The tumour ecology of quiescence: Niches across scales of complexity

Quiescence is a state of cell cycle arrest, allowing cancer cells to evade anti-proliferative cancer therapies. Quiescent cancer stem cells are thought to be responsible for treatment resistance in glioblastoma, an aggressive brain cancer with poor patient outcomes. However, the regulation of quiescence in glioblastoma cells involves a myriad of intrinsic and extrinsic mechanisms that are not fully understood. In this review, we synthesise the literature on quiescence regulatory mechanisms in the context of glioblastoma and propose an ecological perspective to stemness-like phenotypes anchored to the contemporary concepts of niche theory. From this perspective, the cell cycle regulation is multiscale and multidimensional, where the niche dimensions extend to extrinsic variables in the tumour microenvironment that shape cell fate. Within this conceptual framework and powered by ecological niche modelling, the discovery of microenvironmental variables related to hypoxia and mechanosignalling that modulate proliferative plasticity and intratumor immune activity may open new avenues for therapeutic targeting of emerging biological vulnerabilities in glioblastoma

Long-time discrete particle effects versus kinetic theory in the self-consistent single-wave model

The influence of the finite number N of particles coupled to a monochromatic wave in a collisionless plasma is investigated. For growth as well as damping of the wave, discrete particle numerical simulations show an N-dependent long time behavior resulting from the dynamics of individual particles. This behavior differs from the one due to the numerical errors incurred by Vlasov approaches. Trapping oscillations are crucial to long time dynamics, as the wave oscillations are controlled by the particle distribution inhomogeneities and the pulsating separatrix crossings drive the relaxation towards thermal equilibrium.Comment: 11 pages incl. 13 figs. Phys. Rev. E, in pres

Modelling Future Coronary Heart Disease Mortality to 2030 in the British Isles.

OBJECTIVE: Despite rapid declines over the last two decades, coronary heart disease (CHD) mortality rates in the British Isles are still amongst the highest in Europe. This study uses a modelling approach to compare the potential impact of future risk factor scenarios relating to smoking and physical activity levels, dietary salt and saturated fat intakes on future CHD mortality in three countries: Northern Ireland (NI), Republic of Ireland (RoI) and Scotland. METHODS: CHD mortality models previously developed and validated in each country were extended to predict potential reductions in CHD mortality from 2010 (baseline year) to 2030. Risk factor trends data from recent surveys at baseline were used to model alternative future risk factor scenarios: Absolute decreases in (i) smoking prevalence and (ii) physical inactivity rates of up to 15% by 2030; relative decreases in (iii) dietary salt intake of up to 30% by 2030 and (iv) dietary saturated fat of up to 6% by 2030. Probabilistic sensitivity analyses were then conducted. RESULTS: Projected populations in 2030 were 1.3, 3.4 and 3.9 million in NI, RoI and Scotland respectively (adults aged 25-84). In 2030: assuming recent declining mortality trends continue: 15% absolute reductions in smoking could decrease CHD deaths by 5.8-7.2%. 15% absolute reductions in physical inactivity levels could decrease CHD deaths by 3.1-3.6%. Relative reductions in salt intake of 30% could decrease CHD deaths by 5.2-5.6% and a 6% reduction in saturated fat intake might decrease CHD deaths by some 7.8-9.0%. These projections remained stable under a wide range of sensitivity analyses. CONCLUSIONS: Feasible reductions in four cardiovascular risk factors (already achieved elsewhere) could substantially reduce future coronary deaths. More aggressive polices are therefore needed in the British Isles to control tobacco, promote healthy food and increase physical activity

