10 research outputs found
The Cityscapes Dataset for Semantic Urban Scene Understanding
Visual understanding of complex urban street scenes is an enabling factor for
a wide range of applications. Object detection has benefited enormously from
large-scale datasets, especially in the context of deep learning. For semantic
urban scene understanding, however, no current dataset adequately captures the
complexity of real-world urban scenes.
To address this, we introduce Cityscapes, a benchmark suite and large-scale
dataset to train and test approaches for pixel-level and instance-level
semantic labeling. Cityscapes is comprised of a large, diverse set of stereo
video sequences recorded in streets from 50 different cities. 5000 of these
images have high quality pixel-level annotations; 20000 additional images have
coarse annotations to enable methods that leverage large volumes of
weakly-labeled data. Crucially, our effort exceeds previous attempts in terms
of dataset size, annotation richness, scene variability, and complexity. Our
accompanying empirical study provides an in-depth analysis of the dataset
characteristics, as well as a performance evaluation of several
state-of-the-art approaches based on our benchmark.Comment: Includes supplemental materia
Glyco-Engineered Anti-Human Programmed Death-Ligand 1 Antibody Mediates Stronger CD8 T Cell Activation Than Its Normal Glycosylated and Non-Glycosylated Counterparts
The programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) axis plays a central role in suppression of anti-tumor immunity. Blocking the axis by targeting PD-L1 with monoclonal antibodies is an effective and already clinically approved approach to treat cancer patients. Glyco-engineering technology can be used to optimize different properties of monoclonal antibodies, for example, binding to FcγRs. We generated two glycosylation variants of the same anti-PD-L1 antibody: one bearing core fucosylated N-glycans in its Fc part (92%) and its de-fucosylated counterpart (4%). The two glycosylation variants were compared to a non-glycosylated commercially available anti-PD-L1 antibody in various assays. No differences were observed regarding binding to PD-L1 and blocking of this interaction with its counter receptors PD-1 or CD80. The de-fucosylated anti-PD-L1 antibody showed increased FcγRIIIa binding resulting in enhanced antibody dependent cellular cytotoxicity (ADCC) activity against PD-L1+ cancer cells compared to the “normal”-glycosylated variant. Both glycosylation variants showed no antibody-mediated lysis of B cells and monocytes. The non-glycosylated reference antibody showed no FcγRIIIa engagement and no ADCC activity. Using mixed leukocyte reaction it was observed that the de-fucosylated anti-PD-L1 antibody induced the strongest CD8 T cell activation determined by expression of activation markers, proliferation, and cytotoxicity against cancer cells. The systematic comparison of anti-PD-L1 antibody glycosylation variants with different Fc-mediated potencies demonstrates that our glyco-optimization approach has the potential to enhance CD8 T cell-mediated anti-tumor activity which may improve the therapeutic benefit of anti-PD-L1 antibodies
A-Life - Increasing Survival Chances in Avalanches by Wearable Sensors
Avalanches are one of the major threats to life in high mountain terrain. Once buried by an avalanche, survival chances dramatically drop from 92% after 15 minutes to only 30% after 35 minutes mostly due to the lack of oxygen. It is therefore extremely important to rescue any victims as fast as possible in order to maximize survival chances. Today 's technology, so called electronic avalanche beacons, only allow to localize buried victims. In this paper we propose a novel avalanche rescue system enhanced with wearable sensors. Those sensors provide information about the vital state of buried victims such as heart rate, respiration activity, and blood oxygen saturation, as well as the orientation of the victim. We believe that this knowledge can empower non-professional companion rescuers with a tool to perform triage, i.e. sorting victims into categories of priority for treatment. Better allocation of resources can help to maximize survival chances of avalanche victims
Chronic Serotonergic Overstimulation Mimicking Panic Attacks in a Patient with Parkinson’s Disease Receiving Additional Antidepressant Treatment with Moclobemide
Background. The pharmacological treatment options of Parkinson’s disease (PD) have considerably evolved during the last decades. However, therapeutic regimes are complicated due to individual differences in disease progression as well as the occurrence of complex nonmotor impairments such as mood and anxiety disorders. Antidepressants in particular are commonly prescribed for the treatment of depressive symptoms and anxiety in PD. Case Presentation. In this case report, we describe a case of a 62-year-old female patient with PD and history of depressive symptoms for which she had been treated with moclobemide concurrent with anti-Parkinson medications pramipexole, rasagiline, and L-DOPA+benserazide retard. An increase in the dosage of moclobemide 12 months prior to admission progressively led to serotonergic overstimulation and psychovegetative exacerbations mimicking the clinical picture of an anxiety spectrum disorder. After moclobemide and rasagiline were discontinued based on the hypothesis of serotonergic overstimulation, the patient’s psychovegetative symptoms subsided. Conclusions. The specific pharmacological regime in this case probably caused drug-drug interactions resulting in a plethora of psychovegetative symptoms. Likely due to the delayed onset of adverse effects, physicians had difficulties in determining the pharmacologically induced serotonin toxicity. This case report emphasizes the complexity of pharmacological treatments and the importance of drug-drug interaction awareness in the treatment of PD patients with complicating nonmotor dysfunctions such as depression
data_sheet_1_Glyco-Engineered Anti-Human Programmed Death-Ligand 1 Antibody Mediates Stronger CD8 T Cell Activation Than Its Normal Glycosylated and Non-Glycosylated Counterparts.docx
<p>The programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) axis plays a central role in suppression of anti-tumor immunity. Blocking the axis by targeting PD-L1 with monoclonal antibodies is an effective and already clinically approved approach to treat cancer patients. Glyco-engineering technology can be used to optimize different properties of monoclonal antibodies, for example, binding to FcγRs. We generated two glycosylation variants of the same anti-PD-L1 antibody: one bearing core fucosylated N-glycans in its Fc part (92%) and its de-fucosylated counterpart (4%). The two glycosylation variants were compared to a non-glycosylated commercially available anti-PD-L1 antibody in various assays. No differences were observed regarding binding to PD-L1 and blocking of this interaction with its counter receptors PD-1 or CD80. The de-fucosylated anti-PD-L1 antibody showed increased FcγRIIIa binding resulting in enhanced antibody dependent cellular cytotoxicity (ADCC) activity against PD-L1<sup>+</sup> cancer cells compared to the “normal”-glycosylated variant. Both glycosylation variants showed no antibody-mediated lysis of B cells and monocytes. The non-glycosylated reference antibody showed no FcγRIIIa engagement and no ADCC activity. Using mixed leukocyte reaction it was observed that the de-fucosylated anti-PD-L1 antibody induced the strongest CD8 T cell activation determined by expression of activation markers, proliferation, and cytotoxicity against cancer cells. The systematic comparison of anti-PD-L1 antibody glycosylation variants with different Fc-mediated potencies demonstrates that our glyco-optimization approach has the potential to enhance CD8 T cell-mediated anti-tumor activity which may improve the therapeutic benefit of anti-PD-L1 antibodies.</p
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac