Towards an efficient segmentation of small rodents brain: a short critical review

One of the most common tasks in small rodents MRI pipelines is the voxel-wise segmentation of the volume in multiple classes. While many segmentation schemes have been developed for the human brain, fewer are available for rodent MRI, often by adaptation from human neuroimaging. Common methods include atlas-based and clustering schemes. The former labels the target volume by registering one or more pre-labeled atlases using a deformable registration method, in which case the result depends on the quality of the reference volumes, the registration algorithm and the label fusion approach, if more than one atlas is employed. The latter is based on an expectation maximization procedure to maximize the variance between voxel categories, and is often combined with Markov Random Fields and the atlas based approach to include spatial information, priors, and improve the classification accuracy. Our primary goal is to critically review the state of the art of rat and mouse segmentation of neuro MRI volumes and compare the available literature on popular, readily and freely available MRI toolsets, including SPM, FSL and ANTs, when applied to this task in the context of common pre-processing steps. Furthermore, we will briefly address the emerging Deep Learning methods for the segmentation of medical imaging, and the perspectives for applications to small rodents

Light Ion Accelerating Line (L3IA): Test Experiment at ILIL-PW

The construction of a novel Laser driven Light Ions Acceleration Line(L3IA) is progressing rapidly towards the operation, following the recent upgrade of the ILIL-PW laser facility. The Line was designed following the pilot experimental activity carried out earlier at the same facility to define design parameters and to identify main components including target control and diagnostic equipment, also in combination with the numerical simulations for the optimization of laser and target parameters. A preliminary set of data was acquired following the successful commissioning of the laser system >100 TW upgrade. Data include output from a range of different ion detectors and optical diagnostics installed for qualification of the laser-target interaction. An overview of the results is given along with a description of the relevant upgraded laser facility and features.Comment: 6 pages, 7 figures, 18 references, presented at the EAAC 201

Energy metabolism and glutamate-glutamine cycle in the brain: a stoichiometric modeling perspective

Background: The energetics of cerebral activity critically relies on the functional and metabolic interactions between neurons and astrocytes. Important open questions include the relation between neuronal versus astrocytic energy demand, glucose uptake and intercellular lactate transfer, as well as their dependence on the level of activity. Results: We have developed a large-scale, constraint-based network model of the metabolic partnership between astrocytes and glutamatergic neurons that allows for a quantitative appraisal of the extent to which stoichiometry alone drives the energetics of the system. We find that the velocity of the glutamate-glutamine cycle (Vcyc) explains part of the uncoupling between glucose and oxygen utilization at increasing Vcyc levels. Thus, we are able to characterize different activation states in terms of the tissue oxygen-glucose index (OGI). Calculations show that glucose is taken up and metabolized according to cellular energy requirements, and that partitioning of the sugar between different cell types is not significantly affected by Vcyc. Furthermore, both the direction and magnitude of the lactate shuttle between neurons and astrocytes turn out to depend on the relative cell glucose uptake while being roughly independent of Vcyc. Conclusions: These findings suggest that, in absence of ad hoc activity-related constraints on neuronal and astrocytic metabolism, the glutamate-glutamine cycle does not control the relative energy demand of neurons and astrocytes, and hence their glucose uptake and lactate exchange. © 2013 Massucci et al.; licensee BioMed Central Ltd

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 s and no pre-processing requirements. We trained and validated MU-Net on 128 T2-weighted mouse MRI volumes as well as on the publicly available MRM NeAT dataset of 10 MRI volumes. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). Further, we explored the effectiveness of our network in the presence of different architectural features, including skip connections and recently proposed framing connections, and the effects of the age range of the training set animals. These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net

Exploring Relationships between Demersal Resources and Environmental Factors in the Ionian Sea (Central Mediterranean)

The relationships between the abundance of demersal resources, environmental variables, and fishing pressure in the north-western Ionian Sea in the last two decades were evaluated. Data on the density collected during seventeen trawl surveys carried out from 1985 to 2005 were used. The following species were considered:Aristaeomorpha foliacea, Nephrops norvegicus,andParapenaeus longirostrisfor crustaceans;Merluccius merluccius, Phycis blennoides,andMullus barbatusfor teleost fish. The recruitment index was also considered forN. norvegicus, P. longirostris, M. merlucciusandMullus barbatus. Six candidate models were evaluated for each density and recruitment data set either combining fishing effort with global (NAO) and regional (SST and precipitation) climatic indices, or models separately involving fishing effort, NAO, or regional climatic indices as the only predictive variable. Model selection was carried out using an information-theoretical approach that applies Akaike's Information Criterion (AIC). High changes over time were observed for the density data and recruitment indices in each species. Apart from hake abundance and recruitment data, for which a clear positive relationship with the NAO index alone was detected, the changes observed in the other species seem to be the consequence of the interaction between bottom-up effects linked to changes in physical environment and top-down ones due to the fishing pressure

Highly selective recovery of Ni(II) in neutral and acidic media using a novel Ni(II)-ion imprinted polymer

In this work, an original ion-imprinted polymer (IIP) was synthetized for the highly selective removal of Ni(II) ions in neutral and acidic media. First a novel functional monomer (AMP-MMA) was synthetized through the amidation of 2-(aminomethyl)pyridine (AMP) with methacryloylchloride. Following Ni(II)/AMP-MMA complex formation study, the Ni(II)-IIP was produced via inverse suspension polymerization (DMSO in mineral oil) and characterized with solid state 13C CPMAS NMR, FT-IR, SEM and nitrogen adsorption/desorption experiments. The Ni(II)-IIP was then used in solid-phase extraction of Ni(II) exploring a wide range of pH (from neutral to strongly acidic solution), several initial concentrations of Ni(II) (from 0.02 to 1 g/L), and the presence of competitive ions (Co(II), Cu(II), Cd(II), Mn(II), and Mg(II)). The maximum Ni(II) adsorption capacity at pH 2 and pH 7 reached values of 138.9 mg/g and 169.5 mg/g, that are among the best reported in literature. The selectivity coefficients toward Cd(II), Mn(II), Co(II), Mg(II) and Cu(II) are also very high, with values up to 38.6, 32.9, 25.2, 23.1 and 15.0, respectively. The Ni(II)-IIP showed good reusability of up to 5 cycles both with acidic and basic Ni(II) eluents.Peer reviewe

Lignin as polymer electrolyte precursor for stable and sustainable potassium batteries

Potassium batteries show interesting peculiarities as large-scale energy storage systems and, in this scenario, the formulation of polymer electrolytes obtained from sustainable resources or waste-derived products represents a milestone activity. In this study, a lignin-based membrane is designed by crosslinking a pre-oxidized Kraft lignin matrix with an ethoxylated difunctional oligomer, leading to self-standing membranes that are able to incorporate solvated potassium salts. The in-depth electrochemical characterization highlights a wide stability window (up to 4 V) and an ionic conductivity exceeding 10−3 S cm−1 at ambient temperature. When potassium metal cell prototypes are assembled, the lignin-based electrolyte attains significant electrochemical performances, with an initial specific capacity of 168 mAh g−1 at 0.05 A g−1 and an excellent operation for more than 200 cycles, which is an unprecedented outcome for biosourced systems in potassium batteries