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In the present study, we use Cognitive Metrics Profiling (CMP) to capture variance in cognitive load within a complex unmanned vehicle control task. We aim to demonstrate convergent validity with existing workload measurement methods, and to decompose workload into constituent cognitive resources to aid in diagnosing causes of workload. A cognitive model of the task was developed and examined to determine the extent to which it could predict behavioral performance, subjective workload, and validated physiological workload metrics. We also examined model activity to draw insights regarding loaded cognitive capacities. We found that composite workload from the model predicted physiological metrics, performance, and subjective workload. Moreover, the model indicates that differences in workload were driven largely by procedural, declarative, and temporal memory demands. We have found preliminary evidence of correspondence between workload predictions of a CMP model and physiological measures of workload. This suggests our approach captures interesting aspects of workload in a complex task environment and may provide a theoretical link between behavioral, physiological, and subjective metrics. This approach may provide a means to design effective workload mitigation interventions and improve decision-making about personnel tasking and automation.
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Análisis y Desempeño de Tareas , Carga de Trabajo , Carga de Trabajo/psicología , Automatización , CogniciónRESUMEN
T-cell receptors (TCR) have considerable potential as therapeutics and antibody-like reagents to monitor disease progression and vaccine efficacy. Whereas antibodies recognize only secreted and surface-bound proteins, TCRs recognize otherwise inaccessible disease-associated intracellular proteins when they are presented as processed peptides bound to major histocompatibility complexes (pMHC). TCRs have been primarily explored for cancer therapy applications but could also target infectious diseases such as cytomegalovirus (CMV). However, TCRs are more difficult to express and engineer than antibodies, and advanced methods are needed to enable their widespread use. Here, we engineered the human CMV-specific TCR RA14 for high-affinity and robust soluble expression. To achieve this, we adapted our previously reported mammalian display system to present TCR extracellular domains and used this to screen CDR3 libraries for clones with increased pMHC affinity. After three rounds of selection, characterized clones retained peptide specificity and activation when expressed on the surface of human Jurkat T cells. We obtained high yields of soluble, monomeric protein by fusing the TCR extracellular domains to antibody hinge and Fc constant regions, adding a stabilizing disulfide bond between the constant domains and disrupting predicted glycosylation sites. One variant exhibited 50 nm affinity for its cognate pMHC, as measured by surface plasmon resonance, and specifically stained cells presenting this pMHC. Our work has identified a human TCR with high affinity for the immunodominant CMV peptide and offers a new strategy to rapidly engineer soluble TCRs for biomedical applications.
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Citomegalovirus/inmunología , Expresión Génica , Biblioteca de Genes , Ingeniería de Proteínas , Receptores de Antígenos de Linfocitos T/inmunología , Animales , Células CHO , Cricetulus , Citomegalovirus/genética , Humanos , Regiones Constantes de Inmunoglobulina/genética , Regiones Constantes de Inmunoglobulina/inmunología , Células Jurkat , Ratones , Dominios Proteicos , Receptores de Antígenos de Linfocitos T/genética , Proteínas Recombinantes de Fusión/genética , Proteínas Recombinantes de Fusión/inmunología , SolubilidadRESUMEN
We present a novel approach toward carboxylate-terminated, low-density monolayers on gold, which provides exceptional adsorbate stability and conformational freedom of interfacial functional groups. Adsorbates are synthesized through the thiol-yne addition of two thiol-containing head groups to an alkyne-containing tail group. The resulting monolayers have two distinct phases: a highly crystalline head phase adjacent to the gold substrate, and a reduced density tail phase, which is in contact with the environment. The ellipsometric thickness of 27 Å is consistent with the proposed structure, where a densely packed decanedithiol monolayer is capped with an 11 carbon long, second layer at 50% lateral chain density. The Fourier transform infrared peak at 1710 cm(-1) supports the presence of the carbonyl group. Further, the peaks associated with asymmetric and symmetric methylene stretching are shifted toward higher wavenumbers compared to those of well-packed self-assembled monolayers (SAMs), which shows a lower average crystallinity of the thiol-yne monolayers compared to a typical monolayer. Contact angle measurements indicate an intermediate surface energy for the thiol-yne monolayer surface, owing to the contribution of exposed methylene functionality at the surface in addition to the carbonyl terminal group. The conformational freedom at the surface was demonstrated through remodeling the thiol-yne surface under an applied potential. Changes in the receding contact angle in response to an external potential support the capacity for reorientation of the surface presenting groups. Despite the low packing at the solution interface, thiol-yne monolayers are resistant to water and ion transport (R(f) ~ 10(5)), supporting the presence of a densely structured layer at the gold surface. Further, the electrochemical stability of the thiol-yne adsorbates exceeded that of well-packed SAMs, requiring a more reductive potential to desorb the thiol-yne monolayers from the gold surface. The thiol-yne monolayer approach is not limited to carboxylate functionality and is readily adapted for low-density monolayers of varied functionality.
