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Ascribing mental states to non-human agents has been shown to increase their likeability and lead to better joint-task performance in human-robot interaction (HRI). However, it is currently unclear what physical features non-human agents need to possess in order to trigger mind attribution and whether different aspects of having a mind (e.g., feeling pain, being able to move) need different levels of human-likeness before they are readily ascribed to non-human agents. The current study addresses this issue by modeling how increasing the degree of human-like appearance (on a spectrum from mechanistic to humanoid to human) changes the likelihood by which mind is attributed towards non-human agents. We also test whether different internal states (e.g., being hungry, being alive) need different degrees of humanness before they are ascribed to non-human agents. The results suggest that the relationship between physical appearance and the degree to which mind is attributed to non-human agents is best described as a two-linear model with no change in mind attribution on the spectrum from mechanistic to humanoid robot, but a significant increase in mind attribution as soon as human features are included in the image. There seems to be a qualitative difference in the perception of mindful versus mindless agents given that increasing human-like appearance alone does not increase mind attribution until a certain threshold is reached, that is: agents need to be classified as having a mind first before the addition of more human-like features significantly increases the degree to which mind is attributed to that agent.

Sediment transport in rivers and channels can be understood as a diffusive phenomenon, where sediment particles separate as they move downstream. This diffusive behavior can be Fickian or anomalous (superdiffusive, subdiffusive, ballistic, etc.), depending on the time evolution of the sediment displacement variance. Many authors have observed transitions from ballistic to Fickian or subdiffusive regimes as the observation timescale increases, aligning with the conceptual model of Nikora et al. (2002), . Despite progress, the mechanisms driving these transitions remain unclear. To investigate them, we simulate a flat‐bed channel entraining sediment using Direct Numerical Simulations (DNS) to solve the flow and a point‐particle Discrete Element Method (DEM) to resolve particle dynamics, including collisions. The DNS–DEM algorithm is two‐way coupled, with the flow responding to particles through a cell‐based projection of drag forces. In parallel, we implement stochastic models calibrated from DNS‐DEM results, including linear and non‐linear advection‐diffusion equations and autoregressive Markov models (correlated/non‐correlated with Gaussian/non‐Gaussian distributions). We study eight cases spanning Shields numbers from 0.03 to 0.85, focusing on the evolution of the mean, variance, skewness, and kurtosis of particle displacement. We observe a ballistic regime at short timescales and near‐Fickian at longer ones, though subdiffusion is also present at low Shields numbers. Variance, skewness, and kurtosis approach Fickian behavior as time increases, with convergence rates depending on the Shields number. We find that particle motion correlation—an indirect measure of particle inertia—drives the transition from ballistic to Fickian regimes. In contrast, transitions to subdiffusive states are governed by resting times, not particle inertia.

The chronic inflammation observed during type 2 diabetes (T2D) is associated with spinal pathologies, including intervertebral disc (IVD) degeneration and chronic spine pain. Despite confounding factors, such as obesity, studies show that, after adjusting for age, body mass index and genetics (e.g. twins), patients with T2D experience disproportionate severity of IVD degeneration and/or back pain than individuals without T2D. We hypothesized that chronic T2D fosters a proinflammatory microenvironment within the IVD that promotes degeneration and disrupts homeostasis. To test this, we evaluated two common mouse models of T2D – leptin-receptor deficient (db/db) mice and mice with a chronic high-fat diet and impaired β-cell function (STZ-HFD). IVDs of STZ-HFD mice exhibited more severe degeneration and elevated chemokine expression than controls. RNA sequencing further revealed extensive transcriptional dysregulation in STZ-HFD IVDs not observed in db/db IVDs. STZ-HFD IVDs expressed enzymes that enhance advanced glycation end product precursors, impaired non-AGE DAMP pathways and suppressed RAGE turnover. These results suggest that, under controlled genetic and environmental conditions, the STZ-HFD model more accurately reflects the multifactorial inflammatory milieu characteristic of T2D-induced IVD degeneration.

