A comprehensive study on the deformation behavior of ultra-fine grained and ultra-fine porous Au at elevated temperatures

Modern design and engineering of highly efficient devices and machines demand innovative materials to satisfy requirements such as high strength at low density. The purpose of this study was to compare mechanical properties and deformation behavior of ultra-fine grained Au and its ultra-fine porous counterpart, both fabricated from the same base material. Microstructural investigations of the foam surrendered a ligament size of approximately 100 nm consisting of ~60 nm grains in average. The ultra-fine grained Au features a mean grain size of 250 nm. Nanoindentation is a convenient technique to obtain materials properties at ambient but also at non-ambient conditions and elevated temperatures. In this work, a broad indentation test series was performed in order to determine hardness, Young’s modulus, strain-rate sensitivity, and activation volume between room and elevated temperatures up to 300 °C for both materials. Due to the small characteristic dimensions, high hardness values were noted for both materials, which rapidly drop at elevated temperatures. In addition, an enhanced strain-rate sensitivity accompanied by low activation volumes was determined, increasing with elevated temperatures for both states. This can clearly be associated with interactions between dislocations and interphases. Moreover, for ultra-fine porous Au, a considerable increase of hardness was observed after annealing, which potentially can be attributed to starvation of mobile dislocations not occurring in the ultra-fine grained state. Cross-sections of indentations in ultra-fine porous Au combined with quantitative analysis of the resulting porosity maps allow visualizing the occurring deformation of the foam properly, showing distinct differences for tests at varying conditions. While the as-fabricated material exhibits distributed plasticity underneath the indent, this changes to strongly localized failure events in the annealed condition. At increased temperature, the deformation morphology reverts to more distributed deformation favored by the additional thermal activation

Phase transformations in a model mesenchymal tissue

doi: 10.1088/1478-3967/1/2/006 http://iopscience.iop.org/1478-3975/1/2/006/Connective tissues, the most abundant tissue type of the mature mammalian body, consist of cells suspended in complex microenvironments known as extracellular matrices (ECMs). In the immature connective tissues (mesenchymes) encountered in developmental biology and tissue engineering applications, the ECMs contain varying amounts of randomly arranged fibers, and the physical state of the ECM changes as the fibers secreted by the cells undergo fibril and fiber assembly and organize into networks. In vitro composites consisting of assembling solutions of type I collagen, containing suspended polystyrene latex beads (~6 µm in diameter) with collagen-binding surface properties, provide a simplified model for certain physical aspects of developing mesenchymes. In particular, assembly-dependent topological (i.e., connectivity) transitions within the ECM could change a tissue from one in which cell-sized particles (e.g., latex beads or cells) are mechanically unlinked to one in which the particles are part of a mechanical continuum. Any particle-induced alterations in fiber organization would imply that cells could similarly establish physically distinct microdomains within tissues. Here we show that the presence of beads above a critical number density accelerates the sol-gel transition that takes place during the assembly of collagen into a globally interconnected network of fibers. The presence of this suprathreshold number of beads also dramatically changes the viscoelastic properties of the collagen matrix, but only when the initial concentration of soluble collagen is itself above a critical value. Our studies provide a starting point for the analysis of phase transformations of more complex biomaterials including developing and healing tissues as well as tissue substitutes containing living cells.This work was supported by the Deutsche Forschungsgemeinschaft (Sa. 246/22-4, a group grant SFB 266), the Fonds der Chemischen Industrie, and grants from the National Science Foundation to GF (IBN-97100010) and SAN (IBN-9603838) and NASA to GF (NAG2-1611)

Europe's plans for responsible science

In the past, European framework programs for research and innovation have included funding for the integration of science and society (1). Collaborative projects have brought together diverse sets of actors to co-create and implement common agendas through citizen science, science communication, public engagement, and responsible research and innovation (RRI) and have built an evidence base about science-society interaction (2, 3). In the proposal for the upcoming Horizon Europe program, however, there is no sustained support for RRI, nor is there a program line dedicated to co-creating knowledge and agendas with civil society (4). These serious oversights must be corrected before the Horizon Europe program is adopted by the Council and the European Parliament

Beiträge zur Geschichte des Landkreises Regensburg 38

Marginalien von 11 Autoren, darin: Fendl, Josef: Kurzer Abriss der Geschichte des Gebietes des Landkreises Regensburg (S. 3); Koch, Robert: Die Ausgrabungen 1985 in Obertraubling (S. 4-5); Regensburger Bistumsblatt: Die Emmerams-Reliquien sind echt (S. 6-7); Kaiserdiplom Ottos II. im lateinischen Original und in deutscher Übersetzung (S. 8-10); Fendl, Josef: Gebelkofen - bereits vor 850 Jahren urkundlich erwähnt (S. 11); Fendl, Josef: Von Wappen und Siegeln (S. 12-14); Fendl, Josef: Unsere Familiennamen im Regensburger Raum (S. 15); Mittelbayerische Zeitung: Wolfsegger Höhle nach Jahren wiederentdeckt (S. 16); Als der Regierungspräsident Schrubber zuteilte (S. 16); Mittelbayerische Zeitung: Wie der Selfmademann Kronseder zu seiner Weltfirma kam ( S. 18-19); Sparkasse Regensburg: Gedenkmünze zur 800-Jahr-Feier von Pfakofen (S. 22); Le Forgeron, Emanuel: Ein barockes Fest in Alteglofsheim (S. 22-24); Fendl, Josef: Festvortrag 850 Jahre Johannishof (S. 25-29); Maier, Erich: Die Renovierung der Egglfinger Kirche 1983-1986 (S. 30); Straßenbauamt Regensburg: Einblicke in die Geschichte der Brücke von Donaustauf (S. 31-36); Peter, Helmut: Donaubrücke Donaustauf (S. 36-37); Schindlbeck, Georg: Ein "Gänshänger"-Brief (S. 38

Impact of route of access and stenosis subtype on outcome after transcatheter aortic valve replacement

Introduction: Previous analyses have reported the outcomes of transcatheter aortic valve replacement (TAVR) for patients with low-flow, low-gradient (LFLG) aortic stenosis (AS), without stratifying according to the route of access. Differences in mortality rates among access routes have been established for high-gradient (HG) patients and hypothesized to be even more pronounced in LFLG AS patients. This study aims to compare the outcomes of patients with LFLG or HG AS following transfemoral (TF) or transapical (TA) TAVR. Methods: A total of 910 patients, who underwent either TF or TA TAVR with a median follow-up of 2.22 (IQR: 1.22-4.03) years, were included in this multicenter cohort study. In total, 146 patients (16.04%) suffered from LFLG AS. The patients with HG and LFLG AS were stratified according to the route of access and compared statistically. Results: The operative mortality rates of patients with HG and LFLG were found to be comparable following TF access. The operative mortality rate was significantly increased for patients who underwent TA access [odds ratio (OR): 2.91 (1.54-5.48), p = 0.001] and patients with LFLG AS [OR: 2.27 (1.13-4.56), p = 0.02], which could be corroborated in a propensity score-matched subanalysis. The observed increase in the risk of operative mortality demonstrated an additive effect [OR for TA LFLG: 5.45 (2.35-12.62), p < 0.001]. LFLG patients who underwent TA access had significantly higher operative mortality rates (17.78%) compared with TF LFLG (3.96%, p = 0.016) and TA HG patients (6.36%, p = 0.024). Conclusions: HG patients experienced a twofold increase in operative mortality rates following TA compared with TF access, while LFLG patients had a fivefold increase in operative mortality rates. TA TAVR appears suboptimal for patients with LFLG AS. Prospective studies should be conducted to evaluate alternative options in cases where TF is not possible

Impact of route of access and stenosis subtype on outcome after transcatheter aortic valve replacement.

INTRODUCTION Previous analyses have reported the outcomes of transcatheter aortic valve replacement (TAVR) for patients with low-flow, low-gradient (LFLG) aortic stenosis (AS), without stratifying according to the route of access. Differences in mortality rates among access routes have been established for high-gradient (HG) patients and hypothesized to be even more pronounced in LFLG AS patients. This study aims to compare the outcomes of patients with LFLG or HG AS following transfemoral (TF) or transapical (TA) TAVR. METHODS A total of 910 patients, who underwent either TF or TA TAVR with a median follow-up of 2.22 (IQR: 1.22-4.03) years, were included in this multicenter cohort study. In total, 146 patients (16.04%) suffered from LFLG AS. The patients with HG and LFLG AS were stratified according to the route of access and compared statistically. RESULTS The operative mortality rates of patients with HG and LFLG were found to be comparable following TF access. The operative mortality rate was significantly increased for patients who underwent TA access [odds ratio (OR): 2.91 (1.54-5.48), p = 0.001] and patients with LFLG AS [OR: 2.27 (1.13-4.56), p = 0.02], which could be corroborated in a propensity score-matched subanalysis. The observed increase in the risk of operative mortality demonstrated an additive effect [OR for TA LFLG: 5.45 (2.35-12.62), p < 0.001]. LFLG patients who underwent TA access had significantly higher operative mortality rates (17.78%) compared with TF LFLG (3.96%, p = 0.016) and TA HG patients (6.36%, p = 0.024). CONCLUSIONS HG patients experienced a twofold increase in operative mortality rates following TA compared with TF access, while LFLG patients had a fivefold increase in operative mortality rates. TA TAVR appears suboptimal for patients with LFLG AS. Prospective studies should be conducted to evaluate alternative options in cases where TF is not possible

Deep-learning-based reconstruction of undersampled MRI to reduce scan times:a multicentre, retrospective, cohort study

BACKGROUND: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p&lt;0·0001). INTERPRETATION: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. FUNDING: Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.</p

Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study

Background: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. Methods: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. Findings: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). Interpretation: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation

Deep-learning-based reconstruction of undersampled MRI to reduce scan times:a multicentre, retrospective, cohort study

BACKGROUND: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p&lt;0·0001). INTERPRETATION: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. FUNDING: Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.</p