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Abstract High levels of thyroid hormones are linked to increased risk and advanced stages of breast cancer. Our previous work demonstrated that the biologically active triiodothyronine (T3) facilitates mitochondrial ATP production by upregulating Ca2+ handling proteins, thereby boosting mitochondrial Ca2+ uptake and Krebs cycle activity. In this study, different cell types were utilized to investigate whether T3 activates a Ca2+-induced signaling pathway to boost cancer cell proliferation. Using live-cell imaging, biochemical assays, and molecular profiling, differences in intracellular signaling among MCF7 and MDA-MB-468 breast cancer cells, non-cancerous breast cells hTERT-HME1, and PC3 prostate carcinoma cells, previously found to be insensitive to thyroid hormones in terms of proliferation, were investigated. Our findings revealed that T3 upregulates 1,4,5-trisphosphate receptor 3 via thyroid hormone receptor α. This boosts mitochondrial Ca2+ uptake, reduction equivalent yield, and mitochondrial ATP production, supporting the viability and proliferation of breast cancer cells without affecting non-cancerous hTERT-HME1 or PC3 prostate carcinoma cells. Understanding the interplay between T3 signaling, organellar interaction, and breast cancer metabolism could lead to targeted therapies that exploit cancer cell vulnerabilities. Our findings highlight T3 as a crucial regulator of cancer metabolism, reinforcing its potential as a therapeutic target in breast cancer. Graphical Abstract

Abstract. This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

This study investigates how people assign blame to autonomous vehicles (AVs) when involved in an accident. Our experiment (N = 2647) revealed that people placed more blame on AVs than on human drivers when accident details were unspecified. To examine whether people assess major classes of blame-relevant information differently for AVs and humans, we developed a causal model and introduced a novel concept of prevention effort, which emerged as a crucial factor for blame judgement alongside intentionality. Finally, we addressed the “many hands” problem by exploring how people assign blame to entities associated with AVs and human drivers, such as the car company or an accident victim. Our findings showed that people assigned high blame to these entities in scenarios involving AVs, but not with human drivers. This necessitates adapting a model of blame for AVs to include other agents and thus allow for blame allocation “outside” of autonomous vehicles.

What makes Lassie a smart dog? People have strong intui-tions about dogs' intelligence, yet the content and organiza-tion of these intuitions remain unknown. Two studies ex-amined the structure of laypeople's concepts of dog intelli-gence, creating a conceptual map of what people represent as a “smart” or “dumb” dog. Study 1 elicited open-ended ideas about dog intelligence. We turned consistent themes into items in a 50-item survey. Study 2 asked participants to picture either a smart or dumb dog and rate that dog on the items derived from Study 1. Participants strongly agreed in their ratings of smart and dumb dogs, and we discovered a coherent dimensional structure underlying people's intui-tions. They represent smart dogs as socially skilled with a good temperament, and dumb dogs as bad at physical rea-soning and avoiding threats. These representations align well with findings from canine research and with dog train-ers' practical knowledge.

Empirical work on people's perceptions of AI advisors has found evidence for both “algorithm aversion” and “algorithm appreciation.” We investigated whether these differing reactions stem from two different paths of processing: assessing the content of the advice and evaluating the source (AI vs. human advisor). In two survey studies, people were as strongly persuaded by the advice of an AI as that of a human advisor; nonetheless, people's approval of and trust in the AI advisor was consistently lower. This pattern of dissociation suggests that algorithm aversion and algorithm appreciation can occur at the same time, but along different response paths.

Land surface processes, crucial for exchanging carbon, nitrogen, water, and energy between the atmosphere and terrestrial Earth, significantly impact the climate system. Many of these processes vary considerably at small spatial and temporal scales, in particular in mountainous terrain and complex topography. To examine the impact of spatial resolution and representativeness of input data on modelled land surface processes, we conducted simulations using the Community Land Model 5 (CLM5) at different resolutions and based on a range of input datasets over the spatial extent of Switzerland. Using high-resolution meteorological forcing and land use data, we found that increased resolution substantially improved the representation of snow cover in CLM5 (up to 52 % enhancement), allowing CLM5 to closely match performance of a dedicated snow model. However, a simple lapse-rate-based temperature downscaling provided large positive effects on model performance, even if simulations were based on coarse-resolution forcing datasets only. Results demonstrate the need for resolutions higher than 0.25° for accurate snow simulations in topographically complex terrain. These findings have profound implications for climate impact studies. As improvements were observed across the cascade of dependencies in the land surface model, high spatial resolution and high-quality forcing data become necessary for accurately capturing the effects of a declining snow cover and consequent shifts in the vegetation period, particularly in mountainous regions. This study further highlights the utility of multi-resolution modelling experiments when aiming to improve representation of variables in land surface models. By embracing high-resolution modelling, we can enhance our understanding of the land surface and its response to climate change.

