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Purpose: Tumor hypoxia is a paradigmatic negative prognosticator of treatment resistance in head and neck squamous cell carcinoma (HNSCC). The lack of robust and reliable hypoxia classifiers limits the adaptation of stratified therapies. We hypothesized that the tumor DNA methylation landscape might indicate epigenetic reprogramming induced by chronic intratumoral hypoxia. Experimental Design: A DNA-methylome–based tumor hypoxia classifier (Hypoxia-M) was trained in the TCGA (The Cancer Genome Atlas)-HNSCC cohort based on matched assignments using gene expression–based signatures of hypoxia (Hypoxia-GES). Hypoxia-M was validated in a multicenter DKTK-ROG trial consisting of human papillomavirus (HPV)–negative patients with HNSCC treated with primary radiochemotherapy (RCHT). Results: Although hypoxia-GES failed to stratify patients in the DKTK-ROG, Hypoxia-M was independently prognostic for local recurrence (HR, 4.3; P = 0.001) and overall survival (HR, 2.34; P = 0.03) but not distant metastasis after RCHT in both cohorts. Hypoxia-M status was inversely associated with CD8 T-cell infiltration in both cohorts. Hypoxia-M was further prognostic in the TCGA-PanCancer cohort (HR, 1.83; P = 0.04), underscoring the breadth of this classifier for predicting tumor hypoxia status. Conclusions: Our findings highlight an unexplored avenue for DNA methylation–based classifiers as biomarkers of tumoral hypoxia for identifying high-risk features in patients with HNSCC tumors.

Supplementary Table S2. Treatment Information for the validation cohort (n=88, DKTK-ROG)

Supplementary Figure 4 (Top). CD8 T cell enrichment is prognostic for Overall Survival (OS), local recurrence (LR) and disease progression but not for distant metastasis (DM) in HPV negative HNSCC in the DKTK-ROG cohort (n=88). (Bottom) After adjusting for Immune cell Infiltration, Hypoxia-M remained an independent prognosticator for local recurrence (HR=3.06, p

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Supplementary Methods, Results, Table of Contents

Purpose:Tumor hypoxia is a paradigmatic negative prognosticator of treatment resistance in head and neck squamous cell carcinoma (HNSCC). The lack of robust and reliable hypoxia classifiers limits the adaptation of stratified therapies. We hypothesized that the tumor DNA methylation landscape might indicate epigenetic reprogramming induced by chronic intratumoral hypoxia.Experimental Design:A DNA-methylome–based tumor hypoxia classifier (Hypoxia-M) was trained in the TCGA (The Cancer Genome Atlas)-HNSCC cohort based on matched assignments using gene expression–based signatures of hypoxia (Hypoxia-GES). Hypoxia-M was validated in a multicenter DKTK-ROG trial consisting of human papillomavirus (HPV)–negative patients with HNSCC treated with primary radiochemotherapy (RCHT).Results:Although hypoxia-GES failed to stratify patients in the DKTK-ROG, Hypoxia-M was independently prognostic for local recurrence (HR, 4.3; P = 0.001) and overall survival (HR, 2.34; P = 0.03) but not distant metastasis after RCHT in both cohorts. Hypoxia-M status was inversely associated with CD8 T-cell infiltration in both cohorts. Hypoxia-M was further prognostic in the TCGA-PanCancer cohort (HR, 1.83; P = 0.04), underscoring the breadth of this classifier for predicting tumor hypoxia status.Conclusions:Our findings highlight an unexplored avenue for DNA methylation–based classifiers as biomarkers of tumoral hypoxia for identifying high-risk features in patients with HNSCC tumors.

Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Methods: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes: T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20,101 MRI images) was used for training, while C2 (197, 11,333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class’ added value was evaluated via radiomics model performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). Results: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95% Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. A total of 620 images were misclassified; manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased the PFS model C-I by 14.6% on average, compared to a model trained without MR-Class. Conclusions: We provide a DCNN-based method for the sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.

Purpose: This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models’ performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). Methods: MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods’ performance. Results: Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13), however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman’s rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. Conclusion: The IN method impacted the predictive power of survival models. Thus, performance is sequence-dependent.

Targeting immunosuppressive and protumorigenic glioblastoma-associated macrophages and microglial cells (GAMs) holds great potential to improve patient outcomes. Although CSF1R has emerged as a promising target to reprogram anti-inflammatory M2-like GAMs, relevant treatment data on human, tumor-educated GAMs and innovative patient-derived 3D tumor organoid models to study the influence on adaptive immunity and the effectiveness of treatment in a complex and entirely autologous setting are largely lacking. We performed a comprehensive phenotypical, transcriptional and functional analysis of primary, patient-derived GAMs upon treatment with the CSF1R-targeting drugs PLX3397, BLZ945, and GW2580. The most effective reprogramming of GAMs was observed upon GW2580 treatment, which led to a downregulation of M2-related markers and signaling pathways, while M1-like markers, phagocytosis, and T-cell killing were substantially increased. Moreover, treatment of patient-derived glioblastoma organoids with GW2580 confirmed successful reprogramming together with reduced tumor cell proliferation, indicating that treatment with GW2580 could be an important pillar in the future therapy of GBM.

