132 research outputs found
Atmospheric bending effects in GNSS tomography
In Global Navigation Satellite System (GNSS) tomography, precise information about the tropospheric water vapor
distribution is derived from integral measurements like ground-based GNSS
slant wet delays (SWDs). Therefore, the functional relation between
observations and unknowns, i.e., the signal paths through the atmosphere, have
to be accurately known for each stationâsatellite pair involved. For GNSS
signals observed above a 15â elevation angle, the signal path is well
approximated by a straight line. However, since electromagnetic waves are
prone to atmospheric bending effects, this assumption is not sufficient
anymore for lower elevation angles. Thus, in the following, a mixed 2-D
piecewise linear ray-tracing approach is introduced and possible error
sources in the reconstruction of the bended signal paths are analyzed in more
detail. Especially if low elevation observations are considered, unmodeled
bending effects can introduce a systematic error of up to 10â20 ppm, on
average 1â2 ppm, into the tomography solution. Thereby, not only the
ray-tracing method but also the quality of the a priori field can have a
significant impact on the reconstructed signal paths, if not reduced by
iterative processing. In order to keep the processing time within acceptable
limits, a bending model is applied for the upper part of the neutral
atmosphere. It helps to reduce the number of processing steps by up to 85 %
without significant degradation in accuracy. Therefore, the developed
mixed ray-tracing approach allows not only for the correct treatment of low
elevation observations but is also fast and applicable for near-real-time
applications.</p
Simple Systematic Synthesis of Periodic Mesoporous Organosilica Nanoparticles with Adjustable Aspect Ratios
One-dimensional periodic mesoporous organosilica (PMO) nanoparticles with tunable aspect ratios are obtained from a chain-type molecular precursor octaethoxy-1,3,5-trisilapentane. The aspect ratio can be tuned from 2:1 to >20:1 simply by variation in the precursor concentration in acidic aqueous solutions containing constant amounts of triblock copolymer Pluronic P123. The mesochannels are highly ordered and are oriented parallel to the longitudinal axis of the PMO particles. No significant SiâC bond cleavage occurs during the synthesis according to29Si MAS NMR. The materials exhibit surface areas between 181 and 936 m2 gâ1
Periodic Mesoporous Organosilica Nanorice
A periodic mesoporous organosilica (PMO) with nanorice morphology was successfully synthesized by a template assisted solâgel method using a chain-type precursor. The PMO is composed of D and T sites in the ratio 1:2. The obtained mesoporous nanorice has a surface area of 753 m2 gâ1, one-dimensional channels, and a narrow pore size distribution centered at 4.3 nm. The nanorice particles have a length of ca. 600 nm and width of ca. 200 nm
Remodeling of secretory lysosomes during education tunes functional potential in NK cells
Inhibitory signaling during natural killer (NK) cell education translates into increased responsiveness to activation; however, the intracellular mechanism for functional tuning by inhibitory receptors remains unclear. Secretory lysosomes are part of the acidic lysosomal compartment that mediates intracellular signalling in several cell types. Here we show that educated NK cells expressing self-MHC specific inhibitory killer cell immunoglobulin-like receptors (KIR) accumulate granzyme B in dense-core secretory lysosomes that converge close to the centrosome. This discrete morphological phenotype is independent of transcriptional programs that regulate effector function, metabolism and lysosomal biogenesis. Meanwhile, interference of signaling from acidic Ca2+ stores in primary NK cells reduces target-specific Ca2+-flux, degranulation and cytokine production. Furthermore, inhibition of PI(3,5)P2 synthesis, or genetic silencing of the PI(3,5)P2-regulated lysosomal Ca2+-channel TRPML1, leads to increased granzyme B and enhanced functional potential, thereby mimicking the educated state. These results indicate an intrinsic role for lysosomal remodeling in NK cell education
Tumors induce de novo steroid biosynthesis in T cells to evade immunity
Abstract: Tumors subvert immune cell function to evade immune responses, yet the complex mechanisms driving immune evasion remain poorly understood. Here we show that tumors induce de novo steroidogenesis in T lymphocytes to evade anti-tumor immunity. Using a transgenic steroidogenesis-reporter mouse line we identify and characterize de novo steroidogenic immune cells, defining the global gene expression identity of these steroid-producing immune cells and gene regulatory networks by using single-cell transcriptomics. Genetic ablation of T cell steroidogenesis restricts primary tumor growth and metastatic dissemination in mouse models. Steroidogenic T cells dysregulate anti-tumor immunity, and inhibition of the steroidogenesis pathway is sufficient to restore anti-tumor immunity. This study demonstrates T cell de novo steroidogenesis as a mechanism of anti-tumor immunosuppression and a potential druggable target
The importance of nerve microenvironment for schwannoma development
Schwannomas are predominantly benign nerve sheath neoplasms caused by Nf2 gene inactivation. Presently, treatment options are mainly limited to surgical tumor resection due to the lack of effective pharmacological drugs. Although the mechanistic understanding of Nf2 gene function has advanced, it has so far been primarily restricted to Schwann cell-intrinsic events. Extracellular cues determining Schwann cell behavior with regard to schwannoma development remain unknown. Here we show pro-tumourigenic microenvironmental effects on Schwann cells where an altered axonal microenvironment in cooperation with injury signals contribute to a persistent regenerative Schwann cell response promoting schwannoma development. Specifically in genetically engineered mice following crush injuries on sciatic nerves, we found macroscopic nerve swellings in mice with homozygous nf2 gene deletion in Schwann cells and in animals with heterozygous nf2 knockout in both Schwann cells and axons. However, patient-mimicking schwannomas could only be provoked in animals with combined heterozygous nf2 knockout in Schwann cells and axons. We identified a severe re-myelination defect and sustained macrophage presence in the tumor tissue as major abnormalities. Strikingly, treatment of tumor-developing mice after nerve crush injury with medium-dose aspirin significantly decreased schwannoma progression in this disease model. Our results suggest a multifactorial concept for schwannoma formation-emphasizing axonal factors and mechanical nerve irritation as predilection site for schwannoma development. Furthermore, we provide evidence supporting the potential efficacy of anti-inflammatory drugs in the treatment of schwannomas
A computational framework for complex disease stratification from multiple large-scale datasets.
BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine
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