human-machine interaction, distinct factors must be considered for its implementation within human-swarm interaction. This paper reviews the multidisciplinary literature on different aspects contributing to the facilitation of adaptive autonomy in human-swarm interaction. Specifically, five aspects that are necessary for an adaptive agent to operate properly are considered and discussed, including mission objectives, interaction, mission complexity, automation levels, and human states. We distill the corresponding indicators in each of the five aspects, and propose a framework, named MICAH (i.e., Mission-Interaction-Complexity-Automation-Human), which maps the primitive state indicators needed for adaptive human-swarm teaming.</p

The Role of C-reactive Protein in Diagnosis of Acute Complicated Appendicitis: A Diagnostic Accuracy Study

Introduction: Acute appendicitis is one of the most common emergencies of general surgery. Contrary to simple appendicitis, the complicated cases are associated with higher morbidity and mortality. Except for pathology, no accurate diagnostic test has been found to identify complicated cases.&nbsp;Objective: Here in, we aim to evaluate the serum C-Reactive Protein (CRP) level in both acute simple and complicated appendicitis.&nbsp;Methods: In this diagnostic accuracy study, 199 patients with acute appendicitis were enrolled. The serum CRP level was evaluated in patients. Post-operatively, the patients were divided into simple and complicated appendicitis based on histopathological examination. Eventually, analysis of the CRP level and type of appendicitis was performed.&nbsp;Results: Fifty-three patients were categorized into complicated appendicitis and 146 patients into simple appendicitis. The median of CRP was significantly higher in the complicated group. Additionally, the optimal cutoff point was as follows: [65.0 (25.0) vs 25.0 (51.0); P-value&lt; 0.001]. The optimal cutoff point for CRP was more than 42 with 81.1% sensitivity (95% CI: 68.0 to 90.6), and 67.8% specificity (95% CI: 59.6 to 75.3). The positive (PPV) and negative predictive values (NPV), based on the prevalence of complicated appendicitis (26.6%) for optimal cutoff point, were 47.8% (95% CI: 37.1 to 58.6) and 90.8% (95% CI: 83.8 to 95.5).&nbsp;Conclusion: Our study revealed that evaluation of serum CRP levels could be useful and beneficial in the diagnosis of acute complicated appendicitis

The Immune Epitope Database 2.0

The Immune Epitope Database (IEDB, www.iedb.org) provides a catalog of experimentally characterized B and T cell epitopes, as well as data on Major Histocompatibility Complex (MHC) binding and MHC ligand elution experiments. The database represents the molecular structures recognized by adaptive immune receptors and the experimental contexts in which these molecules were determined to be immune epitopes. Epitopes recognized in humans, nonhuman primates, rodents, pigs, cats and all other tested species are included. Both positive and negative experimental results are captured. Over the course of 4 years, the data from 180 978 experiments were curated manually from the literature, which covers ∼99% of all publicly available information on peptide epitopes mapped in infectious agents (excluding HIV) and 93% of those mapped in allergens. In addition, data that would otherwise be unavailable to the public from 129 186 experiments were submitted directly by investigators. The curation of epitopes related to autoimmunity is expected to be completed by the end of 2010. The database can be queried by epitope structure, source organism, MHC restriction, assay type or host organism, among other criteria. The database structure, as well as its querying, browsing and reporting interfaces, was completely redesigned for the IEDB 2.0 release, which became publicly available in early 2009

Curation of complex, context-dependent immunological data

BACKGROUND: The Immune Epitope Database and Analysis Resource (IEDB) is dedicated to capturing, housing and analyzing complex immune epitope related data . DESCRIPTION: To identify and extract relevant data from the scientific literature in an efficient and accurate manner, novel processes were developed for manual and semi-automated annotation. CONCLUSION: Formalized curation strategies enable the processing of a large volume of context-dependent data, which are now available to the scientific community in an accessible and transparent format. The experiences described herein are applicable to other databases housing complex biological data and requiring a high level of curation expertise

Design and utilization of epitope-based databases and predictive tools

In the last decade, significant progress has been made in expanding the scope and depth of publicly available immunological databases and online analysis resources, which have become an integral part of the repertoire of tools available to the scientific community for basic and applied research. Herein, we present a general overview of different resources and databases currently available. Because of our association with the Immune Epitope Database and Analysis Resource, this resource is reviewed in more detail. Our review includes aspects such as the development of formal ontologies and the type and breadth of analytical tools available to predict epitopes and analyze immune epitope data. A common feature of immunological databases is the requirement to host large amounts of data extracted from disparate sources. Accordingly, we discuss and review processes to curate the immunological literature, as well as examples of how the curated data can be used to generate a meta-analysis of the epitope knowledge currently available for diseases of worldwide concern, such as influenza and malaria. Finally, we review the impact of immunological databases, by analyzing their usage and citations, and by categorizing the type of citations. Taken together, the results highlight the growing impact and utility of immunological databases for the scientific community

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin