Secondary Mental Models: Introducing Conversational Agents in Financial Advisory Service Encounters

When introducing unfamiliar Artificial Intelligence (AI)-based systems, such as conversational agents (CAs), one needs to ensure that users interact with them according to their design. While past research has studied single-user environments, many practical settings involve multiple parties. This study addresses this gap and focuses on financial advisory service encounters and how mental models evolve in multi-party contexts. A multimodal interactive CA is developed and tested in financial consultations with 24 clients. The observations of these consultations and subsequent interviews provide insights into the challenges of using CAs in unfamiliar contexts. The clients have difficulties effectively using the system. This is linked to the institutional setting of financial advisory service encounters and a mismatch between the designer’s conceptual model and the client’s mental model, which we call secondary mental model

Secondary Mental Models: Introducing Conversational Agents in Financial Advisory Service Encounters

When introducing unfamiliar Artificial Intelligence (AI)-based systems, such as conversational agents (CAs), one needs to ensure that users interact with them according to their design. While past research has studied single-user environments, many practical settings involve multiple parties. This study addresses this gap and focuses on financial advisory service encounters and how mental models evolve in multi-party contexts. A multimodal interactive CA is developed and tested in financial consultations with 24 clients. The observations of these consultations and subsequent interviews provide insights into the challenges of using CAs in unfamiliar contexts. The clients have difficulties effectively using the system. This is linked to the institutional setting of financial advisory service encounters and a mismatch between the designer’s conceptual model and the client’s mental model, which we call secondary mental model

Morphology of Influenza B/Lee/40 Determined by Cryo-Electron Microscopy

Cryo-electron microscopy projection image analysis and tomography is used to describe the overall architecture of influenza B/Lee/40. Algebraic reconstruction techniques with utilization of volume elements (blobs) are employed to reconstruct tomograms of this pleomorphic virus and distinguish viral surface spikes. The purpose of this research is to examine the architecture of influenza type B virions by cryo-electron tomography and projection image analysis. The aims are to explore the degree of ribonucleoprotein disorder in irregular shaped virions; and to quantify the number and distribution of glycoprotein surface spikes (hemagglutinin and neuraminidase) on influenza B. Projection image analysis of virion morphology shows that the majority (∼83%) of virions are spherical with an average diameter of 134±19 nm. The aspherical virions are larger (average diameter = 155±47 nm), exhibit disruption of the ribonucleoproteins, and show a partial loss of surface protein spikes. A count of glycoprotein spikes indicates that a typical 130 nm diameter type B virion contains ∼460 surface spikes. Configuration of the ribonucleoproteins and surface glycoprotein spikes are visualized in tomogram reconstructions and EM densities visualize extensions of the spikes into the matrix. The importance of the viral matrix in organization of virus structure through interaction with the ribonucleoproteins and the anchoring of the glycoprotein spikes to the matrix is demonstrated

Gene Constellation of Influenza A Virus Reassortants with High Growth Phenotype Prepared as Seed Candidates for Vaccine Production

BACKGROUND: Influenza A virus vaccines undergo yearly reformulations due to the antigenic variability of the virus caused by antigenic drift and shift. It is critical to the vaccine manufacturing process to obtain influenza A seed virus that is antigenically identical to circulating wild type (wt) virus and grows to high titers in embryonated chicken eggs. Inactivated influenza A seasonal vaccines are generated by classical reassortment. The classical method takes advantage of the ability of the influenza virus to reassort based on the segmented nature of its genome. In ovo co-inoculation of a high growth or yield (hy) donor virus and a low yield wt virus with antibody selection against the donor surface antigens results in progeny viruses that grow to high titers in ovo with wt origin hemagglutinin (HA) and neuraminidase (NA) glycoproteins. In this report we determined the parental origin of the remaining six genes encoding the internal proteins that contribute to the hy phenotype in ovo. METHODOLOGY: The genetic analysis was conducted using reverse transcription-polymerase chain reaction (RT-PCR) and restriction fragment length polymorphism (RFLP). The characterization was conducted to determine the parental origin of the gene segments (hy donor virus or wt virus), gene segment ratios and constellations. Fold increase in growth of reassortant viruses compared to respective parent wt viruses was determined by hemagglutination assay titers. SIGNIFICANCE: In this study fifty-seven influenza A vaccine candidate reassortants were analyzed for the presence or absence of correlations between specific gene segment ratios, gene constellations and hy reassortant phenotype. We found two gene ratios, 6:2 and 5:3, to be the most prevalent among the hy reassortants analyzed, although other gene ratios also conferred hy in certain reassortants

HA1 (Hemagglutinin) Quantitation for Influenza A H1N1 and H3N2 High Yield Reassortant Vaccine Candidate Seed Viruses by RP-UPLC

The only effective measure to decrease morbidity and mortality caused by the influenza virus in the human population is worldwide vaccination. Vaccination produces neutralizing antibodies that target the HA1 subunit of the HA (hemagglutinin) protein and are strain specific. The effectiveness of new influenza vaccines are linked to two factors, the correct prediction of the circulating strains in the population in a particular season and the concentration of the HA1 protein in the vaccine formulation. With the advent of the licensing of quadrivalent vaccines, pharmaceutical manufacturers are under considerable pressure due to time constraints and dedicated resources to deliver 194-198 million doses (2020-2021 U.S. market) of vaccine. Considering the valuable resources needed to produce the influenza vaccine in a timely manner, the efficient quantitation of the HA1 protein (the main component in the influenza vaccine) is required. Currently the only method approved by regulatory agencies for quantitation of the HA antigen in vaccines is the single radial immunodiffusion assay (SRID), an antibody dependent assay that is not time efficient. Time efficient methods that are antibody independent e.g. reverse phase-high performance liquid chromatography (RP-HPLC) or size exclusion-HPLC (SE-HPLC) are available. An improved method implementing reverse phase-ultra performance liquid chromatography (RP-UPLC) has been developed to quantitate the HA1 protein antigen present in the high yield reassortant vaccine seed viruses from influenza A H1N1 and H3N2 subtypes harvested from inoculated embryonated chicken eggs. This method differentiates between high yield and lower yielding reassortants in order to select the best vaccine candidate seed virus with the highest growth \u27in ovo\u27. This direct capability to monitor the HA1 concentration of potential reassortant seed viruses and to choose the best yielding HA influenza reassortant when faced with multiple viral seed candidates provides a major advantage on the industrial scale to the influenza vaccine process

“Garbage In, Garbage Out”: Mitigating Human Biases in Data Entry by Means of Artificial Intelligence

Current HCI research often focuses on mitigating algorithmic biases. While such algorithmic fairness during model training is worthwhile, we see fit to mitigate human cognitive biases earlier, namely during data entry. We developed a conversational agent with voice-based data entry and visualization to support financial consultations, which are human-human settings with information asymmetries. In a pre-study, we reveal data-entry biases in advisors by a quantitative analysis of 5 advisors consulting 15 clients in total. Our main study evaluates the conversational agent with 12 advisors and 24 clients. A thematic analysis of interviews shows that advisors introduce biases by “feeling” and “forgetting” data. Additionally, the conversational agent makes financial consultations more transparent and automates data entry. These findings may be transferred to various dyads, such as doctor visits. Finally, we stress that AI not only poses a risk of becoming a mirror of human biases but also has the potential to intervene in the early stages of data entry