Modellgestützte Optimierung des energetischen Eigenverbrauchs von Wohngebäuden bei sektorgekoppelter Wärmeversorgung – Vorstellung des POPART-Modells

Auf die Versorgung von Wohngebäuden mit Raumwärme und Warmwasser entfällt in Deutschland ein erheblicher Teil des Endenergiebedarfs. Ihrer Transformation kommt folglich eine gewichtige Rolle bei der Erreichung der energie- und klimapolitischen Ziele der Bundesregierung zu. Sogenannte sektorgekoppelte Wärmeerzeuger wie Wärmepumpen und Kraft-Wärme-Kopplungsanlagen ermöglichen es, nicht nur einen Beitrag zur Zielerreichung im Gebäudesektor zu leisten, sondern auch im Elektrizitätssektor durch die Bereitstellung elektrischer Flexibilität durch Wärmespeicherung. Das einschlägige wissenschaftliche Schrifttum weist gesamtwirtschaftliche Potenziale für diesen Ansatz nach. Das Interesse der vorliegenden Arbeit ist hingegen auf die Untersuchung der entsprechenden Potenziale aus einzelwirtschaftlicher Perspektive gerichtet. Ziel der vorliegenden Arbeit ist daher die Entwicklung eines geeigneten Analyseinstruments zur Untersuchung der Entscheidungssituation bzw. zur Entscheidungsunterstützung bei der Investition in und dem Betrieb von Anlagen zur Energieversorgung einzelner Wohngebäude. Dabei sollen neben erneuerbaren Energietechniken zur Wärme- und Stromversorgung insbesondere Wärmepumpen und objektbasierte Kraft-Wärme-Kopplungs-Anlagen betrachtet werden. Wesentlicher Inhalt der vorliegenden Arbeit ist die Entwicklung und Beschreibung des resultierenden POPART-Modells und seine Anwendung auf o.g. Fragestellungen. Der Fokus liegt dabei auf der Betrachtung älterer Bestandsgebäude mit Gasnetzanschluss. Die Auswertung der Modellergebnisse zeigt ein hohes einzelwirtschaftliches Potenzial für objektbasierte Energiekonzepte. Dabei spielen vor allem die Photovoltaik und objektbasierte Kraft-Wärme-Kopplungs-Anlagen eine wichtige Rolle, bei steigenden Gaspreisen auch die Solarthermie. Wärmepumpen spielen allerdings unter den untersuchten Rahmenbedingungen nur in Einzelfällen eine Rolle. Thermische und elektrische Speicher kommen bestenfalls in vernachlässigbaren Größenordnungen zum Einsatz. Die Ergebnisse erlauben die Schlussfolgerung, dass ein entscheidender Hebel zur Ausgabenminderung in der Verringerung des Strombezugs aus dem Netz durch Eigenverbrauch liegt. Anstelle der derzeit verbreiteten Praxis, die elektrische Eigenerzeugungsquote durch Investition in Speicherkapazitäten zu erhöhen, ist den Modellergebnissen zufolge vielmehr eine größere Dimensionierung der Umwandlungskapazitäten vorteilhaft. Insgesamt belegt die Ergebnisauswertung ein Spannungsverhältnis zwischen Sektorenkopplung und Eigenversorgung unter gegebenen regulatorischen Rahmenbedingungen, welches die Realisierung gesamtwirtschaftlicher Flexibilitätspotenziale in Frage stellt

Modellgestützte Untersuchung des wirtschaftlichen Potenzials sektorgekoppelter Wärmeversorgung in Wohngebäuden im Kontext der Transformation des Energiesystems in Deutschland

Im Kontext der Energie- und Klimaziele der Bundesregierung untersucht diese Arbeit das wirtschaftliche Potenzial sektorgekoppelter Wärmeerzeugertechniken in Wohngebäuden zur Flexibilisierung der Elektrizitätsversorgung in Deutschland. Dazu werden ein integriertes Energiesystemmodell zur Langfristplanung der Energieversorgung der Wohngebäude sowie ein Entscheidungsmodell zur Energieversorgung einzelner Wohngebäude entwickelt und angewendet

Wirkungsabschätzung von Flächenbelegungen in Ökobilanzen: Arbeitsstand einer Methodenentwicklung

Die Ökobilanz hat unter den in Anwendung befindlichen Umweltbewertungsmethoden einen besonderen Stellenwert und ist zwischenzeitlich bereits in einigen Gesetzen verankert. Für die Betrachtung der Flächennutzung in Ökobilanzen ist eine allgemein anerkannte methodische Lösung bislang allerdings noch nicht vorhanden. Zwar gibt es erste Ansätze, doch wird dabei auch der Bedarf an methodischer Erweiterung und insbesondere die Verbesserung der Datengrundlagen herausgestellt. Dieser Beitrag thematisiert eine Methodenentwicklung am Umweltbundesamt zur Berücksichtigung von temporärer Flächenbelegung und direkter sowie indirekter Flächennutzungsänderung von Produkten und Dienstleistungen im Rahmen der Ökobilanzierung. Zentraler Bestandteil ist die qualitative Bewertung der Flächennutzung und -änderung anhand von Charakterisierungsfaktoren auf Basis eines erweiterten Hemerobieansatzes. Die bisher erarbeitete Methode wurde u. a. für Biogas testweise angewendet, allerdings steht eine umfangreiche Erprobung noch aus. Dieser Beitrag stellt den bisherigen Arbeitsstand vor

Artificial intelligence‐based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargement

Background: Patients with Marfan syndrome are at risk for aortic enlargement and are routinely monitored by computed tomography (CT) imaging. The purpose of this study is to analyse body composition using artificial intelligence (AI)-based tissue segmentation in patients with Marfan syndrome in order to identify possible predictors of progressive aortic enlargement. Methods: In this study, the body composition of 25 patients aged <= 50 years with Marfan syndrome and no prior aortic repair was analysed at the third lumbar vertebra (L3) level from a retrospective dataset using an AI-based software tool (Visage Imaging). All patients underwent electrocardiography-triggered CT of the aorta twice within 2 years for suspected progression of aortic disease, suspected dissection, and/or pre-operative evaluation. Progression of aortic enlargement was defined as an increase in diameter at the aortic sinus or the ascending aorta of at least 2 mm. Patients meeting this definition were assigned to the 'progressive aortic enlargement' group (proAE group) and patients with stable diameters to the 'stable aortic enlargement' group (staAE group). Statistical analysis was performed using the Mann-Whitney U test. Two possible body composition predictors of aortic enlargement-skeletal muscle density (SMD) and psoas muscle index (PMI)-were analysed further using multivariant logistic regression analysis. Aortic enlargement was defined as the dependent variant, whereas PMI, SMD, age, sex, body mass index (BMI), beta blocker medication, and time interval between CT scans were defined as independent variants. Results: There were 13 patients in the proAE group and 12 patients in the staAE group. AI-based automated analysis of body composition at L3 revealed a significantly increased SMD measured in Hounsfield units (HUs) in patients with aortic enlargement (proAE group: 50.0 +/- 8.6 HU vs. staAE group: 39.0 +/- 15.0 HU; P = 0.03). PMI also trended towards higher values in the proAE group (proAE group: 6.8 +/- 2.3 vs. staAE group: 5.6 +/- 1.3; P = 0.19). Multivariate logistic regression revealed significant prediction of aortic enlargement for SMD (P = 0.05) and PMI (P = 0.04). Conclusions: Artificial intelligence-based analysis of body composition at L3 in Marfan patients is feasible and easily available from CT angiography. Analysis of body composition at L3 revealed significantly higher SMD in patients with progressive aortic enlargement. PMI and SMD significantly predicted aortic enlargement in these patients. Using body composition as a predictor of progressive aortic enlargement may contribute information for risk stratification regarding follow-up intervals and the need for aortic repair

Hepatocellular adenomas: is there additional value in using Gd-EOB-enhanced MRI for subtype differentiation?

Purpose: To differentiate subtypes of hepatocellular adenoma (HCA) based on enhancement characteristics in gadoxetic acid (Gd-EOB) magnetic resonance imaging (MRI). Materials and methods: Forty-eight patients with 79 histopathologically proven HCAs who underwent Gd-EOB-enhanced MRI were enrolled (standard of reference: surgical resection). Two blinded radiologists performed quantitative measurements (lesion-to-liver enhancement) and evaluated qualitative imaging features. Inter-reader variability was tested. Advanced texture analysis was used to evaluate lesion heterogeneity three-dimensionally. Results: Overall, there were 19 (24%) hepatocyte nuclear factor (HNF)-1a-mutated (HHCAs), 37 (47%) inflammatory (IHCAs), 5 (6.5%) b-catenin-activated (bHCA), and 18 (22.5%) unclassified (UHCAs) adenomas. In the hepatobiliary phase (HBP), 49.5% (39/79) of all adenomas were rated as hypointense and 50.5% (40/79) as significantly enhancing (defined as > 25% intralesional GD-EOB uptake). 82.5% (33/40) of significantly enhancing adenomas were IHCAs, while only 4% (1/40) were in the HHCA subgroup (p < 0.001). When Gd-EOB uptake behavior was considered in conjunction with established MRI features (binary regression model), the area under the curve (AUC) increased from 0.785 to 0.953 for differentiation of IHCA (atoll sign + hyperintensity), from 0.859 to 0.903 for bHCA (scar + hyperintensity), and from 0.899 to 0.957 for HHCA (steatosis + hypointensity). Three-dimensional region of interest (3D ROI) analysis showed significantly increased voxel heterogeneity for IHCAs (p = 0.038). Conclusion: Gd-EOB MRI is of added value for subtype differentiation of HCAs and reliably identifies the typical heterogeneous HBP uptake of IHCAs. Diagnostic accuracy can be improved significantly by the combined analysis of established morphologic MR appearances and intralesional Gd-EOB uptake. Key points: •Gd-EOB-enhanced MRI is of added value for subtype differentiation of HCA. •IHCA and HHCA can be identified reliably based on their typical Gd-EOB uptake patterns, and accuracy increases significantly when additionally taking established MR appearances into account. •The small numbers of bHCAs and UHCAs remain the source of diagnostic uncertainty

