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Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.

We study sub-GeV dark matter (DM) particles that may annihilate or decay into Standard Model particles producing an exotic injection component in the Milky Way that leaves an imprint in both photon and cosmic-ray (CR) fluxes. Specifically, the DM particles may annihilate or decay into e + e −, μ + μ −, or π + π − and may radiate photons through their e ± products. The resulting e ± products can be directly observed in probes such as Voyager 1. Alternatively, the e ± products may produce bremsstrahlung radiation and upscatter the low-energy Galactic photon fields via the inverse Compton process, generating a broad emission from X-ray to γ-ray energies observable in experiments such as XMM-Newton. We find that we get a significant improvement in the DM annihilation and decay constraints from XMM-Newton (excluding thermally averaged cross sections of 10−31 cm3 s−1 ≲ 〈σ v〉 ≲ 10−26 cm3 s−1 and decay lifetimes of 1026 s ≲ τ ≲ 1028 s, respectively) by including best-fit CR propagation and diffusion parameters. This yields the strongest astrophysical constraints for this mass range of DM of 1 MeV to a few GeV and even surpasses cosmological bounds across a wide range of masses as well.

In conventional MR examinations glioblastomas multiforme (GBMs), metastases and primary CNS lymphomas (PCNSLs) may show very similar appearance. The aim of the study was to evaluate usefulness of multiparametric T2*DSC perfusion and diffusion MR imaging in the preoperative differentiation of these tumors. Seventy four solitary enhancing tumors (27 GBMs, 30 metastases, 17 PCNSLs) were enrolled in the study. Parameters of cerebral blood volume (rCBV), peak height (rPH), percentage of signal recovery (rPSR) and apparent diffusion coefficient (ADC) were assessed from the tumor core and the peritumoral non-enhancing T2-hyperintense zone. Within the tumor core there were no differences in perfusion and diffusion parameters between GBMs and metastases. Compared to GBMs and metastases, PCNSLs showed significantly lower rCBV and rPH, ADC as well as higher rPSR values. Max rCBV with a cut-off value of 2.18 demonstrated the highest accuracy of 0.98 in differentiating PCNSLs from other tumors. To distinguish GBMs from metastases analysis of the peritumoral zone was performed showing significantly higher rCBV, rPH and lower ADC values in GBMs with the highest accuracy of 0.94 found for max rCBV at a cut-off value of 0.98. Max rCBV seems to be the most important parameter to differentiate GBMs, metastases and PCNSLs. Analysis of max rCBV within the tumor core enables to distinguish hypoperfused PCNSLs from hyperperfused GBMs and metastases while evaluation of max rCBV within the peritumoral zone is helpful to distinguish GBMs showing peritumoral infiltration from metastases surrounded by pure edema.

Novel insights into the behavior of the diffusion coefficients of charged particles in the inner heliosphere are of great importance to any study of the transport of these particles and are especially relevant with regard to the transport of low-energy electrons. The present study undertakes an exhaustive investigation into the diffusion parameters needed to reproduce low-energy electron intensities as observed at Earth, using a state-of-the-art 3D cosmic ray transport code. To this end, the transport of Jovian electrons is considered, as Jupiter represents the predominant source of these particles in the inner heliosphere, and because a careful comparison of model results with observations taken during periods of good and poor magnetic connectivity between Earth and Jupiter allows for conclusions to be drawn as to both parallel and perpendicular diffusion coefficients. This study then compares these results with the predictions made by various scattering theories. Best-fit parameters for parallel and perpendicular mean free paths at 1 au fall reasonably well within the span of observational values reported by previous studies, but best-fit radial and rigidity dependences vary widely. However, a large number of diffusion parameters lead to reasonable to-good fits to observations, and it is argued that considerable caution must be exercised when comparing theoretical results for diffusion coefficients with diffusion parameters calculated from particle transport studies.

Volcanic eruptions can cause significant disruption to society, and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3∘ N, 153.2∘ E). The eruption emitted around 1.5±0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption but is unable, in its standard configuration, to capture the extent and precise location of the highest magnitude vertical column density (VCD) regions within the observed volcanic cloud. Using the structure–amplitude–location (SAL) score and the fractional skill score (FSS) as metrics for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 d after the eruption where VCDs of SO2 (in Dobson units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 d only. The lower skill for these high-SO2-VCD regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard horizontal diffusion parameters used in NAME by a factor of 4 results in a slightly increased model skill during the first 5 d of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. The skill of NAME to simulate high SO2 VCDs and the temporal evolution of the NH-mean SO2 mass burden is dominated by the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), the NAME simulations show a similar peak in SO2 mass burden to that derived from TROPOMI (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 d in the NH. Our work illustrates how the synergy between high-resolution satellite retrievals and dispersion models can identify potential limitations of dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

We present a systematic study of particle transport by diffusion in young pulsar wind nebulae (PWNe). We selected nine bright sources that are well resolved with the Chandra X-ray Observatory. We analyzed archival data to obtain their radial profiles of photon index (Γ) and surface brightness (Σ) in a consistent way. These profiles were then fit with a pure diffusion model that was tested on Crab, 3C 58, and G21.5−0.9 before. In addition to the spectral softening due to the diffusion, we calculated the synchrotron power and built up the theoretical surface brightness profile. For each source, we performed separate fits to the Γ and Σ profiles. We found that these two profiles of most PWNe are similar, except for Crab and Vela. Both profiles can be well described by our model, suggesting that diffusion dominates the particle transport in most sampled PWNe. The discrepancy of parameters between the Γ and Σ profiles is relatively large for 3C 58 and G54.1+0.3. This difference could be attributed to the elongated shape, reflecting boundary, and the nonuniform magnetic field. Finally, we found no significant correlations between diffusion parameters and physical parameters of PWNe and pulsars.

The correct estimation of fibre orientations is a crucial step for reconstructing human brain tracts. Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques (bedpostx) is able to estimate several fibre orientations and their diffusion parameters per voxel using Markov Chain Monte Carlo (MCMC) in a whole brain diffusion MRI data, and it is capable of running on GPUs, achieving speed-up of over 100 times compared to CPUs. However, few studies have looked at whether the results from the CPU and GPU algorithms differ. In this study, we compared CPU and GPU bedpostx outputs by running multiple trials of both algorithms on the same whole brain diffusion data and compared each distribution of output using Kolmogorov-Smirnov tests. We show that distributions of fibre fraction parameters and principal diffusion direction angles from bedpostx and bedpostx_gpu display few statistically significant differences in shape and are localized sparsely throughout the whole brain. Average output differences are small in magnitude compared to underlying uncertainty. Despite small amount of differences in output between CPU and GPU bedpostx algorithms, results are comparable given the difference in operation order and library usage between CPU and GPU bedpostx.

The caudate nucleus has been thought to be involved in the control of motor commands by the cerebrum, and recent studies suggest that it may play a role in the control of attachment behavior, cognition, emotion, and mental functions. Implied by the basal ganglia’s involvement in the execution, planning and control of movement, the caudate nucleus functions in a situation-dependent manner where processing of external stimuli is important on the basis of learning and memory. Sensory processing, which determines the response to external stimuli, has been shown to be related to various brain regions but it remains unknown how sensory processing is associated with the structure of the caudate nucleus and white matter microstructures of the caudate. Using four diffusion parameters derived from diffusion tensor imaging (DTI) (i.e., fractional anisotropy (FA), mean diffusivity (MD), axonal diffusivity (AD), and radial diffusivity (RD)) and the Adolescent/Adult Sensory Profile (AASP) questionnaire of 99 healthy subjects [42 males and 57 females; mean age:26.9 years, standard deviation 6.9], we investigated the relationship between white matter structure in the caudate nucleus and sensory processing. In consistent with what had been suggested by the results of previous studies, we found significant correlations between AD, MD and tactile sensation. Furthermore, we found a significant correlation between AD, MD and tactile sensory avoidance, the AASP sub-scores regarding the tactile senses. To the best of our knowledge, this is the first study to show that DTI diffusion parameters correlate with AASP scores in specific brain regions.

Animals translocate nutrients by consuming nutrients at one point and excreting them or dying at another location. Such lateral fluxes may be an important mechanism of nutrient supply in many ecosystems, but lack quantification and a systematic theoretical framework for their evaluation. This paper presents a mathematical framework for quantifying such fluxes in the context of mammalian herbivores. We develop an expression for lateral diffusion of a nutrient, where the diffusivity is a biologically determined parameter depending on the characteristics of mammals occupying the domain, including size-dependent phenomena such as day range, metabolic demand, food passage time, and population size. Three findings stand out: (a) Scaling law-derived estimates of diffusion parameters are comparable to estimates calculated from estimates of each coefficient gathered from primary literature. (b) The diffusion term due to transport of nutrients in dung is orders of magnitude large than the coefficient representing nutrients in bodymass. (c) The scaling coefficients show that large herbivores make a disproportionate contribution to lateral nutrient transfer. We apply the diffusion equation to a case study of Kruger National Park to estimate the conditions under which mammal-driven nutrient transport is comparable in magnitude to other (abiotic) nutrient fluxes (inputs and losses). Finally, a global analysis of mammalian herbivore transport is presented, using a comprehensive database of contemporary animal distributions. We show that continents vary greatly in terms of the importance of animal-driven nutrient fluxes, and also that perturbations to nutrient cycles are potentially quite large if threatened large herbivores are driven to extinction.

There has been significant progress in the last few decades in addressing the biomedical applications of polymer hydrogels. Particularly, stimuli responsive hydrogels have been inspected as elegant drug delivery systems capable to deliver at the appropriate site of action within the specific time. The present work describes the synthesis of pH responsive semi-interpenetrating network (semi-IPN) hydrogels of N-succinyl-chitosan (NSC) via Schiff base mechanism using glutaraldehyde as a crosslinking agent and Poly (acrylamide-co-acrylic acid)(Poly (AAm-co-AA)) was embedded within the N-succinyl chitosan network. The physico-chemical interactions were characterized by Fourier transform infrared (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and field emission scanning electron microscope (FESEM). The synthesized hydrogels constitute porous structure. The swelling ability was analyzed in physiological mediums of pH 7.4 and pH 1.2 at 37°C. Swelling properties of formulations with various amounts of NSC/ Poly (AAm-co-AA) and crosslinking agent at pH 7.4 and pH 1.2 were investigated. Hydrogels showed higher swelling ratios at pH 7.4 while lower at pH 1.2. Swelling kinetics and diffusion parameters were also determined. Drug loading, encapsulation efficiency, and in vitro release of 5-fluorouracil (5-FU) from the synthesized hydrogels were observed. In vitro release profile revealed the significant influence of pH, amount of NSC, Poly (AAm-co-AA), and crosslinking agent on the release of 5-FU. Accordingly, rapid and large release of drug was observed at pH 7.4 than at pH 1.2. The maximum encapsulation efficiency and release of 5-FU from SP2 were found to be 72.45% and 85.99%, respectively. Kinetics of drug release suggested controlled release mechanism of 5-FU is according to trend of non-Fickian. From the above results, it can be concluded that the synthesized hydrogels have capability to adapt their potential exploitation as targeted oral drug delivery carriers.