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Modeling real estate prices in the context of hedonic models often involves fitting a Generalized Additive Model, where only the mean of a (lognormal) distribution is regressed on a set of variables without taking other parameters of the distribution into account. Thus far, the application of regression models that model the full conditional distribution of the prices, has been infeasible for large data sets, even on powerful machines. Moreover, accounting for heterogeneity of effects regarding time and locale, is often achieved by naive stratification of the data rather than on a model basis. A novel batchwise backfitting algorithm is applied in the context of a structured additive distributional regression model, which enables us to efficiently model all distributional parameters of the price distribution. Using a large German dataset of apartment asking prices with over one million observations, we employ a model-based clustering algorithm to capture the heterogeneity of covariate effects on the parameters with respect to dwelling locale. We thus identify clusters that are homogeneous with respect to the influence of dwelling locale on price. A boosting type algorithm of the batchwise backfitting algorithm is then used to automatically determine the variables relevant for modelling the location and scale parameters in each regional cluster. This allows for a different influence of variables on the distribution of prices depending on the locale and price segment of the dwelling.

Abstract Hematopoietic stem cells (HSCs) react to various stress conditions. However, it is unclear whether and how HSCs respond to severe anemia. Here, we demonstrate that upon induction of acute anemia, HSCs rapidly proliferate and enhance their erythroid differentiation potential. In severe anemia, lipoprotein profiles largely change and the concentration of ApoE increases. In HSCs, transcription levels of lipid metabolism-related genes, such as very low-density lipoprotein receptor (Vldlr), are upregulated. Stimulation of HSCs with ApoE enhances their erythroid potential, whereas HSCs in Apoe knockout mice do not respond to anemia induction. VldlrhighHSCs show higher erythroid potential, which is enhanced after acute anemia induction. VldlrhighHSCs are epigenetically distinct because of their low chromatin accessibility, and more chromatin regions are closed upon acute anemia induction. Chromatin regions closed upon acute anemia induction are mainly binding sites of Erg. Inhibition of Erg enhanced the erythroid differentiation potential of HSCs. Our findings indicate that lipoprotein metabolism plays an important role in HSC regulation under severe anemic conditions.

Background and aimsGeneral guideline recommendations in patients with intracerebral hemorrhage (ICH) include blood pressure-, temperature- and glucose management. The therapeutic effect of such a “care bundle” (blood pressure lowering, glycemic control, and treatment of pyrexia) on clinical outcomes becomes increasingly established. For the present study, we aimed to investigate associations of strict bundled care treatment (BCT) with clinical outcomes and characterize associations with key outcome effectors such as hematoma enlargement (HE) and peak perihemorrhagic edema (PHE).MethodsWe screened consecutive ICH patients (n = 1,322) from the prospective UKER-ICH cohort study. BCT was defined as achieving and maintaining therapeutic ranges for systolic blood pressure (110–160 mmHg), glucose (80–180 mg/dL), and body temperature (35.5–37.5°C) over the first 72 h. The primary outcome was the functional outcome at 12 months (modified Rankin Scale (mRS) 0–3). Secondary outcomes included mortality at 12 months, the occurrence of hematoma enlargement, and the development of peak perihemorrhagic edema. Confounding was addressed by a doubly robust methodology to calculate the absolute treatment effect (ATE) and by calculating e-values.ResultsA total of 681 patients remained for analysis, and 182 patients fulfilled all three BCT criteria and were compared to 499 controls. The ATE of BCT to achieve the primary outcome was 9.3%, 95% CI (1.7 to 16.9), p

In the adult murine brain, neural stem cells (NSCs) can be found in two main niches: the dentate gyrus (DG) and the subventricular zone (SVZ). In the DG, NSCs produce intermediate progenitors (IPs) that differentiate into excitatory neurons, while progenitors in the SVZ migrate to the olfactory bulb (OB), where they mainly differentiate into inhibitory interneurons. Neurogenesis, the process of generating new neurons, persists throughout life but decreases dramatically with aging, concomitantly with increased inflammation. Although many cell types, including microglia, undergo significant transcriptional changes, few such changes have been detected in neural progenitors. Furthermore, transcriptional profiles in progenitors from different neurogenic regions have not been compared on a single-cell level, and little is known about how they are affected by aging-related inflammation. We have generated a single cell RNA sequencing dataset enriched for IPs, which revealed that most aged neural progenitors only acquire minor transcriptional changes. However, progenitors set to become excitatory neurons decrease faster than others. In addition, a population in the aged SVZ, not detected in the OB, acquired major transcriptional activation related to immune responses. This suggests that differences in age related neurogenic decline between regions is not due to tissue differences but rather cell type specific intrinsic transcriptional programs, and that subset of neuroblasts in the SVZ react strongly to age related inflammatory cues.

Dwelling information is essential for humanitarian emergency response during or in the aftermath of disasters, especially in temporary settlement areas hosting forcibly displaced people. To map dwellings, the integration of very high-resolution remotely sensed imagery in computer vision models plays a key role. However, state-of-the-art deep learning models have two known downsides: 1) lack of generalization across space and time under changing scenes and object characteristics, and 2) extensive demand for annotated samples for training and validation. Both could pose a critical challenge during an emergency. To bypass this problem, this study deals with unsupervised domain adaptation for instance segmentation using a single-stage instance segmentation model, namely segmenting objects by location (SOLO). The goal is to adapt a SOLO model trained on a labeled source domain to detect dwellings in an unlabeled target domain. In this context, we study three domain adaptation techniques based on adversarial learning, domain discrepancy, and domain alignment mapping. We also propose domain similarity at different levels to understand its implication on domain adaptation. Experiments are conducted on very high-resolution satellite images obtained from four temporary settlement areas located in different countries and exhibiting various spatial characteristics. Analysis results show that in most source–target combinations unsupervised domain adaptation improves the performance by a large margin even surpassing a model trained with supervised learning. There is also an observed performance deviation among implemented strategies and different source–target dataset combinations. From the in-depth analysis of domain similarity at the image, object, and deep feature space levels, the former is more correlated with unsupervised domain adaptation performance.

Die Leibniz-Wolffsche Tradition hat im Bereich der Geschichte der Philosophie an Bedeutung gewonnen.[1] Zu dieser Tradition zählen neben den Namensgebern, Gottfried Wilhelm Leibniz und Christian Wolff, die Vertreter der deutschen Schulmetaphysik des 18. Jahrhunderts und die Leibnizianer wie Moses Mendelssohn oder Johann August Eberhard.[2] Zu der deutschen Schulmetaphysik des 18. Jahrhunderts gehören neben Wolff die „Wolffianer“, bspw. Alexander Gottlieb Baumgarten, Johann Christoph Gottsched, Georg Friedrich Meier oder Hermann Samuel Reimarus, während Leibniz und (zumeist) auch Mendelssohn nicht zur deutschen Schulmetaphysik gezählt werden (Heimsoeth, 1967, Krijnen, 2017, Wundt, 1964).[3]Indes ist Leibniz auch für Wolff und die Wolffianer eine Schlüsselfigur gewesen. [1] Seit dem Ende des zweiten Weltkriegs hat die Erforschung der Leibniz-Wolffschen Tradition in den 1960er und den 1970er Jahren einen ersten Höhepunkt erreicht (vgl. bspw. Arndt, 1971, Bissinger, 1970, École, 1973, 1979). In dieser Einleitung werden vor allem Arbeiten erwähnt, die nach der Jahrtausendwende erschienen sind, um auf diese Weise das aktuelle Interesse an der Leibniz-Wolffschen Tradition zu verdeutlichen. [2] Von der deutschen Schulmetaphysik des 18. Jahrhunderts ist die deutsche Schulmetaphysik des 17. Jahrhunderts zu unterscheiden. Zur deutschen Schulmetaphysik des 17. Jahrhunderts siehe Wundt (1939). [3] Gideon Stiening zählt Mendelssohn jedoch zur deutschen Schulmetaphysik (Stiening, 2014, S. 55).

ABSTRACTAiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this paper consists of two parts. In the previous Part 1, existing EO optical sensory image-derived Level 2/Analysis Ready Data (ARD) products and processes are critically compared, to overcome their lack of harmonization/ standardization/ interoperability and suitability in a new notion of Space Economy 4.0. In the present Part 2, original contributions comprise, at the Marr five levels of system understanding: (1) an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification. First, in the pursuit of third-level semantic/ontological interoperability, a novel ARD symbolic (categorical and semantic) co-product, known as Scene Classification Map (SCM), adopts an augmented Cloud versus Not-Cloud taxonomy, whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization. Second, a novel ARD subsymbolic numerical co-product, specifically, a panchromatic or multi-spectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure, ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values, in a five-stage radiometric correction sequence. (2) An original ARD process requirements specification. (3) An innovative ARD processing system design (architecture), where stepwi se SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence. (4) An original modular hierarchical hybrid (combined deductive and inductive) computer vision subsystem design, provided with feedback loops, where software solutions at the Marr two shallowest levels of system understanding, specifically, algorithm and implementation, are selected from the scientific literature, to benefit from their technology readiness level as proof of feasibility, required in addition to proven suitability. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.

ABSTRACTAiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this two-part paper identifies an innovative, but realistic EO optical sensory image-derived semantics-enriched Analysis Ready Data (ARD) product-pair and process gold standard as linchpin for success of a new notion of Space Economy 4.0. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, it is regarded as necessary-but-not-sufficient “horizontal” (enabling) precondition for: (I) Transforming existing EO big raster-based data cubes at the midstream segment, typically affected by the so-called data-rich information-poor syndrome, into a new generation of semantics-enabled EO big raster-based numerical data and vector-based categorical (symbolic, semi-symbolic or subsymbolic) information cube management systems, eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery. (II) Boosting the downstream segment in the development of an ever-increasing ensemble of “vertical” (deep and narrow, user-specific and domain-dependent) value–adding information products and services, suitable for a potentially huge worldwide market of institutional and private end-users of space technology. For the sake of readability, this paper consists of two parts. In the present Part 1, first, background notions in the remote sensing metascience domain are critically revised for harmonization across the multi-disciplinary domain of cognitive science. In short, keyword “information” is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation. Moreover, buzzword “artificial intelligence” is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI. Second, based on a better-defined and better-understood vocabulary of multidisciplinary terms, existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system. To overcome their drawbacks, an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.

ObjectiveHematoma expansion (HE) is the most important therapeutic target during acute care of patients with intracerebral hemorrhage (ICH). Imaging biomarkers such as non-contrast CT (NCCT) markers have been associated with increasing risk for HE. The aim of the present study was to evaluate the influence of NCCT markers with functional long-term outcome and with HE in patients with deep (basal ganglia and thalamus) ICH who represent an important subgroup of patients at the highest risk for functional deterioration with HE due to the eloquence of the affected brain region.MethodsFrom our prospective institutional database, all patients maximally treated with deep ICH were included and retrospectively analyzed. NCCT markers were recorded at diagnostic imaging, ICH volume characteristics were volumetrically evaluated, and all patients received follow-up imaging within 0–48 h. We explored associations of NCCT makers with unfavorable functional outcome, defined as modified Rankin scale 4–6, after 12 months and with HE. Bias and confounding were addressed by multivariable regression modeling.ResultsIn 322 patients with deep ICH, NCCT markers were distributed as follows: irregular shape: 69.6%, heterogenous density: 55.9%, hypodensities: 52.5%, island sign: 19.3%, black hole sign: 11.5%, and blend sign: 4.7%. Upon multivariable regression analyses, independent associations were documented with the functional outcome for irregular shape (aOR: 2.73, 95%CI: 1.42–5.22, p = 0.002), heterogenous density (aOR: 2.62, 95%CI: 1.40–4.90, p = 0.003) and island sign (aOR: 2.54, 95%CI: 1.05–6.14, p = 0.038), and with HE for heterogenous density (aOR: 5.01, 95%CI: 1.93–13.05, p = 0.001) and hypodensities (aOR: 3.75, 95%CI: 1.63–8.62, p = 0.002).ConclusionNCCT markers are frequent in deep ICH patients and provide important clinical implications. Specifically, markers defined by diverging intra-hematomal densities provided associations with a 5-times higher risk for HE and a 2.5-times higher likelihood for unfavorable functional long-term outcome. Hence, these markers allow the identification of patients with deep ICH at high risk for clinical deterioration due to HE.

This study delves into the analysis of a vineyard in Carinthia, Austria, focusing on the automated derivation of ecosystem structures of individual vine parameters, including vine heights, leaf area index (LAI), leaf surface area (LSA), and the geographic positioning of single plants. For the derivation of these parameters, intricate segmentation processes and nuanced UAS-based data acquisition techniques are necessary. The detection of single vines was based on 3D point cloud data, generated at a phenological stage in which the plants were in the absence of foliage. The mean distance from derived vine locations to reference measurements taken with a GNSS device was 10.7 cm, with a root mean square error (RMSE) of 1.07. Vine height derivation from a normalized digital surface model (nDSM) using photogrammetric data showcased a strong correlation (R2 = 0.83) with real-world measurements. Vines underwent automated classification through an object-based image analysis (OBIA) framework. This process enabled the computation of ecosystem structures at the individual plant level post-segmentation. Consequently, it delivered comprehensive canopy characteristics rapidly, surpassing the speed of manual measurements. With the use of uncrewed aerial systems (UAS) equipped with optical sensors, dense 3D point clouds were computed for the derivation of canopy-related ecosystem structures of vines. While LAI and LSA computations await validation, they underscore the technical feasibility of obtaining precise geometric and morphological datasets from UAS-collected data paired with 3D point cloud analysis and object-based image analysis.

Abstract Background The Federal Ministry of Education and Research of Germany (BMBF) funds a network of university medicines (NUM) to support COVID-19 and pandemic research at national level. The “COVID-19 Data Exchange Platform” (CODEX) as part of NUM establishes a harmonised infrastructure that supports research use of COVID-19 datasets. The broad consent (BC) of the Medical Informatics Initiative (MII) is agreed by all German federal states and forms the legal base for data processing. All 34 participating university hospitals (NUM sites) work upon a harmonised infrastructural as well as legal basis for their data protection-compliant collection and transfer of their research dataset to the central CODEX platform. Each NUM site ensures that the exchanged consent information conforms to the already-balloted HL7 FHIR consent profiles and the interoperability concept of the MII Task Force “Consent Implementation” (TFCI). The Independent Trusted Third-Party (TTP) of the University Medicine Greifswald supports data protection-compliant data processing and provides the consent management solutions gICS. Methods Based on a stakeholder dialogue a required set of FHIR-functionalities was identified and technically specified supported by official FHIR experts. Next, a “TTP-FHIR Gateway” for the HL7 FHIR-compliant exchange of consent information using gICS was implemented. A last step included external integration tests and the development of a pre-configured consent template for the BC for the NUM sites. Results A FHIR-compliant gICS-release and a corresponding consent template for the BC were provided to all NUM sites in June 2021. All FHIR functionalities comply with the already-balloted FHIR consent profiles of the HL7 Working Group Consent Management. The consent template simplifies the technical BC rollout and the corresponding implementation of the TFCI interoperability concept at the NUM sites. Conclusions This article shows that a HL7 FHIR-compliant and interoperable nationwide exchange of consent information could be built using of the consent management software gICS and the provided TTP-FHIR Gateway. The initial functional scope of the solution covers the requirements identified in the NUM-CODEX setting. The semantic correctness of these functionalities was validated by project-partners from the Ludwig-Maximilian University in Munich. The production rollout of the solution package to all NUM sites has started successfully.

International policy and humanitarian guidance emphasize the need for precise, subnational malaria risk assessments with cross-regional comparability. Spatially explicit indicator-based assessments can support humanitarian aid organizations in identifying and localizing vulnerable populations for scaling resources and prioritizing aid delivery. However, the reliability of these assessments is often uncertain due to data quality issues. This article introduces a data evaluation framework to assist risk modelers in evaluating data adequacy. We operationalize the concept of “data adequacy” by considering “quality by design” (suitability) and “quality of conformance” (reliability). Based on a use case we developed in collaboration with Médecins Sans Frontières, we assessed data sources popular in spatial malaria risk assessments and related domains, including data from the Malaria Atlas Project, a healthcare facility database, WorldPop population counts, Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation estimates, European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation forecast, and Armed Conflict Location and Event Data Project (ACLED) conflict events data. Our findings indicate that data availability is generally not a bottleneck, and data producers effectively communicate contextual information pertaining to sources, methodology, limitations and uncertainties. However, determining such data’s adequacy definitively for supporting humanitarian intervention planning remains challenging due to potential inaccuracies, incompleteness or outdatedness that are difficult to quantify. Nevertheless, the data hold value for awareness raising, advocacy and recognizing trends and patterns valuable for humanitarian contexts. We contribute a domain-agnostic, systematic approach to geodata adequacy evaluation, with the aim of enhancing geospatial risk assessments, facilitating evidence-based decisions.

Diamond-Blackfan anemia is a rare genetic bone marrow failure disorder which is usually caused by mutations in ribosomal protein genes. In the present study, we generated a traceable RPS19-deficient cell model using CRISPR-Cas9 and homology-directed repair to investigate the therapeutic effects of a clinically applicable lentiviral vector at single-cell resolution. We developed a gentle nanostraw delivery platform to edit the RPS19 gene in primary human cord bloodderived CD34+ hematopoietic stem and progenitor cells. The edited cells showed expected impaired erythroid differentiation phenotype, and a specific erythroid progenitor with abnormal cell cycle status accompanied by enrichment of TNFα/NF-κB and p53 signaling pathways was identified by single-cell RNA sequencing analysis. The therapeutic vector could rescue the abnormal erythropoiesis by activating cell cycle-related signaling pathways and promoted red blood cell production. Overall, these results establish nanostraws as a gentle option for CRISPR-Cas9- based gene editing in sensitive primary hematopoietic stem and progenitor cells, and provide support for future clinical investigations of the lentiviral gene therapy strategy.

Background: Previous studies suggest that brain atrophy can not only be defined by its morphological extent, but also by the cerebral blood flow (CBF) within a certain area of the brain, including white and gray matter. The aim of this study is to investigate known atrophy patterns in different forms of dementia and to compare segmented brain volumetrics and pulsed arterial spin labeling (pASL) data to explore the correlation between brain maps with atrophy and this non-contrast-enhanced brain-perfusion method. Methods: Our study comprised 17 patients with diagnosed cognitive impairment (five Alzheimer’s disease = AD, five frontotemporal dementia = FTD, seven mild cognitive impairment = MCI) and 19 healthy control subjects (CO). All patients and controls underwent 4D-pASL brain-perfusion MR imaging and T1w MPRAGE. The data were assessed regarding relative brain volume on the basis of 286 brain regions, and absolute and relative cerebral blood flow (CBF/rCBF) were derived from pASL data in the corresponding brain regions. Mini-Mental State Examination (MMSE) was performed to assess cognitive functions. Results: FTD patients demonstrated significant brain atrophy in 43 brain regions compared to CO. Patients with MCI showed significant brain atrophy in 18 brain regions compared to CO, whereas AD patients only showed six brain regions with significant brain atrophy compared to CO. There was good correlation of brain atrophy and pASL perfusion data in five brain regions of patients with diagnosed FTD, especially in the superior temporal gyrus (r = 0.900, p = 0.037), the inferior frontal white matter (pars orbitalis; r = 0.968, p = 0.007) and the thalami (r = 0.810, p = 0.015). Patients with MCI demonstrated a correlation in one brain region (left inferior fronto-occipital fasciculus; r = 0.786, p = 0.036), whereas patients with diagnosed AD revealed no correlation. Conclusions: pASL can detect affected brain regions in cognitive impairment and corresponds with brain atrophy, especially for patients suffering from FTD and MCI. However, there was no correlation of perfusion alterations and brain atrophy in AD. pASL perfusion might thus represent a promising tool for noninvasive brain-perfusion evaluation in specific dementia subtypes as a complimentary imaging-based bio marker in addition to brain volumetry.

Dwelling information is very important for various applications in humanitarian emergency response. For this, Earth observation is crucial to have spatially explicit and temporally frequent observations. Coupled with advances in computer vision, especially with the proliferation of state-of-the-art deep learning models, are providing a new opportunity for automatic information retrieval from remotely sensed imagery. Despite their proven performance, they have two known limitations, viz, the requirement of intensive data for training and lack of universal generalization under changing scene characteristics and respective data distributions. To tackle this problem, the current study has investigated the relevance of a meta-learning approach for the creation of a spatially transferable optimal model for dwelling extraction in IDP/refugee settlements. The study followed a Model Agnostic meta-Learning (MAML) strategy with newly designed and tested variates with weighted loss gradient update plus self-supervision in the adaptation phase to the target locations using a few samples. The approach is tested using multi-sensor, multi-temporal satellite imagery from eight IDP settlements. Furthermore, a thorough investigation is undertaken on task-specific transfers and their association with deep-embedded feature space and image structural similarity. Results indicate that for some target sites, task-specific transfers perform better than MAML approaches. When MAML is trained with a weighted loss gradient update, it yielded better performance. The best performance (MIoU 0.623 and an F-1 score of 76.7 %) was achieved when MAML is aided with self-supervision using pseudo-labels from unlabelled target data. In all experimental setups, though increasing adaptation samples contribute to positive transfer, the marginal contribution from additional samples is decreasing and stagnates when the adaptation sample size reaches ∼ 35 % of the target dataset.

Hematopoiesis is regulated by the bone marrow (BM) stroma. However, cellular identities and functions of the different BM stromal elements in humans remain poorly defined. Based on single-cell RNA sequencing (scRNAseq), we systematically characterized the human non-hematopoietic BM stromal compartment and we investigated stromal cell regulation principles based on the RNA velocity analysis using scVelo and studied the interactions between the human BM stromal cells and hematopoietic cells based on ligand-receptor (LR) expression using CellPhoneDB. scRNAseq led to the identification of six transcriptionally and functionally distinct stromal cell populations. Stromal cell differentiation hierarchy was recapitulated based on RNA velocity analysis and in vitro proliferation capacities and differentiation potentials. Potential key factors that might govern the transition from stem and progenitor cells to fate-committed cells were identified. In situ localization analysis demonstrated that different stromal cells were localized in different niches in the bone marrow. In silico cell-cell communication analysis further predicted that different stromal cell types might regulate hematopoiesis through distinct mechanisms. These findings provide the basis for a comprehensive understanding of the cellular complexity of the human BM microenvironment and the intricate stroma-hematopoiesis crosstalk mechanisms, thus refining our current view on human hematopoietic niche organization.

