Real-time estimation of EEG-based engagement in different tasks

: Objective.Recent trends in brain-computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG).Approach.Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects' perceived engagement was quantified using a questionnaire.Main results.The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects' perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p= 0.025 andp<0.001, respectively); greater during medium and fast compared to slow Tetris speed (p<0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (rrm= 0.44,p<0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states.Significance.This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications

Phagocytosis and digestion of pH-sensitive fluorescent dye (Eos-FP) transfected E. coli in whole blood assays from patients with severe sepsis and septic shock

The function of phagocytic and antigen presenting cells is of crucial importance to sustain immune competence against infectious agents as well as malignancies. We here describe a reproducible procedure for the quantification of phagocytosis by leukocytes in whole blood. For this, a pH-sensitive green-fluorescent protein- (GFP) like dye (Eos-FP) is transfected into infectious microroganisms. After UV-irradiation, the transfected bacteria emit green (≈5160 nm) and red (≈581 nm) fluorescent light at 490 nm excitation. Since the red fluorescent light is sensitive to acidic pH, the phagocytosed bacteria stop emitting red fluorescent light as soon as the phagosomes fuse with lysosomes. The green fluorescence is maintained in the phagolysosome until pathogen degradation is completed. Fluorescence emission can be followed by flow cytometry with filter settings documenting fluorescence 1 (FL 1, FITC) and fluorescence 2 (FL 2, phycoerythrin, PE). Eos-FP transfected bacteria can also be traced within phagocytes using microscopical techniques. A standardized assay has been developed which is suitable for clinical studies by providing clinicians with syringes pre-filled with fixed and appropriately UV-irradiated Eos-FP E. coli (TruCulture™). After adding blood or body fluids to these containers and starting the incubation at 37°C, phagocytosis by granulocytes proceeds over time. Cultures can be terminated at a given time by lysing red blood cells followed by flow cytometry. A pilot study demonstrated that Eos-FP E. coli phagocytosis and digestion was up-regulated in the majority of patients with either severe sepsis or septic shock as compared to healthy donors (p < 0.0001 after o/n incubation). Following treatment with recombinant human granulocyte colony-stimulating factor (rhG-CSF) in selected patients with sepsis, phagolysosome fusion appeared to be accelerated

Phagozytose und Abbau von EosFP E.coli-Bakterien (transfiziert mit pH-sensitivem Fluoreszenzfarbstoff) in Vollblutproben von Patienten mit schwerer Sepsis und septischem Schock