Effects of Artificial Intelligence-Derived Body Composition on Kidney Graft and Patient Survival in the Eurotransplant Senior Program

The Eurotransplant Senior Program allocates kidneys to elderly transplant patients. The aim of this retrospective study is to investigate the use of computed tomography (CT) body composition using artificial intelligence (AI)-based tissue segmentation to predict patient and kidney transplant survival. Body composition at the third lumbar vertebra level was analyzed in 42 kidney transplant recipients. Cox regression analysis of 1-year, 3-year and 5-year patient survival, 1-year, 3-year and 5-year censored kidney transplant survival, and 1-year, 3-year and 5-year uncensored kidney transplant survival was performed. First, the body mass index (BMI), psoas muscle index (PMI), skeletal muscle index (SMI), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) served as independent variates. Second, the cut-off values for sarcopenia and obesity served as independent variates. The 1-year uncensored and censored kidney transplant survival was influenced by reduced PMI (p = 0.02 and p = 0.03, respectively) and reduced SMI (p = 0.01 and p = 0.03, respectively); 3-year uncensored kidney transplant survival was influenced by increased VAT (p = 0.04); and 3-year censored kidney transplant survival was influenced by reduced SMI (p = 0.05). Additionally, sarcopenia influenced 1-year uncensored kidney transplant survival (p = 0.05), whereas obesity influenced 3-year and 5-year uncensored kidney transplant survival. In summary, AI-based body composition analysis may aid in predicting short- and long-term kidney transplant survival

Effects of Artificial Intelligence-Derived Body Composition on Kidney Graft and Patient Survival in the Eurotransplant Senior Program

The Eurotransplant Senior Program allocates kidneys to elderly transplant patients. The aim of this retrospective study is to investigate the use of computed tomography (CT) body composition using artificial intelligence (AI)-based tissue segmentation to predict patient and kidney transplant survival. Body composition at the third lumbar vertebra level was analyzed in 42 kidney transplant recipients. Cox regression analysis of 1-year, 3-year and 5-year patient survival, 1-year, 3-year and 5-year censored kidney transplant survival, and 1-year, 3-year and 5-year uncensored kidney transplant survival was performed. First, the body mass index (BMI), psoas muscle index (PMI), skeletal muscle index (SMI), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) served as independent variates. Second, the cut-off values for sarcopenia and obesity served as independent variates. The 1-year uncensored and censored kidney transplant survival was influenced by reduced PMI (p = 0.02 and p = 0.03, respectively) and reduced SMI (p = 0.01 and p = 0.03, respectively); 3-year uncensored kidney transplant survival was influenced by increased VAT (p = 0.04); and 3-year censored kidney transplant survival was influenced by reduced SMI (p = 0.05). Additionally, sarcopenia influenced 1-year uncensored kidney transplant survival (p = 0.05), whereas obesity influenced 3-year and 5-year uncensored kidney transplant survival. In summary, AI-based body composition analysis may aid in predicting short- and long-term kidney transplant survival

First PACS‐integrated artificial intelligence‐based software tool for rapid and fully automatic analysis of body composition from CT in clinical routine

Background: To externally evaluate the first picture archiving communications system (PACS)-integrated artificial intelligence (AI)-based workflow, trained to automatically detect a predefined computed tomography (CT) slice at the third lumbar vertebra (L3) and automatically perform complete image segmentation for analysis of CT body composition and to compare its performance with that of an established semi-automatic segmentation tool regarding speed and accuracy of tissue area calculation. Methods: For fully automatic analysis of body composition with L3 recognition, U-Nets were trained (Visage) and compared with a conventional image segmentation software (TomoVision). Tissue was differentiated into psoas muscle, skeletal muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Mid-L3 level images from randomly selected DICOM slice files of 20 CT scans acquired with various imaging protocols were segmented with both methods. Results: Success rate of AI-based L3 recognition was 100%. Compared with semi-automatic, fully automatic AI-based image segmentation yielded relative differences of 0.22% and 0.16% for skeletal muscle, 0.47% and 0.49% for psoas muscle, 0.42% and 0.42% for VAT and 0.18% and 0.18% for SAT. AI-based fully automatic segmentation was significantly faster than semi-automatic segmentation (3 ± 0 s vs. 170 ± 40 s, P < 0.001, for User 1 and 152 ± 40 s, P < 0.001, for User 2). Conclusion: Rapid fully automatic AI-based, PACS-integrated assessment of body composition yields identical results without transfer of critical patient data. Additional metabolic information can be inserted into the patient’s image report and offered to the referring clinicians

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN