Oxytocin modulates neural activity during early perceptual salience attribution.

Leading hypotheses of oxytocin's (OT) role in human cognition posit that it enhances salience attribution. However, whether OT exerts its effects predominantly in social (vs non-social) contexts remains debatable, and the time-course of intranasal OT's effects' on salience attribution processing is still unknown. We used the social Salience Attribution Task modified (sSAT) in a double-blind, placebo-controlled intranasal OT (inOT) administration, between-subjects design, with 54 male participants, to test existing theories of OT's role in cognition. Namely, we aimed to test whether inOT would differently affect salience attribution processing of social stimuli (expressing fearfulness) and non-social stimuli (fruits) made relevant via monetary reinforcement, and its neural processing time-course. During electroencephalography (EEG) recording, participants made speeded responses to emotional social (fearful faces) and non-emotional non-social (fruits) stimuli - which were matched for task-relevant motivational salience through their (color-dependent) probability of monetary reinforcement. InOT affected early (rather than late, P3b and LPP) EEG components, increasing N170 amplitude (p = .041) and P2b latency (p .001; albeit not of P1), regardless of stimuli's (emotional) socialness or reinforcement probability. Fear-related socialness affected salience attribution processing EEG (p .05) across time (N170, P2b and P3b), being later modulated by reinforcement probability (LPP). Our data suggest that OT's effects on neural activity during early perception, may exist irrespective of fear-related social- or reward-contexts. This partially supports the tri-phasic model of OT (which posits OT enhances salience attribution in an early perception stage regardless of socialness), and not the social salience nor the general approach-withdrawal hypotheses of OT, for early salience processing event-related potentials.

Integrated Spatial Simulation of Population and Urban Land Use: a Pan-European Model Validation.

Spatial models jointly simulating population and land-use change provide support for policy-making, by allowing to explore territorial developments under alternative scenarios and resulting impacts in the environment, economy and society. However, their ability to reproduce observed spatial patterns is rarely evaluated through model validation. This lack of insight prevents researchers and policy-makers of fully grasping the ability of existing models to provide sensible projections of future land use and population density. In this article, we address this gap by performing a model validation of the LUISA Territorial Modelling Platform, a spatial model jointly simulating population and land use at a fine resolution (100 m) in the European Union and United Kingdom. In particular, we compare observed and simulated patterns of population and urban residential land-use change for the period of 1990-2015, and evaluate the model performance according to different degrees of urbanisation. The results show that model performance can vary depending on the context, even when the same data and methods are uniformly applied. The model performed consistently well in urban areas characterized by compact urban growth, but poorly where residential development occurred predominantly in scattered patterns across rural areas. Overall, the model tends to favour the formation of densely populated, highly accessible urban conglomerations, which often do not entirely correspond to the observed patterns. Based on the validation results, we propose directions for further model improvement and development. Model validation should be regarded as a critical step, and an integral part, in the process of developing models for policy support. Supplementary Information: The online version contains supplementary material available at 10.1007/s12061-023-09518-x.

Why has farming in Europe changed? A farmers' perspective on the development since the 1960s.

Farming in Europe has been the scene of several important socio-economic and environmental developments and crises throughout the last century. Therefore, an understanding of the historical driving forces of farm change helps identifying potentials for navigating future pathways of agricultural development. However, long-term driving forces have so far been studied, e.g. in anecdotal local case studies or in systematic literature reviews, which often lack context dependency. In this study, we bridged local and continental scales by conducting 123 oral history interviews (OHIs) with elderly farmers across 13 study sites in 10 European countries. We applied a driving forces framework to systematically analyse the OHIs. We find that the most prevalent driving forces were the introduction of new technologies, developments in agricultural markets that pushed farmers for farm size enlargement and technological optimisation, agricultural policies, but also cultural aspects such as cooperation and intergenerational arrangements. However, we find considerable heterogeneity in the specific influence of individual driving forces across the study sites, implying that generic assumptions about the dynamics and impacts of European agricultural change drivers hold limited explanatory power on the local scale. Our results suggest that site-specific factors and their historical development will need to be considered when addressing the future of agriculture in Europe in a scientific or policy context. Supplementary Information: The online version contains supplementary material available at 10.1007/s10113-023-02150-y.

Farmer surveys in Europe suggest that specialized, intensive farms were more likely to perceive negative impacts from COVID-19.