Die vorliegende experimentelle Arbeit stellt einen neuartigen Assay zur simultanen Beurteilung von Phagozytoseleistung und Degradation (intrazellulärer Abbau) vor. Hierfür wurden fluoreszierende „EosFP-E.coli.“ -Bakterien verwendet, Escherichia coli – Bakterien welche mit dem Fluoreszenz-Protein („EosFP“) einer Koralle (Lobophyllia hemrichi) transfiziert worden sind. Dieser Fluoreszenzfarbstoff konvertiert durch Foto-Stimulation von einem grünen zu einem stark pH-abhängigen (<pH4) roten Farbstoff und kann damit die für intrazellulären Abbau hauptverantwortliche Fusion von Phagosom und Lysosom nachweisen. Der grüne Farbstoff selbst ist auch bei niedrigen pH-Werten stabil. Die Fluoreszenzbereiche sind denen etablierter Antikörpermarkierungen sehr ähnlich und lassen sich daher mit einer Anregungswellenlänge von 490nm und Emissionswellenlängenfiltern um 530nm und 570nm messen. Im Eos-FP E. coli-Phagozytosetest können folgende Parameter dokumentiert werden: 1. Phagozytoseleistung durch Anstieg der Grünfluoreszenz über die Zeit. 2. Abbauleistung durch Abnahme der Rotfluoreszenz über die Zeit. 3. Verhältnis von Grün-zu-Rotfluoreszenz über die Beziehung zwischen Phagozytoseleistung und Abbau.Vollblutproben wurden dazu mit den simultan rot und grün fluoreszierenden Bakterien inkubiert, so dass Zellen des Immunsystems diese phagozytieren konnten. Die Fluoreszenzintensität ist damit über die Zeit ein Maß für die Phagozytoseleistung pro Zelle (1). Die pH-Veränderungen mit konsekutivem Verlust der roten Fluoreszenz ermöglicht den Nachweis der Phagosomen-Lysosomen-Fusion und ist ein Maß für die Abbauleistung über die Zeit (2). Die Ratio von Grün zu Rot gibt einen Anhalt für das Verhältnis von Phagozytose zu Abbau. Ein wesentlicher Unterschied zwischen Kontrollen und septischen bzw. Patienten im septischen Schock besteht darin, dass die Grünfluoreszenz über alle gemessene Zeitpunkte bei den Kontrollen deutlich weniger ansteigen als bei den Patienten. Das bedeutet, dass die Probanden in der Geschwindigkeit der Phagozytose reduziert sind, aber die Gesamtphagozytoseleistung bei allen Schockpatienten und bei etwa der Hälfte der Sepsispatienten deutlich stimuliert ist. Das könnte mit einer Ausreifung der meistens linksverschobenen Granulozytoenpopulation bei Sepsis erklärt werden. Die aktivierten Granulozyten von Patienten mit Sepsis weisen nach einer anfänglichen Verzögerung eine höhere Phagozytose - Aktivität auf, als gesunde Probanden. Zugleich konnten wir nun indirekt über die pH-Senkung den Eintritt in den Phagosomen-Lysosomen-Degradationskomplex messen. Dieser war zu jedem Inkubationszeitpunkt bei Patienten mit Sepsis signifikant höher, als bei gesunden Probanden. Die Daten zeigen demnach, dass Granulozyten von Patienten mit Sepsis schneller die phagozytierten Bakterien intrazellulär abbauen als gesunde Probanden. Aktuelle Studien über Phagozytose weisen gegenteilige Ergebnisse auf, unterscheiden sich jedoch deutlich von der Methodik (Inkubationszeit, Konzentration und Art des "target" sowie damit verbundenem Phagozytoseweg). In ergänzenden Einzelfall-Untersuchungen wurde die Wirkung von rhG-CSF (recombinant human granulocyte-colony-stimulating factor) auf Patienten mit Neutropenie und Sepsis anhand derselben Methodik ausgewertet. Die Veränderungen hierbei korrelieren mit einer Angleichung an die Ergebnisse gesunder Probanden. Der neuartige Assay ließ sich demnach auch als Verfahren erstmals standardisiert anwenden

Real-time estimation of EEG-based engagement in different tasks.

Objective.Recent trends in brain-computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG).Approach.Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects' perceived engagement was quantified using a questionnaire.Main results.The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects' perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p= 0.025 andp<0.001, respectively); greater during medium and fast compared to slow Tetris speed (p<0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (rrm= 0.44,p<0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states.Significance.This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications

Real-time estimation of EEG-based engagement in different tasks

Objective. Recent trends in brain–computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG). Approach. Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects’ perceived engagement was quantified using a questionnaire. Main results. The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects’ perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p = 0.025 and p < 0.001, respectively); greater during medium and fast compared to slow Tetris speed (p < 0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (r rm = 0.44, p < 0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states. Significance. This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications

Real-time estimation of EEG-based engagement in different tasks

Objective. Recent trends in brain–computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG). Approach. Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects’ perceived engagement was quantified using a questionnaire. Main results. The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects’ perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p = 0.025 and p < 0.001, respectively); greater during medium and fast compared to slow Tetris speed (p < 0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (r rm = 0.44, p < 0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states. Significance. This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications

Order from disorder in the sarcomere:FATZ forms a fuzzy but tight complex and phase-separated condensates with α-actinin

In sarcomeres, α-actinin cross-links actin filaments and anchors them to the Z-disk. FATZ (filamin-, α-actinin-, and telethonin-binding protein of the Z-disk) proteins interact with α-actinin and other core Z-disk proteins, contributing to myofibril assembly and maintenance. Here, we report the first structure and its cellular validation of α-actinin-2 in complex with a Z-disk partner, FATZ-1, which is best described as a conformational ensemble. We show that FATZ-1 forms a tight fuzzy complex with α-actinin-2 and propose an interaction mechanism via main molecular recognition elements and secondary binding sites. The obtained integrative model reveals a polar architecture of the complex which, in combination with FATZ-1 multivalent scaffold function, might organize interaction partners and stabilize α-actinin-2 preferential orientation in Z-disk. Last, we uncover FATZ-1 ability to phase-separate and form biomolecular condensates with α-actinin-2, raising the question whether FATZ proteins can create an interaction hub for Z-disk proteins through membraneless compartmentalization during myofibrillogenesis