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Olfactory receptors (ORs) are seven transmembrane domains G protein-coupled receptors (GPCRs) located in the olfactory sensory neurons (OSNs) of the nasal olfactory epithelium. Although OR expression was initially hypothesized to be restricted to the OSNs, an ecnomotopic expression has been identified and associated with the modulation of different physiological functions, such as glucose and lipid metabolism, hypoxia sensing, wound healing and sperm chemiotaxis. However, the role of most ORs in non-olfactory tissues is still a matter of debate, as well as their specific ligands and mechanisms of action. High-density lipoproteins (HDL) are heterogenous, and multifunctional nanoparticles constituted primarily of proteins and lipids. Their main structural protein, namely, apolipoprotein A-I (A-I) has been recognized as the major determinant of the biological activities of HDL. Recently, our group, by using unique mouse models and microarray methodology, has demonstrated that human A-II (hA-II) and A-IMilano (A-IM), a molecular variant of A-I, strongly modulate the hepatic expression of different genes involved in lipid metabolism and immune/inflammatory pathways. Therefore, aiming at investigating the impact of these apolipoproteins on the hepatic expression of mouse ORs (Olfrs), we have performed a new bioinformatic analysis of the differentially expressed genes (DEGs) found in the liver of hA-II/A-I k-in versus hA-II/A-IM k-in; A-IM k-in versus hA-II/A-IM k-in; A-I k-in versus A-IM k-in; A-I k-in versus hA-II/A-I k-in mice. Our results suggest that the presence of A-IM, either alone or in combination with hA-II, is critical for the efficient trafficking and functional expression of Olfrs at the cell surface. Moreover, co-expression of hA-II with A-I resulted in down-regulation of previously uncharacterized Olfrs genes an up-regulation of several known Olfrs, which are likely responsive to short-chain fatty acids and signal through the cAMP/CREB pathway.
Revised Trauma Quality of Life (RT-QOL) measurement among violence survivors is challenging because of loss to follow-up. This study evaluated RT-QOL instrument completion during follow-up phone calls after hospital discharge and assessed if time to follow-up call was associated with instrument completion. This was an observational cohort study of intentional interpersonal violence survivors treated at a Level 1 urban trauma center from March 2018-April 2024. Depression (Beck's Depression Inventory II), Post-Traumatic Stress Disorder (Breslau Post Traumatic Stress Disorder Scale 7-item), and Revised Trauma-specific Quality of Life (RT-QOL) instruments were phone administered after discharge in English or Spanish. Multivariable regression tested if time to follow-up call was associated with instrument completion while controlling for survivors' demographic, injury, hospital course and follow-up characteristics. A total of 566 intentional interpersonal violence survivors were eligible. Survivors were mostly male (82.0 %), 25-64 years old (77.9 %), Black (65.2 %), and injured by firearm (44.7 %). Among the 566 eligible survivors, 115 survivors (20.3 %) had an inaccurate phone number in the medical record, and 32 (5.7 %) died after hospital discharge. Two survivors (0.4 %) partially completed and 51 (9 %) completed instruments. Survivors who completed instruments were called fewer times, 2 calls (IQR: 1-2.5) versus 3 calls (IQR: 1-3). Approximately 43 % of survivors who completed instruments, completed them on the first call. Time to follow-up call was not associated with instrument completion. Females had 2.45 higher adjusted odds of instrument completion after controlling for time to follow-up call, age, race, ethnicity and injury mechanism (p = 0.008). Among survivors who completed instruments, 21(41.1 %) screened positive on Beck's Depression Inventory II, 31 (60.8 %) screened positive on Breslau Post Traumatic Stress Disorder Scale 7-item, and 39 (76.5 %) reported RT-QOL symptoms impairing work. Only female sex, not time to follow-up call, was associated with increased instrument completion among violence survivors.
Oral contraceptives are a popular choice of contraception globally. The objective was to assess whether oral contraceptive use is associated with diagnosis, treatment, and symptoms of depression in healthy women. Databases (MEDLINE, EMBASE, and PsycINFO databases via OvidSP for relevant studies, from database inception to 01 July 2025) were searched for prospective and observational studies comparing users of any currently prescribed oral contraceptives to non-users. Binary outcomes were summarised via relative risks (RR) with 95% confidence intervals (95% CI). Continuous outcomes were evaluated via standardised mean differences (SMD) along with 95% CI. Main outcomes and measures included incident depression diagnosis, antidepressant initiation, and depressive symptom scores measured with externally validated depression scales. Out of 438 screened references, 14 (2,425,648 participants) were included in the analysis. Of the five studies (1,607,461 participants) that examined depression diagnoses, oral contraceptives were associated with a 31% increased relative risk of being diagnosed with depression compared to non-users (RR: 1.31; 95% CI: 1.07 to 1.61, I2 = 94.18%, moderate certainty, high heterogeneity). Three studies (2,150,352 participants) with incidence of antidepressant use showed oral contraceptive users were 25% more likely to take antidepressants than non-users (RR: 1.25; 95% CI: 1.20 to 1.30 I2 = 75.4%, moderate certainty). Eight studies (2,525 participants) measuring depressive symptom scores found oral contraceptive users had statistically significantly higher, but clinically small, differences in depressive symptom scores than non-users (SMD: 0.12, 95% CI: 0.05 to 0.20). Subgroup analyses of depressive symptom scores showed no evidence of group differences by study design, follow-up length or progestogen content. Evidence from this meta-analysis suggests oral contraceptive use is associated with increased risks of depression diagnoses, antidepressant initiation, and higher depressive symptom scores. The findings, which reflect the association between hormonal oral contraceptives and depression in women without pre-existing psychological or gynaecological conditions, suggest that adverse effects on mood should be closely monitored by contraception prescribers.
We conducted an international AI negotiation competition in which participants designed and refined prompts for AI negotiation agents. We then facilitated over 180,000 negotiations between these agents across multiple scenarios with diverse characteristics and objectives. Our findings revealed that principles from human negotiation theory remain crucial even in AI-AI contexts. Surprisingly, warmth-a traditionally human relationship-building trait-was consistently associated with superior outcomes across all key performance metrics. Dominant agents, meanwhile, were especially effective at claiming value. Our analysis also revealed unique dynamics in AI-AI negotiations not fully explained by negotiation theory, including AI-specific technical strategies like chain-of-thought reasoning and prompt injection. When we applied natural language processing methods to the full transcripts of all negotiations, we found positivity, gratitude, and question-asking (associated with warmth) were strongly associated with reaching deals as well as objective and subjective value, whereas conversation lengths (associated with dominance) were strongly associated with impasses. The results suggest the need to establish a new theory of AI negotiation, which integrates classic negotiation theory with AI-specific negotiation theories to better understand autonomous negotiations and optimize agent performance.
The rapid incorporation of solar energy (PV) systems into electrical grids has increased the demand for accurate short-term forecasts to ensure stability and improve processes. Although hybrid artificial intelligence (AI) models are increasingly being suggested to address this challenge, there is a lack of systematic compilation of their structures, effectiveness, and readiness for use in real-world applications. This paper provides a detailed analysis of 58 peer-reviewed articles (2020-2025) focused on hybrid models for short-term (1-24 h) solar photovoltaic power forecasting. We propose an innovative classification that groups hybrids into four categories: AI-AI (28%), AI with optimization (21%), decomposition-based (17%), and image-based (7%). Our research indicates that weather conditions (34%) and historical photovoltaic energy records (32%) are the most frequent inputs, and that optimized hybrids and those using decomposition achieve the best balance between effectiveness and computational efficiency. From a geographical perspective, the study focuses mainly on the United States (29%) and China (22%), suggesting that more extensive climate validation is crucial. Essentially, we have identified ongoing obstacles to implementation, such as high computational costs, data quality issues, and gaps in interpretation. In addition, we present a plan for future research focusing on hybrid architectures that are lightweight, understandable, and interactive with the grid. This analysis provides a thorough assessment of the current landscape and a strategic framework to guide the creation of operational forecasting systems capable of supporting highly solar-integrated grids.