Extreme value laws in dynamical systems under physical observables

Extreme value theory for chaotic dynamical systems is a rapidly expanding area of research. Given a system and a real function (observable) defined on its phase space, extreme value theory studies the limit probabilistic laws obeyed by large values attained by the observable along orbits of the system. Based on this theory, the so-called block maximum method is often used in applications for statistical prediction of large value occurrences. In this method, one performs inference for the parameters of the Generalised Extreme Value (GEV) distribution, using maxima over blocks of regularly sampled observations along an orbit of the system. The observables studied so far in the theory are expressed as functions of the distance with respect to a point, which is assumed to be a density point of the system's invariant measure. However, this is not the structure of the observables typically encountered in physical applications, such as windspeed or vorticity in atmospheric models. In this paper we consider extreme value limit laws for observables which are not functions of the distance from a density point of the dynamical system. In such cases, the limit laws are no longer determined by the functional form of the observable and the dimension of the invariant measure: they also depend on the specific geometry of the underlying attractor and of the observable's level sets. We present a collection of analytical and numerical results, starting with a toral hyperbolic automorphism as a simple template to illustrate the main ideas. We then formulate our main results for a uniformly hyperbolic system, the solenoid map. We also discuss non-uniformly hyperbolic examples of maps (H\'enon and Lozi maps) and of flows (the Lorenz63 and Lorenz84 models). Our purpose is to outline the main ideas and to highlight several serious problems found in the numerical estimation of the limit laws

Deep learning enables spatial mapping of the mosaic microenvironment of myeloma bone marrow trephine biopsies

Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphological, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect the diverse cellular communities and mosaic tissue habitats, we developed a superpixel-inspired deep learning method (MoSaicNet) that adapts to complex tissue architectures and a cell imbalance aware deep learning pipeline (AwareNet) to enable accurate detection and classification of rare cell types in multiplex immunohistochemistry images. MoSaicNet and AwareNet achieved an area under the curve of >0.98 for tissue and cellular classification on separate test datasets. Application of MoSaicNet and AwareNet enabled investigation of bone heterogeneity and thickness as well as spatial histology analysis of bone marrow trephine samples from monoclonal gammopathies of undetermined significance (MGUS) and from paired newly diagnosed and post-treatment multiple myeloma. The most significant difference between MGUS and newly diagnosed multiple myeloma (NDMM) samples was not related to cell density but to spatial heterogeneity, with reduced spatial proximity of BLIMP1+ tumor cells to CD8+ cells in MGUS compared with NDMM samples. Following treatment of multiple myeloma patients, there was a reduction in the density of BLIMP1+ tumor cells, effector CD8+ T cells, and T regulatory cells, indicative of an altered immune microenvironment. Finally, bone heterogeneity decreased following treatment of MM patients. In summary, deep-learning based spatial mapping of bone marrow trephine biopsies can provide insights into the cellular topography of the myeloma marrow microenvironment and complement aspirate-based techniques

Glioblastoma cell fate is differentially regulated by the microenvironments of the tumor bulk and infiltrative margin

Glioblastoma (GBM) recurrence originates from invasive margin cells that escape surgical debulking, but to what extent these cells resemble their bulk counterparts remains unclear. Here, we generated three immunocompetent somatic GBM mouse models, driven by subtype-associated mutations, to compare matched bulk and margin cells. We find that, regardless of mutations, tumors converge on common sets of neural-like cellular states. However, bulk and margin have distinct biology. Injury-like programs associated with immune infiltration dominate in the bulk, leading to the generation of lowly proliferative injured neural progenitor-like cells (iNPCs). iNPCs account for a significant proportion of dormant GBM cells and are induced by interferon signaling within T cell niches. In contrast, developmental-like trajectories are favored within the immune-cold margin microenvironment resulting in differentiation toward invasive astrocyte-like cells. These findings suggest that the regional tumor microenvironment dominantly controls GBM cell fate and biological vulnerabilities identified in the bulk may not extend to the margin residuum

