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Human signals of need: Testing predictions from evolutionary and signaling theory
Get PDFIn times of need or opportunity, humans, like many other cooperative species, use signals in attempt to elicit increased support or better treatment from others. Such signals reveal private information about one’s needs and wants (and their relevance to an individual's fitness prospects), which are often not apparent to potential helpers. With this information, signal receivers are expected to provide support when they benefit from the continued well-being of those helped in ways that outweigh the costs of helping. This collection of research uses evolutionary and signaling theory to investigate what leads to various signals of need, how these signals are responded to, and why responses to signals of need take the forms they do. A theoretical review of human signals of need found that there are often many ways to signal need within societies, and that individuals often use multiple strategies, either at the same time or sequentially. Signals of need were also found to be more common among those with low social and physical power, are associated with anger and conflict, and can result in beneficial, negative, or neutral treatment from others.
An experimental vignette study found that participants were more likely to support and believe a female character claiming substantial adversity that could not be easily verified when her signaling involved depression or a suicide attempt, compared to when it involved only verbal requests and crying. These results are consistent with models which suggest that the costs of depression and suicidality function to help victims of adversity elicit support when their true level of need is private information and they have conflicts with social partners.
An exploratory field study established the natural history of child signaling in Utila, Honduras. It revealed that crying, temper tantrums, and sadness were common in both sexes and across all ages, with the frequency of child signaling as a whole decreasing with age and neighborhood quality. Consistent with signaling theory, the frequency of signals of need increased with the frequency of conflict between children and caretakers, and children who were sad more frequently were perceived as needier within the household and were more likely to receive investment within the family.
Together, this work advances the hypothesis that many signals of need are the result of adaptations that allow those with low social power to bargain for better treatment from others. It also provides broad support for the hypothesis that signaling strategies are sensitive to the degree to which one’s needs are private information and the extent of conflicts of interest between signalers and receivers
Perspective Chapter Integrating Large Language Models and Blockchain in Telemedicine
Get PDFThis perspective paper examines how combining artificial intelligence in the form of large language models (LLMs) with blockchain technology can potentially solve ongoing issues in telemedicine, such as personalized care, system integration, and secure patient data sharing. The strategic integration of LLMs for swift medical data analysis and decentralized blockchain ledgers for secure data exchange across organizations could establish a vital learning loop essential for advanced telemedicine. Although the value of combining LLMs with blockchain technology has been demonstrated in non-healthcare fields, wider adoption in medicine requires careful attention to reliability, safety measures, and prioritizing access to ensure ethical use for enhancing patient outcomes. The perspective article posits that a thoughtful convergence could facilitate comprehensive improvements in telemedicine, including automated triage, improved subspecialist access to records, coordinated interventions, readily available diagnostic test results, and secure remote patient monitoring. This article looks at the latest uses of LLMs and blockchain in telemedicine, explores potential synergies, discusses risks and how to manage them, and suggests ways to use these technologies responsibly to improve care quality
Down woody debris microsites for growing black huckleberry shrubs
Get PDFBlack huckleberry (Vaccinium membranaceum) is a shrub in the heather family (Ericaceae) that is ecologically, economically, and culturally important in the Pacific and Inland Northwest. Despite its abundance in many forest environments, black huckleberry requires very specific habitat conditions to successfully establish, grow, and produce berries. Ideal black huckleberry habitat includes a partial canopy (~40% shade) of mature conifers like Douglas-fir or mountain hemlock, often at elevations above approximately 3,000 ft. Because of these specific growing conditions and other factors, black huckleberry has not been commercially cultivated so usually has to be accessed by picking from naturally established shrub fields. As previously burned areas fill in with trees, with fewer new burned areas due to fire suppression efforts and curtailment of Native burning, black huckleberry habitat has declined from the mid-twentieth century forward, putting huckleberry restoration at the forefront of today’s conservation efforts. Our study identified down woody debris (DWD) as a tool to facilitate favorable black huckleberry growing conditions in the absence of one or more of their habitat requirements. This structure occurs naturally in forest environments but can also be supplemented through logging or tree-felling operations, especially with “cull” logs that will not be used for forest products. Recent research has shown that DWD on the forest floor creates small areas of more favorable growing conditions for seedling establishment, known as microsites, compared to surrounding environments
Development of Hydrogen-Assisted Gas Fermentation Technology to Convert Carbon Dioxide Emissions into Renewable Natural Gas Using Methanogenic Archaea