Abstract The YPEL family genes are highly conserved across a diverse range of eukaryotic organisms and thus potentially involved in essential cellular processes. Ypel4, one of five YPEL family gene orthologs in mouse and human, is highly and specifically expressed in late terminal erythroid differentiation (TED). In this study, we investigated the role of Ypel4 in murine erythropoiesis, providing for the first time an in-depth description of a Ypel4-null phenotype in vivo. We demonstrated that the Ypel4-null mice displayed a secondary polycythemia with macro- and reticulocytosis. While lack of Ypel4 did not affect steady-state TED in the bone marrow or spleen, the anemia-recovering capacity of Ypel4-null cells was diminished. Furthermore, Ypel4-null red blood cells (RBC) were cleared from the circulation at an increased rate, demonstrating an intrinsic defect of RBCs. Scanning electron micrographs revealed an ovalocytic morphology of Ypel4-null RBCs and functional testing confirmed reduced deformability. Even though Band 3 protein levels were shown to be reduced in Ypel4-null RBC membranes, we could not find support for a physical interaction between YPEL4 and the Band 3 protein. In conclusion, our findings provide crucial insights into the role of Ypel4 in preserving normal red cell membrane integrity.

The transforming world evokes changes in social, environmental, and economic dimensions, pushed by the digitalisation of many, if not all, aspects of our lives. Satellite Earth observation, while being “digital” from early on, has experienced a boost by digitalisation in recent years, with new trend s of cloud processing, data cube infrastructure, computer vision, machine learning, at unprecedented speeds. This Special Issue on “Digital | Earth | Observation” is dedicated to the fruitful interplay between the Digital Earth concept and Earth observation, embedded in the great technological trends in this field, and demonstrates how this potential can be used in various application contexts.

The availability and usage of optical very high spatial resolution (VHR) satellite images for efficient support of refugee/IDP (internally displaced people) camp planning and humanitarian aid are growing. In this research, an integrated approach was used for dwelling classification from VHR satellite images, which applied the preliminary results of a convolutional neural network (CNN) model as input data for an object-based image analysis (OBIA) knowledge-based semantic classification method. Unlike standard pixel-based classification methods that usually are applied for the CNN model, our integrated approach aggregates CNN results on separately delineated objects as the basic units of a rule-based classification, to include additional prior-knowledge and spatial concepts in the final instance segmentation. An object-based accuracy assessment methodology was used to assess the accuracy of the classified dwelling categories on a single object-level. Our findings reveal accuracies of more than 90% for each applied parameter of precision, recall and F1-score. We conclude that integrating the CNN models with the OBIA capabilities can be considered an efficient approach for dwelling extraction and classification, integrating not only sample derived knowledge but also prior-knowledge about refugee/IDP camp situations, like dwellings size constraints and additional context.

Radiative transfer models (RTM) provide universally applicable, highly accurate prospects for plant parameter retrieval. Due to the ill-posed nature of radiative transfer theory, however, the retrieval of plant parameters requires sophisticated strategies for model inversion. We argue that object-based image analysis (OBIA) works as an effective regularization measure to cope with this ill-posedness. Despite similar findings reported in the literature, OBIA and RTM are rarely used in a combined manner. Additionally, there is a clear lack of software solutions ready for operational usage. Therefore, we propose OBIA4RTM as an approach to combine OBIA and RTM using Python and PostgreSQL/PostGIS spatial databases in a fully Open Geospatial Consortium (OGC) compliant way. First results obtained in agricultural regions in southern Germany and Austria using Sentinel-2 data during the 2017 and 2018 growing season show root mean squared errors (RMSE) in the leaf area index (LAI) of 1.47 m²/m² in the case of silage maize and 1.31 m²/m² in the case of winter cereals. Issues of integrating space and time as well as defining appropriate validation strategies, however, require further research.

Humanitarian action has rapidly adopted Earth observation (EO) and geospatial technologies shaping them according to their needs. Protracted crises and large-scale population displacements require up-to-date information in many facets of humanitarian action support, from mission planning, resource deployment and monitoring, to nutrition and vaccination campaigns, camp plotting, damage assessment, etc. Even though nearly all assets of remote sensing apply in such demanding scenarios, it remains a challenge to fully implement and sustain a trustful and reliable information service. This paper discusses achievements and open issues in the use and uptake of EO technology, from a technical and organisational point of view, motivated by an information service for Médecins Sans Frontières (MSF) and its extension to other NGO’s information needs in the humanitarian sector. With a focus on EO-based population estimation based on (semi-)automated dwelling counting from very high-resolution optical satellite imagery as well as the exploitation of data integration (including radar sensors), the paper also covers potential service elements with respect to environmental and ground- or surface water monitoring. It investigates workflow elements in relation to information extraction and delivery by illustrating a broad range of application scenarios, and discusses first operational solutions of a customized service portfolio.

Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term ‘big Earth data’ emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.

Sentinel-2 scenes are increasingly being used in operational Earth observation (EO) applications at regional, continental and global scales, in near-real time applications, and with multi-temporal approaches. On a broader scale, they are therefore one of the most important facilitators of the Digital Earth. However, the data quality and availability are not spatially and temporally homogeneous due to effects related to cloudiness, the position on the Earth or the acquisition plan. The spatio-temporal inhomogeneity of the underlying data may therefore affect any big remote sensing analysis and is important to consider. This study presents an assessment of the metadata for all accessible Sentinel-2 Level-1C scenes acquired in 2017, enabling the spatio-temporal coverage and availability to be quantified, including scene availability and cloudiness. Spatial exploratory analysis of the global, multi-temporal metadata also reveals that higher acquisition frequencies do not necessarily yield more cloud-free scenes and exposes metadata quality issues, e.g. systematically incorrect cloud cover estimation in high, non-vegetated altitudes. The continuously updated datasets and analysis results are accessible as a Web application called EO-Compass. It contributes to a better understanding and selection of Sentinel-2 scenes, and improves the planning and interpretation of remote sensing analyses.

Summary: Isolation of long-term hematopoietic stem cell (HSC) is possible by utilizing flow cytometry with multiple cell surface markers. However, those cell surface phenotypes do not represent functional HSCs after in vitro culture. Here we show that cultured HSCs express mast cell-related genes including Cd244. After in vitro culture, phenotypic HSCs were divided into CD244- and CD244+ subpopulations, and only CD244- cells that have low mast cell gene expression and maintain HSC-related genes sustain reconstitution potential. The result was same when HSCs were cultured in an efficient expansion medium containing polyvinyl alcohol. Chemically induced endoplasmic reticulum (ER) stress signal increased the CD244+ subpopulation, whereas ER stress suppression using a molecular chaperone, TUDCA, decreased CD244+ population, which was correlated to improved reconstitution output. These data suggest CD244 is a potent marker to exclude non-functional HSCs after in vitro culture thereby useful to elucidate mechanism of functional decline of HSCs during ex vivo treatment.