The overall aim of ROADMAP is to foster transitions towards prudent use of antimicrobials (AMs) in animal production in different contexts to manage antimicrobial resistance (AMR). Prudent antimicrobial use (AMU) will be achieved by enhancing antimicrobial decision-systems along the food and drug supply chains. ROADMAP will focus on supporting animal health and welfare through prevention and health promotion actions. AMR is recognized as a significant threat to global public health and food security. Overuse and improper use of AMs in many parts of the world contribute to the emergence and spread of AMR. Although human and animal health require AMs, it has been estimated that two thirds of the future AMU growth worldwide will be in animal production. Improving the management of AMU in farm animals is therefore a critical component of dealing with AMR and optimizing production in the livestock sector. Nevertheless, the variety of contexts of AMU in the livestock sector is a major challenge to managing AMR. There is no “one-size-fits-all” solution to improve AMU and strategies must be contextually developed (for instance, strategies used in the Danish pig industry are difficult to adapt and adopt in the French free-range poultry farming). Successful solutions must be combined and tailored to the production systems and the social and economic context in which they operate. ROADMAP will meet three general objectives, in line with the EU AMR Action plan: i) Rethink AM decision-systems and animal health management; ii) Develop options for encouraging prudent AMU in animal production; iii) Engage all actors in the food and drug supply chains in fostering a more prudent use of AMs.

In diesem Beitrag wird das KDT-EA (Knowledge Discovery in Time Series for Engineering Applications) vorgestellt, eine Verbesserung des bestehenden KDD-Prozesses, um speziell die Analyse von aufgezeichneten Zeitreihen in Produktionsprozessen zu adressieren. Es werden die Schritte von der Datenerfassung bis zur Implementierung der entwickelten Modelle in die Produktionsprozesse beschrieben und die Schlüsselfaktoren für eine erfolgreiche Machine-Learning-Anwendung identifiziert. Am Beispiel der Vorhersage des Biegewinkels in einem Folgeverbundwerkzeugprozess wird der Einfluss der einzelnen Entscheidungen innerhalb der Prozessschritte auf die Modellleistung aufgezeigt.

Industrial buildings play a major role in sustainable development, producing and expending a significant amount of resources, energy and waste. Due to product individualization and accelerating technological advances in manufacturing, industrial buildings strive for highly flexible building structures to accommodate constantly evolving production processes. However, common sustainability assessment tools do not respect flexibility metrics and manufacturing and building design processes run sequentially, neglecting discipline-specific interaction, leading to inflexible solutions. In integrated industrial building design (IIBD), incorporating manufacturing and building disciplines simultaneously, design teams are faced with the choice of multiple conflicting criteria and complex design decisions, opening up a huge design space. To address these issues, this paper presents a parametric design process for efficient design space exploration in IIBD. A state-of-the-art survey and multiple case study are conducted to define four novel flexibility metrics and to develop a unified design space, respecting both building and manufacturing requirements. Based on these results, a parametric design process for automated structural optimization and quantitative flexibility assessment is developed, guiding the decision-making process towards increased sustainability. The proposed framework is tested on a pilot-project of a food and hygiene production, evaluating the design space representation and validating the flexibility metrics. Results confirmed the efficiency of the process that an evolutionary multi-objective optimization algorithm can be implemented in future research to enable multidisciplinary design optimization for flexible industrial building solutions.

Background and purpose: Glioblastoma multiforme (GBM) is a stroma rich immune cold tumor with still dismal prognosis of despite multimodal therapy consisting of postoperative surgery and radiochemotherapy. Consequently, all randomized Phase III clinical GBM trials attempting to integrate immune checkpoint blockade (ICB) have failed to demonstrate efficacy. We have recently shown that tumor immune microenvironment (TIME) reprograming via Bintrafusp alpha (Anti-PD-L1/TGF-beta bispecific molecule) combined with conventional photon radiotherapy elicits beneficial effects and prolong survival in experimental models (Lan, Knoll, Moustafa et al. Cancer Cell 2021). Moreover, eradication of radioresistant i.e., stem cell like, invasive and hypoxic tumor subpopulations by high linear energy transfer (LET) carbon ion radiotherapy (CIRT) was shown to generate an immune permissive niche in experimental GBM models (Chiblak et al. JCI Insight 2019, Tang et al. IJRBP 2022). Therefore, we aimed to systematically study the effect of CIRT vs. conventional photon radiotherapy (RT) in two (GL261 and SB28) orhotopic syngeneic mouse GBM models. Methods: Tumor growth monitoring was conducted longitudinally by means of bioluminescence and small animal MR-imaging. Mice were irradiated in five fractions (Fx) with a total dose of 15Gy RT vs. 5, 10 and 15Gy CIRT. Results: Intriguingly, all GL261 harbouring mice were cured after 5 × 3Gy Fx CIRT with no sign of tumor recurrence up to 80 days post irradiation. In contrast, no tumor control was found after 5 × 3Gy Fx RT led despite a significant prolongation of OS. The tumor control probability 50% (TCP50) for CIRT in GL261 was estimated to be 10Gy in 5 Fx. To asses if the curative effect was related to tumor eradication or reconstitution of immune surveillance. GL261 tumor cells were implanted in the contralateral hemisphere of cured mice. Single cell and Bulk RNAseq as well as immunostaining were conducted to deconvolute TIME effects post CIRT. Conclusions: In line with mechanistic studies, lack of tumor growth indicating persistent immunity against GL261 was found in CIRT cured mice. Similar experiments are conducted in SB28 model and will be presented. Together, these encouraging data indicate potential benefits for high-LET CIRT in immune modulation of GBM in experimental models that warrants for study and optimization towards clinical translation. Citation Format: Federica Ciamarone, Sarah Meister, Jennifer Furkel, Maximilian Knoll, Christian Schwager, Jovana Bojcevski, Yannik Streibel, Christel Herold-Mende, Michael Breckwoldt, Juergen Debus, Ivana Dokic, Amir Abdollahi, Katharina Schregel. Carbon ion radiotherapy eradicates glioblastoma via tumor immune environment reprograming [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1105.

Objectives Direct toxic effects of transthyretin amyloid in patient plasma upon cardiomyocytes are discussed. However, no data regarding the relevance of this putative effect for clinical outcome are available. In this monocentric prospective study, we analyzed cellular hypertrophy after phenylephrine stimulation in vitro in the presence of patient plasma and correlated the cellular growth response with phenotype and prognosis. Methods and results Progress in automated microscopy and image analysis allows high-throughput analysis of cell morphology. Using the InCell microscopy system, changes in cardiomyocyte’s size after treatment with patient plasma from 89 patients suffering from transthyretin amyloidosis and 16 controls were quantified. For this purpose, we propose a novel metric that we named Hypertrophic Index, defined as difference in cell size after phenylephrine stimulation normalized to the unstimulated cell size. Its prognostic value was assessed for multiple endpoints (HTX: death/heart transplantation; DMP: cardiac decompensation; MACE: combined) using Cox proportional hazard models. Cells treated with plasma from healthy controls and hereditary transthyretin amyloidosis with polyneuropathy showed an increase in Hypertrophic Index after phenylephrine stimulation, whereas stimulation after treatment with hereditary cardiac amyloidosis or wild-type transthyretin patient plasma showed a significantly attenuated response. Hypertrophic Index was associated in univariate analyses with HTX (hazard ratio (HR) high vs low: 0.12 [0.02–0.58], p = 0.004), DMP: (HR 0.26 [0.11–0.62], p = 0.003) and MACE (HR 0.24 [0.11–0.55], p Conclusions Attenuated cardiomyocyte growth response after stimulation with patient plasma in vitro is an independent risk factor for adverse cardiac events in ATTR patients

Background Projection of future cancer incidence is an important task in cancer epidemiology. The results are of interest also for biomedical research and public health policy. Age-Period-Cohort (APC) models, usually based on long-term cancer registry data (> 20 yrs), are established for such projections. In many countries (including Germany), however, nationwide long-term data are not yet available. General guidance on statistical approaches for projections using rather short-term data is challenging and software to enable researchers to easily compare approaches is lacking. Methods To enable a comparative analysis of the performance of statistical approaches to cancer incidence projection, we developed an R package (incAnalysis), supporting in particular Bayesian models fitted by Integrated Nested Laplace Approximations (INLA). Its use is demonstrated by an extensive empirical evaluation of operating characteristics (bias, coverage and precision) of potentially applicable models differing by complexity. Observed long-term data from three cancer registries (SEER-9, NORDCAN, Saarland) was used for benchmarking. Results Overall, coverage was high (mostly > 90%) for Bayesian APC models (BAPC), whereas less complex models showed differences in coverage dependent on projection-period. Intercept-only models yielded values below 20% for coverage. Bias increased and precision decreased for longer projection periods (> 15 years) for all except intercept-only models. Precision was lowest for complex models such as BAPC models, generalized additive models with multivariate smoothers and generalized linear models with age x period interaction effects. Conclusion The incAnalysis R package allows a straightforward comparison of cancer incidence rate projection approaches. Further detailed and targeted investigations into model performance in addition to the presented empirical results are recommended to derive guidance on appropriate statistical projection methods in a given setting.

