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Road expansion risk predicts future hotspots of tropical deforestation
Item type: Journal Article
Engert J.E.; Souza C.M.; Kleinschroth F.; et al. (2025)
Roads act as conduits for human incursions and hence underlie many of humanity’s impacts on nature, including deforestation, wildfires, and natural-resource overexploitation. Unfortunately, existing roadmaps often drastically underestimate the true extent of road networks and future predictions of road-related impacts rely on incomplete and outdated data, undermining development planning and conservation decision-making. Here, we develop a multivariate “road expansion risk” index to identify areas prone to road building and therefore vulnerable to road-related environmental impacts. Using a massive road dataset—137 million 1-ha raster cells drawn from three different sources arrayed across the Amazon and Congo basins and insular Asia-Pacific region—we predict road-prone locations via a statistical model that integrates a range of biophysical, socioeconomic, and administrative data. This highly integrative, large-scale approach allowed us to identify areas likely to experience future road building and regions that may contain unmapped roads. Importantly, our road expansion risk index is a strong predictor of forest loss and degradation and can hence identify future road building and deforestation hotspots, even for the many tropical forest locales with grossly deficient road data.
Neoliberalism versus Socialism
Item type: Review Article
Herbst M. (2026)
Projected increases in environmental resources due to population growth and wealth-driven dietary changes. Data, uncertainties, and prospects
Item type: Journal Article
Scholz R.W.; Scholz S.W. (2026)
Food production causes over 30 % of global environmental emissions and resource use. This paper provides a realist estimation of how population growth and dietary change increase the global Food Resources Requirement (FRR) through 2060. The FRR-Model links GDP-driven caloric and meat uptake with population growth, using wheat equivalents (wheatEQs) as a composite proxy for environmental resource demand. Latent Class Analysis identifies five country clusters reflecting differing meat dependence and meat affinity. Two main groups emerge: OECD+ (81 % of current population) with diet-driven demand growth, and with population-driven pressure on future food resources. Results indicate a magnitude up to 40 % global increase in Food Resources Requirement (FRR) by 2060. Two opposing trends emerge: in OECD + countries, GDP growth of 128 % raises food-related resource use by 17 % without population growth, whereas in Africa+, minimal GDP and diet change coincide with a 162 % population surge—raising Africa's share to 40 % of the global total. Country clusters differ by meat affinity—the GDP-driven rise in meat consumption—and meat dependence, where meat remains an unsubstitutable staple food. Mitigating FRR growth demands policies that integrate these cultural and biophysical realities while acknowledging epistemological limits of forecasting. The discussion outlines cleaner, context-specific, and lower-resource food system pathways.
Lexical meaning is lower dimensional in psychosis
Item type: Journal Article
Palominos, Claudio; Stein, Frederike; Kircher, Tilo; et al. (2025)
Diverse language models (LMs), including large language models (LLMs) based on deep neural networks, allow us to chart how people organize meanings in speech and how this process breaks down in conditions. Recent evidence has pointed to higher mean semantic similarities between words in people with psychosis, conceptualized as a 'shrunk' (more compressed) semantic space. Based on this, we hypothesized that the dimensionality of the vector spaces as defined by the embeddings of speech samples from LMs would also be easier to reduce in psychosis. To test this, we used principal component analysis (PCA) to calculate different metrics serving as proxies for reducibility, including the number of components needed to reach 90% of variance, and the cumulative variance explained by the first two components. For further exploration, intrinsic dimensionality (ID) was also estimated. Results consistent over datasets in three languages confirmed significantly higher reducibility of the semantic space in psychosis. This result points to the existence of an underlying intrinsic geometry of the space of semantic associations in speech, which may underlie more surface-level measurements such as semantic similarity. It also offers a new foundational approach to speech in mental disorders.
Orthogonal Investigation at Single-Particle and Ensemble Levels Uncovers Lipoprotein-Extracellular Vesicle Binding
Item type: Journal Article
Musico, Angelo; Frigerio, Roberto; Normak, Karl; et al. (2026)
Mesoscale interactions critically shape the biological identity of extracellular nanoparticles, including extracellular vesicles. These interactions encompass biomolecular coronas, transient aggregation, and fusion events. Among them, the interaction between extracellular vesicles and lipoproteins has recently garnered significant attention due to their potential impact on functionality and in vivo fate of extracellular vesicles. In this work, we present a first investigation of the binding between human red blood cell-derived extracellular vesicles and lipoproteins across multiple scales, in both buffer and plasma. Red blood cell-derived extracellular vesicles were selected as a model system for their physicochemical homogeneity, potential in personalized medicine, and production scalability. To achieve this, we employed an ad hoc suite of orthogonal analytical techniques: fluorescence cross-correlation spectroscopy (FCCS), super-resolution microscopy, flow cytometry, and Single Molecule Array assays (Simoa). Our results reveal class-specific and context-dependent extracellular vesicle-lipoprotein associations. Notably, lipoproteins bind to extracellular vesicles with affinities ranging from 10 nM to 1 mu M and with up to 100% extracellular vesicles interacting with high-density lipoproteins in the presence of plasma proteins. These findings uncover a complex and dynamic interactome of red blood cell-derived extracellular vesicles across lipoprotein classes. This work establishes a robust methodological framework for studying mesoscale interactions of extracellular nanoparticles under physiologically relevant conditions. Its versatility allows for its application to diverse interaction scenarios, supporting systematic investigation of context-dependent effects on EV-LP binding.