4 research outputs found
HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints
No full textWith approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images like robustness to light variation or color- and texture invariance. Correspondingly, we introduce High- Resolution Depth Net (HRDepthNet)—a machine learning driven approach to detect human joints (body, head, and upper and lower extremities) in purely depth images. HRDepthNet retrains the original HRNet for depth images. Therefore, a dataset is created holding depth (and RGB) images recorded with subjects conducting the timed up and go test—an established geriatric assessment. The images were manually annotated RGB images. The training and evaluation were conducted with this dataset. For accuracy evaluation, detection of body joints was evaluated via COCO’s evaluation metrics and indicated that the resulting depth image-based model achieved better results than the HRNet trained and applied on corresponding RGB images. An additional evaluation of the position errors showed a median deviation of 1.619 cm (x-axis), 2.342 cm (y-axis) and 2.4 cm (z-axis)
TGFα controls checkpoints in CNS resident and infiltrating immune cells to promote resolution of inflammation
No full textAfter acute lesions in the central nervous system (CNS), the interaction of microglia, astrocytes, and infiltrating immune cells decides over their resolution or chronification. However, this CNS-intrinsic cross-talk is poorly characterized. Analyzing cerebrospinal fluid (CSF) samples of Multiple Sclerosis (MS) patients as well as CNS samples of female mice with experimental autoimmune encephalomyelitis (EAE), the animal model of MS, we identify microglia-derived TGFα as key factor driving recovery. Through mechanistic in vitro studies, in vivo treatment paradigms, scRNA sequencing, CRISPR-Cas9 genetic perturbation models and MRI in the EAE model, we show that together with other glial and non-glial cells, microglia secrete TGFα in a highly regulated temporospatial manner in EAE. Here, TGFα contributes to recovery by decreasing infiltrating T cells, pro-inflammatory myeloid cells, oligodendrocyte loss, demyelination, axonal damage and neuron loss even at late disease stages. In a therapeutic approach in EAE, blood-brain barrier penetrating intranasal application of TGFα attenuates pro-inflammatory signaling in astrocytes and CNS infiltrating immune cells while promoting neuronal survival and lesion resolution. Together, microglia-derived TGFα is an important mediator of glial-immune crosstalk, highlighting its therapeutic potential in resolving acute CNS inflammation
The astrocyte-produced growth factor HB-EGF limits autoimmune CNS pathology
No full textCentral nervous system (CNS)-resident cells such as microglia, oligodendrocytes and astrocytes are gaining increasing attention in respect to their contribution to CNS pathologies including multiple sclerosis (MS). Several studies have demonstrated the involvement of pro-inflammatory glial subsets in the pathogenesis and propagation of inflammatory events in MS and its animal models. However, it has only recently become clear that the underlying heterogeneity of astrocytes and microglia can not only drive inflammation, but also lead to its resolution through direct and indirect mechanisms. Failure of these tissue-protective mechanisms may potentiate disease and increase the risk of conversion to progressive stages of MS, for which currently available therapies are limited. Using proteomic analyses of cerebrospinal fluid specimens from patients with MS in combination with experimental studies, we here identify Heparin-binding EGF-like growth factor (HB-EGF) as a central mediator of tissue-protective and anti-inflammatory effects important for the recovery from acute inflammatory lesions in CNS autoimmunity. Hypoxic conditions drive the rapid upregulation of HB-EGF by astrocytes during early CNS inflammation, while pro-inflammatory conditions suppress trophic HB-EGF signaling through epigenetic modifications. Finally, we demonstrate both anti-inflammatory and tissue-protective effects of HB-EGF in a broad variety of cell types in vitro and use intranasal administration of HB-EGF in acute and post-acute stages of autoimmune neuroinflammation to attenuate disease in a preclinical mouse model of MS. Altogether, we identify astrocyte-derived HB-EGF and its epigenetic regulation as a modulator of autoimmune CNS inflammation and potential therapeutic target in MS. Linnerbauer and colleagues find that HB-EGF produced by reactive astrocytes is protective during autoimmune neuroinflammation, but epigenetically suppressed during late stages
Dataset: Mask R-CNN Based C. Elegans Detection with a DIY Microscope
No full textThe dataset consists of images of C. elegans in Petri Dish that were captured at a frequency of 1 Hz at 3280 × 2464 pixels via a Raspberry Pi based DIY Microscope. Further details of the recording setup and the dataset can be found in the corresponding article.
Up on use, please cite the following article https://doi.org/10.3390/bios11080257 such as:
Fudickar, S.; Nustede, E.J.; Dreyer, E.; Bornhorst, J. Mask R-CNN Based C. Elegans Detection with a DIY Microscope. Biosensors 2021, 11, 257. https://doi.org/10.3390/bios11080257Usage:
To train the neural network, the specified folder for the RGB images is checked first with the os library and the paths of all contained jpg files are stored as strings in a list.
This list is then iterated and the first image is loaded via the openCV library.
The filename of each RGB image contains a number and an ID as unique identifier. This is used to identify the associated labels. For example, image with Filename "img_038_id2.jpg" has the ID 2.
In the associated masks folder, all image files are loaded that also have the 038_id2 in the file name, such as "m5_img_038_id2.jpg". So for each RGB image a list of mask images, also loaded with openCV, is kept in temporary memory. All images then run through the sliding window algorithm and are presented to the network in individual parts one after the other
