Hematopoietic stem cell (HSC) transplantation can cure diverse diseases of the blood system, including hematologic malignancies, anemias, and autoimmune disorders. However, patients must undergo ...toxic conditioning regimens that use chemotherapy and/or radiation to eliminate host HSCs and enable donor HSC engraftment. Previous studies have shown that anti-c-Kit monoclonal antibodies deplete HSCs from bone marrow niches, allowing donor HSC engraftment in immunodeficient mice. We show that host HSC clearance is dependent on Fc-mediated antibody effector functions, and enhancing effector activity through blockade of CD47, a myeloid-specific immune checkpoint, extends anti-c-Kit conditioning to fully immunocompetent mice. The combined treatment leads to elimination of >99% of host HSCs and robust multilineage blood reconstitution after HSC transplantation. This targeted conditioning regimen that uses only biologic agents has the potential to transform the practice of HSC transplantation and enable its use in a wider spectrum of patients.
The community structure of graphs is an important feature that gives insight into the high‐level organization of objects within the graph. In real‐world systems, the graph topology is oftentimes not ...static but changes over time and hence, also the community structure changes. Previous timeline‐based approaches either visualize the dynamic graph or the dynamic community structure. In contrast, our approach combines both in a single image and therefore allows users to investigate the community structure together with the underlying dynamic graph. Our optimized ordering of vertices and selection of colours in combination with interactive highlighting techniques increases the traceability of communities along the time axis. Users can identify visual signatures, estimate the reliability of the derived community structure and investigate whether community evolution interacts with changes in the graph topology. The utility of our approach is demonstrated in two application examples.
The community structure of graphs is an important feature that gives insight into the high‐level organization of objects within the graph. In real‐world systems, the graph topology is oftentimes not static but changes over time and hence, also the community structure changes. Previous timeline‐based approaches either visualize the dynamic graph or the dynamic community structure. In contrast, our approach combines both in a single image and therefore allows users to investigate the community structure together with the underlying dynamic graph. Our optimized ordering of vertices and selection of colours in combination with interactive highlighting techniques increases the traceability of communities along the time axis. Users can identify visual signatures, estimate the reliability of the derived community structure and investigate whether community evolution interacts with changes in the graph topology. The utility of our approach is demonstrated in two application examples.
We present an approach for visual analysis of high‐dimensional measurement data with varying sampling rates as routinely recorded in intensive care units. In intensive care, most assessments not only ...depend on one single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate data remains a challenging task. We present a linked‐view post hoc visual analytics application that reduces data complexity by combining projection‐based time curves for overview with small multiples for details on demand. Our approach supports not only the analysis of individual patients but also of ensembles by adapting existing techniques using non‐parametric statistics. We evaluated the effectiveness and acceptance of our approach through expert feedback with domain scientists from the surgical department using real‐world data: a post‐surgery study performed on a porcine surrogate model to identify parameters suitable for diagnosing and prognosticating the volume state, and clinical data from a public database. The results show that our approach allows for detailed analysis of changes in patient state while also summarizing the temporal development of the overall condition.
We present an approach for visual analysis of high‐dimensional measurement data with varying sampling rates as routinely recorded in intensive care units.
Cognitive neuroimaging studies typically require fast whole brain image acquisition with maximal sensitivity to small BOLD signal changes. To increase the sensitivity, higher field strengths are ...often employed, since they provide an increased image signal-to-noise ratio (SNR). However, as image SNR increases, the relative contribution of physiological noise to the total time series noise will be greater compared to that from thermal noise. At 7 T, we studied how the physiological noise contribution can be best reduced for EPI time series acquired at three different spatial resolutions (1.1 mm × 1.1 mm × 1.8 mm, 2 mm × 2 mm × 2 mm and 3 mm × 3 mm × 3 mm). Applying optimal physiological noise correction methods improved temporal SNR (tSNR) and increased the numbers of significantly activated voxels in fMRI visual activation studies for all sets of acquisition parameters. The most dramatic results were achieved for the lowest spatial resolution, an acquisition parameter combination commonly used in cognitive neuroimaging which requires high functional sensitivity and temporal resolution (i.e. 3mm isotropic resolution and whole brain image repetition time of 2s). For this data, physiological noise models based on cardio-respiratory information improved tSNR by approximately 25% in the visual cortex and 35% sub-cortically. When the time series were additionally corrected for the residual effects of head motion after retrospective realignment, the tSNR was increased by around 58% in the visual cortex and 71% sub-cortically, exceeding tSNR ~140. In conclusion, optimal physiological noise correction at 7 T increases tSNR significantly, resulting in the highest tSNR per unit time published so far. This tSNR improvement translates into a significant increase in BOLD sensitivity, facilitating the study of even subtle BOLD responses.
•Reliability of spinal cord resting-state functional connectivity was investigated in 45 healthy participants using fMRI at 3T.•Reliability was in the fair-to-good range, though lower when looking at ...more focal aspects, i.e. segmental connectivity.•Different noise sources (physiological noise and thermal noise) impacted connectivity amplitude and reliability in distinct ways.
The investigation of spontaneous fluctuations of the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord, where it has stimulated interest from a clinical perspective. A number of resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated robust functional connectivity between the time series of BOLD fluctuations in bilateral dorsal horns and between those in bilateral ventral horns, in line with the functional neuroanatomy of the spinal cord. A necessary step prior to extension to clinical studies is assessing the reliability of such resting-state signals, which we aimed to do here in a group of 45 healthy young adults at the clinically prevalent field strength of 3T. When investigating connectivity in the entire cervical spinal cord, we observed fair to good reliability for dorsal-dorsal and ventral-ventral connectivity, whereas reliability was poor for within- and between-hemicord dorsal-ventral connectivity. Considering how prone spinal cord fMRI is to noise, we extensively investigated the impact of distinct noise sources and made two crucial observations: removal of physiological noise led to a reduction in functional connectivity strength and reliability – due to the removal of stable and participant-specific noise patterns – whereas removal of thermal noise considerably increased the detectability of functional connectivity without a clear influence on reliability. Finally, we also assessed connectivity within spinal cord segments and observed that while the pattern of connectivity was similar to that of whole cervical cord, reliability at the level of single segments was consistently poor. Taken together, our results demonstrate the presence of reliable resting-state functional connectivity in the human spinal cord even after thoroughly accounting for physiological and thermal noise, but at the same time urge caution if focal changes in connectivity (e.g. due to segmental lesions) are to be studied, especially in a longitudinal manner.
Many unknowns exist about human immune responses to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. SARS-CoV-2-reactive CD4
T cells have been reported in unexposed ...individuals, suggesting preexisting cross-reactive T cell memory in 20 to 50% of people. However, the source of those T cells has been speculative. Using human blood samples derived before the SARS-CoV-2 virus was discovered in 2019, we mapped 142 T cell epitopes across the SARS-CoV-2 genome to facilitate precise interrogation of the SARS-CoV-2-specific CD4
T cell repertoire. We demonstrate a range of preexisting memory CD4
T cells that are cross-reactive with comparable affinity to SARS-CoV-2 and the common cold coronaviruses human coronavirus (HCoV)-OC43, HCoV-229E, HCoV-NL63, and HCoV-HKU1. Thus, variegated T cell memory to coronaviruses that cause the common cold may underlie at least some of the extensive heterogeneity observed in coronavirus disease 2019 (COVID-19) disease.
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