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Abstract Climate change is currently one of humanity’s greatest threats. To help scholars understand the psychology of climate change, we conducted an online quasi-experimental survey on 59,508 participants from 63 countries (collected between July 2022 and July 2023). In a between-subjects design, we tested 11 interventions designed to promote climate change mitigation across four outcomes: climate change belief, support for climate policies, willingness to share information on social media, and performance on an effortful pro-environmental behavioural task. Participants also reported their demographic information (e.g., age, gender) and several other independent variables (e.g., political orientation, perceptions about the scientific consensus). In the no-intervention control group, we also measured important additional variables, such as environmentalist identity and trust in climate science. We report the collaboration procedure, study design, raw and cleaned data, all survey materials, relevant analysis scripts, and data visualisations. This dataset can be used to further the understanding of psychological, demographic, and national-level factors related to individual-level climate action and how these differ across countries.

Abstract Maize demand in Sub-Saharan Africa is expected to increase 2.3 times during the next 30 years driven by demographic and dietary changes. Over the past two decades, the area cropped with maize has expanded by 17 million hectares in the region, with limited yield increase. Following this trend could potentially result in further maize cropland expansion and the need for imports to satisfy domestic demand. Here, we use data collected from 14,773 smallholder fields in the region to identify agronomic practices that can improve farm yield gains. We find that agronomic practices related to cultivar selection, and nutrient, pest, and crop management can double on-farm yields and provide an additional 82 million tons of maize within current cropped area. Research and development investments should be oriented towards agricultural practices with proven capacity to raise maize yields in the region.

Abstract Somatic copy number alterations (SCNAs) are pervasive in advanced human cancers, but their prevalence and spatial distribution in early-stage, localized tumors and their surrounding normal tissues are poorly characterized. Here, we perform multi-region, single-cell DNA sequencing to characterize the SCNA landscape across tumor-rich and normal tissue in two male patients with localized prostate cancer. We identify two distinct karyotypes: ‘pseudo-diploid’ cells harboring few SCNAs and highly aneuploid cells. Pseudo-diploid cells form numerous small-sized subclones ranging from highly spatially localized to broadly spread subclones. In contrast, aneuploid cells do not form subclones and are detected throughout the prostate, including normal tissue regions. Highly localized pseudo-diploid subclones are confined within tumor-rich regions and carry deletions in multiple tumor-suppressor genes. Our study reveals that SCNAs are widespread in normal and tumor regions across the prostate in localized prostate cancer patients and suggests that a subset of pseudo-diploid cells drive tumorigenesis in the aging prostate.

Abstract The study primarily aimed to understand whether individual factors could predict how people perceive and evaluate artworks that are perceived to be produced by AI. Additionally, the study attempted to investigate and confirm the existence of a negative bias toward AI-generated artworks and to reveal possible individual factors predicting such negative bias. A total of 201 participants completed a survey, rating images on liking, perceived positive emotion, and believed human or AI origin. The findings of the study showed that some individual characteristics as creative personal identity and openness to experience personality influence how people perceive the presented artworks in function of their believed source. Participants were unable to consistently distinguish between human and AI-created images. Furthermore, despite generally preferring the AI-generated artworks over human-made ones, the participants displayed a negative bias against AI-generated artworks when subjective perception of source attribution was considered, thus rating as less preferable the artworks perceived more as AI-generated, independently on their true source. Our findings hold potential value for comprehending the acceptability of products generated by AI technology.

The current study explores the determinants of ChatGPT adoption and utilization among a sample of Norwegian university students. The theoretical perspective of the study is anchored in the Unified Theory of Acceptance and Use of Technology (UTAUT2) and based on a previously tested model. The proposed model integrates six constructs to explain the Behavioral intentions and actual usage patterns of ChatGPT in a higher education context. The study analyzed responses from 104 students attending Universities in West and Central Norway using the partial-least squares approach to structural equation modeling. The data showed that performance expectancy emerged as the construct with the biggest impact on Behavioral intention, followed by Habit. This study contributes to the research on the factors influencing university students’ engagement with generative AI technologies. Furthermore, it contributes to a more comprehensive understanding of how tools like ChatGPT can be integrated effectively in educational contexts in both students learning and instructors teaching.

We study the normalized field statistics inside nested reverberation chambers (NRCs), through the analysis of the shielding effectiveness of enclosures and material samples. The accurate knowledge of field statistics allows to improve the estimation of probability distributions for high field values inside electrically large enclosures. Furthermore, the study allows for better selection of the measurement model for both shielding effectiveness of enclosures and materials and gaskets. We find the correct procedures to achieve such distributions. We study basic cases of single and electrically small coupling apertures filtering single or double field components and we show the theoretical statistics of the field components inside NRCs. The change of inner field distributions is driven by the electrical size of coupling apertures and has been verified experimentally. The theory developed to obtain such statistics is based upon physics and the results are supported by measurements. The procedures and results shown in this paper are applicable to predict the statistics in NRCs as well as in adjacent RCs.

Abstract Africa produces around 60% of the rice the continent consumes, relying heavily on rice imports to fulfill the rest of the domestic demand. Over the past 10 years, the rice-agricultural area increased nearly 40%, while average yield remained stagnant. Here we used a process-based crop simulation modelling approach combined with local weather, soil, and management datasets to evaluate the potential to increase rice production on existing cropland area in Africa and assess cropland expansion and rice imports by year 2050 for different scenarios of yield intensification. We find that Africa can avoid further increases in rice imports, and even reduce them, through a combination of cropland expansion following the historical trend together with closure of the current exploitable yield gap by half or more. Without substantial increase in rice yields, meeting future rice demand will require larger rice imports and/or land conversion than now.

Nutrient budgets help to identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow for the calculation of indicators, such as the nutrient balance (surplus if positive or deficit if negative) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability across the world. We present a global database of country-level budget estimates for nitrogen (N), phosphorus (P) and potassium (K) on cropland. The database, disseminated in FAOSTAT, is meant to provide a global reference, synthesizing and continuously updating the state of the art on this topic. The database covers 205 countries and territories, as well as regional and global aggregates, for the period from 1961 to 2020. Results highlight the wide range in nutrient use and nutrient use efficiencies across geographic regions, nutrients, and time. The average N balance on global cropland has remained fairly steady at about 50–55 kg ha−1 yr−1 during the past 15 years, despite increasing N inputs. Regional trends, however, show recent average N surpluses that range from a low of about 10 kg N ha−1 yr−1 in Africa to more than 90 kg N ha−1 yr−1 in Asia. Encouragingly, average global cropland N use efficiency decreased from about 59 % in 1961 to a low of 43 % in 1988, but it has risen since then to a level of 55 %. Phosphorus deficits are mainly found in Africa, whereas potassium deficits occur in Africa and the Americas. This study introduces improvements over previous work in relation to the key nutrient coefficients affecting nutrient budgets and nutrient use efficiency estimates, especially with respect to nutrient removal in crop products, manure nutrient content, atmospheric deposition and crop biological N fixation rates. We conclude by discussing future research directions and highlighting the need to align statistical definitions across research groups as well as to further refine plant and livestock coefficients and expand estimates to all agricultural land, including nutrient flows in meadows and pastures. Further information is available from https://doi.org/10.5061/dryad.hx3ffbgkh (Ludemann et al., 2023b) as well as the FAOSTAT database (https://www.fao.org/faostat/en/#data/ESB; FAO, 2022a) and is updated annually.

This study investigates the effects of immersion on spatial ability in virtual reality (VR) and the influence of sense of presence, simulation sickness, and cognitive load. Participants performed a spatial ability task using a head-mounted display in an immersive or non-immersive VR condition. Contrary to expectations, immersive VR did not enhance spatial performance compared to non-immersive VR. Interestingly, cognitive load was positively associated with spatial ability in immersive VR but negatively associated with it in non-immersive VR. A higher sense of presence was associated with increased spatial ability for both conditions. Predictably, simulation sickness negatively impacted spatial ability in the more immersive condition. Sense of presence and simulation sickness correlated in the immersive condition but not in the non-immersive condition, indicating the importance of immersion as a covariate. These findings highlight a complex interplay of factors in immersive environments, challenging existing assumptions and providing insights for designing spatial environments in immersive and non-immersive virtual environments.

Abstract Creativity has traditionally been considered an ability exclusive to human beings. However, the rapid development of artificial intelligence (AI) has resulted in generative AI chatbots that can produce high-quality artworks, raising questions about the differences between human and machine creativity. In this study, we compared the creativity of humans (n = 256) with that of three current AI chatbots using the alternate uses task (AUT), which is the most used divergent thinking task. Participants were asked to generate uncommon and creative uses for everyday objects. On average, the AI chatbots outperformed human participants. While human responses included poor-quality ideas, the chatbots generally produced more creative responses. However, the best human ideas still matched or exceed those of the chatbots. While this study highlights the potential of AI as a tool to enhance creativity, it also underscores the unique and complex nature of human creativity that may be difficult to fully replicate or surpass with AI technology. The study provides insights into the relationship between human and machine creativity, which is related to important questions about the future of creative work in the age of AI.

Recent progress in the realm of imaging technologies has swiftly disseminated fresh methodologies for representing objects, broadening the horizons for scholars such as art historians, archaeologists, conservators, and conservation scientists. Reflectance Transformation Imaging (RTI) and three-dimensional (3D) modeling via Structure from Motion (SfM) have rapidly gained popularity and are being employed by a wide range of users, expanding the possibilities for understanding objects of material cultural heritage from different perspectives. This paper explores the outcomes achieved through the application of these techniques to gain deeper insights into the surface of a bas-relief within a confined space. This methodology enhanced our understanding of the original sandstone surfaces of the bas-relief by integrating RTI and 3D visualization, thereby acquiring a novel investigative tool with enhanced illumination possibilities. A data fusion approach is proposed using photogrammetry to produce a Virtual RTI (V-RTI). In the case study reported in this paper, focusing on one inner wall of the chapel of Ellesiya at the Museo Egizio of Turin, V-RTI was employed to obtain useful surface information in a context where conventional RTI would have been impossible to achieve. Finally, the study compares the different techniques through direct qualitative and quantitative analysis, highlighting possible future developments from both instrumental and methodological points of view.

Digitalization techniques, such as photogrammetry (PG), are attracting the interest of experts in the cultural heritage field, as they enable the creation of three-dimensional virtual replicas of historical artifacts with 2D digital images. Indeed, PG allows for acquiring data regarding the overall appearance of an artifact, its geometry, and its texture. Furthermore, among several image-based techniques exploited for the conservation of works of art, multispectral imaging (MSI) finds great application in the study of the materials of historical items, taking advantage of the different responses of materials when exposed to specific wavelengths. Despite their great usefulness, PG and MSI are often used as separate tools. Integrating radiometric and geometrical data can notably expand the information carried by a 3D model. Therefore, this paper presents a novel research methodology that enables the acquisition of multispectral 3D models, combining the outcomes of PG and MSI (Visible (VIS), Ultraviolet-induced Visible Luminescence (UVL), Ultraviolet-Reflected (UVR), and Ultraviolet-Reflected False Color (UVR-FC) imaging) in a single coordinate system, using an affordable tunable set-up and open-source software. The approach has been employed for the study of two wooden artifacts from the Museo Egizio di Torino to investigate the materials present on the surface and provide information that could support the design of suitable conservation treatments.

Future rice systems must produce more grain while minimizing the negative environmental impacts. A key question is how to orient agricultural research & development (R&D) programs at national to global scales to maximize the return on investment. Here we assess yield gap and resource-use efficiency (including water, pesticides, nitrogen, labor, energy, and associated global warming potential) across 32 rice cropping systems covering half of global rice harvested area. We show that achieving high yields and high resource-use efficiencies are not conflicting goals. Most cropping systems have room for increasing yield, resource-use efficiency, or both. In aggregate, current total rice production could be increased by 32%, and excess nitrogen almost eliminated, by focusing on a relatively small number of cropping systems with either large yield gaps or poor resource-use efficiencies. This study provides essential strategic insight on yield gap and resource-use efficiency for prioritizing national and global agricultural R&D investments to ensure adequate rice supply while minimizing negative environmental impact in coming decades.

The development of clinical reasoning abilities is a core competency of emergency medicine (EM) resident education and has historically been accomplished through case conferences and clinical learning. The advent of the SARS-CoV-2 pandemic has fundamentally changed these traditional learning opportunities by causing a nationwide reliance on virtual education environments and reducing the clinical diversity of cases encountered by EM trainees.We propose an innovative case conference that combines low-fidelity simulation with elements of gamification to foster the development of clinical reasoning skills and increase engagement among trainees during a virtual conference. After a team of residents submits a real clinical case that challenged their clinical reasoning abilities, a different team of residents “plays” through a gamified, simulated version of the case live on a video conference call. The case concludes with a facilitated debriefing led by a simulation-trained faculty, where both the resident teams and live virtual audience discuss the challenges of the case. Participants described how the Challenging Case Conference improved their perceptions of their clinical reasoning skills. Audience members reported increased engagement compared to traditional conferences. Participants also reported an unexpected, destigmatizing effect on the discussion of medical errors produced by this exercise. Residency programs could consider implementing a similar case conference as a component of their conference curriculum.

One significant obstacle to gaining a widespread awareness of the ongoing climate change is the nature of its manifestations in relation to our perception: climate change effects are gradual, distributed, and sometimes seemingly contradictory. These features result in a lag in collective climate action and sometimes foster climate skepticism and climate denial. While the literature on climate change perception and belief has thoroughly explored its sociocultural and sociopolitical aspects, research on the potential contribution of psychophysiological factors remains scarce. In this perspective paper, we outline evidence and arguments for the involvement of psychophysiological systems such as thermoception, hygroreception, and interoception in modulating climate change awareness. We discuss psychophysiological mechanisms of climate change awareness in animals and humans, as well as possible sources of individual variance in climate change perception. We conclude by suggesting novel research questions which would be worthwhile to pursue in future studies.

Background & Aims: Alcohol abuse and metabolic disorders are leading causes of hepatocellular carcinoma (HCC) worldwide. Alcohol-related aetiology is associated with a worse prognosis compared with viral agents, because of the lower percentage of patients diagnosed with HCC under routine surveillance and a higher burden of comorbidity in alcohol abusers. This study aimed to describe the evolving clinical scenario of alcohol-related HCC over 15 years (2006–2020) in Italy. Methods: Data from the Italian Liver Cancer (ITA.LI.CA) registry were used: 1,391 patients were allocated to three groups based on the year of HCC diagnosis (2006–2010; 2011–2015; 2016–2020). Patient characteristics, HCC treatment, and overall survival were compared among groups. Survival predictors were also investigated. Results: Approximately 80% of alcohol-related HCCs were classified as cases of metabolic dysfunction-associated fatty liver disease. Throughout the quinquennia,

Abstract Background The “HER2-low” nomenclature identifies breast carcinomas (BCs) displaying a HER2 score of 1+/2+ in immunohistochemistry and lacking ERBB2 amplification. Whether HER2-low BCs (HLBCs) constitute a distinct entity is debated. Methods We performed DNA and RNA high-throughput analysis on 99 HLBC samples (n = 34 cases with HER2 score 1+/HLBC-1, n = 15 cases with HER2 score 2+ and ERBB2 not amplified/HLBC-2N, and n = 50 cases with score 2+ and ERBB2 copy number in the equivocal range/HLBC-2E). We compared the mutation rates with data from 1317 samples in the Memorial Sloan-Kettering Cancer Center (MSKCC) BC cohort and gene expression data with those from an internal cohort of HER2-negative and HER2-positive BCs. Results The most represented mutations affected PIK3CA (31/99, 31%), GATA3 (18/99, 18%), TP53 (17/99, 17%), and ERBB2 (8/99, 8%, private to HLBC-2E). Tumor mutational burden was significantly higher in HLBC-1 compared to HLBC-2E/N (P = 0.04). Comparison of mutation spectra revealed that HLBCs were different from both HER2-negative and HER2-positive BCs, with HLBC-1 resembling more HER2-negative tumors and HLBC-2 mutationally related to HER2-addicted tumors. Potentially actionable alterations (annotated by using OncoKB/ESCAT classes) affected 52 patients. Intra-group gene expression revealed overlapping features between HLBC-1 and control HER2-negative BCs, whereas the HLBC-2E tumors showed the highest diversity overall. The RNA-based class discovery analysis unveiled four subsets of tumors with (i) lymphocyte activation, (ii) unique enrichment in HER2-related features, (iii) stromal remodeling alterations, and (iv) actionability of PIK3CA mutations (LAURA classification). Conclusions HLBCs harbor distinct genomic features when compared with HER2-positive and HER2-negative BCs; however, differences across IHC classes were also unveiled thus dissecting the full picture of heterogeneity across HER2-low disease. The HLBC-2E category harbors most distinctive features, whereas HLBC-1 seems superimposable to HER2-negative disease. Further studies are needed to ascertain whether the four genomic-driver classes of the LAURA classification hold prognostic and/or predictive implications.