Get PDFClimate change is the defining issue of our time. The carbon dioxide that is emitted from the combustion of fossil fuels is the main contributor to climate change. Despite the projected efforts to transition to carbon-negative fuels in the following decades, the demand of fossil natural gas is expected to remain constant at least until 2050. Therefore, the development of cost-effective carbon capture technologies is a priority to counteract the contributions of fossil fuels on climate change. Gas fermentation takes advantage of chemolithotrophic microorganisms to fix carbon dioxide and convert it into renewable value-added products, including methane and acetic acid. The microorganisms involved in gas fermentation use hydrogen as electron donor in anaerobic conditions to catalyze the reduction of carbon dioxide. Gas fermentations can be operated at mild temperature conditions, do not generate hazardous waste, and are compatible with the existing elements in the water-waste-energy nexus worldwide. It makes sense that hydrogen-assisted gas fermentations emerge as a solution for both carbon capture and renewable fuel production, however this technology is limited by three primary elements; the low solubility of hydrogen in water, the relatively high costs associated to cultivation of chemolithotrophic microorganisms in an industrial setting, and the uncertainty about the robustness of non-model chemolithotrophs. In this work, new gas fermentation technology was developed to mitigate these limitations. First, a comprehensive set of strategies to overcome the mass transport limitations of hydrogen were methodically identified in the literature. This knowledge was used to build a continuous lab-scale bioreactor with enhanced gas-liquid mass transport. Later, a novel methanogenic strain with minimum nutritional demands was adapted and isolated in the laboratory. The phenotype and the genome of the new strain were characterized using microbiology principles and genomics. The study of the genome revealed evolutionary adaptations that explained the ability of the strain to grow with ammonium as only nitrogen source. Furthermore, the possibility to use wastewater as alternative low-cost medium for cultivation was demonstrated. The new strain was used to seed the bioreactor with enhanced gas-liquid mass transport, and a series of experiments were conducted to evaluate the performance of the new strain to convert biogas into renewable natural gas. The results showed an outstanding hydrogen conversion efficiency of >0.98 and a final methane titer of >99%. The bioreactor study also provided new insights to improve the performance of biogas upgrading and stablished a stepping stone for upscaling single-culture biogas upgrading technology. The use of the developed microbial strain in biogas upgrading and carbon capture was subject to a provisional patent application. In a last study, the new methanogenic strain was challenged with temperature changes, oxygen contamination, nutrients depletion, and hydrogen starvation during batch cultivation. The robustness of the cellular functions under these perturbations was measured, using a descriptive statistics approach. The measurement of robustness was used to find the perturbations that affected the most the performance of the microbe, providing a starting point for future improvement of the strain. The explanatory power of global proteomics was used to provide a mechanistic framework of what proteins are involved in the stress response and adaptation in methanogens, when exposed to relevant process perturbations. This last study also helped in the identification of genetic engineering targets in methanogens that could further lead to improved performance and robustness
Veterinary Medicine Extension Newsletter, October 2024
No full textIncluded in this newsletter: Antioxidant supplementation strategies for periparturient dairy cows; Differences in oxidative stress levels and their association with lactational performance and health of periparturient dairy cows; Small ruminant Johne s (Mycobacterium avium subspecies paratuberculosis
Exploring Current and Emerging Technologies for Education
Get PDFThis dissertation consists of two separate studies exploring current and emerging technologies for education. The current technology is presented in the first study as an open educational resource (OER) for language learning. This study explored the affordances of OERs for language by comparing a traditional textbook and an OER web-book in terms of perceived quality and task engagement. The study findings provided some insights into how language learners’ perceptions of the quality of OERs may influence their engagement with learning tasks, and consequently, with language learning.The emerging technology is presented in the second study as generative artificial intelligence (GenAI) for education. This study explored the perceptions and experiences of pre-service teachers and teacher educators about GenAI tools. The findings offer valuable insights by outlining key attributes that can influence the adoption of these tools in higher education. Based on this knowledge, this study provides practical recommendations for teacher educators and administrators to address adoption and prepare current and future teachers
EVALUATION OF THE NEED FOR CLINICIAN INVOLVEMENT IN THE DELIVERY OF AN APP-BASED INTERVENTION FOR OLDER ADULTS A RANDOMIZED CLINICAL TRIAL
Get PDFThe aging population is increasing while the caregiver pool is not. Assistive technologies can play a role in mitigating the effects of this aging wave on the healthcare industry. However, we must not only design technologies alongside the end-users, but also ensure the delivery of the technology maximizes its utility. This project sought to better understanding the effect of a clinician providing structured motivational support in uptake and integration of a new health app, the Electronic Memory and Management Aid (EMMA). While the app and the training system integrated in the app were thoughtfully designed to meet the needs of older adults, to understand the most effective way to deliver the app, we wanted to understand if it is most meaningfully offered as a stand-alone, or with a clinician in the loop. Forty-nine older adults with self-reported cognitive complaints were recruited to complete an adaptive web-based training program to learn to use and integrate the EMMA app into their daily life. During the training month, half of the study participants were randomly assigned to receive a weekly motivation and goal setting focused phone call from a clinician (M+T condition), while the other half completed the training independently (T condition) after initial set up sessions. Between group comparisons in app usage for the two-weeks post-training found that there were no differences in engagement with the app between the conditions. Correlational analyses revealed that motivation at baseline and throughout the training were significantly related to outcomes. This study helps inform effective delivery structures for technology-based interventions to support older adults aging in place