Olfactory Conditioning of Positive Performance in Humans

Olfactory conditioning effects have been widely demonstrated in the animal literature but more seldom in human populations and rarely of consciously controlled human behaviors. Building upon previous work on negative performance, we report the first experimental evidence that odors can be used effectively in a classical conditioning paradigm to positively influence human behavior. In the present study, underachieving schoolchildren experienced unexpected success at a paper-and-pencil task in the presence of an ambient odor. When they later experienced the same odor again, performance on other tasks was superior to that of relevant control groups. These data substantially extend previous results on human olfactory classical conditioning and show that odors potentially can be used to exert positive influences on human behavior

Special Observations in the Care of Psychiatric Inpatients: A Review of the Literature and Developments in Practice

Special observations are commonly used on mental health inpatient wards as an intervention with acutely ill patients who are at risk of harm to themselves, harm to others or absconding. Attention has turned to looking for alternatives to special observations, partly because of the resources that are devoted to the practice in the context of the strain on services, and partly because of questions around the efficacy of the practice and the impact on patient care. There have been a number of developments that have tried to reduce levels of special observations on wards with varying success. Here, we review the literature on special observations and recent developments in the efforts to reduce the practice. There is no convincing evidence that special observations exert a positive effect on patient outcomes, but conclusive evidence is difficult to gather and there is a need for stronger evidence to inform practice

The impact of a night confinement policy on patients in a high secure inpatient mental health service.

Purpose – From 2012, all high-secure forensic mental health services in England began operating a policy of confining patients to their locked bedrooms overnight to increase service efficiency and reduce costs. The purpose of this paper is to assess the views of staff and patients concerning the policy and examine the specific impact of the policy on patients. Design/methodology/approach – Measures of patients’ sleep hygiene, patients’ behaviour, ward atmosphere, engagement with therapy and adverse incidents were taken both before and after the night confinement (NC) policy was implemented. Both patients and staff also expressed their views of the impact of the NC policy. Findings – Results provide converging evidence that the impact of the NC policy on patients is negligible. There were no consistent negative effects of confining patients overnight. Rather, patients and staff were broadly positive about the impact that the practice had on patients. Practical implications – Confining patients to locked bedrooms overnight does not exert any consistent influence, positive or negative, on patients’ sleep hygiene, behaviour or engagement with therapy, and patients expressed a broadly positive view of the practice of NC. Thus, a NC policy may have a contribution to make to the provision an effective high-secure mental health service. Originality/value – The study provides convincing evidence that secure inpatient mental health services that are considering the adoption of a NC policy may do so without fear of a negative impact on patients

Cross-sortal Predication and Polysemy

Department of Chinese and Bilingual Studie

"Do it my way!": Impact of Customizations on Trust perceptions in Human-Robot Collaboration

Trust has been shown to be a key factor in effective human-robot collaboration. In the context of assistive robotics, the effect of trust factors on human experience is further pronounced. Personalization of assistive robots is an orthogonal factor positively correlated with robot adoption and user perceptions. In this work, we investigate the relationship between these factors through a within-subjects study (N=17). We provide different levels of customization possibilities over baseline autonomous robot behavior and investigate its impact on trust. Our findings indicate that increased levels of customization was associated with higher trust and comfort perceptions. The assistive robot design process can benefit significantly from our insights for designing trustworthy and customized robots.Comment: 8 pages including reference

Synthetic Patients: Simulating Difficult Conversations with Multimodal Generative AI for Medical Education

Problem: Effective patient-centered communication is a core competency for physicians. However, both seasoned providers and medical trainees report decreased confidence in leading conversations on sensitive topics such as goals of care or end-of-life discussions. The significant administrative burden and the resources required to provide dedicated training in leading difficult conversations has been a long-standing problem in medical education. Approach: In this work, we present a novel educational tool designed to facilitate interactive, real-time simulations of difficult conversations in a video-based format through the use of multimodal generative artificial intelligence (AI). Leveraging recent advances in language modeling, computer vision, and generative audio, this tool creates realistic, interactive scenarios with avatars, or "synthetic patients." These synthetic patients interact with users throughout various stages of medical care using a custom-built video chat application, offering learners the chance to practice conversations with patients from diverse belief systems, personalities, and ethnic backgrounds. Outcomes: While the development of this platform demanded substantial upfront investment in labor, it offers a highly-realistic simulation experience with minimal financial investment. For medical trainees, this educational tool can be implemented within programs to simulate patient-provider conversations and can be incorporated into existing palliative care curriculum to provide a scalable, high-fidelity simulation environment for mastering difficult conversations. Next Steps: Future developments will explore enhancing the authenticity of these encounters by working with patients to incorporate their histories and personalities, as well as employing the use of AI-generated evaluations to offer immediate, constructive feedback to learners post-simulation

Protein stability prediction by fine-tuning a protein language model on a mega-scale dataset.

Protein stability plays a crucial role in a variety of applications, such as food processing, therapeutics, and the identification of pathogenic mutations. Engineering campaigns commonly seek to improve protein stability, and there is a strong interest in streamlining these processes to enable rapid optimization of highly stabilized proteins with fewer iterations. In this work, we explore utilizing a mega-scale dataset to develop a protein language model optimized for stability prediction. ESMtherm is trained on the folding stability of 528k natural and de novo sequences derived from 461 protein domains and can accommodate deletions, insertions, and multiple-point mutations. We show that a protein language model can be fine-tuned to predict folding stability. ESMtherm performs reasonably on small protein domains and generalizes to sequences distal from the training set. Lastly, we discuss our models limitations compared to other state-of-the-art methods in generalizing to larger protein scaffolds. Our results highlight the need for large-scale stability measurements on a diverse dataset that mirrors the distribution of sequence lengths commonly observed in nature

Conditional Generation of Paired Antibody Chain Sequences through Encoder-Decoder Language Model

Protein language models (LMs) have been successful in sequence, structural and functional predictions. However, currently, protein LMs are limited to encoder- or decoder-only architectures for single sequences while many biological contexts involve protein-protein interactions. Here, we introduce pAbT5, which models antibody chain pairing as forward- and back-translations using a T5-based architecture. We show that pAbT5 accurately reflects chain pairing through sequence generation. Our protein LM generates variable-length sequences and its next-word prediction probability agrees with position-specific scoring matrix from sequence alignment. Like other works in protein LM, pAbT5 performs state-of-the-art unsupervised prediction on experimental measurements. To the best of our knowledge, pAbT5 is the first generative encoder-decoder protein LM for protein-protein interactions

I like who you like, but only if I like you: Female character affects mate-choice copying.

Mate-choice copying is shown when women imitate the mate-choice preferences of other women. We propose that the preferences of women with a pleasant character should be more influential than those of women with an unpleasant character and further suggest that this should apply only when the female demonstrates active interest in the male, rather than disinterest. Here, we presented women as having either a pleasant or unpleasant character and found that observing pleasant women looking at men increased women’s preferences for those men, while observing unpleasant women looking at men had no effect on women’s preferences. Furthermore, the effect of being looked at by a pleasant woman was heightened when she was smiling. This suggests that judgements of facial attractiveness can be socially influenced and that character affects the degree of influence