Relative Diffusion Research Articles

Albumin diffusivity coefficient estimation in imitated porous structure of interstitial space by integration of albumin diffusion model (ADM) to electrical impedance tomography (EIT)

Albumin diffusivity coefficient in imitated porous structure of interstitial space has been estimated by means of the integration of albumin diffusion model (ADM) to electrical impedance tomography (EIT) (iADM-EIT) under five different porosity conditions (from 0.922 to 0.990) for transport phenomena quantification. The iADM-EIT was conducted by applying an iterative curve-fitting between spatio-temporal albumin concentration derived from ADM and spatio-temporal distribution of conductivity difference reconstructed by EIT. The essential point of the iADM-EIT is the quantification of experimental from by establishing a constitutive relationship among , , and . ADM is developed based on Fick’s second law implemented in Krogh tissue cylinder. EIT is performed to image the caused by increase of due to albumin diffusion phenomenon from a capillary to the imitated porous structure. The imitated porous structure is manufactured with agarose gel in a dynamic phantom including a capillary. As a result, the relative albumin diffusivity coefficient (: free diffusivity of albumin coefficient) is increased with the increase of in the range from 0.271 to 0.694, which are correspondent to the literature data with percent average relative error = 6.83 ± 2.72%.

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas

Objectives: The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas. Methods: Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters. Results: Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10⁻³ mm²/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92. Conclusion: The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

C-rate related diffusion process of the graphite electrode by in situ experiment and analysis

The diffusion process and mechanism play important roles in the lithium storage performance of electrode materials, which directly affects the electrochemical performance of lithium-ion batteries. In this paper, we propose an in situ experimental method based on the phase evolution to observe the diffusion process. An optical acquisition system with an electrochemical battery as the core was constructed. Using the electrochromic characteristics of graphite electrodes, the phase evolution information is displayed in real time. Based on the above experimental method, diffusion process observation experiments at different charge/discharge rates (C-rates) were carried out, and it was found that there was a rate-dependent corresponding relationship among the potential, phase transition, and lithium content. The results show that a high C-rate accelerates the phase evolution process, reduces the formation potential of the new phase, shortens the duration of the phase transition, and results in a multiphase coexistence state in the electrode. In addition, the effect of acceleration induced by a high C-rate on the diffusion process in the electrode is staged: the early and middle acceleration is significant, but the later acceleration is slow. From the perspective of the energy, this is because there is competition between the diffusion-driving energy and the energy barrier. Therefore, optimization with a balance between the battery capacity and lithiation rate is proposed.

Longitudinal neuroimaging evaluation of the corticospinal tract in patients with stroke treated with autologous bone marrow cells.

Bone marrow mononuclear cells (MNCs) attenuate secondary degeneration and enhance recovery in stroke animal models. In a nonrandomized clinical trial, we imaged 37 patients with stroke: 17 patients treated with MNCs (treated) and 20 patients who received standard of care (nontreated) at 1, 3, and 12 months onset of stroke on 3.0T MRI system. Three‐dimensional anatomical and diffusion tensor images were obtained. The integrity of the corticospinal tract was assessed by measuring absolute and relative fractional anisotropy (FA) and mean diffusivity (MD) in the rostral pons (RP), posterior limb of the internal capsule, and corona radiata by drawing regions of interest. Infarct volume and stroke severity, which was assessed via the NIH Stroke Scale (NIHSS), were higher in the MNC group compared with the nontreated patients, which is a major limitation. Overall, the relative FA (rFA) of the nontreated patients exhibited continued reduction and an increase in relative MD (rMD) from 1 to 12 months, whereas despite larger infarcts and higher severity, treated patients displayed an increase in rFA from 3 to 12 months and no change in rMD. Contrary to the nontreated group, the treated patients' rFA was also significantly correlated (P < .05) with NIHSS score in the RP at all time points, whereas rMD at the last two.