Coronary MR angiography at 3T: fat suppression versus water-fat separation

Objectives: To compare Dixon water-fat suppression with spectral pre-saturation with inversion recovery (SPIR) at 3T for coronary magnetic resonance angiography (MRA) and to demonstrate the feasibility of fat suppressed coronary MRA at 3T without administration of a contrast agent. Materials and methods: Coronary MRA with Dixon water-fat separation or with SPIR fat suppression was compared on a 3T scanner equipped with a 32-channel cardiac receiver coil. Eight healthy volunteers were examined. Contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), right coronary artery (RCA), and left anterior descending (LAD) coronary artery sharpness and length were measured and statistically compared. Two experienced cardiologists graded the visual image quality of reformatted Dixon and SPIR images (1: poor quality to 5: excellent quality). Results: Coronary MRA images in healthy volunteers showed improved contrast with the Dixon technique compared to SPIR (CNR blood-fat: Dixon = 14.9 ± 2.9 and SPIR = 13.9 ± 2.1; p = 0.08, CNR blood-myocardium: Dixon = 10.2 ± 2.7 and SPIR = 9.11 ± 2.6; p = 0.1). The Dixon method led to similar fat suppression (fat SNR with Dixon: 2.1 ± 0.5 vs. SPIR: 2.4 ± 1.2, p = 0.3), but resulted in significantly increased SNR of blood (blood SNR with Dixon: 19.9 ± 4.5 vs. SPIR: 15.5 ± 3.1, p < 0.05). This means the residual fat signal is slightly lower with the Dixon compared to the SIPR technique (although not significant), while the SNR of blood is significantly higher with the Dixon technique. Vessel sharpness of the RCA was similar for Dixon and SPIR (57 ± 7 % vs. 56 ± 9 %, p = 0.2), while the RCA visualized vessel length was increased compared to SPIR fat suppression (107 ± 21 vs. 101 ± 21 mm, p < 0.001). For the LAD, vessel sharpness (50 ± 13 % vs. 50 ± 7 %, p = 0.4) and vessel length (92 ± 46 vs. 90 ± 47 mm, p = 0.4) were similar with both techniques. Consequently, the Dixon technique resulted in an improved visual score of the coronary arteries in the water fat separated images of healthy subjects (RCA: 4.6 ± 0.5 vs. 4.1 ± 0.7, p = 0.01, LAD: 4.1 ± 0.7 vs. 3.5 ± 0.8, p = 0.007). Conclusions: Dixon water-fat separation can significantly improve coronary artery image quality without the use of a contrast agent at 3T

Comparative Transcriptome Atlases Reveal Altered Gene Expression Modules between Two Cleomaceae C-3 and C-4 Plant Species

Külahoglu C, Denton AK, Sommer M, et al. Comparative Transcriptome Atlases Reveal Altered Gene Expression Modules between Two Cleomaceae C-3 and C-4 Plant Species. Plant Cell. 2014;26(8):3243-3260.C-4 photosynthesis outperforms the ancestral C-3 state in a wide range of natural and agro-ecosystems by affording higher water-use and nitrogen-use efficiencies. It therefore represents a prime target for engineering novel, high-yielding crops by introducing the trait into C-3 backgrounds. However, the genetic architecture of C-4 photosynthesis remains largely unknown. To define the divergence in gene expression modules between C-3 and C-4 photosynthesis during leaf ontogeny, we generated comprehensive transcriptome atlases of two Cleomaceae species, Gynandropsis gynandra (C-4) and Tarenaya hassleriana (C-3), by RNA sequencing. Overall, the gene expression profiles appear remarkably similar between the C-3 and C-4 species. We found that known C-4 genes were recruited to photosynthesis from different expression domains in C-3, including typical housekeeping gene expression patterns in various tissues as well as individual heterotrophic tissues. Furthermore, we identified a structure-related module recruited from the C-3 root. Comparison of gene expression patterns with anatomy during leaf ontogeny provided insight into genetic features of Kranz anatomy. Altered expression of developmental factors and cell cycle genes is associated with a higher degree of endoreduplication in enlarged C-4 bundle sheath cells. A delay in mesophyll differentiation apparent both in the leaf anatomy and the transcriptome allows for extended vein formation in the C-4 leaf

Rapidity and centrality dependence of proton and antiproton production from 197Au + 197Au collisions at √SNN = 130 GeV

We report on the rapidity and centrality dependence of proton and antiproton transverse mass distributions from 197Au + 197Au collisions at sqrt[sNN ]=130 GeV as measured by the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). Our results are from the rapidity and transverse momentum range of |y| <0.5 and 0.35< pt <1.00 GeV/c . For both protons and antiprotons, transverse mass distributions become more convex from peripheral to central collisions demonstrating characteristics of collective expansion. The measured rapidity distributions and the mean transverse momenta versus rapidity are flat within |y| <0.5 . Comparisons of our data with results from model calculations indicate that in order to obtain a consistent picture of the proton (antiproton) yields and transverse mass distributions the possibility of prehadronic collective expansion may have to be taken into account