It has been shown that the COVID-19 pandemic affected some agricultural systems more than others, and even within geographic regions, not all farms were affected to the same extent. To build resilience of agricultural systems to future shocks, it is key to understand which farms were affected and why. In this study, we examined farmers' perceived robustness to COVID-19, a key resilience capacity. We conducted standardized farmer interviews (n = 257) in 15 case study areas across Europe, covering a large range of socio-ecological contexts and farm types. Interviews targeted perceived livelihood impacts of the COVID-19 pandemic on productivity, sales, price, labor availability, and supply chains in 2020, as well as farm(er) characteristics and farm management. Our study corroborates earlier evidence that most farms were not or only slightly affected by the first wave(s) of the pandemic in 2020, and that impacts varied widely by study region. However, a significant minority of farmers across Europe reported that the pandemic was "the worst crisis in a lifetime" (3%) or "the worst crisis in a decade" (7%). Statistical analysis showed that more specialized and intensive farms were more likely to have perceived negative impacts. From a societal perspective, this suggests that highly specialized, intensive farms face higher vulnerability to shocks that affect regional to global supply chains. Supporting farmers in the diversification of their production systems while decreasing dependence on service suppliers and supply chain actors may increase their robustness to future disruptions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13593-022-00820-5.

Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach.

BACKGROUND: Early and accurate diagnosis of Alzheimer's disease (AD) is essential for disease management and therapeutic choices that can delay disease progression. Machine learning (ML) approaches have been extensively used in attempts to develop algorithms for reliable early diagnosis of AD, although clinical usefulness, interpretability, and generalizability of the classifiers across datasets and MRI protocols remain limited. METHODS: We report a multi-diagnostic and generalizable approach for mild cognitive impairment (MCI) and AD diagnosis using structural MRI and ML. Classifiers were trained and tested using subjects from the AD Neuroimaging Initiative (ADNI) database (n = 570) and the Open Access Series of Imaging Studies (OASIS) project database (n = 531). Several classifiers are compared and combined using voting for a decision. Additionally, we report tests of generalizability across datasets and protocols (IR-SPGR and MPRAGE), the impact of using graph theory measures on diagnostic classification performance, the relative importance of different brain regions on classification for better interpretability, and an evaluation of the potential for clinical applicability of the classifier. RESULTS: Our "healthy controls (HC) vs. AD" classifier trained and tested on the combination of ADNI and OASIS datasets obtained a balanced accuracy (BAC) of 90.6% and a Matthew's correlation coefficient (MCC) of 0.811. Our "HC vs. MCI vs. AD" classifier trained and tested on the ADNI dataset obtained a 62.1% BAC (33.3% being the by-chance cut-off) and 0.438 MCC. Hippocampal features were the strongest contributors to the classification decisions (approx. 25-45%), followed by temporal (approx. 13%), cingulate, and frontal regions (approx. 8-13% each), which is consistent with our current understanding of AD and its progression. Classifiers generalized well across both datasets and protocols. Finally, using graph theory measures did not improve classification performance. CONCLUSIONS: In sum, we present a diagnostic tool for MCI and AD trained using baseline scans and a follow-up diagnosis regardless of progression, which is multi-diagnostic, generalizable across independent data sources and acquisition protocols, and with transparently reported performance. Rated as potentially clinically applicable, our tool may be clinically useful to inform diagnostic decisions in dementia, if successful in real-world prospective clinical trials.

An approach for comparing agricultural development to societal visions.

There is broad agreement that agriculture has to become more sustainable in order to provide enough affordable, healthy food at minimal environmental and social costs. But what is "more sustainable"? More often than not, different stakeholders have opposing opinions on what a more sustainable future should look like. This normative dimension is rarely explicitly addressed in sustainability assessments. In this study, we present an approach to assess the sustainability of agricultural development that explicitly accounts for the normative dimension by comparing observed development with various societal visions. We illustrate the approach by analyzing farm- and landscape-scale development as well as sustainability outcomes in a Swiss case study landscape. Observed changes were juxtaposed with desired changes by Avenir Suisse, a liberal think tank representing free-market interests; the Swiss Farmers Association, representing a conservative force; and Landwirtschaft mit Zukunft, an exponent of the Swiss agroecological movement. Overall, the observed developments aligned most closely with desired developments of the liberal think-tank (72%). Farmer interviews revealed that in the case study area farms increased in size (+ 57%) and became more specialized and more productive (+ 223%) over the past 20 years. In addition, interpretation of aerial photographs indicated that farming became more rationalized at the landscape level, with increasing field sizes (+ 34%) and removal of solitary field trees (- 18%). The case study example highlights the varying degrees to which current developments in agriculture align with societal visions. By using societal visions as benchmarks to track the progress of agricultural development, while explicitly addressing their narratives and respective systems of values and norms, this approach offers opportunities to inform also the wider public on the extent to which current developments are consistent with different visions. This could help identify mismatches between desired and actual development and pave the way for designing new policies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13593-021-00739-3.

Modelling agricultural land abandonment in a fine spatial resolution multi-level land-use model: An application for the EU.

In the majority of EU Member States, agricultural land is expected to decrease not only due to land-use changes in favour of urban expansion and afforestation but also to land abandonment processes. The knowledge on location and extent of agricultural land abandonment is relevant for estimating local external effects and adapting policy interventions. Currently, multi-level land-use models are able to capture determined processes of demand-driven redevelopment. However, land abandonment is much more difficult to capture because of its more ambiguous definition and the lack of data on its spatial distribution. This paper presents a method to explicitly model agricultural abandonment as a choice of disinvestment, which in turn is embedded in a utility-based land-use modelling framework that projects land-use changes for the EU and the UK. Validation exercises using observed spatial distribution of abandoned farmland show that the proposed method allows to model abandonment with acceptable accuracy.

Assessing potential future urban heat island patterns following climate scenarios, socio-economic developments and spatial planning strategies.

Climate change and urban development will exacerbate current urban heat island effects. While most studies acknowledge the importance of projected temperature increases for raising urban temperatures, little attention is paid to the impacts of future changes in urbanisation patterns. Yet, steering urban development may be an effective strategy to further limit increases in the intensity and spreading of the urban heat island effect. We describe a method that allows exploring the impact of urban development scenarios on the urban heat island effect. This paper starts with a basic analysis of the strength of this effect in a temperate climate under relatively favourable conditions based on data from amateur weather stations and own observations. It explains local variation in observed temperatures and quantifies how the urban heat island effect may develop in the coming 30 years. Using the obtained relations, we assess potential future changes building on existing scenarios of climatic and socio-economic changes and a land use simulation model. Our measurements for the Amsterdam region in the Netherlands indicate that the urban heat island effect induces maximum temperature differences with the surrounding countryside of over 3 °C on moderately warm summer days. The simulations of potential future changes indicate that strong local temperature increases are likely due to urban development. Climate change will, on average, have a limited impact on these changes. Large impacts can, however, be expected from the combination of urban development and potentially more frequent occurrences of extreme climatic events such as heat waves. Spatial planning strategies that reduce the lateral spread of urban development will thus greatly help to limit a further increase in urban heat island values.

Estimating demand for industrial and commercial land use given economic forecasts.

Current developments in the field of land use modelling point towards greater level of spatial and thematic resolution and the possibility to model large geographical extents. Improvements are taking place as computational capabilities increase and socioeconomic and environmental data are produced with sufficient detail. Integrated approaches to land use modelling rely on the development of interfaces with specialized models from fields like economy, hydrology, and agriculture. Impact assessment of scenarios/policies at various geographical scales can particularly benefit from these advances. A comprehensive land use modelling framework includes necessarily both the estimation of the quantity and the spatial allocation of land uses within a given timeframe. In this paper, we seek to establish straightforward methods to estimate demand for industrial and commercial land uses that can be used in the context of land use modelling, in particular for applications at continental scale, where the unavailability of data is often a major constraint. We propose a set of approaches based on 'land use intensity' measures indicating the amount of economic output per existing areal unit of land use. A base model was designed to estimate land demand based on regional-specific land use intensities; in addition, variants accounting for sectoral differences in land use intensity were introduced. A validation was carried out for a set of European countries by estimating land use for 2006 and comparing it to observations. The models' results were compared with estimations generated using the 'null model' (no land use change) and simple trend extrapolations. Results indicate that the proposed approaches clearly outperformed the 'null model', but did not consistently outperform the linear extrapolation. An uncertainty analysis further revealed that the models' performances are particularly sensitive to the quality of the input land use data. In addition, unknown future trends of regional land use intensity widen considerably the uncertainty bands of the predictions.