Advances in generative artificial intelligence (AI) have given rise to relational AI-AI agents that mimic human relational capabilities while possessing unique nonhuman features, including constant availability, extensive malleability, and a lack of intrinsic psychological needs. This article uses self-determination theory to examine how relational AI may support or undermine the three basic psychological needs self-determination theory posits as essential for well-being: relatedness, competence, and autonomy. Across roles including that of tutor, copilot, social mediator, companion, and therapist, relational AI may address critical challenges to these needs, facilitating goal attainment, alleviating loneliness, and promoting mental health. However, relational AI also carries potential risks for well-being, including reduced self-direction, diminished efficacy, and altered expectations within human relationships. We propose that relational AI's ultimate impact on users' well-being will depend on moderating factors that may shape both the strength and direction of its effects on well-being, including users' motivational orientation toward the goal for which they use relational AI, their motivational orientation for using relational AI, their baseline psychological need satisfaction, dynamics particular to each relational AI role, and various situational factors. Crucially, it is likely impossible to fully understand relational AI's impacts on well-being without examining all three psychological needs, as focusing on only a single need risks overlooking dynamics in which impacts on multiple needs interact to amplify or neutralize well-being. Future research on these processes can deepen our understanding of human relationship dynamics, inform responsible AI development, and reveal novel theoretical mechanisms underlying relational AI's impacts on well-being. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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Invasive ants are among the most destructive invaders worldwide, causing ecological disruption, economic losses, and public health risks. While classic traits such as polygyny, colony budding, and supercoloniality are well-known contributors to their success, emerging research reveals a broader suite of mechanisms driving their invasiveness. This review synthesizes recent findings on the microbial, genetic, and behavioral factors that facilitate ant invasions. Microbial interactions play a crucial role; invasive ants often exhibit a loss of natural enemies, including microbial pathogens such as Wolbachia. However, Wolbachia has received growing attention for its potential mutualistic role in enhancing colony productivity and nutrient provisioning. The bridgehead effect, wherein invasive populations establish strategic hubs that facilitate secondary invasions, has been increasingly recognized as a key driver of global ant spread and may promote genetic intermixing among invasive lineages. Genetic mechanisms such as double clonality, sexually antagonistic selection, and tolerance to inbreeding help invasive ants maintain genetic diversity despite founding populations often consisting of relatively few individuals. Additionally, urban environments impose unique selective pressures that may lead to adaptations favoring success across all stages of the invasion process. This framework aligns with the Anthropogenically Induced Adaptation to Invade (AIAI) hypothesis and helps explain why many urban-adapted ants become globally invasive. As urbanization continues to expand, human-modified landscapes may inadvertently serve as breeding grounds for future invasive species. Understanding these multifaceted invasion dynamics provides critical insights for managing invasive ant populations and mitigating their widespread impacts.
Large multimodal models (LMMs) have enabled new AI-powered applications that help people with visual impairments (PVI) receive natural language descriptions of their surroundings through audible text. We investigated how this emerging paradigm of visual assistance transforms how PVI perform and manage their daily tasks. Moving beyond basic usability assessments, we examined both the capabilities and limitations of LMM-based tools in personal and social contexts, while exploring design implications for their future development. Through interviews with 14 visually impaired users and analysis of image descriptions from both participants and social media using Be My AI (an LMM-based application), we identified two key limitations. First, these systems' context awareness suffers from hallucinations and misinterpretations of social contexts, styles, and human identities. Second, their intent-oriented capabilities often fail to grasp and act on users' intentions. Based on these findings, we propose design strategies for improving both human-AI and AI-AI interactions, contributing to the development of more effective, interactive, and personalized assistive technologies.
We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The X-LoRA-Gemma model, inspired by biological principles and featuring 7 billion parameters, dynamically reconfigures its structure through a dual-pass inference strategy to enhance its problem-solving abilities across diverse scientific domains. The model is used to first identify molecular engineering targets through a systematic human-AI and AI-AI self-driving multi-agent approach to elucidate key targets for molecular optimization to improve interactions between molecules. Next, a multi-agent generative design process is used that includes rational steps, reasoning and autonomous knowledge extraction. Target properties of the molecule are identified either using a principal component analysis (PCA) of key molecular properties or sampling from the distribution of known molecular properties. The model is then used to generate a large set of candidate molecules, which are analyzed via their molecular structure, charge distribution, and other features. We validate that as predicted, increased dipole moment and polarizability is indeed achieved in the designed molecules. We anticipate an increasing integration of these techniques into the molecular engineering workflow, ultimately enabling the development of innovative solutions to address a wide range of societal challenges. We conclude with a critical discussion of challenges and opportunities of the use of multi-agent generative AI for molecular engineering, analysis and design.
Artificial intelligence and machine learning (AI/ML) algorithms will transform the childhood otitis media (OM) diagnostic experience. However, there is limited data on parents' current experiences within clinical settings, limited research exploring AI/ML acceptability among consumers generally, and none regarding consumer perspectives on its use for childhood OM. This study aimed to explore current parental experiences of, as well as their perspectives on the use of AI/ML in, clinical care for OM in children. We conducted and thematically analysed semi-structured interviews with parents of children seen for OM within the ENT or audiology departments of an Australian urban teaching hospital. Seven themes were identified: (1) Meeting children's needs; (2) Challenges in accessing and waiting for audiology and ENT care; (3) Urban versus rural healthcare experience; (4) Public versus private health system; (5) Strategies for enhancing paediatric audiology services; (6) Perceived benefits of AI/ML in ear disease diagnosis; and (7) Concerns and considerations regarding AI/ML in ear health diagnosis. Parents have concerns about the use and development of AI/ML tools, but also acknowledge the potential benefits of such tools for healthcare delivery. Currently, the understanding amongst parents of AIAI/ML/ML tools for OM diagnosis was limited, and more education on the use and development of AIAI/ML/ML for OM is warranted. We did not involve patients or the public in the design of this study. However, three authors have lived experience as parents of children who have had recurrent ear infections.
Physical-mental comorbidity in children and adolescents is an emerging global health concern, yet research remains fragmented and lacks a coordinated agenda. We conducted a global priority setting exercise using the Child Health and Nutrition Research Initiative method. A total of 134 research ideas were scored by 45 experts against five criteria: answerability, effectiveness, potential for paradigm shift, potential for translation and implementation, and impact on equity. The highest-ranked priorities focused on treatment strategies, early intervention, reducing disparities in care, and the role of schools and communities in supporting health. Comparative analyses revealed both shared and context-specific needs across income settings. This is the first global consensus on research priorities for child and adolescent physical-mental comorbidity and offers a strategic roadmap to guide future research and policy.
Are large language models (LLMs) biased in favor of communications produced by LLMs, leading to possible antihuman discrimination? Using a classical experimental design inspired by employment discrimination studies, we tested widely used LLMs, including GPT-3.5, GPT-4 and a selection of recent open-weight models in binary choice scenarios. These involved LLM-based assistants selecting between goods (the goods we study include consumer products, academic papers, and film-viewings) described either by humans or LLMs. Our results show a consistent tendency for LLM-based AIs to prefer LLM-presented options. This suggests the possibility of future AI systems implicitly discriminating against humans as a class, giving AI agents and AI-assisted humans an unfair advantage.