The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals’ beliefs about AI’s influence on their lives, careers, and humanity overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale’s structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users’ general attitudes toward AI.

Prevention of infections is crucial in solid organ transplant (SOT) candidates and recipients. These patients are exposed to an increased infectious risk due to previous organ insufficiency and to pharmacologic immunosuppression. Besides infectious-related morbidity and mortality, this vulnerable group of patients is also exposed to the risk of acute decompensation and organ rejection or failure in the pre- and post-transplant period, respectively, since antimicrobial treatments are less effective than in the immunocompetent patients. Vaccination represents a major preventive measure against specific infectious risks in this population but as responses to vaccines are reduced, especially in the early post-transplant period or after treatment for rejection, an optimal vaccination status should be obtained prior to transplantation whenever possible. This review reports the currently available data on the indications and protocols of vaccination in SOT adult candidates and recipients.

Increasing rice yield while improving resource use efficiency is of great importance. This study examines cropping systems globally to highlight areas where rice production can be improved by prioritizing R&D strategies.

Previous studies suggested a role for adipokines in ageing and in several age-related diseases. The purpose of our study was to further elucidate adipokines involvement in neurodegeneration, investigating adiponectin, leptin and resistin in Alzheimer's disease (AD) and Frontotemporal Dementia (FTD). We enrolled for the study 70 subjects: 26 AD, 21 FTD, and 23 with other neurological (but not neurodegenerative) conditions (CTR, control group). According to a standardized protocol, we measured adipokines plasmatic levels, blood parameters of glucidic and lipidic metabolism, ESR, cerebrospinal fluid (CSF) markers of neurodegeneration (beta-amyloid, total-Tau, phosphorylated-Tau) and anthropometric parameters. In comparison with control group, we found lower resistin concentrations in patients with dementia, and in particular in AD (p < 0.001). In multivariate analysis, AD relative risk was reduced by resistin, when controlling for sex, age and anthropometric/metabolic parameters (RR = 0.71, P < 0.0001). Considering CSF biomarkers, we found a direct correlation between resistin and Aβ1-42 CSF concentration in patients (p < 0.001, r = 0.50). Lower resistin characterized AD patients in our study and AD, but not FTD, diagnosis risk was found to be inversely associated with resistin when controlling for confounders. We hypothesize that resistin-linked metabolic profile has to be reconsidered and further investigated in AD.

Abstract Foliar fungicide usage in soybeans in the north-central United States increased steadily over the past two decades. An agronomically-interpretable machine learning framework was used to understand the importance of foliar fungicides relative to other factors associated with realized soybean yields, as reported by growers surveyed from 2014 to 2016. A database of 2738 spatially referenced fields (of which 30% had been sprayed with foliar fungicides) was fit to a random forest model explaining soybean yield. Latitude (a proxy for unmeasured agronomic factors) and sowing date were the two most important factors associated with yield. Foliar fungicides ranked 7th out of 20 factors in terms of relative importance. Pairwise interactions between latitude, sowing date and foliar fungicide use indicated more yield benefit to using foliar fungicides in late-planted fields and in lower latitudes. There was a greater yield response to foliar fungicides in higher-yield environments, but less than a 100 kg/ha yield penalty for not using foliar fungicides in such environments. Except in a few production environments, yield gains due to foliar fungicides sufficiently offset the associated costs of the intervention when soybean prices are near-to-above average but do not negate the importance of disease scouting and fungicide resistance management.