Background and purpose: Based on artificial intelligence (AI), 3D angiography (3DA) is a novel postprocessing algorithm for “DSA-like” 3D imaging of cerebral vasculature. Because 3DA requires neither mask runs nor digital subtraction as the current standard 3D-DSA does, it has the potential to cut the patient dose by 50%. The object was to evaluate 3DA’s diagnostic value for visualization of intracranial artery stenoses (IAS) compared to 3D-DSA. Materials and methods: 3D-DSA datasets of IAS (nIAS = 10) were postprocessed using conventional and prototype software (Siemens Healthineers AG, Erlangen, Germany). Matching reconstructions were assessed by two experienced neuroradiologists in consensus reading, considering image quality (IQ), vessel diameters (VD1/2), vessel-geometry index (VGI = VD1/VD2), and specific qualitative/quantitative parameters of IAS (e.g., location, visual IAS grading [low-/medium-/high-grade] and intra-/poststenotic diameters [dintra-/poststenotic in mm]). Using the NASCET criteria, the percentual degree of luminal restriction was calculated. Results: In total, 20 angiographic 3D volumes (n3DA = 10; n3D-DSA = 10) were successfully reconstructed with equivalent IQ. Assessment of the vessel geometry in 3DA datasets did not differ significantly from 3D-DSA (VD1: r = 0.994, p = 0.0001; VD2:r = 0.994, p = 0.0001; VGI: r = 0.899, p = 0.0001). Qualitative analysis of IAS location (3DA/3D-DSA:nICA/C4 = 1, nICA/C7 = 1, nMCA/M1 = 4, nVA/V4 = 2, nBA = 2) and the visual IAS grading (3DA/3D-DSA:nlow-grade = 3, nmedium-grade = 5, nhigh-grade = 2) revealed identical results for 3DA and 3D-DSA, respectively. Quantitative IAS assessment showed a strong correlation regarding intra-/poststenotic diameters (rdintrastenotic = 0.995, pdintrastenotic = 0.0001; rdpoststenotic = 0.995, pdpoststenotic = 0.0001) and the percentual degree of luminal restriction (rNASCET 3DA = 0.981; pNASCET 3DA = 0.0001). Conclusions: The AI-based 3DA is a resilient algorithm for the visualization of IAS and shows comparable results to 3D-DSA. Hence, 3DA is a promising new method that allows a considerable patient-dose reduction, and its clinical implementation would be highly desirable.

Summary: Single-cell RNAseq is a routinely used method to explore heterogeneity within cell populations. Data from these experiments are often visualized using dimension reduction methods such as UMAP and tSNE, where each cell is projected in two or three dimensional space. Three-dimensional projections can be more informative for larger and complex datasets because they are less prone to merging and flattening similar cell-types/clusters together. However, visualizing and cross-comparing 3D projections using current software on conventional flat-screen displays is far from optimal as they are still essentially 2D, and lack meaningful interaction between the user and the data. Here we present CellexalVR (www.cellexalvr.med.lu.se), a feature-rich, fully interactive virtual reality environment for the visualization and analysis of single-cell experiments that allows researchers to intuitively and collaboratively gain an understanding of their data.

Background: Trigeminal neuralgia (TN) is a severe facial pain condition often requiring surgical treatment. Unfortunately, even technically successful surgery fails to achieve durable pain relief in many patients. The purpose of this study was to use resting-state functional magnetic resonance imaging (fMRI) to: (1) compare functional connectivity between limbic and accessory sensory networks in TN patients vs. healthy controls; and (2) determine if pre-operative variability in these networks can distinguish responders and non-responders to surgery for TN.Methods: We prospectively recruited 22 medically refractory classic or idiopathic TN patients undergoing surgical treatment over a 3-year period, and 19 age- and sex-matched healthy control subjects. fMRI was acquired within the month prior to surgery for all TN patients and at any time during the study period for controls. Functional connectivity analysis was restricted to six pain-relevant brain regions selected a priori: anterior cingulate cortex (ACC), posterior cingulate cortex, hippocampus, amygdala, thalamus, and insula. Two comparisons were performed: (1) TN vs. controls; and (2) responders vs. non-responders to surgical treatment for TN. Functional connectivity was assessed with a two-sample t-test, using a statistical significance threshold of p < 0.050 with false discovery rate (FDR) correction for multiple comparisons.Results: Pre-operative functional connectivity was increased in TN patients compared to controls between the right insular cortex and both the left thalamus [t(39) = 3.67, p = 0.0007] and right thalamus [t(39) = 3.22, p = 0.0026]. TN patients who were non-responders to surgery displayed increased functional connectivity between limbic structures, including between the left and right hippocampus [t(18) = 2.85, p = 0.0106], and decreased functional connectivity between the ACC and both the left amygdala [t(18) = 2.94, p = 0.0087] and right hippocampus [t(18) = 3.20, p = 0.0049]. Across all TN patients, duration of illness was negatively correlated with connectivity between the ACC and left amygdala (r2 = 0.34, p = 0.00437) as well as the ACC and right hippocampus (r2 = 0.21, p = 0.0318).Conclusions: TN patients show significant functional connectivity abnormalities in sensory-salience regions. However, variations in the strength of functional connectivity in limbic networks may explain why some TN patients fail to respond adequately to surgery.

Background and Purpose: The optimal acute management of patients with large vessel occlusion (LVO) and minor clinical deficits on admission [National Institutes of Health Stroke Scale (NIHSS) ≤ 4] remains to be elucidated. The aim of the present study was to investigate the prognostic factors and therapeutic management of those patients.Methods: In this retrospective cohort study, we investigated (1) all patients with acute ischemic stroke due to an LVO who underwent mechanical thrombectomy (MT) and (2) all patients with minor clinical deficits (NIHSS ≤ 4) on admission due to an LVO between January 2013 and December 2016 at the University Medical Center Erlangen. We dichotomized management of patients with minor deficits treated with MT for analysis according to immediate mechanical thrombectomy (IT) and initial medical management with rescue intervention (MM) in case of secondary deterioration. Primary endpoints were secondary deterioration, in-hospital mortality, and functional outcome on day 90 (dichotomized modified Rankin Scale 0–2: favorable, 3–6: poor).Results: Two hundred twenty-three patients (83% with anterior circulation stroke, 13 (6%) with minor deficits) treated with MT and 88 patients with minor deficits due to LVO [13 (15%) treated with MT] were included. Secondary deterioration (n = 19) was independently associated with poor outcome in patients with minor deficits and LVO [odds ratio (OR), 0.060; 95% confidence interval (CI), 0.013–0.280], which in turn was associated with the occlusion site [especially M1 occlusion: 11 (58%) vs. 3 (4%) in patients without secondary deterioration, p < 0.0001]. IT (n = 8) was associated with a lower intrahospital mortality compared to MM (n = 5; 13 vs. 80%; OR, 0.036; 95% CI, 0.002–0.741). Seven of eight patients with IT survived until discharge, with 29% showing a favorable functional outcome on day 90.Conclusions: Secondary deterioration is associated with poor outcome in patients with LVO and minor deficits, which in turn was associated with occlusion site. Future randomized controlled trials should assess whether selected patients, depending on occlusion site and associated characteristics, may benefit from MT.

Abstract Introduction To describe the influence of subretinal fluid (SRF) in analysis of the flow signal from the choriocapillaris (CC) via optical coherence tomography angiography (OCTA) in patients with acute central serous chorioretinopathy (CSC). Methods Observational, longitudinal, cross-sectional case–control study. Twenty-three eyes of 22 patients with acute CSC were compared with 20 eyes of 20 healthy controls. OCTA images (AngioPlex®, Zeiss) were recorded at baseline and follow-up examination and automatically quantified using an image-processing algorithm. Abnormal CC decorrelation signals (increased and decreased flow, IF and DF) were quantified using a custom image-processing algorithm. To analyze the spatial correlation between SRF and OCTA, CC signal heat-map images containing macular thickness information were used. Results Choriocapillaris flow alterations were significantly more pronounced at baseline than at follow-up examination in patients with acute CSC and resolving SRF (IF: p

The improvement in computer vision, sensor quality, and remote sensing data availability makes satellite imagery increasingly useful for studying human settlements. Several challenges remain to be overcome for some types of settlements, particularly for internally displaced populations (IDPs) and refugee camps. Refugee-dwelling footprints and detailed information derived from satellite imagery are critical for a variety of applications, including humanitarian aid during disasters or conflicts. Nevertheless, extracting dwellings remains difficult due to their differing sizes, shapes, and location variations. In this study, we use U-Net and residual U-Net to deal with dwelling classification in a refugee camp in northern Cameroon, Africa. Specifically, two semantic segmentation networks are adapted and applied. A limited number of randomly divided sample patches is used to train and test the networks based on a single image of the WorldView-3 satellite. Our accuracy assessment was conducted using four different dwelling categories for classification purposes, using metrics such as Precision, Recall, F1, and Kappa coefficient. As a result, F1 ranges from 81% to over 99% and approximately 88.1% to 99.5% based on the U-Net and the residual U-Net, respectively.

Refugee-dwelling footprints derived from satellite imagery are beneficial for humanitarian operations. Recently, deep learning approaches have attracted much attention in this domain. However, most refugees are hosted by low- and middle-income countries where accurate label data are often unavailable. The Object-Based Image Analysis (OBIA) approach has been widely applied to this task for humanitarian operations over the last decade. However, the footprints were usually produced urgently, and thus, include delineation errors. Thus far, no research discusses whether these footprints generated by the OBIA approach (OBIA labels) can replace manually annotated labels (Manual labels) for this task. This research compares the performance of OBIA labels and Manual labels under multiple strategies by semantic segmentation. The results reveal that the OBIA labels can produce IoU values greater than 0.5, which can produce applicable results for humanitarian operations. Most falsely predicted pixels source from the boundary of the built-up structures, the occlusion of trees, and the structures with complicated ontology. In addition, we found that using a small number of Manual labels to fine-tune models initially trained with OBIA labels can outperform models trained with purely Manual labels. These findings show high values of the OBIA labels for deep-learning-based refugee-dwelling extraction tasks for future humanitarian operations.

Single-center comparison of postinterventional multislice computed tomography (MS-CT) and flat-detector computed tomography (FD-CT) in patients with subarachnoid haemorrhage (SAH) and endovascularly treated cerebral aneurysms with a focus on detection of posttherapeutical complications. Patients with endovascularly treated aneurysmal SAH undergoing both MS-CT and FD-CT within 24 h after intervention were included. Datasets were compared regarding image quality (IQ) as well as qualitative (detection of SAH, intracerebral haemorrhage [ICH], intraventricular haemorrhage [IVH], external ventricular drain [EVD] position, acute obstructive hydrocephalus [AOH]) and quantitative (cella media distance [CMD], modified Graeb score [GS]) parameters. 410 patients with endovascularly treated aneurysmal SAH were included. IQ was equal between MS-CT and FD-CT. FD-CT allowed equal detection of SAH and ICH in comparison to MS-CT. FD-CT allowed excellent detection of IVH and delineation of EVD position with strong agreement to MS-CT findings. FD-CT allowed equal detection of AOH in comparison to MS-CT. There was no significant difference of CMD and GS between FD-CT and MS-CT. Postinterventional FD-CT yields equivalent diagnostic value in patients with endovascular treated SAH as MS-CT. Enabling reliable detection of SAH-associated complications within the angiosuite, FD-CT might be an efficient and safe imaging modality in these clinical emergencies.

Mild behavioral impairment (MBI) is a neurobehavioral syndrome characterized by later life emergence of sustained neuropsychiatric symptoms, as an at-risk state for incident cognitive decline and dementia. Prior studies have reported that neuropsychiatric symptoms are associated with cognitive abilities in Parkinson’s disease (PD) patients, and we have recently found a strong correlation between MBI and cognitive performance. However, the underlying neural activity patterns of cognitive performance linked to MBI in PD are unknown. Fifty-nine non-demented PD patients and 26 healthy controls were scanned using fMRI during performance of a modified version of the Wisconsin card sorting task. MBI was evaluated using the MBI-checklist, and PD patients were divided into two groups, PD-MBI and PD-noMBI. Compared to the PD-noMBI group and healthy controls, the PD-MBI group revealed less activation in the prefrontal and posterior parietal cortices, and reduced deactivation in the medial temporal region. These results suggest that in PD, MBI reflects deficits in the frontoparietal control network and the hippocampal memory system.

Background: Mild cognitive impairment is a common non-motor symptom of Parkinson's disease (PD-MCI) and has minimal treatment options.Objective: In this double-blind, randomized, sham-controlled trial, we assessed the effect of repeated sessions of intermittent theta-burst stimulation over the left dorsolateral prefrontal cortex on cognition and brain connectivity in subjects with PD-MCI.Methods: Forty-one subjects were randomized to receive real (n = 21) or sham stimulation (n = 20). All subjects underwent neuropsychological assessments before, 1 day, and 1 month after stimulation. Subjects also underwent resting-state functional magnetic resonance imaging before and 48 h after stimulation. The primary outcome was the change in the cognitive domain (executive function, attention, memory, language, and visuospatial abilities) z-scores across time.Results: There was an insignificant effect on cognitive domain z-scores across time when comparing real with sham stimulation and correcting for multiple comparisons across cognitive domains (p > 0.05 Bonferroni correction). However, the real stimulation group demonstrated a trend toward improved executive functioning scores at the 1-month follow-up compared with sham (p < 0.05 uncorrected). After real stimulation, the connectivity of the stimulation site showed decreased connectivity to the left caudate head. There was no change in connectivity within or between the stimulation network (a network of cortical regions connected to the stimulation site) and the striatal network. However, higher baseline connectivity between the stimulation network and the striatal network was associated with improved executive function scores at 1 month.Conclusions: These results suggest that intermittent theta-burst stimulation over the dorsolateral prefrontal cortex in subjects with PD-MCI has minimal effect on cognition compared with sham, although there were trends toward improved executive function. This intervention may be more effective in subjects with higher baseline connectivity between the stimulation network and the striatal network. This trial supports further investigation focusing on executive function and incorporating connectivity-based targeting.Clinical Trial Registration:www.ClinicalTrials.gov, identifier NCT03243214.

Background: Transcranial direct current stimulation (tDCS) has been used extensively in patient populations to facilitate motor network plasticity. However, it has not been studied in patients with brain tumors. We aimed to determine the feasibility of a preoperative motor training and tDCS intervention in patients with glioma. In an exploratory manner, we assessed changes in motor network connectivity following this intervention and related these changes to predicted electrical field strength from the stimulated motor cortex.Methods: Patients with left-sided glioma (n=8) were recruited in an open label proof of concept pilot trial and participated in four consecutive days of motor training combined with tDCS. The motor training consisted of a 60-min period where the subject learned to play the piano with their right hand. Concurrently, they received 40 min of 2 mA anodal tDCS of the left motor cortex. Patients underwent task and resting state fMRI before and after this intervention. Changes in both the connectivity of primary motor cortex (M1) and general connectivity across the brain were assessed. Patient specific finite element models were created and the predicted electrical field (EF) resulting from stimulation was computed. The magnitude of the EF was extracted from left M1 and correlated to the observed changes in functional connectivity.Results: There were no adverse events and all subjects successfully completed the study protocol. Left M1 increased both local and global connectivity. Voxel-wide measures, not constrained by a specific region, revealed increased global connectivity of the frontal pole and decreased global connectivity of the supplementary motor area. The magnitude of EF applied to the left M1 correlated with changes in global connectivity of the right M1.Conclusion: In this proof of concept pilot study, we demonstrate for the first time that tDCS appears to be feasible in glioma patients. In our exploratory analysis, we show preoperative motor training combined with tDCS may alter sensorimotor network connectivity. Patient specific modeling of EF in the presence of tumor may contribute to understanding the dose-response relationship of this intervention. Overall, this suggests the possibility of modulating neural networks in glioma patients.

Background and Purpose: Although outcome in intracerebral hemorrhage (ICH) patients is generally not improved by surgical intervention, the use of minimally invasive surgery (MIS) has shown promising results. However, vitamin K antagonist (VKA)-related ICH patients are underrepresented in surgical treatment trials. We therefore assessed the safety and efficacy of a bedside MIS approach including local application of urokinase in VKA-related ICH.Methods: Patients with a VKA-related ICH > 20 ml who received bedside hematoma evacuation treatment (n = 21) at the University Medical Center Freiburg were retrospectively included for analysis and compared to a historical control group (n = 35) selected from an institutional database (University Medical Center Erlangen) according to identical inclusion criteria. Propensity score matching was performed to obtain comparable cohorts. The evolution of hematoma and peri-hemorrhagic edema (PHE) volumes, midline shift, and the occurrence of adverse events were analyzed. Furthermore, we assessed the modified Rankin Scale and NIHSS scores recorded at discharge.Results: Propensity score matching resulted in 16 patients per group with well-balanced characteristics. Median ICH volume at admission was 45.7 (IQR: 24.2–56.7) ml in the control group and 48.4 (IQR: 28.7–59.6) ml in the treatment group (p = 0.327). ICH volume at day 7 was less pronounced in the treatment group [MIS: 23.2 ml (IQR: 15.8–32.3) vs. control: 43.2 ml (IQR: 27.5–52.4); p = 0.013], as was the increase in midline shift up to day 7 [MIS: −3.75 mM (IQR: −4.25 to −2) vs. control: 1 mM (IQR: 0–2); p < 0.001]. No group differences were observed in PHE volume on day 7 [MIS: 42.4 ml (IQR: 25.0–72.3) vs. control: 31.0 ml (IQR: 18.8–53.8); p = 0.274] or mRS at discharge [MIS: 5 (IQR: 4–5) and 5 (IQR: 4–5); p = 0.949]. No hematoma expansion was observed. The catheter had to be replaced in 1 patient (6%).Conclusions: Bedside catheter-based hematoma evacuation followed by local thrombolysis with urokinase appears to be feasible and safe in cases of large VKA-related ICH. Further studies that assess the functional outcome associated with this technique are warranted.Clinical Trial Registration: DRKS00007908 (German Clinical Trial Register; www.drks.de)

Abstract Introduction To quantify optical coherence tomography angiography (OCTA) signal changes at the level of the choriocapillaris (CC) in patients with different stages of central serous chorioretinopathy (CSC) and to explore any correlation between subretinal fluid (SRF) and retinal pigment epithelium (RPE) alterations and the OCTA CC signal. Methods One hundred one CSC eyes and 42 healthy control eyes were included in this retrospective study. CSC patients were allocated into four groups: acute, non-resolving, chronic atrophic and inactive CSC. CC OCTA images (AngioPlex®, Zeiss) were automatically quantified using an image-processing algorithm. Spatial correlation analysis of OCTA signals was performed by overlapping macular edema heatmaps and fundus autofluorescence images with corresponding OCTA images. Results Active CSC subgroups demonstrated significantly more increased and decreased flow pixels in the CC compared with controls (p

Treated cerebral aneurysms (IA) require follow-up imaging to ensure occlusion. Metal artifacts complicate radiologic assessment. Our aim was to evaluate an innovative metal-artifact-reduction (iMAR) algorithm for flat-detector computed tomography angiography (FD-CTA) regarding image quality (IQ) and detection of aneurysm residua/reperfusion in comparison to 2D digital subtraction angiography (DSA). Patients with IAs treated by endovascular coiling or clipping underwent both FD-CTA and DSA. FD-CTA datasets were postprocessed with/without iMAR algorithm (MAR+/MAR−). Evaluation of all FD-CTA and DSA datasets regarding qualitative (IQ, MAR) and quantitative (coil package diameter/CPD) parameters was performed. Aneurysm occlusion was assessed for each dataset and compared to DSA findings. In total, 40 IAs were analyzed (ncoiling = 24; nclipping = 16). All iMAR+ datasets demonstrated significantly better IQ (pIQ coiling < 0.0001; pIQ clipping < 0.0001). iMAR significantly reduced the metal-artifact burden but did not affect the CPD. iMAR significantly improved the detection of aneurysm residua/reperfusion with excellent agreement with DSA (naneurysm detection MAR+/MAR−/DSA = 22/1/26). The iMAR algorithm significantly improves IQ by effective reduction of metal artifacts in FD-CTA datasets. The proposed algorithm enables reliable detection of aneurysm residua/reperfusion with good agreement to DSA. Thus, iMAR can help to reduce the need for invasive follow-up in treated IAs.

Abstract Background The use of medical data for research purposes requires an informed consent of the patient that is compliant with the EU General Data Protection Regulation. In the context of multi-centre research initiatives and a multitude of clinical and epidemiological studies scalable and automatable measures for digital consent management are required. Modular form, structure, and contents render a patient’s consent reusable for varying project settings in order to effectively manage and minimise organisational and technical efforts. Results Within the DFG-funded project “MAGIC” (Grant Number HO 1937/5-1) the digital consent management service tool gICS was enhanced to comply with the recommendations published in the TMF data protection guideline for medical research. In addition, a structured exchange format for modular consent templates considering established standards and formats in the area of digital informed consent management was designed. Using the new FHIR standard and the HAPI FHIR library, the first version for an exchange format and necessary import-/export-functionalities were successfully implemented. Conclusions The proposed exchange format is a “work in progress”. It represents a starting point for current discussions concerning digital consent management. It also attempts to improve interoperability between different approaches within the wider IHE-/HL7-/FHIR community. Independent of the exchange format, providing the possibility to export, modify and import templates for consents and withdrawals to be reused in similar clinical and epidemiological studies is an essential precondition for the sustainable operation of digital consent management.

Cell-based therapies hold great promise in re-establishing organ function for many diseases, including untreatable lung diseases such as idiopathic pulmonary fibrosis (IPF). However, many hurdles still remain, in part due to our lack of knowledge about the disease-driving mechanisms that may affect the cellular niche and thereby possibly hinder the function of any transplanted cells by imposing the disease phenotype onto the newly generated progeny. Recent findings have demonstrated increased ciliation of lung cells from IPF patients, but how this affects ciliated cell function and the airway milieu is not well-known. Here, we performed single-cell RNA sequencing on primary ciliated (FOXJ1+) cells isolated from IPF patients and from healthy control donors. The sequencing identified multiple biological processes, such as cilium morphogenesis and cell signaling, that were significantly changed between IPF and healthy ciliated cells. Ferritin light chain (FTL) was downregulated in IPF, which suggests that iron metabolism may be affected in the IPF ciliated cells. The RNA expression was confirmed at the protein level with histological localization in lung tissue, prompting future functional assays to reveal the potential role of FTL. Taken together, our data demonstrate the importance of careful analyses in pure cell populations to better understand the IPF disease mechanism.