Relative Concentrations and Diffusion of COVID-19 across the United States in 2020

Les auteurs étudient la propagation de la COVID-19 (maladie à coronavirus 2019) en termes de cas et de décès dans l’ensemble des États-Unis, à l’aide des données relatives aux comtés à différents niveaux de comparaison. Ils utilisent l’indice de Hoover et l’indice de différentiel géographique pour analyser l’évolution de la concentration de COVID-19 à l’échelle des grandes zones économiques ( city systems), de mai 2020 à décembre 2020. Les auteurs cartographient également l’indice du différentiel géographique (DG) des différents comtés afin de montrer la distribution des cas de COVID-19 et des décès qui lui sont attribuables dans les comtés par rapport aux moyennes nationales. Ils constatent que la maladie s’est rapidement répandue et qu’elle s’est déconcentrée dans l’ensemble des zones économiques pour ce qui est des indices de Hoover, et qu’en décembre 2020, de nombreuses zones économiques affichant des niveaux très élevés de concentration de COVID-19 étaient passées à des taux de dispersion supérieurs aux taux observés dans la population en général. Cette évolution semble indiquer un degré appréciable de propagation rurale dans les derniers mois de 2020. Les indices de différentiel géographique pour les mêmes zones économiques révèlent aussi une convergence des géographies de la population et des décès liés à la COVID-19. Les statistiques des différents comtés mettent en relief l’existence de grappes de grands nombres et de faibles nombres de décès comparativement aux normes nationales, qui se sont déplacées du nord-est au sud-est et qui, en décembre, sont aussi apparues dans le Midwest et la portion centrale du pays, entre autres régions.

Nucleation of Y–Si–O Nano-clusters in Multi-microalloyed Nano-structured Ferritic Alloys: a First-principles Study

First-principles energetics analyses were performed to investigate the nucleation of (Y–Si–O) nano-clusters (NCs) in nano-structured ferritic alloys (NFAs). It was predicted that the nucleation of (Y-Si–O) NCs follows the same general pathway as previously found for (Y–Ti/Al/Zr–O) NCs in NFAs: they all tend to begin with the (O–O) pairs and grow to (O–Y)-cores and further to larger NCs by attracting Y and other solute elements nearby. Nucleation of a hexa-atomic –[2Y–Si–3O]– cluster can reduce the total energy by 4.71 eV. Among various microalloying-induced NCs, the nucleation preference ordering was predicted as (Y–Zr–O) > (Y–Ti–O) > (Y–Al–O) > (Y–Si–O). The number densities and chemical compositions of NCs in multi-microalloyed NFAs would largely depend on the number availability of (O,Y)-cores, as well as the relative abundance and atomic diffusivities of microalloying solute elements nearby.

Cell polarisation in a bulk-surface model can be driven by both classic and non-classic Turing instability

The GTPase Cdc42 is the master regulator of eukaryotic cell polarisation. During this process, the active form of Cdc42 is accumulated at a particular site on the cell membrane called the pole. It is believed that the accumulation of the active Cdc42 resulting in a pole is driven by a combination of activation–inactivation reactions and diffusion. It has been proposed using mathematical modelling that this is the result of diffusion-driven instability, originally proposed by Alan Turing. In this study, we developed, analysed and validated a 3D bulk-surface model of the dynamics of Cdc42. We show that the model can undergo both classic and non-classic Turing instability by deriving necessary conditions for which this occurs and conclude that the non-classic case can be viewed as a limit case of the classic case of diffusion-driven instability. Using three-dimensional Spatio-temporal simulation we predicted pole size and time to polarisation, suggesting that cell polarisation is mainly driven by the reaction strength parameter and that the size of the pole is determined by the relative diffusion.

Investigation of Cerebral White Matter Changes After Spinal Cord Injury With a Measure of Fiber Density.

