Levy, M. M. | Search bioRxiv

bioRxiv2023

Decoding Internal Decision Making During Reverse Engineering Tasks

Neural decoding is often limited to tasks with known stimuli and limited response options . Real world tasks, however, are often completely stimulus free with unconstrained user response possibilities. Real time decoding of internal decision making would allow for more complex and interactive Huma Machine Teaming in a way that is not currently possible. To address this problem, we present here a novel method of decoding moments of recognition and their associated internal value judgments in the context of highly complex software reverse engineering tasks. This is done through a combination of P300 detection (a neural marker of recognition) and the Engagement Index (a ratio of neural band powers) to determine whether an item has been identified as relevant to the task (to be further explored) or irrelevant to the task (to be quickly ignored). Artificial neural networks were trained to identify P300s in each subject during the reverse engineering tasks. Dimensionality reduction of neural data during the tasks showed the existence of separately clustering subgroups of P300s with differences in Engagement Index. Subgroups of P300s differentiated by Engagement were further verified as distinct groupings with pupil dilation and user behavior metrics. This decoded information could be used to aid in the reverse engineering process via cognitive offloading of the users own decision making on to the visual interface in a completely automated and personalized fashion. This represents a significant advance in domain of real-time neural decoding, and opens up many further possibilities for usage in a broad range of intelligent human systems integration applications.

neuroscience↗

bioRxiv2022

Identification and Mechanistic Basis of non-ACE2 Blocking Neutralizing Antibodies from COVID-19 Patients with Deep RNA Sequencing and Molecular Dynamics Simulations

Variants of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continue to cause disease and impair the effectiveness of treatments. The therapeutic potential of convergent neutralizing antibodies (NAbs) from fully recovered patients has been explored in several early stages of novel drugs. Here, we identified initially elicited NAbs (Ig Heavy, Ig lambda, Ig kappa) in response to COVID-19 infection in patients admitted to the intensive care unit at a single center with deep RNA sequencing (>100 million reads) of peripheral blood as a diagnostic tool for predicting the severity of the disease and as a means to pinpoint specific compensatory NAb treatments. Clinical data were prospectively collected at multiple time points during ICU admission, and amino acid sequences for the NAb CDR3 segments were identified. Patients who survived severe COVID-19 had significantly more of a Class 3 antibody (C135) to SARS-CoV-2 compared to non-survivors (16,315 reads vs 1,412 reads, p=0.02). In addition to highlighting the utility of RNA sequencing in revealing unique NAb profiles in COVID-19 patients with different outcomes, we provided a physical basis for our findings via atomistic modeling combined with molecular dynamics simulations. We established the interactions of the Class 3 NAb C135 with the SARS-CoV-2 spike protein, proposing a mechanistic basis for inhibition via multiple conformations that can effectively prevent ACE2 from binding to the spike protein, despite C135 not directly blocking the ACE2 binding motif. Overall, we demonstrate that deep RNA sequencing combined with structural modeling offers the new potential to identify and understand novel therapeutic(s) NAbs in individuals lacking certain immune responses due to their poor endogenous production. Our results suggest a possible window of opportunity for administration of such NAbs when their full sequence becomes available. A method involving rapid deep RNA sequencing of patients infected with SARS-CoV-2 or its variants at the earliest infection time could help to develop personalized treatments using the identified specific NAbs.

immunology↗