Recent advancements in Artificial Intelligence (AI), particularly in generative language models and algorithms, have led to significant impacts across diverse domains. AI capabilities to address prompts are growing beyond human capability but we expect AI to perform well also as a prompt engineer. Additionally, AI can serve as a guardian for ethical, security, and other predefined issues related to generated content. We postulate that enforcing dialogues among AI-as-prompt-engineer, AI-as-prompt-responder, and AI-as-Compliance-Guardian can lead to high-quality and responsible solutions. This paper introduces a novel AI collaboration paradigm emphasizing responsible autonomy, with implications for addressing real-world challenges. The paradigm of responsible AI-AI conversation establishes structured interaction patterns, guaranteeing decision-making autonomy. Key implications include enhanced understanding of AI dialogue flow, compliance with rules and regulations, and decision-making scenarios exemplifying responsible autonomy. Real-world applications envision AI systems autonomously addressing complex challenges. We have made preliminary testing of such a paradigm involving instances of ChatGPT autonomously playing various roles in a set of experimental AI-AI conversations and observed evident added value of such a framework.
Pre-adaptation to anthropogenic disturbance is broadly considered key for plant invasion success. Nevertheless, empirical evidence remains scarce and fragmentary, given the multifaceted nature of anthropogenic disturbance itself and the complexity of other evolutionary forces shaping the (epi)-genomes of recent native and invasive plant populations. Here, we review and critically revisit the existing theory and empirical evidence in the field of evolutionary ecology and highlight novel integrative research avenues that work at the interface with archaeology to solve open questions. The approaches suggested so far focus on contemporary plant populations, although their genomes have rapidly changed since their initial introduction in response to numerous selective and stochastic forces. We elaborate that a role of pre-adaptation to anthropogenic disturbance in plant invasion success should thus additionally be validated based on the analyses of archaeobotanical remains. Such materials, in the light of detailed knowledge on past human societies could highlight fine-scale differences in the type and timing of past disturbances. We propose a combination of archaeobotanical, ancient DNA and morphometric analyses of plant macro- and microremains to assess past community composition, and species' functional traits to unravel the timing of adaptation processes, their drivers and their long-term consequences for invasive species. Although such methodologies have proven to be feasible for numerous crop plants, they have not been yet applied to wild invasive species, which opens a wide array of insights into their evolution.
Background: Pharmacotherapy has emerged as a practical option for weight management in pediatrics. This study aims to assess the effectiveness and safety of phentermine use in pediatric patients with obesity. Methods: We performed a retrospective single-center analysis of patients younger than or equal to 18 years of age, over 10 years, who underwent phentermine treatment and recommended lifestyle changes. We evaluated efficacy by the change in the percent of the 95th percentile for BMI (%BMIp95). We deemed a 5% decrease in %BMIp95 as a favorable outcome. Results: We identified 30 pediatric patients who were treated with phentermine. The cohort was primarily female, 63% white, with a mean (standard deviation) baseline age of 15.63 (1.97) years. The average duration of treatment was 10 months, with a period ranging from 2 weeks to 2 years. The average %BMIp95 at the start of treatment was 137%, and that at the time of analysis was 122%, with a mean reduction of 15%. Five patients, 17%, experienced side effects that resolved after dose reduction or discontinuing phentermine. Conclusions: Phentermine monotherapy is an effective and safe means for weight loss in pediatric patients when combined with lifestyle interventions. Twenty-one of 30 (70%) patients achieved at least a 5% decrease in %BMIp95 within a mean duration of treatment of 10 months. We noted no severe adverse events.