Remote neurodegenerative changes in supraspinal white matter (WM) can manifest after central lesions such as spinal cord injury (SCI). The majority of diffusion tensor imaging (DTI) studies use traditional metrics such as fractional anisotropy (FA) and mean diffusivity (MD) to investigate microstructural changes in cerebral WM after SCI. However, interpretation of FA readouts is often challenged by inherent limitations of the tensor model. Recent developments in novel diffusion markers, such as fiber density (FD), allows more accurate depictions of WM pathways and has shown more reliable quantification of WM alterations compared to FA in recent studies of neurological diseases. This study investigated if FD provides useful characterization of supraspinal WM integrity after SCI in addition to the traditional DTI readouts. FA, MD, and FD maps were derived from diffusion datasets of 20 patients with chronic SCI and compared with 19 healthy controls (HC). Group differences were investigated across whole brain WM using tract-based spatial statistics and averaged diffusion values of the corticospinal tract (CST) and thalamic radiation (TR) were extracted for comparisons between HC and SCI subgroups. We also related diffusion readouts of the CST and TR with clinical scores of sensorimotor function. To investigate which diffusion markers of the CST and TR delineate HC and patients with SCI a receiver operating characteristic (ROC) analysis was performed. Overall, patients with an SCI showed decreased FA of the TR and CST. ROC analysis differentiated HC and SCI based on diffusion markers of large WM tracts including FD of the TR. Furthermore, patients' motor function was positively correlated with greater microstructural integrity of the CST. While FD showed the strongest correlation, motor function was also associated with FA and MD of the CST. In summary, microstructural changes of supraspinal WM in patients with SCI can be detected using FD as a complementary marker to traditional DTI readouts and correlates with their clinical characteristics. Future DTI studies may benefit from utilizing this novel marker to investigate complex large WM tracts in patient cohorts with varying presentations of SCI or neurodegenerative diseases.

Importance of deepening integration of crime and conservation sciences.

Conservation crime is a globally distributed societal problem. Conservation crime science, an emerging interdisciplinary field, has the potential to help address this problem. However, its utility depends on serious reflection on the transposition of crime science approaches to conservation contexts, which may differ in meaningful ways from traditional crime contexts. We considered the breadth of crime science approaches being used in conservation as well as the depth of crime science integration in conservation. We used the case of sea cucumber (Holothuria floridana, Isostichopus badionotus) trafficking in Mexico as an example of why the interdisciplinarity of crime and conservation sciences should be deepened and how integration can help ideate new solutions. We first conducted a review of literature to capture the range of interdisciplinarity applications. We identified 6 crime science approaches being applied to the conservation contexts of illegal, unreported, and unregulated fishing; wildlife and plant crime; and illegal logging. We then compared this knowledge base to the case of illegal sea cucumber fishing in Mexico. We identified 5 challenges in the application of these approaches to conservation contexts: the relative diffusion of harms and victims in conservation crimes; scalar mismatches in crime, authority, and the conservation issue itself; interactions between legal and illegal networks; communities and their authority to define and control crime; and the role of natural science in the rule of law. Considering these 5 factors may enhance the depth of interdisciplinarity between crime and conservation sciences. Nurturing interdisciplinary crime and conservation science will expand innovation and help accelerate successful risk management programs and other policy agendas.

Brain morphological and connectivity changes on MRI after stem cell therapy in a rat stroke model.

In animal models of stroke, behavioral assessments could be complemented by a variety of neuroimaging studies to correlate them with recovery and better understand mechanisms of improvement after stem cell therapy. We evaluated morphological and connectivity changes after treatment with human mesenchymal stem cells (hMSCs) in a rat stroke model, through quantitative measurement of T2-weighted images and diffusion tensor imaging (DTI). Transient middle cerebral artery occlusion rats randomly received PBS (PBS-only), FBS cultured hMSCs (FBS-hMSCs), or stroke patients’ serum cultured hMSCs (SS-hMSCs). Functional improvement was assessed using a modified neurological severity score (mNSS). Quantitative analyses of T2-weighted ischemic lesion and ventricular volume changes were performed. Brain microstructure/connectivity changes were evaluated in the ischemic recovery area by DTI-derived microstructural indices such as relative fractional anisotropy (rFA), relative axial diffusivity (rAD), and relative radial diffusivity (rRD), and relative fiber density (rFD) analyses. According to mNSS results, the SS-hMSCs group showed the most prominent functional improvement. Infarct lesion volume of the SS-hMSCs group was significantly decreased at 2 weeks when compared to the PBS-only groups, but there were no differences between the FBS-hMSCs and SS-hMSCs groups. Brain atrophy was significantly decreased in the SS-hMSCs group compared to the other groups. In DTI, rFA and rFD values were significantly higher and rRD value was significant lower in the SS-hMSCs group and these microstructure/connectivity changes were correlated with T2-weighted morphological changes. T2-weighted volume alterations (ischemic lesion and brain atrophy), and DTI microstructural indices and rFD changes, were well matched with the results of behavioral assessment. These quantitative MRI measurements could be potential outcome predictors of functional recovery after treatment with stem cells for stroke.