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Natively paired sequencing (NPS) of B cell receptors [variable heavy (VH) and light (VL)] and T cell receptors (TCRb and TCRa) is essential for the understanding of adaptive immunity in health and disease. Despite many recent technical advances, determining the VH:VL or TCRb:a repertoire with high accuracy and throughput remains challenging. We discovered that the recently engineered xenopolymerase, RTX, is exceptionally resistant to cell lysate inhibition in single-cell emulsion droplets. We capitalized on the characteristics of this enzyme to develop a simple, rapid, and inexpensive in-droplet overlap extension reverse transcription polymerase chain reaction method for NPS not requiring microfluidics or other specialized equipment. Using this technique, we obtained high yields (5000 to >20,000 per sample) of paired VH:VL or TCRb:a clonotypes at low cost. As a demonstration, we performed NPS on peripheral blood plasmablasts and T follicular helper cells following seasonal influenza vaccination and discovered high-affinity influenza-specific antibodies and TCRb:a.
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Training negotiation is difficult because it is a complex, dynamic activity that involves multiple parties. It is often not clear how to create situations in which students can practice negotiation or how to measure students' progress. Some have begun to address these issues by creating artificial software agents with which students can train. These agents have the advantage that they can be "reset," and played against multiple times. This allows students to learn from their mistakes and try different strategies. However, these agents are often based on normative theories of how negotiators should conduct themselves, not necessarily how people actually behave in negotiations. Here, we take a step toward addressing this gap by developing an agent grounded in a cognitive architecture, ACT-R. This agent contains a model of theory-of-mind, the ability of humans to reason about the mental states of others. It uses this model to try to infer the strategy of the opponent and respond accordingly. In a series of experiments, we show that this agent replicates some aspects of human performance, is plausible to human negotiators, and can lead to learning gains in a small-scale negotiation task.
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Many studies have examined how people recall the locations of objects in spatial layouts. However, little is known about how people monitor the accuracy of judgments based on those memories. The goal of the present experiments was to examine the effect of reference frame characteristics on metacognitive accuracy for spatial judgments. Reference frame characteristics include the alignment of one's viewpoint with the structure of the environment (allocentric alignment), direction of the target with respect to one's current viewpoint (egocentric direction), and the type of perspective used to solve the task (egocentric vs. allocentric). Participants were tested on their knowledge of a well-known location in which they had experience navigating. They were asked to orient themselves toward a particular heading and point to target landmarks from this heading. They then rated their confidence in their pointing judgments. Confidence judgments were sensitive to the effects of allocentric alignment and egocentric direction on performance. However, they underestimate the magnitude of these effects. Follow-up regression analyses indicate that confidence in individual landmarks was a stronger predictor of confidence than reference frame characteristics. Overall, the results suggest that people use reference frame features and landmark confidence when monitoring performance in directional judgments. (PsycINFO Database Record
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Juicio/fisiología , Metacognición/fisiología , Orientación/fisiología , Percepción Espacial/fisiología , Navegación Espacial/fisiología , Análisis de Varianza , Femenino , Humanos , Masculino , Estimulación Luminosa , Distribución Aleatoria , Tiempo de Reacción/fisiología , Regresión Psicológica , Factores Sexuales , Estudiantes , Encuestas y Cuestionarios , UniversidadesRESUMEN
In this article, we examine the advantages of simple metacognitive capabilities in a repeated social dilemma. Two types of metacognitive agent were developed and compared with a non-metacognitive agent and two fixed-strategy agents. The first type of metacognitive agent (opponent perspective) takes the perspective of the opponent to anticipate the opponent's future actions and respond accordingly. The other metacognitive agent (modeler) predicts the opponent's next move based on the previous moves of the agent and the opponent. The modeler agent achieves better individual outcomes than a non-metacognitive agent and is more successful at encouraging cooperation. The opponent-perspective agent, by contrast, fails to achieve these outcomes because it lacks important information about the opponent. These simple agents provide insights regarding modeling of metacognition in more complex tasks.