Comparison of NASA and contractor results from aeroacoustic tests of QCSEE OTW engine

The aerodynamics and acoustics of the over-the-wing (OTW) Quiet, Clean, Short Haul Experimental Engine (QCSEE) were tested. A boilerplate (nonflight weight), high-throat Mach number, acoustically treated inlet and a D-shaped OTW exhaust nozzle with variable position side doors were used. Some acoustic directivity results for the type "D" nozzle and acoustic effects of variations in the nozzle side door positions are included. It was found that the results are in agreement with those previously obtained

Changes in the milk metabolome of the Giant Panda (Ailuropoda melanoleuca) with time after birth: three phases in early lactation and progressive individual differences

Ursids (bears) in general, and giant pandas in particular, are highly altricial at birth. The components of bear milks and their changes with time may be uniquely adapted to nourish relatively immature neonates, protect them from pathogens, and support the maturation of neonatal digestive physiology. Serial milk samples collected from three giant pandas in early lactation were subjected to untargeted metabolite profiling and multivariate analysis. Changes in milk metabolites with time after birth were analysed by Principal Component Analysis, Hierarchical Cluster Analysis and further supported by Orthogonal Partial Least Square-Discriminant Analysis, revealing three phases of milk maturation: days 1–6 (Phase 1), days 7–20 (Phase 2), and beyond day 20 (Phase 3). While the compositions of Phase 1 milks were essentially indistinguishable among individuals, divergences emerged during the second week of lactation. OPLS regression analysis positioned against the growth rate of one cub tentatively inferred a correlation with changes in the abundance of a trisaccharide, isoglobotriose, previously observed to be a major oligosaccharide in ursid milks. Three artificial milk formulae used to feed giant panda cubs were also analysed, and were found to differ markedly in component content from natural panda milk. These findings have implications for the dependence of the ontogeny of all species of bears, and potentially other members of the Carnivora and beyond, on the complexity and sequential changes in maternal provision of micrometabolites in the immediate period after birth

Prolonged transition time between colostrum and mature milk in a bear, the giant panda, Ailuropoda melanoleuca

Bears produce the most altricial neonates of any placental mammal. We hypothesized that the transition from colostrum to mature milk in bears reflects a temporal and biochemical adaptation for altricial development and immune protection. Comparison of bear milks with milks of other eutherians yielded distinctive protein profiles. Proteomic and metabolomic analysis of serial milk samples collected from six giant pandas showed a prolonged transition from colostrum to main-phase lactation over approximately 30 days. Particularly striking are the persistence or sequential appearance of adaptive and innate immune factors. The endurance of immunoglobulin G suggests an unusual duration of trans-intestinal absorption of maternal antibodies, and is potentially relevant to the underdeveloped lymphoid system of giant panda neonates. Levels of certain milk oligosaccharides known to exert anti-microbial activities and/or that are conducive to the development of neonatal gut microbiomes underwent an almost complete changeover around days 20–30 postpartum, coincident with the maturation of the protein profile. A potential metabolic marker of starvation was detected, the prominence of which may reflect the natural postpartum period of anorexia in giant panda mothers. Early lactation in giant pandas, and possibly in other ursids, appears to be adapted for the unique requirements of unusually altricial eutherian neonates

Photolysis of H2O-H2O2 Mixtures: The Destruction of H2O2

We present laboratory results on the loss of H2O2 in solid H2O + H2O2 mixtures at temperatures between 21 and 145 K initiated by UV photolysis (193 nm). Using infrared spectroscopy and microbalance gravimetry, we measured the decrease of the 3.5 micrometer infrared absorption band during UV irradiation and obtained a photodestruction cross section that varies with temperature, being lowest at 70 K. We use our results, along with our previously measured H2O2 production rates via ionizing radiation and ion energy fluxes from the spacecraft to compare H2O2 creation and destruction at icy satellites by ions from their planetary magnetosphere and from solar UV photons. We conclude that, in many cases, H2O2 is not observed on icy satellite surfaces because the H2O2 photodestruction rate is much higher than the production rate via energetic particles, effectively keeping the H2O2 infrared signature at or below the noise level

Changeover from signalling to energy-provisioning lipids during transition from colostrum to mature milk in the giant panda (Ailuropoda melanoleuca)

Among the large placental mammals, ursids give birth to the most altricial neonates with the lowest neonatal:maternal body mass ratios. This is particularly exemplified by giant pandas. To examine whether there is compensation for the provision of developmentally important nutrients that other species groups may provide in utero, we examined changes in the lipids of colostrum and milk with time after birth in giant pandas. Lipids that are developmental signals or signal precursors, and those that are fundamental to nervous system construction, such as docosahexaenoic acid (DHA) and phosphatidylserines, appear early and then fall dramatically in concentration to a baseline at 20-30 days. The dynamics of lysophosphatidic acid and eicosanoids display similar patterns, but with progressive differences between mothers. Triglycerides occur at relatively low levels initially and increase in concentration until a plateau is reached at about 30 days. These patterns indicate an early provision of signalling lipids and their precursors, particularly lipids crucial to brain, retinal and central nervous system development, followed by a changeover to lipids for energy metabolism. Thus, in giant pandas, and possibly in all bears, lactation is adapted to provisioning a highly altricial neonate to a degree that suggests equivalence to an extension of gestation

High-Resolution Cartography of the Transcriptome and Methylome Landscapes of Diffuse Gliomas

Molecular mechanisms of lower-grade (II–III) diffuse gliomas (LGG) are still poorly understood, mainly because of their heterogeneity. They split into astrocytoma- (IDH-A) and oligodendroglioma-like (IDH-O) tumors both carrying mutations(s) at the isocitrate dehydrogenase (IDH) gene and into IDH wild type (IDH-wt) gliomas of glioblastoma resemblance. We generated detailed maps of the transcriptomes and DNA methylomes, revealing that cell functions divided into three major archetypic hallmarks: (i) increased proliferation in IDH-wt and, to a lesser degree, IDH-O; (ii) increased inflammation in IDH-A and IDH-wt; and (iii) the loss of synaptic transmission in all subtypes. Immunogenic properties of IDH-A are diverse, partly resembling signatures observed in grade IV mesenchymal glioblastomas or in grade I pilocytic astrocytomas. We analyzed details of coregulation between gene expression and DNA methylation and of the immunogenic micro-environment presumably driving tumor development and treatment resistance. Our transcriptome and methylome maps support personalized, case-by-case views to decipher the heterogeneity of glioma states in terms of data portraits. Thereby, molecular cartography provides a graphical coordinate system that links gene-level information with glioma subtypes, their phenotypes, and clinical context

Mathematical modeling of cell population dynamics in the colonic crypt and in colorectal cancer

Colorectal cancer is initiated in colonic crypts. A succession of genetic mutations or epigenetic changes can lead to homeostasis in the crypt being overcome, and subsequent unbounded growth. We consider the dynamics of a single colorectal crypt by using a compartmental approach [Tomlinson IPM, Bodmer WF (1995) Proc Natl Acad Sci USA 92: 11130-11134], which accounts for populations of stem cells, differential cells, and transit cells. That original model made the simplifying assumptions that each cell popuation divides synchronously, but we relax these assumptions by adopting an age-structured approach that models asynchronous cell division, and by using a continuum model. We discuss two mechanims that could regulate the growth of cell numbers and maintain the equilibrium that is normally observed in the crypt. The first will always maintain an equilibrium for all parameter values, whereas the second can allow unbounded proliferation if the net per capita growth rates are large enough. Results show that an increase in cell renewal, which is equivalent to a failure of programmed cell death or of differentiation, can lead to the growth of cancers. The second model can be used to explain the long lag phases in tumor growth, during which news, higher equilibria are reached, before unlimited growth in cell number ensues

Towards the interpretability of deep learning models for human neuroimaging

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear.We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n=2637, 18-82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37-3.86 years). We find that BA estimates capture aging at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as lesions, iron accumulations and atrophies that appear throughout the brain. Divergence from expected aging reflected cardiovascular riskfactors and accelerated aging was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-aging in healthy and at-risk individuals throughout adulthood

Real-time polarization microscopy of fibrillar collagen in histopathology

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Keikhosravi, A., Shribak, M., Conklin, M. W., Liu, Y., Li, B., Loeffler, A., Levenson, R. M., & Eliceiri, K. W. Real-time polarization microscopy of fibrillar collagen in histopathology. Scientific Reports, 11(1), (2021): 19063, https://doi.org/10.1038/s41598-021-98600-w.Over the past two decades, fibrillar collagen reorganization parameters such as the amount of collagen deposition, fiber angle and alignment have been widely explored in numerous studies. These parameters are now widely accepted as stromal biomarkers and linked to disease progression and survival time in several cancer types. Despite all these advances, there has not been a significant effort to make it possible for clinicians to explore these biomarkers without adding steps to the clinical workflow or by requiring high-cost imaging systems. In this paper, we evaluate previously described polychromatic polarization microscope (PPM) to visualize collagen fibers with an optically generated color representation of fiber orientation and alignment when inspecting the sample by a regular microscope with minor modifications. This system does not require stained slides, but is compatible with histological stains such as H&E. Consequently, it can be easily accommodated as part of regular pathology review of tissue slides, while providing clinically useful insight into stromal composition.This work was supported by NIH R01 CA238191 (KWE), NIH P41GM135019 (KWE), NIH R01 GM101701 (MS), funding from the Morgridge Institute for Research (KWE), the Semiconductor Research Corporation (SRC) (KWE), and the William T. Golden Endowment (MS)

Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n=2637, 18-82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37-3.86 years). We find that BA estimates capture aging at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as white matter lesions, and atrophies that appear throughout the brain. Divergence from expected aging reflected cardiovascular risk factors and accelerated aging was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-aging in healthy and at-risk individuals throughout adulthood