Dielectric spectroscopy of red blood cells in sickle cell disease.

Hypoxia-induced polymerization of sickle hemoglobin and the related ion diffusion across cell membrane can lead to changes in cell dielectric properties, which can potentially serve as label-free, diagnostic biomarkers for sickle cell disease. This article presents a microfluidic-based approach with on-chip gas control for the impedance spectroscopy of suspended cells within the frequency range of 40Hz to 110MHz. A comprehensive bioimpedance of sickle cells under both normoxia and hypoxia is achieved rapidly (within ∼7min) and is appropriated by small sample volumes (∼2.5μL). Analysis of the sensing modeling is performed to obtain optimum conditions for dielectric spectroscopy of sickle cell suspensions and for extraction of single cell properties from the measured impedance spectra. The results of sickle cells show that upon hypoxia treatment, cell interior permittivity and conductivity increase, while cell membrane capacitance decreases. Moreover, the relative changes in cell dielectric parameters are found to be dependent on the sickle and fetal hemoglobin levels. In contrast, the changes in normal red blood cells between the hypoxia and normoxia states are unnoticeable. The results of sickle cells may serve as a reference to design dielectrophoresis-based cell sorting and electrodeformation testing devices that require cell dielectric characteristics as input parameters. The demonstrated method for dielectric characterization of single cells from the impedance spectroscopy of cell suspensions can be potentially applied to other cell types and under varied gas conditions.

Relative Dispersion in the Antarctic Circumpolar Current

AbstractStirring in the subsurface Southern Ocean is examined using RAFOS float trajectories, collected during the Diapycnal and Isopycnal Mixing Experiment in the Southern Ocean (DIMES), along with particle trajectories from a regional eddy permitting model. A central question is the extent to which the stirring is local, by eddies comparable in size to the pair separation, or nonlocal, by eddies at larger scales. To test this, we examine metrics based on averaging in time and in space. The model particles exhibit nonlocal dispersion, as expected for a limited resolution numerical model that does not resolve flows at scales smaller than ~10 days or ~20–30 km. The different metrics are less consistent for the RAFOS floats; relative dispersion, kurtosis, and relative diffusivity suggest nonlocal dispersion as they are consistent with the model within error, while finite-size Lyapunov exponents (FSLE) suggests local dispersion. This occurs for two reasons: (i) limited sampling of the inertial length scales and a relatively small number of pairs hinder statistical robustness in time-based metrics, and (ii) some space-based metrics (FSLE, second-order structure functions), which do not average over wave motions and are reflective of the kinetic energy distribution, are probably unsuitable to infer dispersion characteristics if the flow field includes energetic wave motions that do not disperse particles. The relative diffusivity, which is also a space-based metric, allows averaging over waves to infer the dispersion characteristics. Hence, given the error characteristics of the metrics and data used here, the stirring in the DIMES region is likely to be nonlocal at scales of 5–100 km.

Effects of long-term tillage systems on soil physical quality and crop yield in a Brazilian Ferralsol

Long-term soil tillage trials can provide important knowledge about sustainable changes in soil physical quality and crop yield. This study evaluated soil physical quality indicators under different long-term tillage systems and examined the relationships between quality indicators and crop yield. The study was carried out on a Rhodic Ferralsol with three tillage systems established in 1989: conventional tillage (CT), strategic tillage (ST), and no-tillage (NT). All treatments had long-term crop rotation. The soil parameters evaluated were total organic carbon (TOC), bulk density (ρs), macro and microporosity (Mac and Mic), relative gas diffusivity (D/D0), pore tortuosity (τ), relative field capacity (RFC), structural stability index (SSI), least-limiting water range (LLWR), and degree of compactness (DC, taking as reference the soil bulk density in which LLWR = 0). Soybean and maize yields in two consecutive summer seasons were measured. Conventional and strategic tillage provided higher ρs in the 0.15–0.30 m layer depth, leading to higher DC in CT. Using soil bulk density at LLWR = 0 as reference proved useful to assess soil DC and plant response. No-tillage provided lower DC in the 0–0.15 m (86 %) and 0.15–0.30 m (78 %) layers than CT (91 % and 94 %, respectively). The maize yield had a negative linear relationship to DC, with the lower values at DC > 87 %. All tillage systems affected D/D0, even at similar porosity values. The better soil physical quality under NT provided 1211 kg ha−1 higher maize yield compared with CT. The differences in soybean yield between treatments were not significant, but NT provided 381 kg ha−1 more than CT. These findings indicate that NT is the best system studied. Our results strongly suggest that ST does not improve physical properties of soils under NT with crop rotation, and that a diversified crop rotation in NT was efficient to avoid soil physical degradation.

A molecular interaction-diffusion framework for predicting organic solar cell stability.

Rapid increase in the power conversion efficiency of organic solar cells (OSCs) has been achieved with the development of non-fullerene small-molecule acceptors (NF-SMAs). Although the morphological stability of these NF-SMA devices critically affects their intrinsic lifetime, their fundamental intermolecular interactions and how they govern property-function relations and morphological stability of OSCs remain elusive. Here, we discover that the diffusion of an NF-SMA into the donor polymer exhibits Arrhenius behaviour and that the activation energy Ea scales linearly with the enthalpic interaction parameters χH between the polymer and the NF-SMA. Consequently, the thermodynamically most unstable, hypo-miscible systems (high χ) are the most kinetically stabilized. We relate the differences in Ea to measured and selectively simulated molecular self-interaction properties of the constituent materials and develop quantitative property-function relations that link thermal and mechanical characteristics of the NF-SMA and polymer to predict relative diffusion properties and thus morphological stability.

Diffusivity of cement paste via a continuum-based microstructure and hydration model: Influence of cement grain shape

A microstructure-guided diffusivity model of cement paste is devised to explore the dependence of the relative diffusivity on the microstructure evolutions of cement paste from fresh state via non-spherical cement particles hydration to hardened state. The microstructure-guided diffusivity model contains three main components: (1) the microstructure of fresh cement paste is generated to simulate the initial state of non-spherical cement particles in water; (2) a continuum-based hydration model of non-spherical cement particles (HYD-NSP) is proposed to describe the evolutions of various phases (hydration products and pores) from fresh state to hardened state; (3) a random walk model is implemented to the microstructures of hydrated cement paste digitalized at the resolution of 0.1 μm/voxel for the determination of relative diffusivity of cement paste. Although this study takes five kinds of Platonic particles and sphere as an introductory example, this microstructure-guided model is readily applicable to other complex microstructures induced by the hydration of non-spherical cement particles. Finally, we utilize the framework to evaluate the influence of the geometrical shape of cement particles on the hydrated microstructure and relative diffusivity of cement paste. Results shed light on that the relative diffusivity increases with the increase of sphericity as a shape descriptor of cement particle. This is due to the specific surface area decreasing with the increase of sphericity, resulting in less hydration products and more porosity, which ultimately increases the relative diffusivity.

Barriers to the Diffusion of Medical Technologies Within Healthcare: A Systematic Review

Implementing medical devices into a clinical setting is a complex and lengthy process. Existing models, such as Rogers’ Diffusion of Innovation, try to elucidate this process, however, need to be updated to the evolving healthcare context as fewer than 7% of devices achieve implementation. The aim of this systematic review was to describe the barriers to diffusion in relation to this model to understand why so few technologies are implemented and how to address these challenges to mitigate risk during the translation process. To do this, we searched PubMed, Medline, and Embase databases for studies published between 01/01/1999 – 30/10/2019. Theoretical and application studies were included, and thematic analysis was employed using the Braun and Clarke framework to generate broad themes from the specific concepts described by included studies. A total of 33 articles were eligible for inclusion. Innovation processes constituting an obstacle to diffusion included: technology-specific challenges (8/33), clinical evidence/uncertainty (5/33), regulatory affairs (6/33), health technology assessment (7/33), reimbursement (15/33), and adoption (6/33). The factors that contributed to these themes were identified as being associated to the 11 tenets of Rogers’ Diffusion of Innovation. This allowed the discussion of the identified barriers to medical device diffusion in relation with the Rogers’ model. This analysis enabled the development and proposal of a framework that incorporates considerations within commercialization and translation strategies for these barriers to ultimately facilitate medical technology implementation.

Asymptotic windings of the block determinants of a unitary Brownian motion and related diffusions

We study several matrix diffusion processes constructed from a unitary Brownian motion. In particular, we use the Stiefel fibration to lift the Brownian motion of the complex Grassmannian to the complex Stiefel manifold and deduce a skew-product decomposition of the Stiefel Brownian motion. As an application, we prove asymptotic laws for the determinants of the block entries of the unitary Brownian motion.

Social Stability Risk Diffusion of Large Complex Engineering Projects Based on an Improved SIR Model: A Simulation Research on Complex Networks

The development of China’s new urbanization has driven the rapid increase in large complex engineering projects, which have the characteristics of large‐scale investment, long‐term construction, and wide social influence, easily causing benefit conflicts among relevant stakeholders, and breaking out social stability risks. In the previous research, the risks of large complex engineering projects mainly concentrated on the assessment of economic risk, schedule risk, etc. However, there were few studies on social risks, and they did not consider how the risks spread on the complex networks based on the social connections such as interpersonal relationship. From the subject of social stability risk diffusion of large complex engineering projects, this paper constructs a related risk diffusion model based on the SIR model to analyze risk diffusion mechanism. Through NetLogo simulation platform, the model is placed under a small‐world network environment that is closest to the topology structure of real social interpersonal relationship network for simulation research, aiming to find out key factors of social stability risk intervention for large complex engineering projects, which greatly contributes to the social stability risk management of large complex engineering projects.

Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks

Data-dependent metrics are powerful tools for learning the underlying structure of high-dimensional data. This article further develops and analyzes a data-dependent metric known as diffusion state distance (DSD), which compares points using a data-driven diffusion process. Unlike related diffusion methods, DSDs incorporate information across time scales, which allows for the intrinsic data structure to be inferred in a parameter-free manner. This article develops a theory for DSD based on the multitemporal emergence of mesoscopic equilibria in the underlying diffusion process. New algorithms for denoising and dimension reduction with DSD are also proposed and analyzed. These approaches are based on a weighted spectral decomposition of the underlying diffusion process, and experiments on synthetic datasets and real biological networks illustrate the efficacy of the proposed algorithms in terms of both speed and accuracy. Throughout, comparisons with related methods are made in order to illustrate the distinct advantages of DSD for datasets exhibiting multiscale structure.

Effects of bio-subsoilers on subsoil pore-system functionality: Case study with intact soil columns

Field traffic-induced subsoil compaction poses an escalating problem in relation to key soil functions because of the use of increasingly heavier machinery. Natural recovery from subsoil compaction is slow and therefore there is a need for measures such as bio-subsoilers to stimulate the recovery. This study thus investigated the influence of potential bio-subsoilers on pore functionality and morphology of a sandy loam soil, with an emphasis on the transition between the topsoil and compacted subsoil and on the compacted subsoil per se, in an intact soil column experiment. The control crop was spring barley, and potential bio-subsoilers included chicory, lucerne, fodder radish, tall fescue and kernza. The intact soil columns (Ø = 0.20 m, h = 0.50 m) were X-ray CT-scanned prior to and two years after crop establishment. Undisturbed subsamples (246 cm3) were collected one and two years after crop establishment in five replicates at 0.10, 0.25 and 0.35-m depth from the soil columns. The laboratory measurements and derived indices showed that after one-year non-restrictive air-filled porosity (εa = 0.13 m3 m−3) and relative gas diffusivity (Ds/Do = 0.0059) was reached by lucerne in the Transition layer (0.25 m). In the second year, lucerne also showed a significantly higher εa (0.09 m3 m−3), and a non-significantly higher Ds/Do (0.0029) compared to barley in the Compacted layer (0.30 m). Pore geometry indices derived from the relationship between εa, Ds/Do and air permeability indicated the formation of less tortuous and more connected small pores (at −100 hPa matric potential) throughout the Transition and Compacted layers by chicory, lucerne and tall fescue. A significantly higher CT-number of pores < 1 mm3 was found for lucerne and a higher CT-volume of pores < 1.5 mm in diameter for lucerne and chicory in the Compacted layer. Root number density was significantly higher for chicory, lucerne and tall fescue compared to barley from the first year, and also for kernza from the second year. Lucerne had the greatest subsoil compaction mitigation potential in our experiment in intact columns. Further studies in real field situations are needed.