We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, an algorithm designed to assess protein activity in tissue-independent fashion by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactomes. We confirm the algorithm’s value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures. VIPER has been successfully used to assess the regulatory activities of proteins from gene expression data, but its dependence on tissue-specific molecular profiles limits its applicability. MetaVIPER, introduced here, enables inference of the protein activities in orphan tissues and single cells.

The \"South American Research Network for the Surveillance and Control of Insecticide-Resistance in Arthropod Vectors\" (WINSA), established in 2024 by the IRD and FIOCRUZ with support from the US-CDC VecNet initiative and WHO-TDR, aims to coordinate research on insecticide resistance in arthropod vectors in South America, provide a platform for regional collaboration, and develop effective mitigation strategies. WINSA brings together leading technical experts representing research institutions from 14 countries and territories located in South America, the USA, and France to promote collaboration and information exchange, identify research gaps and priorities, enhance technical capacity in insecticide resistance monitoring, and support national and regional programs on vector resistance issues. This network seeks to contribute to the reduction and elimination of vector-borne diseases in South America.

Sediment transport controls the evolution of river channels, playing a fundamental role in physical, ecological, and biogeochemical processes across a wide range of spatial and temporal scales on the Earth surface. However, developing predictive transport models from first principles and understanding scale interactions on sediment fluxes remain as formidable research challenges in fluvial systems. Here we simulate the smallest scales of transport using direct numerical simulations (DNS) to explore the dynamics of bed-load and discover how turbulence and grain-scale processes influence transport rates, showing that their interplay gives rise to a critical regime dominated by fluctuations that propagate across scales. These connections are represented using a stochastic differential equation, and a statistical description through a path integral formulation and Feynman diagrams, thus providing a framework that incorporates nonlinear and turbulence effects to model the dynamics of bed-load across scales.

The present study utilized digestives tracts from adult largemouth bass (LMB) to hydrolyze Bighead carp muscle and obtain an optimal profile of muscle protein hydrolysates that would be easily assimilated within the primitive digestive tract of larval LMB. Specifically, muscle protein source was digested for the larva using the fully developed digestive system of the same species. The objectives of this study were: 1) to develop an optimal in vitro methodology for carp muscle hydrolysis using LMB endogenous digestive enzymes, and 2) to evaluate the effect of dietary inclusion of the carp muscle protein hydrolysate on LMB growth, survival, occurrence of skeletal deformities, and whole-body free amino acid composition. The study found that the in vitro hydrolysis method using carp intact muscle and LMB digestive tracts incubated at both acid and alkaline pH (to mimic digestive process of LMB) yielded a wide range of low molecular weight fractions (peptides), as opposed to the non-hydrolyzed muscle protein or muscle treated only with acid pH or alkaline pH without enzymes from LMB digestive tracts, which were comprised of large molecular weight fractions (polypeptides above 150 kDa). Overall, the dietary inclusion of the carp muscle hydrolysate improved growth performance of larval LMB in terms of final average weight, weight gain, DGC, SGR, and body length after 21 days of feeding compared to fish that received the diet based on non-hydrolyzed carp muscle. The study also found that hydrolysate-based feed significantly reduced skeletal deformities. The positive growth performance presented by fish in the hydrolysate-fed group possibly resulted from matching the specific requirements of the larvae with respect to their digestive organ development, levels of digestive enzymes present in the gut, and nutritional requirements.

First Person is a series of interviews with the first authors of a selection of papers published in Disease Models & Mechanisms, helping researchers promote themselves alongside their papers. Christian Gonzalez is first author on ‘ Degenerating intervertebral discs in the streptozotocin-high-fat diet model of type 2 diabetes show extensive inflammation’, published in DMM. Christian is a PhD student in the lab of Simon Tang at Washington University in St. Louis, St. Louis, MO, USA, investigating how chronic inflammation and diabetes promote intervertebral disc degeneration and lower-back pain.