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"Topographic Shift": a new digital approach to evaluating topographic changes of the female breast
(2021)
Purpose
To assess precise topographic changes of the breast, objective documentation and evaluation of pre- and postoperative results are crucial. New technologies for mapping the body using digital, three-dimensional surface measurements have offered novel ways to numerically assess the female breast. Due to the lack of clear demarcation points of the breast contour, the selection of landmarks on the breast is highly dependent on the examiner, and, therefore, is prone to error when conducting before-after comparisons of the same breast. This study describes an alternative to volumetric measurements, focusing on topographic changes of the female breast, based on three-dimensional scans.
Method
The study was designed as an interventional prospective study of 10 female volunteers who had planned on having aesthetic breast augmentation with anatomical, textured implants. Three dimensional scans of the breasts were performed intraoperatively, first without and then with breast implants. The topographic change was determined as the mean distance between two three-dimensional layers before and after augmentation. This mean distance is defined as the Topographic Shift. Results The mean implant volume was 283 cc (SD = 68.6 cc, range = 210-395 cc). The mean Topographic Shift was 7.4 mm (SD = 1.9 mm, range = 4.8-10.7 mm). The mean Topographic Shifts per quadrant were: I: 8.0 mm (SD = 3.3 mm); II: 9.2 mm (SD = 3.1 mm); III: 6.9 mm (SD = 3.5 mm); IV: 1.9 mm (SD = 4.3 mm).
Conclusion
The Topographic Shift, describing the mean distance between two three-dimensional layers (for example before and after a volume changing therapy), is a new approach that can be used for assessing topographic changes of a body area. It was found that anatomical, textured breast implants cause a topographic change, particularly on the upper breast, in quadrant II, the decollete.
Background:
Over the past ten years, we carried out several comprehensive studies in the area of entrepreneurship education using business games that simulate the business processes of startups. The studies researched the learning effects of these games on German university students.
Aim:
The studies we present investigated if the simulation game-based educational entrepreneurship programs in Germany reached their goals. These goals included the fostering of participants’ entrepreneurial competencies and desire to create a startup.
Method:
We show traditional summative output-oriented results and more profound results based on our theory-based evaluation approach. A so-called logic model provides a framework for the interpretation of what takes place during the entrepreneurship business simulations. Currently, we have conducted research on more than 50 regular university courses with more than 1000 students. Another part of our studies is the evaluation of entrepreneurship education in the German national business game-based competition, EXIST priME Cup (further called EPC). More than 12,000 students attended more than 500 of these cup seminars. We also show new results from an online survey that was carried out to measure the long-term effects of the startup simulation-based seminars.
Results:
The results of five studies are summarized, and show an overall increase in the participants’ knowledge of business administration and business plan preparation skills (required in start-up context). Apart from other results, significant gender-based differences are identified. The differences relate to entrepreneurial attitudes and motivation. Some test scenarios with cup seminars for women only with extended debriefing seem to reduce this decrease in the motivation of women; further research with larger samples is necessary. A new study on the long-term effects shows a very high acceptance rate and demonstrates the satisfaction of former students. The simulation game-based program resulted in participants starting startups at a rate that is approximately twice as high (around 16%) as the normal rate in Germany.
BACKGROUND:
Due to their corrugated profile, dragonfly wings have special aerodynamic characteristics during flying and gliding. OBJECTIVE: The aim of this study was to create a realistic 3D model of a dragonfly wing captured with a high-resolution micro-CT. To represent geometry changes in span and chord length and their aerodynamic effects, numerical investigations are carried out at different wing positions. METHODS:
The forewing of a Camacinia gigantea was captured using a micro-CT. After the wing was adapted an error-free 3D model resulted. The wing was cut every 5 mm and 2D numerical analyses were conducted in Fluent® 2020 R2 (ANSYS, Inc., Canonsburg, PA, USA). RESULTS: The highest lift coefficient, as well as the highest lift-to-drag ratio, resulted at 0 mm and an angle of attack (AOA) of 5∘. At AOAs of 10∘ or 15∘, the flow around the wing stalled and a Kármán vortex street behind the wing becomes
CONCLUSIONS:
The velocity is higher on the upper side of the wing compared to the lower side. The pressure acts vice versa. Due to the recirculation zones that are formed in valleys of the corrugation pattern the wing resembles the form of an airfoil.
New structural sheet metal parts are developed in an iterative, time-consuming manner. To improve the reproducibility and speed up the iterative drawability assessment, we propose a novel low-dimensional multi-fidelity inspired machine learning architecture. The approach utilizes the results of low-fidelity and high-fidelity finite element deep drawing simulation schemes. It hereby relies not only on parameters, but also on additional features to improve the generalization ability and applicability of the drawability assessment compared to classical approaches. Using the machine learning approach on a generated data set for a wide range of different cross-die drawing configurations, a classifier is trained to distinguish between drawable and non-drawable setups. Furthermore, two regression models, one for drawable and one for non-drawable designs are developed that rank designs by drawability. At instantaneous evaluation time, classification scores of high accuracy as well as regression scores of high quality for both regressors are achieved. The presented models can substitute low-fidelity finite element models due to their low evaluation times while at the same time, their predictive quality is close to high-fidelity models. This approach may enable fast and efficient assessments of designs in early development phases at the accuracy of a later design phase in the future.
In fatigue strength assessment, the methods based on ideal elastic stresses according to Basquin and the less established method based on elastic-plastic stress quantities according to Manson, Coffin and Morrow are applied. The former calculates loads using linear-elastic stresses, the latter requires elasticplastic evaluation parameters, such as stresses and strains. These can be determined by finite element analysis (FEA) with a linear-elastic constitutive law, and subsequent conversion to elastic-plastic loads, using the macro support formula by Neuber. In this contribution, an alternative approach to approximate elastic-plastic parameters proposed by Glinka is compared to the the strain-life method using Neuber's formula, as well as the stress-life method of Basquin. Several component tests on 42CrMoS4-QT specimens are investigated. To determine the input data for the fatigue strength evaluations, the entire test setup is computed by FEA. The nodal displacements from these validated full-model simulations are used as boundary conditions for a submodel simulation of a notch, whose results serve as input for the fatigue strength assessments. It is shown that all approaches provide a reliable assessment of components. Our key result is that the strain-life method using the concept by Glinka for notch stress computation, yields improved results in fatigue strength assessments.
Purpose
Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm.
Design Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study.
Participants.
We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study.
Methods.
We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study.
Main Outcome Measures.
κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification.
Results.
A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%–92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm detected 84.2% of all fundus images with definite signs of early or late AMD. Overall, 94.3% of healthy fundus images were classified correctly.
Conclusions
Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age.
Musculoskeletal research questions regarding the prevention or rehabilitation of the hand can be addressed using inverse dynamics simulations when experiments are not possible. To date, no complete human hand model implemented in a holistic human body model has been fully developed. The aim of this work was to develop, implement, and validate a fully detailed hand model using the AnyBody Modelling System (AMS) (AnyBody, Aalborg, Denmark). To achieve this, a consistent multiple cadaver dataset, including all extrinsic and intrinsic muscles, served as a basis. Various obstacle methods were implemented to obtain with the correct alignment of the muscle paths together with the full range of motion of the fingers. These included tori, cylinders, and spherical ellipsoids. The origin points of the lumbrical muscles within the tendon of the flexor digitorum profundus added a unique feature to the model. Furthermore, the possibility of an entire patient-specific scaling based on the hand length and width were implemented in the model. For model validation, experimental datasets from the literature were used, which included the comparison of numerically calculated moment arms of the wrist, thumb, and index finger muscles. In general, the results displayed good comparability of the model and experimental data. However, the extrinsic muscles showed higher accordance than the intrinsic ones. Nevertheless, the results showed, that the proposed developed inverse dynamics hand model offers opportunities in a broad field of applications, where the muscles and joint forces of the forearm play a crucial role.
In recent years, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques and automatic speech recognition (ASR) techniques that are robust to reverberation. In this paper, we describe the REVERB challenge, which is an evaluation campaign that was designed to evaluate such speech enhancement (SE) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions. Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single- channel scenario, a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation, based on both real, single- channel, and multichannel recordings. This challenge attracted 27 papers, which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes. This paper describes the problems dealt within the challenge, provides an overview of the submitted systems, and scrutinizes them to clarify what current processing strategies appear effective in reverberant speech processing.
To reduce particulate emissions, the use of particulate filters in diesel engines is meanwhile state of the art, while the integration of such systems in gasoline engines is now also necessary in order to comply with today's regulations. Over its lifetime, a gasoline particulate filter (GPF) collects ash components of fuel, lubrication oil, and materials originating from the catalytic coating and from engine abrasion. In the development and application process, synthetic ashing from GPFs is challenging. The ash of the lubrication oil can be increased in various ways, like oil-doped fuel, a separate oil burner, or changes in the piston-cylinder system of the engine. However, these methods show major disadvantages. For this reason, an improved approach is presented in this study, which allows a quick response to changes in combustion (e.g., engine knocking) and producing ash, which is more realistic due to its primary particle size and the density of ash plugs, in a shorter time. Therefore, an approach to accelerate ash loading by active oil injection using a multi-point injection (MPI) system is introduced. With the help of this methodology, an ashing capacity of 1.21 g/h is implemented, which is a high rate compared to other investigations. The primary particle size (evaluated by means of a transmission electron microscope), is in the same size range as those detected at the full-load curve during regular operation. A computer tomographic (CT) analysis of the incinerated particulate filter also shows that a very high density of ash plugs can be realized, which has also been found in the literature for real applications. In addition, with the help of a mass spectrometer (MS) and an intermediate weighing of the particulate filter, the current ash loading of the GPF could be determined with an accuracy of 1%.
Clot formation within membrane oxygenators (MOs) remains a critical problem during extracorporeal membrane oxygenation (ECMO). The composition of the clots-in particular, the presence of von Willebrand factor (vWF)-may be an indicator for prevalent nonphysiological flow conditions, foreign body reactions, or coagulation abnormalities in critically ill patients. Mats of interwoven gas exchange fibers from randomly collected MOs (PLS, Maquet, Rastatt, Germany) of 21 patients were stained with antibodies (anti-vWF and anti-P-selectin) and counterstained with 4 ',6-diamidino-2-phenylindole. The extent of vWF-loading was correlated with patient and technical data. While 12 MOs showed low vWF-loadings, 9 MOs showed high vWF-loading with highest accumulations close to crossing points of adjacent gas fibers. The presence and the extent of vWF-fibers/"cobwebs," leukocytes, platelet-leukocyte aggregates (PLAs), and P-selectin-positive platelet aggregates were independent of the extent of vWF-loading. However, the highly loaded MOs were obtained from patients with a significantly elevated SOFA score, severe thrombocytopenia, and persistent liver dysfunction. The coagulation abnormalities of these critically ill patients may cause an accumulation of the highly thrombogenic and elongated high-molecular-weight vWF multimers in the plasma which will be trapped in the MOs during the ECMO therapy.