Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
**Axon Diameter Mapping** **Overview:** Multi-shell diffusion-weighted MRI of the human brain that was optimized for axon diameter mapping using the power-law approach of Veraart et al. (2020) **Hardware requirements:** The modeling approach leverages (a) high *b*-values to suppress extra-axonal signal, and (b) strong diffusion-weighted strengths to maximize the sensitivity of diffusion-weighted MRI signal to restricted diffusion within micrometer-thin axons. Therefore, axon diameter mapping is currently limited to MRI scanners that are equipped with ultra-strong diffusion-weighting gradients, i.e. 300mT/m. Examples include Siemens 3T Connectom, Siemens 3T Connectom.X, and GE 3T Magnus. The protocol was optimized and tested on Siemens 3T Connectom. **Code:** Code to analyze the data is provided in https://github.com/NYU-DiffusionMRI/AxonRadiusMapping. **Supporting data:** Rician signal biases impact the accuracy of Axon Diameter Mapping. Therefore it is important to collect supporting data from which a noise map can be derived. **References:** *Model:* Veraart J, Nunes D, Rudrapatna U, Fieremans E, Jones DK, Novikov DS, Shemesh N. Noninvasive quantification of axon radii using diffusion MRI. Elife. 2020 Feb 12;9:e49855. doi: 10.7554/eLife.49855. *Reproducibility and protocol:* Veraart J, Raven EP, Edwards LJ, Weiskopf N, Jones DK. The variability of MR axon radii estimates in the human white matter. Hum Brain Mapp. 2021 May;42(7):2201-2213. doi: 10.1002/hbm.25359. *Interpretation:* Karat BG, Wren-Jarvis J, Raven EP, Khan AR, Jones DK, Palombo M, Veraart J. Revisiting the interpretation of axon diameter mapping using higher-order signal representations. Imaging Neurosci (Camb). 2026 Jan 9;4:IMAG.a.1080. doi: 10.1162/IMAG.a.1080. This is a dicompare validation schema. View, browse, and use it at https://dicompare.neurodesk.org/schema/Axon_diameter_mapping_v1.2.
A rational design for next-generation thermo-responsive nanocarriers is proposed, in which polymer chemistry, nanoparticle structure, experimental characterization, and mechanistic modelling are integrated from the earliest stages of material development.
M. Schifone, Giuseppe Nunziata, Filippo Rossi· Advances in Colloid and Inte...· 2 citations
This paper describes the formulation of a numerical model for simulating environmentally driven one-dimensional (1D) ground movements of expansive clay. The formulation is based on a finite-element model that simulates the redistribution of matric suction through a diffusion-type equation, explicitly accounting for volume changes due to wetting and drying of the clay. We synthesize and modify highly nonlinear constitutive relationships for (1) hysteretic soil water retention; (2) reversible soil shrinkage and expansion of clay; and (3) hydraulic conductivity, explicitly incorporating desiccation cracks through a multidomain framework and assuming a critical surface crack depth. These models are well-calibrated to published laboratory tests on a reference expansive clay, Denver bentonite. We demonstrate capabilities of the proposed formulation to simulate the response of a homogeneous expansive clay to periods of drying and wetting, considering the initial matric suction, saturated hydraulic conductivity of the intact clay, and critical crack depth as three primary sources of uncertainty. We compare ensemble model simulations with measured ground movements from an instrumented expansive clay test site in Texas over a 3-year period using detailed records of potential evapotranspiration and precipitation. By assigning weights to the ensemble simulations based on their performance, we constrain the ranges of the three key uncertain parameters. The results showed very reasonable first-order agreement with the measured data and highlight the potential of the proposed formulation. We anticipate that more reliable predictions can be achieved through direct measurements of actual in situ evaporation rates and local soil properties.
Mahdi Seyyedan, Jiali Ma, Ivo Rosa Montenegro et al.· Journal of Geotechnical and...· 1 citation
This paper examines whether differences in the speed with which traded assets respond to a common market shock can predict subsequent relative returns. The framework combines a lagged rolling factor model with Absorption Gap (AG), which measures an asset’s response error, and Shock Coherence (SC), which characterizes the contemporaneous market state. The public specification is evaluated using executable next-open timing, explicit transaction costs, dependence-aware inference, randomized-signal benchmarks, chronological diagnostics, and machine-learning extensions. The study uses 24 ETFs from 4 January 2010 through 28 August 2026, with eight factor proxies excluded from the 16-asset traded cross-section. The corrected public baseline produces a combined Rank IC of -0.00592, an approximately flat zero-cost gross result, and materially negative performance after transaction costs. A within-date randomized-signal benchmark yields an empirical two-sided p-value of 0.299, while standalone Absorption Gap, coherence-conditioned tests, chronological subsamples, and machine-learning models provide no robust evidence of economically viable public alpha. The contribution is therefore methodological as much as empirical: the paper connects an economic hypothesis about heterogeneous information absorption to an executable trading test, documents why the disclosed implementation fails, separates diagnostic and exploratory analysis from confirmatory evidence, and establishes a reproducible public baseline while keeping the proprietary alpha layer outside the evidence package.
Khaybullina Alina· Zenodo (CERN European Organi...· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.