Although stakeholder involvement in policymaking is attracting attention in the fields of medicine and healthcare, a practical methodology has not yet been established. Rare-disease policy, specifically research priority setting for the allocation of limited research resources, is an area where evidence generation through stakeholder involvement is expected to be effective. We generated evidence for rare-disease policymaking through stakeholder involvement and explored effective collaboration among stakeholders. We constructed a space called 'Evidence-generating Commons', where patients, family members, researchers, and former policymakers can share their knowledge and experiences and engage in continual deliberations on evidence generation. Ten rare diseases were consequently represented. In the 'Commons', 25 consecutive workshops were held predominantly online, from 2019 to 2021. These workshops focused on (1) clarification of difficulties faced by rare-disease patients, (2) development and selection of criteria for priority setting, and (3) priority setting through the application of the criteria. For the first step, an on-site workshop using sticky notes was held. The data were analysed based on KJ method. For the second and third steps, workshops on specific themes were held to build consensus. The workshop agendas and methods were modified based on participants' feedback. The 'Commons' was established with 43 participants, resulting in positive effects such as capacity building, opportunities for interactions, mutual understanding, and empathy among the participants. The difficulties faced by patients with rare diseases were classified into 10 categories. Seven research topics were identified as priority issues to be addressed including 'impediments to daily life', 'financial burden', 'anxiety', and 'burden of hospital visits'. This was performed by synthesising the results of the application of the two criteria that were particularly important to strengthen future research on rare diseases. We also clarified high-priority research topics by using criteria valued more by patients and family members than by researchers and former policymakers, and criteria with specific perspectives. We generated evidence for policymaking in the field of rare diseases. This study's insights into stakeholder involvement can enhance evidence-informed policymaking. We engaged in comprehensive discussions with policymakers regarding policy implementation and planned analysis of the participants' experiences in this project. Stakeholder involvement is significant for effective policymaking in the field of rare diseases. However, practical methods for this involvement have not yet been established. Therefore, we developed the ‘Commons project’ to generate valuable policymaking information and explore effective ways for stakeholders’ collaboration. This article explains the process and results of 25 continuous workshops, held from 2019 to 2021 with 43 participants, including patients, family members, researchers, and former policymakers. The main achievements of the discussion that took place in the ‘Commons’ included a presentation of the overview of the difficulties faced by patients with rare diseases and formulation of high priority research topics.First, the difficulties faced by patients with rare diseases were grouped into 10 categories. Second, seven research topics were identified as priority issues including ‘impediments to daily life’, ‘financial burden’, ‘anxiety’, and ‘burden of hospital visits’. During the project process, positive effects such as capacity building, opportunities for interactions, mutual understanding, and empathy among the participants, were identified. Beyond the context of the field of rare diseases and science of policy, these findings are useful for the future of society, including co-creation among stakeholders and patient and public involvement. Based on this study’s results, we have initiated communications with policy stakeholders in the field of rare diseases, with the aim of policy implementation.
This study aimed to (1) determine whether the hip to ankle (HA) line or hip to calcaneus (HC) line better reflects knee coronal plane kinetics, (2) to examine whether the HC line reflects ankle coronal plane kinetics, and (3) to evaluate the radiological and biomechanical aspects of ankle in varus knee osteoarthritis (OA). Full-length, postero-anterior radiographs (hip-to-calcaneus radiographs) were taken and gait analysis was performed in 21 varus knee OA patients. The %HA where the HA lines pass through the tibial plateau, and the %HC and the mechanical ankle joint axis point (MAJAP), where the HC line passes through the tibial plateau and tibial plafond, respectively, were calculated. Knee adduction angular impulse (KAAI) and ankle inversion angular impulse (AIAI) were collected as kinetic data. Finally, we divided the patients into two groups with and without ankle OA, and compared each parameter between both groups. The %HA and %HC were correlated with KAAI (%HA; r = -0.68, P = 0.001, %HC; r = -0.81, P < 0.001, respectively) and MAJAP was correlated with AIAI (r = -0.55, P = 0.009). MAJAP was significantly smaller, and KAAI and AIAI were significantly larger in the ankle OA group. Radiographic analysis using the HC line was more strongly correlated to knee joint kinetics than the HA line and was also correlated to ankle joint kinetics. Assessing lower limb alignment using the HC line could be useful to evaluate the knee and ankle joints for varus knee OA.