De perforatione capitis fetus : dissertatio inauguralis obstetricia

http://tartu.ester.ee/record=b1972542~S1*es

Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets

Recent advances in computer vision have led to significant progress in the generation of realistic image data, with denoising diffusion probabilistic models proving to be a particularly effective method. In this study, we demonstrate that diffusion models can effectively generate fully-annotated microscopy image data sets through an unsupervised and intuitive approach, using rough sketches of desired structures as the starting point. The proposed pipeline helps to reduce the reliance on manual annotations when training deep learning-based segmentation approaches and enables the segmentation of diverse datasets without the need for human annotations. This approach holds great promise in streamlining the data generation process and enabling a more efficient and scalable training of segmentation models, as we show in the example of different practical experiments involving various organisms and cell types.Comment: 9 pages, 2 figure

In situ control of As dimer orientation on Ge(100) surfaces

We investigated the preparation of single domain Ge(100):As surfaces in a metal-organic vapor phase epitaxy reactor. In situ reflection anisotropy spectra (RAS) of vicinal substrates change when arsenic is supplied either by tertiarybutylarsine or by background As4 during annealing. Low energy electron diffraction shows mutually perpendicular orientations of dimers, scanning tunneling microscopy reveals distinct differences in the step structure, and x-ray photoelectron spectroscopy confirms differences in the As coverage of the Ge(100): As samples. Their RAS signals consist of contributions related to As dimer orientation and to step structure, enabling precise in situ control over preparation of single domain Ge(100): As surfaces

Prevalence and antimicrobial susceptibility of Arcobacter species in human stool samples derived from out- and inpatients: the prospective German Arcobacter prevalence study Arcopath

Background: Arcobacter species, particularly A. butzleri, but also A. cryaerophilus constitute emerging pathogens causing gastroenteritis in humans. However, isolation of Arcobacter may often fail during routine diagnostic procedures due to the lack of standard protocols. Furthermore, defined breakpoints for the interpretation of antimicrobial susceptibilities of Arcobacter are missing. Hence, reliable epidemiological data of human Arcobacter infections are scarce and lacking for Germany. We therefore performed a 13-month prospective Arcobacter prevalence study in German patients. Results: A total of 4636 human stool samples was included and Arcobacter spp. were identified from 0.85% of specimens in 3884 outpatients and from 0.40% of specimens in 752 hospitalized patients. Overall, A. butzleri was the most prevalent species (n = 24; 67%), followed by A. cryaerophilus (n = 10; 28%) and A. lanthieri (n = 2; 6%). Whereas A. butzleri, A. cryaerophilus and A. lanthieri were identified in outpatients, only A. butzleri could be isolated from samples of hospitalized patients. Antimicrobial susceptibility testing of Arcobacter isolates revealed high susceptibilities to ciprofloxacin, whereas bimodal distributions of MICs were observed for azithromycin and ampicillin. Conclusions: In summary, Arcobacter including A. butzleri, A. cryaerophilus and A. lanthieri could be isolated in 0.85% of German outpatients and ciprofloxacin rather than other antibiotics might be appropriate for antibiotic treatment of infections. Further epidemiological studies are needed, however, to provide a sufficient risk assessment of Arcobacter infections in humans

Chemical compositions at Mars landing sites subject to Mars Odyssey Gamma Ray Spectrometer constraints

The Mars Odyssey Gamma Ray Spectrometer (GRS) is the first instrument suite to return elemental abundances throughout the midlatitudes of Mars. Concentrations of Cl, Fe, H, K, Si, and Th have been determined to tens of centimeter depths as mass fractions with reasonable confidence. Comparing such data with, or normalizing them to, in situ compositional data is difficult due to issues such as dramatic differences in spatial resolution; difficulties in convolving densities, abundances, and compositions of different regolith components; and a limited number of elements observed in common. We address these concerns in the context of the GRS, using Si at Pathfinder to normalize remote data. In addition, we determine representative in situ compositions for Spirit (both with and without Columbia Hills rocks), Opportunity, and Viking 1 landing sites using GRS-derived H content to hydrate the soil component. Our estimate of the Si mass fraction at Pathfinder, with 13% areal fraction of rocks, is 21%. The composition of major elements, such as Si and Fe, is similar across the four landing sites, while minor elements show significant variability. Areal dominance of soil at all four landing sites causes representative compositions to be driven by the soil component, while proportionally large uncertainties of bulk densities dominate the net uncertainties. GRS compositional determinations compare favorably with the in situ estimates for Cl and K, and for Si by virtue of the normalization. However, the GRS-determined Fe content at each landing site is consistently higher than the in situ value. Copyright 2007 by the American Geophysical Union

Tomographic reconstruction with a generative adversarial network

This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps. The GAN has been developed to fit the input sinogram with the model sinogram generated from the predicted reconstruction. Good quality reconstructions can be obtained during the minimization of the fitting errors. The reconstruction is a self-training procedure based on the physics model, instead of on training data. The algorithm showed significant improvements in the reconstruction accuracy, especially for missing-wedge tomography acquired at less than 180° rotational range. It was also validated by reconstructing a missing-wedge X-ray ptychographic tomography (PXCT) data set of a macroporous zeolite particle, for which only 51 projections over 70° could be collected. The GANrec recovered the 3D pore structure with reasonable quality for further analysis. This reconstruction concept can work universally for most of the ill-posed inverse problems if the forward model is well defined, such as phase retrieval of in-line phase-contrast imaging

In situ control of the GE(100)surface domain structure for III-V multijunction solar cells

Vicinal Ge(100) is the common substrate for state of the art multi-junction solar cells grown by metal-organic vapor phase epitaxy (MOVPE). While triple junction solar cells based on Ge(100) present efficiencies mayor que 40%, little is known about the microscopic III-V/Ge(100) nucleation and its interface formation. A suitable Ge(100) surface preparation prior to heteroepitaxy is crucial to achieve low defect densities in the III-V epilayers. Formation of single domain surfaces with double layer steps is required to avoid anti-phase domains in the III-V films. The step formation processes in MOVPE environment strongly depends on the major process parameters such as substrate temperature, H2 partial pressure, group V precursors [1], and reactor conditions. Detailed investigation of these processes on the Ge(100) surface by ultrahigh vacuum (UHV) based standard surface science tools are complicated due to the presence of H2 process gas. However, in situ surface characterization by reflection anisotropy spectroscopy (RAS) allowed us to study the MOVPE preparation of Ge(100) surfaces directly in dependence on the relevant process parameters [2, 3, 4]. A contamination free MOVPE to UHV transfer system [5] enabled correlation of the RA spectra to results from UHV-based surface science tools. In this paper, we established the characteristic RA spectra of vicinal Ge(100) surfaces terminated with monohydrides, arsenic and phosphorous. RAS enabled in situ control of oxide removal, H2 interaction and domain formation during MOVPE preparation

In situ control of Si(100) and Ge(100) surface preparation for the heteroepitaxy of III-V solar cell architectures

Si(100) and Ge(100) substrates essential for subsequent III-V integration were studied in the hydrogen ambient of a metalorganic vapor phase epitaxy reactor. Reflectance anisotropy spectroscopy (RAS) enabled us to distinguish characteristic configurations of vicinal Si(100) in situ: covered with oxide, cleaned by thermal removing in H2, and terminated with monohydrides when cooling in H2 ambient. RAS measurements during cooling in H2 ambient after the oxide removal process revealed a transition from the clean to the monohydride terminated Si(100) surface dependent on process temperature. For vicinal Ge(100) we observed a characteristic RA spectrum after annealing and cooling in H2 ambient. According to results from X-ray photo electron spectroscopy and Fourier-transform infrared spectroscopy the spectrum corresponds to the monohydride terminated Ge(100) surface

Datenbank Biomassepotenziale - Erstellung einer Datenbank zur Berechnung regionaler Biomassepotenziale aus dem landwirtschaftlichen Anbau und ausgewählten Reststoffen für den Non-Food-Bereich im Freistaat Sachsen bis zum Jahr 2020

Ziele des FuE-Vorhabens waren die Bereitstellung von Daten über die im Freistaat Sachsen bis 2020 regional tatsächlich verfügbaren Potenziale an Biomasse und die Gewinnung von mehr Sicherheit im Hinblick auf die Auswirkungen der Einflussfaktoren, denen diese Potenziale unterliegen. Dazu wurde eine Datenbank zur Berechnung der sächsischen Biomassepotenziale aus der Landwirtschaft und von ausgewählten Reststoffen für den Non-Food-Bereich erarbeitet. Diese ist durch folgende Punkte gekennzeichnet: - Durch die Berücksichtigung von Einflussfaktoren wie regional anzutreffende landwirtschaftliche Standorte, standortabhängige vom Nutzer festzulegende Fruchtfolgen, Ertragsentwicklung, Bevölkerungsentwicklung und verfügbare Ackerflächen werden differenzierte Daten berechnet. - Die Berechnungen können auf drei Ebenen erfolgen: für Sachsen, Regierungsbezirke und je Landkreis. - Es wurden zwei Beispielsszenarien berechnet, an Hand derer die Potenziale bei variierten Annahmen verdeutlicht werden. - Hinweise zur Arbeitsweise der Datenbank wurden in einem Handbuch zusammengestellt. Die Datenbank ermöglicht mit den flexiblen Eingabedaten die Berechnung konkreter regionalspezifischer Daten für das Potenzial aus landwirtschaftlicher Biomasse. Dies verbessert die Voraussetzungen für regionale Planungen von Vorhaben, die Biomasse als Rohstoff beanspruchen, erheblich