Using seaweed-based antimicrobial edible films containing antimicrobial seaweed extracts in food products has maintained product quality during storage. Caulerpa extracts have the potential to be antimicrobial and antioxidant agents. However, no research has been conducted regarding applying Caulerpa extract edible film to semi-wet food products, especially seaweed dodol. This study examines changes in the chemical composition and shelf life of seaweed dodol packaged with antimicrobial edible films made from agar added with crude extract of Caulerpa racemosa. The study employed a completely randomized design with two variables: packaging type and storage duration. The packaging types included an unpackaged (control), an antimicrobial edible coating (AEC), and the antimicrobial edible film (AEF), while the storage period was set to 6 days. The observed research parameters included changes in chemical quality and total microbes. Data on changes in the composition of free fatty acids and total plate numbers are also used to calculate shelf life. The results showed that using AEF as the primary packaging of seaweed dodol can inhibit changes in chemical composition during storage, especially moisture, carbohydrates and fats. The barrier to moisture and oxygen from AEF inhibits the chemical composition deterioration of the coated food product, which has an impact on increasing the product's shelf life. Seaweed dodol packaged with AEF demonstrated an extended shelf life by 2 days compared to both the control and AEC-packaged samples. Antimicrobial edible film of crude extract of C. racemosa has the potential to be developed as primary packaging in semi-wet food products.

Abstract Wolfram syndrome is a rare genetic disease caused by mutations in the WFS1 or CISD2 gene. A primary defect in Wolfram syndrome involves poor ER Ca2+ handling, but how this disturbance leads to the disease is not known. The current study, performed in primary neurons, the most affected and disease-relevant cells, involving both Wolfram syndrome genes, explains how the disturbed ER Ca2+ handling compromises mitochondrial function and affects neuronal health. Loss of ER Ca2+ content and impaired ER-mitochondrial contact sites in the WFS1- or CISD2-deficient neurons is associated with lower IP3R-mediated Ca2+ transfer from ER to mitochondria and decreased mitochondrial Ca2+ uptake. In turn, reduced mitochondrial Ca2+ content inhibits mitochondrial ATP production leading to an increased NADH/NAD+ ratio. The resulting bioenergetic deficit and reductive stress compromise the health of the neurons. Our work also identifies pharmacological targets and compounds that restore Ca2+ homeostasis, enhance mitochondrial function and improve neuronal health.

IntroductionRobots are being introduced into increasingly social environments. As these robots become more ingrained in social spaces, they will have to abide by the social norms that guide human interactions. At times, however, robots will violate norms and perhaps even deceive their human interaction partners. This study provides some of the first evidence for how people perceive and evaluate robot deception, especially three types of deception behaviors theorized in the technology ethics literature: External state deception (cues that intentionally misrepresent or omit details from the external world: e.g., lying), Hidden state deception (cues designed to conceal or obscure the presence of a capacity or internal state the robot possesses), and Superficial state deception (cues that suggest a robot has some capacity or internal state that it lacks).MethodsParticipants (N = 498) were assigned to read one of three vignettes, each corresponding to one of the deceptive behavior types. Participants provided responses to qualitative and quantitative measures, which examined to what degree people approved of the behaviors, perceived them to be deceptive, found them to be justified, and believed that other agents were involved in the robots’ deceptive behavior.ResultsParticipants rated hidden state deception as the most deceptive and approved of it the least among the three deception types. They considered external state and superficial state deception behaviors to be comparably deceptive; but while external state deception was generally approved, superficial state deception was not. Participants in the hidden state condition often implicated agents other than the robot in the deception.ConclusionThis study provides some of the first evidence for how people perceive and evaluate the deceptiveness of robot deception behavior types. This study found that people people distinguish among the three types of deception behaviors and see them as differently deceptive and approve of them differently. They also see at least the hidden state deception as stemming more from the designers than the robot itself.

Purpose: To evaluate retinal thickness changes of individual retinal layers using spectral-domain optical coherence tomography (SD-OCT) after uneventful cataract surgery over a 3-months period. Design: Prospective cohort study. Methods: 41 patients who underwent uneventful cataract surgery were included. Retinal SD-OCT images of both eyes were acquired preoperatively, 1 day after surgery as well as 1 month and 3 months postoperatively. Changes of retinal layer thickness were estimated after semi-automated segmentation for the following individual layers in the central subfield (CS, 1 mm) and inner ring (IR, 1–3 mm) of the early treatment diabetic retinopathy study (ETDRS) grid: retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), RNFL-GCL-IPL complex, inner nuclear layer (INL), outer plexiform layer (OPL), INL-OPL complex, outer nuclear layer (ONL), inner retina layer (IRL) and the total retina (TR). Furthermore, a sub-analysis with exclusion of patients devoid CME and an analysis in regard of patient age, lens status of the fellow eye, best corrected visual acuity and duration of surgery was conducted. Results: This study found significant alterations in all analysed retinal layers except for the RNFL (p = 0.33) and the GCL (p = 0.06) in the central subfield and the INL-OPL complex (p = 0.07) in the inner ring over the 3-months period (all p

Multi-institutional educational partnerships are a promising approach to developing the skilled technical workforce. Inexorably, the ability to maintain such partnership networks that support skilled technical workforce education was disrupted by COVID-19. The purpose of this study is to explore Southwest Virginia's science, technology, engineering, and mathematics (STEM)-focused multi-institutional partnership networks, to inventory the disruptive impacts of COVID-19, and to identify how partnership stakeholders navigated these challenges to prepare students for the skilled technical workforce. This work presents a single-case study design, highlighting the evolving landscape of STEM workforce education partnership networks in Southwest Virginia throughout the pandemic. The team conducted interviews with 19 regional stakeholders focused on the participants' role throughout the pandemic, barriers to STEM workforce education presented by public health and economic factors, and innovative strategies to sustain and expand partnership networks through COVID-19. Two key themes emerged from this study: successful partners maintained network connections through adaptive interactions and actors within the network served as brokers to leverage their connections and expand partnerships in the face of adversity. By taking a contextual view of the role of partnership networks in creating equitable STEM workforce pathways during COVID-19, we develop rich insights into partnership formation, collaboration, resource allocation, and programming amidst challenges to their success.

Objective The objective was to assess the association between nutritional and clinical characteristics and quantitative PCR (qPCR)-diagnosis of bacterial diarrhoea in a multicentre cohort of children under 2 years of age with moderate to severe diarrhoea (MSD).Design A secondary cross-sectional analysis of baseline data collected from the AntiBiotics for Children with Diarrhoea trial (NCT03130114).Patients Children with MSD (defined as >3 loose stools within 24 hours and presenting with at least one of the following: some/severe dehydration, moderate acute malnutrition (MAM) or severe stunting) enrolled in the ABCD trial and collected stool sample.Study period June 2017–July 2019.Interventions None.Main outcome measures Likely bacterial aetiology of diarrhoea. Secondary outcomes included specific diarrhoea aetiology.Results A total of 6692 children with MSD had qPCR results available and 28% had likely bacterial diarrhoea aetiology. Compared with children with severe stunting, children with MAM (adjusted OR (aOR) (95% CI) 1.56 (1.18 to 2.08)), some/severe dehydration (aOR (95% CI) 1.66 (1.25 to 2.22)) or both (aOR (95% CI) 2.21 (1.61 to 3.06)), had higher odds of having likely bacterial diarrhoea aetiology. Similar trends were noted for stable toxin-enterotoxigenic Escherichia coli aetiology. Clinical correlates including fever and prolonged duration of diarrhoea were not associated with likely bacterial aetiology; children with more than six stools in the previous 24 hours had higher odds of likely bacterial diarrhoea (aOR (95% CI) 1.20 (1.05 to 1.36)) compared with those with fewer stools.Conclusion The presence of MAM, dehydration or high stool frequency may be helpful in identifying children with MSD who might benefit from antibiotics.

Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate change impacts on biodiversity and ecosystem functioning. Here we combine a network of microclimate temperature measurements across different habitats and vertical heights with a novel radiative transfer model to map daily temperatures during the vegetation period at 10 m spatial resolution across Switzerland. Our results reveal strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 ∘C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 ∘C, respectively. We also found that, in open grasslands, maximum temperatures at 5 cm above ground are, on average, 3.4 ∘C warmer than those of the macroclimate, suggesting that, in such habitats, heat exposure close to the ground is often underestimated when using macroclimatic data. Spatial interpolation was achieved by using a hybrid approach based on linear mixed-effect models with input from detailed radiation estimates from radiative transfer models that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography, and vegetation height. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by root mean squared errors ranging from 1.18 to 3.43 ∘C, with minimum temperatures being predicted more accurately overall than maximum temperatures. The microclimate-mapping methodology presented here enables a biologically relevant perspective when analysing climate–species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Rural educational contexts often go under addressed in engineering education research and practice. Given the specific nuances associated with rural places and contexts, engineering education outreach should be better aligned with ongoing efforts to recognize and engage students’ assets in education. This paper introduces a conceptual model that proposes tenets, or considerations, for engineering engagement in rural places. The tenets include: understand self and context rooted in a place; seek to (re)contextualize curriculum; encourage students’ assets and other ways of knowing, and engage critically and hopefully. The tenets of this paper are grounded in literature from rural education, P-12 engineering education, asset-based education, and literature pertaining to the rural Appalachian context and spatial justice. Each tenet section presents some of this literature as well as questions for individual reflection to further engage with the tenets. The conceptual model presented in this provides a framework for ongoing reflection on the importance of place and how place can inform engineering education outreach and engagement. Though this paper focuses on rural contexts, the tenets used to organize the model and the associated reflection questions could prove useful to work in various contexts (e.g. suburban, urban). Ultimately, this paper encourages the recognition of how geographies impact communities and how to engage with place in P-12 engineering education settings.

Norms are central to social life. They help people select actions that benefit the community and facilitate behavior prediction and coordination. However, little is known about the cognitive properties of norms. Here we focus on norm activation, context specificity, and how those properties differ for the two major types of norms: prescriptions and prohibitions. In two studies, participants are exposed to a variety of contexts by way of scene images and either (a) freely generate norms that apply to the context or (b) decide whether each of a series of candidate norms applies to a given context. Across both studies, people showed high levels of context specificity and fast norm activation, and these properties were substantially stronger for prescriptions than for prohibitions.