Lipid lyotropic liquid crystalline nanoparticles (LCNPs) have attracted enormous attention for biomedical applications, such as drug delivery, due to their high biocompatibility and different structures giving rise to distinct properties. For e.g., tuning the nanostructure of LCNPs results in a significant difference in the release profile of encapsulated bioactives. However, it is uncertain whether these LCNPs retain their original structure upon contact with physiological relevant media at body temperature, and the link between their internal nanostructure and cell cytotoxicity. Here we will use SAXS to unravel the real-time phase behaviour of LCNPs with different cell media, and further investigate the relationship between nanostructure, cell cytotoxicity and uptake. The results will shed light on future strategies to transform LCNPs into a viable therapeutic entity ideal for biomedical applications.

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This dataset arises from a cross-sectional survey conducted to explore how Generation Z's cognition of the post-COVID-19 economic recession influences their Pro-Environmental Behaviors (PEBs) across distinct spheres. The data was collected from respondents aged 18–26, residing in six major cities across Pakistan. The survey instrument measured variables related to emergency relevance, emergency coping, positive and negative environmental affective reactions, and self-reported PEBs in each sphere.The dataset serves as the empirical foundation for testing an integrative model informed by Affective Events Theory, which examines the interplay of emergency cognition and affective reactions in driving environmental behavior during economic crises. The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) for analysis, offering insights into differential behavioral patterns across the identified spheres and advancing understanding of the challenges and opportunities for environmental action during economic downturns.This resource is valuable for researchers and policymakers interested in behavioral responses to intersecting economic and environmental challenges.

We provide datasets and scripts for reproducing the results of "Attractor dynamics of a whole-cortex network model predicts emergence and structure of fMRI co-activation patterns in the mouse brain". The datasets include the 34 x 34 anatomical connectivity of the mouse brain between excitatory populations, the empirical fMRI time series of 15 mice, and their organization into 6 co-activation patterns (CAPs). The Python script “Empirical_VS_Model.py” implements 3 whole-cortex models with their corresponding best-fit parameters (our full model with directed connectivity and non-linear neural dynamics, an undirected model with non-linear dynamics but undirected anatomical connections, and a linear model with directed anatomical connections but linear activation function). The script uses those models to calculate network statistics averaged over long-time scales (e.g. the functional connectivity matrix), and it finally compares them to the corresponding empirical statistics of the real mouse brain. The script also plots the figures showing the balance between the excitatory and inhibitory currents in our full model. To conclude, the Python script “Attractors_and_CAPs.py” implements our full best-fit model with directed connectivity and non-linear neural dynamics, and it uses the model to calculate the topography and probability of occupancy of its activation state attractors. The script also contains our mapping algorithm, which reconstructs the probability of occupancy of the attractors from the empirical data and compares it with the model distribution. Then the script uses linear combinations of the model attractors to reconstruct the topography of CAPs, and it finally compares the model topography to the empirical one obtained from the real mouse brain.

We provide datasets and scripts for reproducing the results of "Attractor dynamics of a whole-cortex network model predicts emergence and structure of fMRI co-activation patterns in the mouse brain". The datasets include the 34 x 34 anatomical connectivity of the mouse brain between excitatory populations, the empirical fMRI time series of 15 mice, and their organization into 6 co-activation patterns (CAPs). The Python script “Empirical_VS_Model.py” implements 3 whole-cortex models with their corresponding best-fit parameters (our full model with directed connectivity and non-linear neural dynamics, an undirected model with non-linear dynamics but undirected anatomical connections, and a linear model with directed anatomical connections but linear activation function). The script uses those models to calculate network statistics averaged over long-time scales (e.g. the functional connectivity matrix), and it finally compares them to the corresponding empirical statistics of the real mouse brain. The script also plots the figures showing the balance between the excitatory and inhibitory currents in our full model. To conclude, the Python script “Attractors_and_CAPs.py” implements our full best-fit model with directed connectivity and non-linear neural dynamics, and it uses the model to calculate the topography and probability of occupancy of its activation state attractors. The script also contains our mapping algorithm, which reconstructs the probability of occupancy of the attractors from the empirical data and compares it with the model distribution. Then the script uses linear combinations of the model attractors to reconstruct the topography of CAPs, and it finally compares the model topography to the empirical one obtained from the real mouse brain.