Glioblastoma (GBM) derived from the “stem cell” rich subventricular zone (SVZ) may constitute a therapy-refractory subgroup of tumors associated with poor prognosis. Risk stratification for these cases is necessary but is curtailed by error prone imaging-based evaluation. Therefore, we aimed to establish a robust DNA methylome-based classification of SVZ GBM and subsequently decipher underlying molecular characteristics. MRI assessment of SVZ association was performed in a retrospective training set of IDH-wildtype GBM patients (n = 54) uniformly treated with postoperative chemoradiotherapy. DNA isolated from FFPE samples was subject to methylome and copy number variation (CNV) analysis using Illumina Platform and cnAnalysis450k package. Deep next-generation sequencing (NGS) of a panel of 130 GBM-related genes was conducted (Agilent SureSelect/Illumina). Methylome, transcriptome, CNV, MRI, and mutational profiles of SVZ GBM were further evaluated in a confirmatory cohort of 132 patients (TCGA/TCIA). A 15 CpG SVZ methylation signature (SVZM) was discovered based on clustering and random forest analysis. One third of CpG in the SVZM were associated with MAB21L2/LRBA. There was a 14.8% (n = 8) discordance between SVZM vs. MRI classification. Re-analysis of these patients favored SVZM classification with a hazard ratio (HR) for OS of 2.48 [95% CI 1.35–4.58], p = 0.004 vs. 1.83 [1.0–3.35], p = 0.049 for MRI classification. In the validation cohort, consensus MRI based assignment was achieved in 62% of patients with an intraclass correlation (ICC) of 0.51 and non-significant HR for OS (2.03 [0.81–5.09], p = 0.133). In contrast, SVZM identified two prognostically distinct subgroups (HR 3.08 [1.24–7.66], p = 0.016). CNV alterations revealed loss of chromosome 10 in SVZM– and gains on chromosome 19 in SVZM– tumors. SVZM– tumors were also enriched for differentially mutated genes (p

BackgroundSelective uptake of (18)F-fluoro-ethyl-tyrosine (18F-FET) is used in high-grade glioma (HGG) to assess tumor metabolic activity via positron emission tomography (PET). We aim to investigate its value for target volume definition, as a prognosticator, and associations with whole-blood transcriptome liquid biopsy (WBT lbx) for which we recently reported feasibility to mirror tumor characteristics and response to particle irradiation in recurrent HGG (rHGG).Methods18F-FET-PET data from n = 43 patients with primary glioblastoma (pGBM) and n = 33 patients with rHGG were assessed. pGBM patients were irradiated with photons and sequential proton/carbon boost, and rHGG patients were treated with carbon re-irradiation (CIR). WBT (Illumina HumanHT-12 Expression BeadChips) lbx was available for n = 9 patients from the rHGG cohort. PET isocontours (40%–70% SUVmax, 10% steps) and MRI-based treatment volumes (MRIvol) were compared using the conformity index (CI) (pGBM, n = 16; rHGG, n = 27). Associations with WBT lbx data were tested on gene expression level and inferred pathways activity scores (PROGENy) and from transcriptome estimated cell fractions (CIBERSORT, xCell).ResultsIn pGBM, median SUVmax was higher in PET acquired pre-radiotherapy (4.1, range (R) 1.5–7.8; n = 20) vs. during radiotherapy (3.3, R 1.5–5.7, n = 23; p = 0.03) and in non-resected (4.7, R 2.9–7.9; n = 11) vs. resected tumors (3.3, R 1.5–7.8, n = 32; p = 0.01). In rHGG, a trend toward higher SUVmax values in grade IV tumors was observed (p = 0.13). Median MRIvol was 32.34 (R 8.75–108.77) cm3 in pGBM (n = 16) and 20.77 (R 0.63–128.44) cm3 in rHGG patients (n = 27). The highest median CI was observed for 40% (pGBM, 0.31) and 50% (rHGG, 0.43, all tumors) isodose, with 70% (40%) isodose in grade III (IV) rHGG tumors (median CI, 0.38 and 0.49). High SUVmax was linked to shorter survival in pGBM (>3.3, p = 0.001, OR 6.0 [2.1–17.4]) and rHGG (>2.8, p = 0.02, OR 4.1 [1.2–13.9]). SUVmax showed associations with inferred monocyte fractions, hypoxia, and TGFbeta pathway activity and links to immune checkpoint gene expression from WBT lbx.ConclusionThe benefits of 18F-FET-PET imaging on gross tumor volume (GTV) definition for particle radiotherapy warrant further evaluation. SUVmax might assist in prognostic stratification of HGG patients for particle radiotherapy, highlights heterogeneity in rHGG, and is positively associated with unfavorable signatures in peripheral whole-blood transcriptomes.

Background Targeted immunotherapies are of growing interest in the treatment of various cancers. B7 homolog 3 protein (B7-H3), a member of the co-stimulatory/-inhibitory B7-family, exerts immunosuppressive and pro-tumorigenic functions in various cancer types and is under evaluation in ongoing clinical trials. Unfortunately, interaction partner(s) remain unknown which restricts the druggability. Methods Aiming to identify potential binding partner(s) of B7-H3, a yeast two-hybrid and a mass spectrometry screen were performed. Potential candidates were evaluated by bimolecular fluorescence complementation (BiFC) assay, co-immunoprecipitation (co-IP), and functionally in a 3H-thymidine proliferation assay of Jurkat cells, a T-cell lineage cell line. Prognostic value of angio-associated migratory cell protein (AAMP) and B7-H3 expression was evaluated in isocitrate dehydrogenase 1 wildtype (IDH1wt) glioblastoma (GBM) patients from The Cancer Genome Atlas (TCGA)-GBM cohort. Results Of the screening candidates, CD164, AAMP, PTPRA, and SLAMF7 could be substantiated via BiFC. AAMP binding could be further confirmed via co-IP and on a functional level. AAMP was ubiquitously expressed in glioma cells, immune cells, and glioma tissue, but did not correlate with glioma grade. Finally, an interaction between AAMP and B7-H3 could be observed on expression level, hinting toward a combined synergistic effect. Conclusions AAMP was identified as a novel interaction partner of B7-H3, opening new possibilities to create a targeted therapy against the pro-tumorigenic costimulatory protein B7-H3.

Summary Cellular morphology has the capacity to serve as a surrogate for cellular state and functionality. However, primary cardiomyocytes, the standard model in cardiovascular research, are highly heterogeneous cells and therefore impose methodological challenges to analysis. Hence, we aimed to devise a robust methodology to deconvolute cardiomyocyte morphology on a single-cell level: C-MORE (cellular morphology recognition) is a workflow from bench to data analysis tailored for heterogeneous primary cells using our R package cmoRe. We demonstrate its utility in proof-of-principle applications such as modulation of canonical hypertrophy pathways and linkage of genotype-phenotype in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). In our pilot study, exposure of cardiomyocytes to blood plasma prior to versus after aortic valve replacement allows identification of a disease fingerprint and reflects partial reversibility following therapeutic intervention. C-MORE is a valuable tool for cardiovascular research with possible fields of application in basic research and personalized medicine., Graphical abstract, Highlights C-MORE deconvolutes cardiomyocyte morphology on single-cell level C-MORE could be utilized for high-throughput pharmacological screens C-MORE detects pathological phenotypes caused by genetic alterations C-MORE liquid biopsy setup reflects disease state in individuals with aortic stenosis, Furkel et al. present an integrative single-cell morphology-based strategy (C-MORE) to detect cardiomyocyte activation status and response to pharmacological treatment. Deconvolution of disease state in cardiomyocyte cultures exposed to liquid biopsies and in genetically engineered models by C-MORE may pave the way for development of next-generation personalized cardiovascular medicine.

To investigate brain tissue response to ultra-high dose rate (uHDR, FLASH) and standard dose rate (SDR) proton irradiations in the Bragg peak region.Active scanning uHDR delivery was established for proton beams for investigation of dose rate effects between clinical SDR and uHDR at ∼10 Gy in the Bragg peak region (dose-averaged linear energy transfer [LETAveraged dose rates achieved were 0.17 Gy/s (SDR) and 120 Gy/s (uHDR). The fraction of RIF-positive cells increased after SDR ∼10-fold, whereas a significantly lower fraction of RIF-positive cells was found after uHDR versus SDR (∼2 fold, P.0001). Moreover, uHDR substantially preserved the microvascular architecture and reduced microglia/macrophage regulated associated inflammation as compared with SDR.The feasibility of uHDR raster scanning proton irradiation is demonstrated to elicit FLASH sparing neuroprotective effects compared to SDR in a preclinical in vivo model.

Facing the increasing amount of available data, supervision of processes is experiencing a vast upheaval. Especially time series recorded during high-speed manufacturing processes like shear-cutting challenge the interpretation of the data. This work shows how to extract features from shear cutting force data that help to explain process variations. The ability to predict the product quality based on these features, however, plays a decisive role. Here the classic approach of feature engineering, in which features are selected using domain-specific knowledge of the engineer, is compared to statistical feature extraction which only bases on the actual process data. The use of these features aims at identifying the process state and product properties using predictive models. Both feature extraction methods are applied on force data and evaluated empirically in three different shear cutting processes. It turns out that both methods perform similar but differ in the presence of measurement uncertainty. Although simple prediction models have been used in this study, the features provide an excellent basis for predicting process or product properties.

MicroRNAs (miRs) are non-coding master regulators of transcriptome that could act as tumor suppressors (TSs) or oncogenes (oncomiRs). We aimed to systematically investigate the relevance of miRs as prognostic biomarkers in primary glioblastoma multiforme (GBM) treated with postoperative radio(chemo)therapy (PORT). For hypothesis generation, tumor miR expression by Agilent 8x15K human microRNA microarrays and survival data from 482 GBM patients of The Cancer Genome Atlas (TCGA cohort) were analyzed using Cox-PH models. Expression of candidate miRs with prognostic relevance (miR-221/222, miR-17-5p, miR-18a, miR-19b) was validated by qRT-PCR using Taqman technology on an independent validation cohort of GBM patients (n = 109) treated at Heidelberg University Hospital (HD cohort). In TCGA, 50 miRs showed significant association with survival. Among the top ranked prognostic miRs were members of the two miR families miR-221/222 and miR-17-92. Loss of miR-221/222 was correlated with improved prognosis in both cohorts (TCGA, HD) and was an independent prognostic marker in a multivariate analysis considering demographic characteristics (age, sex, Karnofsky performance index (KPI)), molecular markers (O-6-methylguanine-DNA methyltransferase (MGMT) methylation, IDH mutation status) and PORT as co-variables. The prognostic value of miR-17-92 family members was ambiguous and in part contradictory by direct comparison of the two cohorts, thus warranting further validation in larger prospective trials.

The potential existence of spatial clusters in childhood cancer incidence is a debated topic. Identification of rare disease clusters in general may help to better understand disease etiology and develop preventive strategies against such entities. The incidence of newly diagnosed childhood malignancies under 15 years of age is 140/1,000,000. In this context, the subgroup of nephroblastoma represents an extremely rare entity with an annual incidence of 7/1,000,000. We evaluated widely used statistical approaches for spatial cluster detection in childhood cancer (Ref. [22] Schundeln et al., 2021, Cancer Epidemiology). For the simulation study, random high risk clusters of 1 to 50 adjacent districts (NUTS-level 3, nomenclature des unites territoriales statistiques) were generated on the basis of the 402 German administrative districts. Each cluster was simulated with different relative risk levels (1 to 100). For each combination of cluster size and risk level 2000 iterations were performed. Simulated data was then analyzed by three local clustering tests: Besag-Newell method, spatial scan statistic and the Bayesian Besag-York-Mollie approach (fit by Integrated Nested Laplace Approximation). The performance characteristics of all three methods were systematically documented (sensitivity, specificity, positive/negative predictive values, exact- and minimum power, correct classification, positive/negative diagnostic likelihood and false positive/negative rate). This data article links to a Mendeley online repository which includes the raw data of simulated high-risk clusters and simulated cases on the district level for an all-childhood-malignancy scenario as well as for cases of nephroblastoma. These data was used for the evaluation of the three cluster detection methods. The R code for simulation and analysis are available from GitHub. The article also includes analyzed data summarizing the performance of the cluster detection tests in very rare disease entities, using the example of simulated nephroblastoma cases. The raw data from the study can be used for benchmarking analyses applying different spatial statistical methods systematically and evaluating their performance characteristics comparatively. The analyzed data from the nephroblastoma example can be useful to interpret the performance of the three applied local cluster detection tests in the setting of extremely rare disease entities. As a practical application, data and R code can be used for performance analyses when planning to establish surveillance systems for rare disease entities.

Uncertainty affects all phases of the product life cycle of technical systems, from design and production to their usage, even beyond the phase boundaries. Its identification, analysis and representation are discussed in the previous chapter. Based on the gained knowledge, our specific approach on mastering uncertainty can be applied. These approaches follow common strategies that are described in the subsequent chapter, but require individual methods and technologies. In this chapter, first legal and technical aspects for mastering uncertainty are discussed. Then, techniques for product design of technical systems under uncertainty are presented. The propagation of uncertainty is analysed for particular examples of process chains. Finally, semi-active and active technical systems and their relation to uncertainty are discussed.

This chapter serves as an introduction to the main topic of this book, namely to master uncertainty in technical systems. First, the difference of our approach to previous ones is highlighted. We then discuss process chains as an important type of technical systems, in which uncertainty propagates along the chain. Five different approaches to master uncertainty in process chains are presented: uncertainty identification, uncertainty propagation, robust optimisation, sensitivity analysis and model adaption. The influence of the process on uncertainty and methods depends on whether it is dynamic/time-varying and/or active. This brings us to the main strategies for mastering uncertainty: robustness, flexibility and resilience. Finally, three different concrete technical systems that are used to demonstrate our methods are presented.