Earth-observation-based mapping plays a critical role in humanitarian responses by providing timely and accurate information in inaccessible areas, or in situations where frequent updates and monitoring are required, such as in internally displaced population (IDP)/refugee settlements. Manual information extraction pipelines are slow and resource inefficient. Advances in deep learning, especially convolutional neural networks (CNNs), are providing state-of-the-art possibilities for automation in information extraction. This study investigates a deep convolutional neural network-based Mask R-CNN model for dwelling extractions in IDP/refugee settlements. The study uses a time series of very high-resolution satellite images from WorldView-2 and WorldView-3. The model was trained with transfer learning through domain adaptation from nonremote sensing tasks. The capability of a model trained on historical images to detect dwelling features on completely unseen newly obtained images through temporal transfer was investigated. The results show that transfer learning provides better performance than training the model from scratch, with an MIoU range of 4.5 to 15.3%, and a range of 18.6 to 25.6% for the overall quality of the extracted dwellings, which varied on the bases of the source of the pretrained weight and the input image. Once it was trained on historical images, the model achieved 62.9, 89.3, and 77% for the object-based mean intersection over union (MIoU), completeness, and quality metrics, respectively, on completely unseen images.

The present study proposes a workflow to extract vegetation height for urban areas from Pléiades stereo and tri-stereo satellite imagery. The workflow was applied on a stereo image pair for Szeged, Hungary and on tri-stereo imagery for Salzburg, Austria. Digital surface models (DSMs) of the study areas were computed using the semi-global matching algorithm. Normalised digital surface models (nDSMs) were then generated. Objects of vegetation and non-vegetation were delineated based on the spectral information of the multispectral images by applying multi-resolution segmentation and support vector machine classifier. Mean object height values were then computed from the overlaid pixels of the nDSMs and assigned to the objects. Finally, the delineated vegetation was classified into six vegetation height classes based on their assigned height values by using hierarchical classification. The vegetation discrimination resulted in very high accuracy, while the vegetation height extraction was moderately accurate. The results of the vegetation height extraction provided a vertical stratification of the vegetation in the two study areas which is readily applicable for decision support purposes. The elaborated workflow will contribute to a green monitoring and valuation strategy and provide input data for an urban green accessibility study.

Urban green mapping has become an operational task in city planning, urban land management, and quality of life assessments. As a multi-dimensional, integrative concept, urban green comprising several ecological, socio-economic, and policy-related aspects. In this paper, the author advances the representation of urban green by deriving scale-adapted, policy-relevant units. These so-called geons represent areas of uniform green valuation under certain size and homogeneity constraints in a spatially explicit representation. The study accompanies a regular monitoring scheme carried out by the urban municipality of the city of Salzburg, Austria, using optical satellite data. It was conducted in two stages, namely SBG_QB (10.2 km², QuickBird data from 2005) and SBG_WV (140 km², WorldView-2 data from 2010), within the functional urban area of Salzburg. The geon delineation was validated by several quantitative measures and spatial analysis techniques, as well as ground documentation, including panorama photographs and visual interpretation. The spatial association pattern was assessed by calculating Global Moran’s I with incremental search distances. The final geonscape, consisting of 1083 units with an average size of 13.5 ha, was analyzed by spatial metrics. Finally, categories were derived for different types of functional geons. Future research paths and improvements to the described strategy are outlined.

The challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).

Crop maps are key inputs for crop inventory production and yield estimation and can inform the implementation of effective farm management practices. Producing these maps at detailed scales requires exhaustive field surveys that can be laborious, time-consuming, and expensive to replicate. With a growing archive of remote sensing data, there are enormous opportunities to exploit dense satellite image time series (SITS), temporal sequences of images over the same area. Generally, crop type mapping relies on single-sensor inputs and is solved with the help of traditional learning algorithms such as random forests or support vector machines. Nowadays, deep learning techniques have brought significant improvements by leveraging information in both spatial and temporal dimensions, which are relevant in crop studies. The concurrent availability of Sentinel-1 (synthetic aperture radar) and Sentinel-2 (optical) data offers a great opportunity to utilize them jointly; however, optimizing their synergy has been understudied with deep learning techniques. In this work, we analyze and compare three fusion strategies (input, layer, and decision levels) to identify the best strategy that optimizes optical-radar classification performance. They are applied to a recent architecture, notably, the pixel-set encoder–temporal attention encoder (PSE-TAE) developed specifically for object-based classification of SITS and based on self-attention mechanisms. Experiments are carried out in Brittany, in the northwest of France, with Sentinel-1 and Sentinel-2 time series. Input and layer-level fusion competitively achieved the best overall F-score surpassing decision-level fusion by 2%. On a per-class basis, decision-level fusion increased the accuracy of dominant classes, whereas layer-level fusion improves up to 13% for minority classes. Against single-sensor baseline, multi-sensor fusion strategies identified crop types more accurately: for example, input-level outperformed Sentinel-2 and Sentinel-1 by 3% and 9% in F-score, respectively. We have also conducted experiments that showed the importance of fusion for early time series classification and under high cloud cover condition.

Abstract Background The loss of the swallow-tail sign of the substantia nigra has been proposed for diagnosis of Parkinson’s disease. Aim was to evaluate, if the sign occurs consistently in healthy subjects and if it can be reliably detected with high-resolution 7T susceptibility weighted imaging (SWI). Methods Thirteen healthy adults received SWI at 7T. 3 neuroradiologists, who were blinded to patients’ diagnosis, independently classified subjects regarding the swallow-tail sign to be present or absent. Accuracy, positive and negative predictive values (PPV and NPV) as well as inter- and intra-rater reliability and internal consistency were analyzed. Results The sign could be detected in 81% of the cases in consensus reading. Accuracy to detect the sign compared to the consensus was 100, 77 and 96% for the three readers with PPV reader 1/2/3 = 1/0.45/0.83 and NPV = 1/1/1. Inter-rater reliability was excellent (inter-class correlation coefficient = 0.844, alpha = 0.871). Intra-rater reliability was good to excellent (reader 1 R/L = 0.625/0.786; reader 2 = 0.7/0.64; reader 3 = 0.9/1). Conclusion The swallow-tail sign can be reliably detected. However, our data suggest its occurrence is not consistent in healthy subjects. It may be possible that one reason is an individually variable molecular organization of nigrosome 1 so that it does not return a uniform signal in SWI.

During development, hematopoietic cells originate from endothelium in a process known as endothelial-to-hematopoietic transition (EHT). To study human EHT, we coupled flow cytometry and single-cell transcriptional analyses of human pluripotent stem cell-derived CD34+ cells. The resulting transcriptional hierarchy showed a continuum of endothelial and hematopoietic signatures. At the interface of these two signatures, a unique group of cells displayed both an endothelial signature and high levels of key hematopoietic stem cell-associated genes. This interphase group was validated via sort and subculture as an immediate precursor to hematopoietic cells. Differential expression analyses further divided this population into subgroups, which, upon subculture, showed distinct hematopoietic lineage differentiation potentials. We therefore propose that immediate precursors to hematopoietic cells already have their hematopoietic lineage restrictions defined prior to complete downregulation of the endothelial signature. These findings increase our understanding of the processes of de novo hematopoietic cell generation in the human developmental context.