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Brain size among normal humans varies as much as twofold. Reardon et al. surveyed the cortical and subcortical structure of more than 3000 human brains by noninvasive imaging (see the Perspective by Van Essen). They found that the scaling of different regions across the range of brain sizes is not consistent: Some brain regions are metabolically costly and are favored in larger brains. This shifts the balance between associative and sensorimotor brain systems in a brain size–dependent way. Science , this issue p. 1222 ; see also p. 1184 A metabolically expensive brain network is preferentially expanded in individuals that have larger brains. Brain size variation over primate evolution and human development is associated with shifts in the proportions of different brain regions. Individual brain size can vary almost twofold among typically developing humans, but the consequences of this for brain organization remain poorly understood. Using in vivo neuroimaging data from more than 3000 individuals, we find that larger human brains show greater areal expansion in distributed frontoparietal cortical networks and related subcortical regions than in limbic, sensory, and motor systems. This areal redistribution recapitulates cortical remodeling across evolution, manifests by early childhood in humans, and is linked to multiple markers of heightened metabolic cost and neuronal connectivity. Thus, human brain shape is systematically coupled to naturally occurring variations in brain size through a scaling map that integrates spatiotemporally diverse aspects of neurobiology.

Dexter is the biggest, strongest, and most patient goat at the petting zoo, but that does not make it easier to stand still when the Keepers come to trim his hooves.

The European FP7 project DIANA has performed a coherent analysis of a large set of observational data of protoplanetary disks by means of thermo-chemical disk models. The collected data include extinction-corrected stellar UV and X-ray input spectra (as seen by the disk), photometric fluxes, low and high resolution spectra, interferometric data, emission line fluxes, line velocity profiles and line maps, which probe the dust, polycyclic aromatic hydrocarbons (PAHs) and the gas in these objects. We define and apply a standardized modeling procedure to fit these data by state-of-the-art modeling codes ( ProDiMo , MCFOST , MCMax ), solving continuum and line radiative transfer (RT), disk chemistry, and the heating and cooling balance for both the gas and the dust. 3D diagnostic RT tools (e.g., FLiTs) are eventually used to predict all available observations from the same disk model, the DIANA-standard model. Our aim is to determine the physical parameters of the disks, such as total gas and dust masses, the dust properties, the disk shape, and the chemical structure in these disks. We allow for up to two radial disk zones to obtain our best-fitting models that have about 20 free parameters. This approach is novel and unique in its completeness and level of consistency. It allows us to break some of the degeneracies arising from pure Spectral Energy Distribution (SED) modeling. In this paper, we present the results from pure SED fitting for 27 objects and from the all inclusive DIANA-standard models for 14 objects. Our analysis shows a number of Herbig Ae and T Tauri stars with very cold and massive outer disks which are situated at least partly in the shadow of a tall and gas-rich inner disk. The disk masses derived are often in excess to previously published values, since these disks are partially optically thick even at millimeter wavelength and so cold that they emit less than in the Rayleigh–Jeans limit. We fit most infrared to millimeter emission line fluxes within a factor better than 3, simultaneously with SED, PAH features and radial brightness profiles extracted from images at various wavelengths. However, some line fluxes may deviate by a larger factor, and sometimes we find puzzling data which the models cannot reproduce. Some of these issues are probably caused by foreground cloud absorption or object variability. Our data collection, the fitted physical disk parameters as well as the full model output are available to the community through an online database ( http://www.univie.ac.at/diana ).

\"To apply the same approaches to analysing spoken and written formulaic language is problematic; to do so masks the fact that the contextual meaning of spoken formulaic language is encoded, to a large extent, in its prosody. In The Prosody of Formulaic Sequences, Phoebe Lin offers a new perspective on formulaic language, arguing that while past research often treats formulaic language as a lexical phenomenon, the phonological aspect of it is a more fundamental facet. This book draws its conclusions from three original, empirical studies of spoken formulaic language, assessing intonation unit boundaries as well as features such as tempo and stress placement. Across all studies, Lin considers questions of methodology and conceptual framework. The corpus-based descriptions of prosody outlined in this book not only deepen our understanding of the nature of formulaic language but have important implications for English Language Teaching and automatic speech synthesis\"-- Provided by publisher.

The limited number of targetable tumor-specific antigens and the immunosuppressive nature of the microenvironment within solid malignancies represent major barriers to the success of chimeric antigen receptor (CAR)-T cell therapies. Here, using epithelial cell adhesion molecule (EpCAM) as a model antigen, we used alanine scanning of the complementarity-determining region to fine-tune CAR affinity. This allowed us to identify CARs that could spare primary epithelial cells while still effectively targeting EpCAM high tumors. Although affinity-tuned CARs showed suboptimal antitumor activity in vivo, we found that inducible secretion of interleukin-12 (IL-12), under the control of the NFAT promoter, can restore CAR activity to levels close to that of the parental CAR. This strategy was further validated with another affinity-tuned CAR specific for intercellular adhesion molecule-1 (ICAM-1). Only in affinity-tuned CAR-T cells was NFAT activity stringently controlled and restricted to tumors expressing the antigen of interest at high levels. Our study demonstrates the feasibility of specifically gearing CAR-T cells towards recognition of solid tumors by combining inducible IL-12 expression and affinity-tuned CAR. The clinical benefits of chimaeric antigen receptor (CAR) T therapy are limited by ‘on-target, off-tumour’ effects. In this study, the authors describe a strategy that promotes the recognition of antigen on tumour, but not normal, cells by combining affinity tuning with inducible interleukin-12 expression.