BACKGROUND AND OBJECTIVE: The potential existence of spatial clusters in childhood cancer incidence is a debated topic. Identification of such clusters may help to better understand etiology and develop preventive strategies. We evaluated widely used statistical approaches to cluster detection in this context.; METHODS: Incidence of newly diagnosed childhood cancer (140/1,000,000 children under 15 years) and nephroblastoma (7/1,000,000) was simulated. Clusters of defined size (1-50) were randomly assembled on the district level in Germany. Each cluster was simulated with different relative risk levels (1-100). For each combination 2000 iterations were done. Simulated data was then analyzed by three local clustering tests: Besag-Newell method, spatial scan statistic and Bayesian Besag-York-Mollie with Integrated Nested Laplace Approximation approach. The operating characteristics (sensitivity, specificity, predictive values, power and correct classification) of all three methods were systematically described.; RESULTS: Performance varied considerably within and between methods, depending on the simulated setting. Sensitivity of all methods was positively associated with increasing size, incidence and RR of the high-risk area. Besag-York-Mollie showed highest specificity for minimally increased RR in most scenarios. The performance of all methods was lower in the nephroblastoma scenario compared with the scenario including all cancer cases.; CONCLUSION: This study illustrates the challenge to make reliable inferences on the existence of spatial clusters based on single statistical approaches in childhood cancer. Application of multiple methods, ideally with known operating characteristics, and a critical discussion of the joint evidence seems recommendable when aiming to identify high-risk clusters. Copyright © 2020 Elsevier Ltd. All rights reserved.

Cellular morphology has the capacity to serve as surrogate for cellular state and functionality. However, primary cardiomyocytes, the standard model in cardiovascular research, are highly heterogeneous cells and thereby impose methodological challenges to analysis. Hence, we aimed to devise a robust methodology to deconvolute cardiomyocytes’ morphology on a single cell level: C-MORE (cellular morphology recognition) is a workflow from bench to data analysis tailored for heterogeneous primary cells using our new R-package cmoRe. We demonstrate its utility in proof-of-principle applications such as modulation of canonical hypertrophy pathways and linkage of genotype-phenotypes in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM). In our pilot study, exposure of cardiomyocytes to patients’ blood plasma prior vs. post aortic valve replacement allowed identification of a disease fingerprint and reflected partial reversibility following therapeutic intervention. In summary, C-MORE constitutes a valuable tool for cardiovascular research with possible fields of application in basic research and personalized medicine.

Purpose: To develop a model to accurately segment mouse lungs with varying levels of fibrosis and investigate its applicability to mouse images with different resolutions.Materials and Methods: In this experimental retrospective study, a U-Net was trained to automatically segment lungs on mouse CT images. The model was trained (n = 1200), validated (n = 300), and tested (n = 154) on longitudinally acquired and semiautomatically segmented CT images, which included both healthy and irradiated mice (group A). A second independent group of 237 mice (group B) was used for external testing. The Dice score coefficient (DSC) and Hausdorff distance (HD) were used as metrics to quantify segmentation accuracy. Transfer learning was applied to adapt the model to high-spatial-resolution mouse micro-CT segmentation (n = 20; group C [n = 16 for training and n = 4 for testing]).Results: The trained model yielded a high median DSC in both test datasets: 0.984 (interquartile range [IQR], 0.977-0.988) in group A and 0.966 (IQR, 0.955-0.972) in group B. The median HD in both test datasets was 0.47 mm (IQR, 0-0.51 mm [group A]) and 0.31 mm (IQR, 0.30-0.32 mm [group B]). Spatially resolved quantification of differences toward reference masks revealed two hot spots close to the air-tissue interfaces, which are particularly prone to deviation. Finally, for the higher-resolution mouse CT images, the median DSC was 0.905 (IQR, 0.902-0.929) and the median 95th percentile of the HD was 0.33 mm (IQR, 2.61-2.78 mm).Conclusion: The developed deep learning-based method for mouse lung segmentation performed well independently of disease state (healthy, fibrotic, emphysematous lungs) and CT resolution.Keywords: Deep Learning, Lung Fibrosis, Radiation Therapy, Segmentation, Animal Studies, CT, Thorax, Lung Supplemental material is available for this article. Published under a CC BY 4.0 license.

This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.