Biodiversity in human-dominated landscapes is declining, but evidence-based conservation targets to guide international policies for such landscapes are lacking. We present a framework for informing habitat conservation policies based on the enhancement of habitat quantity and quality and define thresholds of habitat quantity at which it becomes effective to also prioritize habitat quality. We applied this framework to pollinators, an important part of agroecosystem biodiversity, by synthesizing 59 studies from 19 countries. Given low habitat quality, hoverflies had the lowest threshold at 6% semi-natural habitat cover, followed by solitary bees (16%), bumble bees (18%), and butterflies (37%). These figures represent minimum habitat thresholds in agricultural landscapes, but when habitat quantity is restricted, marked increases in quality are required to reach similar outcomes.

Apatite fission track (AFT) and (U–Th)/He thermochronology data from the Anti-Atlas belt of Morocco. Samples were collected from the intrusive Precambrian rocks exposed in the western (Kerdous inlier), central (Agadir-Melloul, Zenaga, and West Saghro inliers), and eastern (East Saghro and Ougnat inliers) Anti-Atlas belt. The samples were processed and analyzed for AFT and (U–Th)/He at the geochronology laboratory of the VU University of Amsterdam. Full description, analysis, and interpretation of the dataset can be found in Gouiza et al., (2017).

Apatite fission track (AFT) and (U–Th)/He thermochronology data from the Anti-Atlas belt of Morocco. Samples were collected from the intrusive Precambrian rocks exposed in the western (Kerdous inlier), central (Agadir-Melloul, Zenaga, and West Saghro inliers), and eastern (East Saghro and Ougnat inliers) Anti-Atlas belt. The samples were processed and analyzed for AFT and (U–Th)/He at the geochronology laboratory of the VU University of Amsterdam. Full description, analysis, and interpretation of the dataset can be found in Gouiza et al., (2017).

Input and output parameters of the simulations presented in Frasson et al. (2025).Parameters:nu: ViscosityOmega: Rotation rateD: Width of the outer coreeta: Magnetic diffusivityg_o: Acceleration of gravity at the top of the coreF_i: Buoyancy flux at the bottom of the corekappa: Codensity diffusivityrho_0: Reference outer core densityU: RMS flow amplitudeB: RMS magnetic field amplitudeList of inputs:lmax: Maximum spherical harmonic degreemmax: Maximum spherical harmonic orderNr_OC: Number of nodal points in the radial direction within the outer coreNr_IC: Number of nodal points in the radial direction within the inner coredns: True for direct numerical simulation or False for hyperdiffusive treatmentgprof: Radial profile of the acceleration of gravitybc_icb: Flow boundary condition at the ICBbc_cmb: Flow boundary condition at the CMBGC: Gravitational couplingCC: Inner Core conductivityrratio: Aspect ratio of the outer coreE: Ekman number E=nu/(OmegaD^2)Pm: Magnetic Prandtl number Pm=nu/etaRa: Rayleigh number Ra=g_oF_iD^2/(4pikappanu^2rho_0)Pr: Prandtl number Pr=nu/kappapattern: Heterogeneous heat flux pattern at the top of the coredqo/qi: Peak-to-peak amplitude of the heat flux pattern normalized by the ICB heat fluxList of outputs:duration (dip. decay time): Duration of the simulation in dipole decay time unitf_dip: Amplitude of the magnetic dipole at the CMB (l=1) relative to the total magnetic field at the CMBE_mag: Magnetic energyE_kin: Kinetic energyM_Za: Modified energy ratio (see Frasson et al. 2025, https://doi.org/10.1093/gji/ggae457)ell_u: Characteristic degree of the flowell_b: Characteristic degree of the magnetic fieldRo_l: Local Rossby number Ro_l = Uell_u/(piOmegaD)Rm: Magnetic Reynolds numberElsasser: Elsasser number Elsasser=B^2/(rho_0Omegamu_0eta)Ro_ml: Local magnetic Rossby number (see Soderlund et al. 2025, https://doi.org/10.1093/mnras/staf1081)f_ohm: Fraction of ohmic dissipationReference:Frasson, T., Schaeffer, N., Nataf, H. C., & Labrosse, S. (2025). Geomagnetic dipole stability and zonal flows controlled by mantle heat flux heterogeneities. Geophysical Journal International, 240(3), 1481-1504.