The two-dimensional electron gas in a graphene bilayer in the Bernal stacking supports a variety of uniform broken-symmetry ground states in Landau level N = 0 at integer filling factors ν ∈ [−3,4]. When an electric potential difference (or bias) is applied between the layers at filling factors ν = 1,3, the ground state evolves from an interlayer coherent state at small bias to a state with orbital coherence at higher bias, where electric dipoles associated with the orbital pseudospins order spontaneously in the plane of the layers. In this paper, we show that, by further increasing the bias at these two filling factors, the two-dimensional electron gas goes first through an electron crystal with an orbital pseudospin texture at each site and then into a helical state where the pseudospins rotate in space. The pseudospin textures in the electron crystal and the helical state are due to the presence of a Dzyaloshinskii-Moriya interaction in the effective pseudospin Hamiltonian when orbital coherence is present in the ground state. We study in detail the electronic structure of the helical and electron crystal states as well as their collective excitations and then compute their electromagnetic absorption.

Purpose: The radiation dose delivered to brain tumors is limited by the possibility to induce vascular damage and necrosis in surrounding healthy tissue. In the present study, we assessed the ability of MRI to monitor the cascade of events occurring in the healthy rat brain after stereotactic radiosurgery, which could be used to optimize the radiation treatment planning. Methods: The primary somatosensory forelimb area (S1FL) and the primary motor cortex in the right hemisphere of Fischer rats (n ¼ 6) were irradiated with a single dose of Gamma Knife radiation (Leksell Perfexion, Elekta AG, Stockholm, Sweden). Rats were scanned with a small-animal 7 Tesla MRI scanner before treatment and 16, 21, 54, 82, and 110 days following irradiation. At every imaging session, T 2 -weighted (T 2 w), Gd-DTPA dynamic contrast-enhanced MRI (DCE-MRI), and T Ã 2 -weighted (T Ã 2 w) images were acquired to measure changes in fluid content, blood vessel permeability, and structure, respectively. At days 10, 110, and 140, histopathology was performed on brain sections. Locomotion and spatial memory ability were assessed longitudinally by behavioral tests. Results: No vascular changes were initially observed. After 54 days, a small necrotic volume in the white matter below the S1FL, surrounded by an area presenting significant vascular permeability, was revealed. Between 54 and 110 days, the necrotic volume increased and was accompanied by the formation of a ring-like region, where a mixture of necrosis and permeable blood vessels were observed, as confirmed by histology. Behavioral changes were only observed after day 82.Conclusion: Together, DCE-MRI and T Ã 2 w images supported by histology provided a coherent picture of the phenomena involved in the formation of new, leaky blood vessels, which was followed by the detection of radionecrosis in a preclinical model of brain irradiation.

Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may not be immediately available in the traditional wet-lab environment. Here, we propose a simple and accessible workflow to automate detection and quantification of brain epithelial metastases on digitized histological slides. We leverage 100 Hematoxylin & Eosin (H&E)-stained whole slide images (WSIs) from 25 Balb/c mice with various level of brain metastatic tumor burden. A supervised training of the Trainable Weka Segmentation (TWS) from Fiji was achieved from annotated WSIs. Upon comparison with manually drawn regions, it is apparent that the algorithm learned to identify and segment cancer cell-specific nuclei and normal brain tissue. Our approach resulted in a robust and highly concordant correlation between automated metastases quantification of brain metastases and manual human assessment (R2 = 0.8783; P < 0.0001). This simple approach is amenable to other similar analyses, including that of human tissues. Widespread adoption of these tools aims to democratize ML and improve precision in traditionally qualitative tasks in histopathology-based research.

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O-(2-18 F-fluoroethyl)-L-tyrosine ( 18 F-FET) is a radiolabeled artificial amino acid used in PET for tumor delineation and grading. The present study compares different kinetic models to determine which are more appropriate for 18 F-FET in rats. Methods: Rats were implanted with F98 glioblastoma cells in the right hemisphere and scanned 9-15 d later. PET data were acquired during 50 min after a 1-min bolus of 18 F-FET. Arterial blood samples were drawn for arterial input function determination. Two compartmental pharmacokinetic models were tested: the 2-tissue model and the 1-tissue model. Their performance at fitting concentration curves from regions of interest was evaluated using the Akaike information criterion, F test, and residual plots. Graphical models were assessed qualitatively. Results: Metrics indicated that the 2-tissue model was superior to the 1-tissue model for the current dataset. The 2-tissue model allowed adequate decoupling of 18 F-FET perfusion and internalization by cells in the different regions of interest. Of the 2 graphical models tested, the Patlak plot provided adequate results for the tumor and brain, whereas the Logan plot was appropriate for muscles. Conclusion: The 2-tissue-compartment model is appropriate to quantify the perfusion and internalization of 18 F-FET by cells in various tissues of the rat, whereas graphical models provide a global measure of uptake. Ther adiolabeled artificial amino acid O-(2-18 F-fluoroethyl)-Ltyrosine ( 18 F-FET) has proven useful for the PET assessment of brain tumors in preclinical and clinical settings (1-3). Its high uptake in tumor tissue compared with normal brain and inflamed tissues allows for efficient tumor delineation (4), but the typical SUVs and tumor-to-brain ratios are of limited use for tumor grading (5,6). In contrast, the shape of time-activity curves are indicative of tumor grade and aggressiveness (7). For example, in untreated or recurring gliomas, continuously ascending curves are associated with a better prognosis than curves that reach a maximum a few minutes after injection (6,8), but the underlying mechanisms remain to be clarified (7,9,10). A pharmacokinetic model could help explain these differences and would allow quantitative comparison of cohorts.There have been few reports on 18 F-FET pharmacokinetic modeling (11,12), and a consensus on the most appropriate models has not been proposed. The present study aims at identifying the best models in different tissue types. MATERIALS AND METHODS Animal ModelExperiments were conducted in accordance with the recommendations of the Canadian Council on Animal Care and the local Ethics Committee. F98 glioblastoma cells were implanted in the right hemisphere of 17 male Fischer rats (254.6 6 15.9 g, Charles River Laboratories) according to a previously published protocol (13). The animals underwent dynamic PET scans 9-15 d after implantation. All imaging procedures were performed under isoflurane anesthesia with breathing rate and temperature continuously monitored. An automatic in...