Exploratory behaviour is an essential part of the behavioural repertoire of pigs. Providing pigs with a rooting area filled with appropriate material enables such behaviour and is therefore considered to improve animal welfare. Managing the hygiene of a rooting area, however, is often challenging when pigs use it also for elimination. Mixing corn pellets into the rooting material could increase use and exploration while reducing elimination behaviour. To investigate this hypothesis, we constructed rooting areas filled with compost produced from garden waste in four pens on a commercial organic farm. We compared two experimental pens (E) with rooting areas filled with compost, in which we mixed 2 kg of corn pellets every morning, with two control pens (C, rooting areas filled with compost only). The experiment started in October 2019 and lasted 34 weeks with seven replicates in total. Group size ranged between 21–35 pigs (N = 386). We registered behaviour once a week through direct observations of the complete outdoor area and additional video recordings of the rooting area. Behavioural variables of interest were activity status (i.e. standing/sitting or lying), rooting, agonistic and play behaviour. We assessed cleanliness of the rooting material via visual scoring and chemical analysis of compost samples. The latter included tests on dry matter content, conductivity, and ammonium concentration. Data were analysed with linear mixed-effects models. Results showed that there was a tendency for a higher total number of pigs in the rooting area in E than in C (p = 0.06). In E, more pigs were lying in the rooting area than in C (p = 0.04). There was no difference between treatments in rooting behaviour. In addition, the overall use of the outdoor run did not differ between treatments. Time of day influenced all recorded behaviours in the rooting area (p < 0.001). With increasing temperature, more pigs were present in the outdoor run (p < 0.001) and in the rooting area (p < 0.01) for both treatments. Conductivity and ammonium concentration in the compost increased the longer the compost remained in the rooting area (p < 0.001), but there was no difference between the two treatments. We conclude that mixing corn pellets into rooting material increases the use of the rooting area by heightening the overall presence of pigs in it but not its cleanliness.

BACKGROUND Elevated interleukin (IL)-6-levels have been described in familial variant transthyretin amyloidosis (ATTRv) associated polyneuropathy and heart failure. However, IL-6 in cardiac ATTR amyloidosis (ATTR-CM) and its prognostic value have not been investigated yet. AIM We aim to study the correlation between IL-6 levels with clinical presentation (Gillmore-class) and outcome [heart transplantation or death (htx/death)], or the combined endpoint of cardiac decompensation or htx/death in ATTR-CM. METHODS IL-6 levels of 106 ATTR-CM patients [54 wild-type ATTRwt, 52 ATTRv-CM], 15 asymptomatic carriers of ATTR mutations (aATTRv-CM) and 27 healthy donors were quantified using Luminex technology. Statistical analysis was performed using parametric survival regression models. RESULTS We found that IL-6 levels from wild-type ATTR patients were significantly elevated compared to healthy controls, while aATTRv-CM carriers and ATTRv-CM patients did not show a significant difference. IL-6 levels showed significantly higher values in increasing Gillmore classes. Univariate analyses revealed association of low IL-6 levels with cardiac decompensation and htx/death [odds ratio: 0.26 (0.09-0.72), P = 0.01] and htx/death [odds ratio: 0.15 (0.04-0.58), P = 0.006]. However, in the multivariate model, no significant improvement of risk prediction was seen for IL-6, while established prognostic factors were significantly associated with outcome. CONCLUSION Raised IL-6 levels correlate with clinical presentation and are associated with worse outcome in ATTR-CM but do not improve stratification in addition to established risk factors.

Background Projection of future cancer incidence is of high importance for epidemiological and biomedical research. Age-Period-Cohort (APC) models are established for such projections, usually based on long-term cancer registry data (>20yrs). In many countries (including Germany), however, nationwide long-term data are not yet available. It is unclear which statistical approach should be recommended for projections using rather short-term data. Methods An R package (incAnalysis) for the comparative analysis of projection methods was developed, supporting in particular Bayesian models fitted by Integrated Nested Laplace Approximations (INLA). It was used to assess operating characteristics (bias, coverage, precision) of various statistical approaches to cancer incidence projection based on artificially shortened long-term data from three cancer registries (SEER-9, NORDCAN, Saarland). Additionally, a novel age-period INLA z-model with multivariate tensor product smoothers is described. Results Coverage was high (mostly >90%) for Bayesian APC models (BAPC), whereas less complex models showed differences in coverage dependent on projection-period. Intercept-only models yielded values below 20% for coverage. Bias increased and precision decreased for longer projection periods for all except intercept-only models. Precision was lowest for BAPC, multivariate smoother and Age-Period GLMs with interaction effect. Conclusion The incAnalysis R package allows for straightforward comparison of (cancer incidence) rate projection approaches. Of the evaluated model classes, more complex models showed an increase in coverage at the cost of precision; bias increased for longer projection intervals, mostly irrespective of the level of complexity.

Future products will require new forming processes in order to meet the increasing requirements for innovative products. These processes will enable new possibilities in product manufacturing and the control of product properties beyond geometry control. For this purpose, a new process called punch-hole-rolling has been developed, which enables the production of a double-sided collar in thin sheet structures. Punch-hole-rolling is a two-stage process combination of conventional shear cutting and novel rolling which is carried out in one tool. The process combination allows to derive product data from the punching process for the control of the flexible hole-rolling in order to control product properties such as geometry, hardness and microstructure. In order to increase the process understanding and classification, an FE-simulation is built and validated by experiments.