\"A new translation of a foundational text of yoga philosophy\"-- Provided by publisher.

Advances in image segmentation of magnetic resonance images (MRI) have demonstrated that multi-atlas approaches improve segmentation over regular atlas-based approaches. These approaches often rely on a large number of manually segmented atlases (e.g. 30–80) that take significant time and expertise to produce. We present an algorithm, MAGeT-Brain (Multiple Automatically Generated Templates), for the automatic segmentation of the hippocampus that minimises the number of atlases needed whilst still achieving similar agreement to multi-atlas approaches. Thus, our method acts as a reliable multi-atlas approach when using special or hard-to-define atlases that are laborious to construct. MAGeT-Brain works by propagating atlas segmentations to a template library, formed from a subset of target images, via transformations estimated by nonlinear image registration. The resulting segmentations are then propagated to each target image and fused using a label fusion method. We conduct two separate Monte Carlo cross-validation experiments comparing MAGeT-Brain and basic multi-atlas whole hippocampal segmentation using differing atlas and template library sizes, and registration and label fusion methods. The first experiment is a 10-fold validation (per parameter setting) over 60 subjects taken from the Alzheimer's Disease Neuroimaging Database (ADNI), and the second is a five-fold validation over 81 subjects having had a first episode of psychosis. In both cases, automated segmentations are compared with manual segmentations following the Pruessner-protocol. Using the best settings found from these experiments, we segment 246 images of the ADNI1:Complete 1Yr 1.5T dataset and compare these with segmentations from existing automated and semi-automated methods: FSL FIRST, FreeSurfer, MAPER, and SNT. Finally, we conduct a leave-one-out cross-validation of hippocampal subfield segmentation in standard 3T T1-weighted images, using five high-resolution manually segmented atlases (Winterburn et al., 2013). In the ADNI cross-validation, using 9 atlases MAGeT-Brain achieves a mean Dice's Similarity Coefficient (DSC) score of 0.869 with respect to manual whole hippocampus segmentations, and also exhibits significantly lower variability in DSC scores than multi-atlas segmentation. In the younger, psychosis dataset, MAGeT-Brain achieves a mean DSC score of 0.892 and produces volumes which agree with manual segmentation volumes better than those produced by the FreeSurfer and FSL FIRST methods (mean difference in volume: 80mm3, 1600mm3, and 800mm3, respectively). Similarly, in the ADNI1:Complete 1Yr 1.5T dataset, MAGeT-Brain produces hippocampal segmentations well correlated (r>0.85) with SNT semi-automated reference volumes within disease categories, and shows a conservative bias and a mean difference in volume of 250mm3 across the entire dataset, compared with FreeSurfer and FSL FIRST which both overestimate volume differences by 2600mm3 and 2800mm3 on average, respectively. Finally, MAGeT-Brain segments the CA1, CA4/DG and subiculum subfields on standard 3T T1-weighted resolution images with DSC overlap scores of 0.56, 0.65, and 0.58, respectively, relative to manual segmentations. We demonstrate that MAGeT-Brain produces consistent whole hippocampal segmentations using only 9 atlases, or fewer, with various hippocampal definitions, disease populations, and image acquisition types. Additionally, we show that MAGeT-Brain identifies hippocampal subfields in standard 3T T1-weighted images with overlap scores comparable to competing methods. •We propose an automated MR image hippocampus (and subfield) segmentation method.•Our method is optimised for use with a small number (<10) of training images.•Consistent, accurate identification of the whole hippocampus and subfields•Validated on healthy, Alzheimer's disease, and first episode psychosis subjects•Source code and high-resolution training subfield atlases available online

Significant heterogeneity across aetiologies, neurobiology and clinical phenotypes have been observed in individuals with autism spectrum disorder (ASD). Neuroimaging-based neuroanatomical studies of ASD have often reported inconsistent findings which may, in part, be attributable to an insufficient understanding of the relationship between factors influencing clinical heterogeneity and their relationship to brain anatomy. To this end, we performed a large-scale examination of cortical morphometry in ASD, with a specific focus on the impact of three potential sources of heterogeneity: sex, age and full-scale intelligence (FIQ). To examine these potentially subtle relationships, we amassed a large multi-site dataset that was carefully quality controlled (yielding a final sample of 1327 from the initial dataset of 3145 magnetic resonance images; 491 individuals with ASD). Using a meta-analytic technique to account for inter-site differences, we identified greater cortical thickness in individuals with ASD relative to controls, in regions previously implicated in ASD, including the superior temporal gyrus and inferior frontal sulcus. Greater cortical thickness was observed in sex specific regions; further, cortical thickness differences were observed to be greater in younger individuals and in those with lower FIQ, and to be related to overall clinical severity. This work serves as an important step towards parsing factors that influence neuroanatomical heterogeneity in ASD and is a potential step towards establishing individual-specific biomarkers.