Refine
Has Fulltext
- yes (8)
Is part of the Bibliography
- no (8)
Keywords
Institute
- Medizin (4)
- Frankfurt Institute for Advanced Studies (FIAS) (3)
- Informatik (3)
- Physik (3)
- Informatik und Mathematik (2)
Flächenbezogene Artenzahlen sind besonders im Kontext von Monitoringprojekten grundlegend für die Beurteilung von Veränderungen der Biodiversität. Diese Studie vergleicht die von neun Bearbeitern (5 Einzelbearbeiter, 2 Zweierteams) erfasste Zahl an Gefäßpflanzenarten bei Vegetationserhebungen auf markierten Flächen von 4, 100 und 400 m2 Größe in einem artenreichen Kalkbuchenwald im Göttinger Stadtwald. Dabei wurden Bearbeiter- und Zeiteffekte untersucht, sowie artspezifische Übersehensraten, Fehlbestimmungsraten und Ungenauigkeiten bei der Zuordnung von Pflanzenindividuen zur jeweiligen Aufnahmefläche (Fehlzuordnungsraten) abgeschätzt.
Protokollierte Fragen ließen keine systematischen Unterschiede bei der Vertrautheit der Bearbeiter mit der Vegetation vor Ort erkennen, so dass Ausbildung und Erfahrung für gefundene Unterschiede ausschlaggebend sein dürften. Bei den 4 m2 großen Erhebungseinheiten ergaben sich bei der Artenzahl relative Abweichungen der Bearbeiter vom Erwartungswert von 8 bis 26 % (1 bis 4 Arten absolut). Diese waren bei den 100 m2 großen Erhebungseinheiten mit 9 bis 27 % (2 bis 6 Arten absolut) höher. Mit zunehmender Flächengröße nahm der Flächenidentitätseffekt tendenziell ab und der Bearbeitereffekt signifikant zu. Bei den 100 m2 großen Flächen hatte eine längere Bearbeitungszeit einen positiven Effekt auf die Artenzahl.
Mit Hilfe artbezogener Auswertungen wurden Übersehens-, Fehlbestimmungs- und Fehlzuordnungsraten ermittelt. Nicht eine Art wurde von allen Bearbeitern auf allen Flächen gefunden, auf denen sie jeweils auftrat. Schwer differenzierbare Arten sowie Arten in ungünstigen Entwicklungsstadien wiesen höhere Übersehens-, aber auch höhere Fehlbestimmungsraten auf. Bei morphologisch gut charakterisierten Arten wurde bei Einzelfunden von einer Fehlzuordnung zur Erhebungseinheit ausgegangen.
Die erzielten Ergebnisse sind auch für andere Projekte zur Erfassung der Biodiversität relevant und Bemühungen zur Reduzierung entsprechender Bearbeitereffekte sollten unternommen werden. Eine organisatorische Einbindung entsprechender Bemühungen wird vorgeschlagen.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p–Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.