Directional dark field retrieval with single-grid x-ray imaging

Directional dark-field imaging is an emerging x-ray modality that is sensitive to unresolved anisotropic scattering from sub-pixel sample microstructures. A single-grid imaging set-up can be used to capture dark-field images by looking at changes in a grid pattern projected upon the sample. By creating analytical models for the experiment, we have developed a single-grid directional dark field retrieval algorithm that can extract dark-field parameters such as the dominant scattering direction, and the semi-major and -minor scattering angles. We show that this method is effective even in the presence of high image noise, allowing for low dose and time sequence imaging

Targeting a therapeutic LIF transgene to muscle via the immune system ameliorates muscular dystrophy.

Many potentially therapeutic molecules have been identified for treating Duchenne muscular dystrophy. However, targeting those molecules only to sites of active pathology is an obstacle to their clinical use. Because dystrophic muscles become extensively inflamed, we tested whether expressing a therapeutic transgene in leukocyte progenitors that invade muscle would provide selective, timely delivery to diseased muscle. We designed a transgene in which leukemia inhibitory factor (LIF) is under control of a leukocyte-specific promoter and transplanted transgenic cells into dystrophic mice. Transplantation diminishes pathology, reduces Th2 cytokines in muscle and biases macrophages away from a CD163+/CD206+ phenotype that promotes fibrosis. Transgenic cells also abrogate TGFβ signaling, reduce fibro/adipogenic progenitor cells and reduce fibrogenesis of muscle cells. These findings indicate that leukocytes expressing a LIF transgene reduce fibrosis by suppressing type 2 immunity and highlight a novel application by which immune cells can be genetically modified as potential therapeutics to treat muscle disease

Test–Retest Reliability and Correlates of Vertebral Bone Marrow Lipid Composition by Lipidomics Among Children With Varying Degrees of Bone Fragility

The reliability of lipidomics, an approach to identify the presence and interactions of lipids, to analyze the bone marrow lipid composition among pediatric populations with bone fragility is unknown. The objective of this study was to assess the test–retest reliability, standard error of measurement (SEM), and the minimal detectable change (MDC) of vertebral bone marrow lipid composition determined by targeted lipidomics among children with varying degrees of bone fragility undergoing routine orthopedic surgery. Children aged 10 to 19 years, with a confirmed diagnosis of adolescent idiopathic scoliosis (n = 13) or neuromuscular scoliosis and cerebral palsy (n = 3), undergoing posterior spinal fusion surgery at our institution were included in this study. Transpedicular vertebral body bone marrow samples were taken from thoracic vertebrae (T11, 12) or lumbar vertebrae (L1 to L4). Lipid composition was assessed via targeted lipidomics and all samples were analyzed in the same batch. Lipid composition measures were examined as the saturated, monounsaturated, and polyunsaturated index and as individual fatty acids. Relative and absolute test–retest reliability was assessed using the intraclass correlation coefficient (ICC), SEM, and MDC. Associations between demographics and index measures were explored. The ICC, SEM, and MDC were 0.81 (95% CI, 0.55–0.93), 1.6%, and 4.3%, respectively, for the saturated index, 0.66 (95% CI, 0.25–0.87), 3.5%, and 9.7%, respectively, for the monounsaturated index, and 0.60 (95% CI, 0.17–0.84), 3.6%, and 9.9%, respectively, for the polyunsaturated index. For the individual fatty acids, the ICC showed a considerable range from 0.04 (22:2n‐6) to 0.97 (18:3n‐3). Age was positively correlated with the saturated index (r2 = 0.36; p = 0.014) and negatively correlated with the polyunsaturated index (r2 = 0.26; p = 0.043); there was no difference in index measures by sex (p > 0.58). The test–retest reliability was moderate‐to‐good for index measures and poor to excellent for individual fatty acids; this information can be used to power research studies and identify measures for clinical or research monitoring. © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163414/2/jbm410400_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163414/1/jbm410400.pd

Mapping tissue microstructure of brain white matter in vivo in health and disease using diffusion MRI

Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challenge is to develop biomarkers that are specific to micrometer-scale cellular features in a human MRI scan of a few minutes. Here we quantify the sensitivity and specificity of a multicompartment diffusion modeling framework to the density, orientation and integrity of axons. We demonstrate that using a machine learning based estimator, our biophysical model captures the morphological changes of axons in early development, acute ischemia and multiple sclerosis (total N=821). The methodology of microstructure mapping is widely applicable in clinical settings and in large imaging consortium data to study development, aging and pathology.Comment: 4 figures, 5 supplementary figures, 1 supplementary tabl

Reduced Satb1 expression predisposes CD4+ T conventional cells to Treg suppression and promotes transplant survival

Limiting CD4+ T cell responses is important to prevent solid organ transplant rejection. In a mouse model of costimulation blockade-dependent cardiac allograft tolerance, we previously reported that alloreactive CD4+ conventional T cells (Tconvs) develop dysfunction, losing proliferative capacity. In parallel, induction of transplantation tolerance is dependent on the presence of regulatory T cells (Tregs). Whether susceptibility of CD4+ Tconvs to Treg suppression is modulated during tolerance induction is unknown. We found that alloreactive Tconvs from transplant tolerant mice had augmented sensitivity to Treg suppression when compared with memory T cells from rejector mice and expressed a transcriptional profile distinct from these memory T cells, including down-regulated expression of the transcription factor Special AT-rich sequence-binding protein 1 (Satb1). Mechanistically, Satb1 deficiency in CD4+ T cells limited their expression of CD25 and IL-2, and addition of Tregs, which express higher levels of CD25 than Satb1-deficient Tconvs and successfully competed for IL-2, resulted in greater suppression of Satb1-deficient than wild-type Tconvs in vitro. In vivo, Satb1-deficient Tconvs were more susceptible to Treg suppression, resulting in significantly prolonged skin allograft survival. Overall, our study reveals that transplantation tolerance is associated with Tconvs’ susceptibility to Treg suppression, via modulated expression of Tconv-intrinsic Satb1. Targeting Satb1 in the context of Treg-sparing immunosuppressive therapies might be exploited to improve transplant outcomes

Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño

The 2015–2016 El Niño led to historically high temperatures and low precipitation over the tropics, while the growth rate of atmospheric carbon dioxide (CO_2) was the largest on record. Here we quantify the response of tropical net biosphere exchange, gross primary production, biomass burning, and respiration to these climate anomalies by assimilating column CO_2, solar-induced chlorophyll fluorescence, and carbon monoxide observations from multiple satellites. Relative to the 2011 La Niña, the pantropical biosphere released 2.5 ± 0.34 gigatons more carbon into the atmosphere in 2015, consisting of approximately even contributions from three tropical continents but dominated by diverse carbon exchange processes. The heterogeneity of the carbon-exchange processes indicated here challenges previous studies that suggested that a single dominant process determines carbon cycle interannual variability

A method to improve protein subcellular localization prediction by integrating various biological data sources

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large; distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different; and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.</p> <p>Results</p> <p>In this paper we propose a new predicting method which combines two key ideas: 1) Information of neighbour proteins in a probabilistic gene network is integrated to enrich the prediction features. 2) Fuzzy k-NN, a classification method based on fuzzy set theory is applied to predict protein locating in multiple sites. Experiment was conducted on a dataset consisting of 22 locations from Budding yeast proteins and significant improvement was observed.</p> <p>Conclusion</p> <p>Our results suggest that the neighbourhood information from functional gene networks is predictive to subcellular localization. The proposed method thus can be integrated and complementary to other available prediction methods.</p