Introduction: Deletion 1p36 syndrome is the most common terminal deletion syndrome. The authors describe a gene within the deletion that is responsible for the cardiomyopathy associated with monosomy 1p36, and confirm its role in nonsyndromic left ventricular noncompaction cardiomyopathy (LVNC) and dilated cardiomyopathy (DCM).
The classification and the position estimation of objects become more and more relevant as the field of robotics is expanding in diverse areas of society. In this Bachelor Thesis, we developed a cone detection algorithm for an autonomous car using a LiDAR sensor and a colour camera. By evaluating simple constraints, the LiDAR detection algorithm preselects cone candidates in the 3 dimensional space. The candidates are projected into the image plane of the colour camera and an image candidate is cropped out. A convolutional neural networks classifies the image candidates as cone or not a cone. With the fusion of the precise position estimation of the LiDAR sensor and the high classification accuracy of a neural network, a reliable cone detection algorithm was implemented. Furthermore, a path planning algorithm generates a path around the detected cones. The final system detects cones even at higher velocity and has the potential to drive fully autonomous around the cones.
Acute kidney injury (AKI) is a common complication following transcatheter aortic valve implantation (TAVI) leading to increased mortality and morbidity. Urinary G1 cell cycle arrest proteins TIMP-2 and IGFBP7 have recently been suggested as sensitive biomarkers for early detection of AKI in critically ill patients. However, the precise role of urinary TIMP-2 and IGFBP7 in patients undergoing TAVI is unknown. In a prospective observational trial, 40 patients undergoing TAVI (either transaortic or transapical) were enrolled. Serial measurements of TIMP-2 and IGFBP7 were performed in the early post interventional course. The primary clinical endpoint was the occurrence of AKI stage 2/3 according to the KDIGO classification. Now we show, that ROC analyses of [TIMP-2]*[IGFBP7] on day one after TAVI reveals a sensitivity of 100 % and a specificity of 90 % for predicting AKI 2/3 (AUC 0.971, 95 % CI 0.914-1.0, SE 0.0299, p = 0.001, cut-off 1.03). In contrast, preoperative and postoperative serum creatinine levels as well as glomerular filtration rate (GFR) and perioperative change in GFR did not show any association with the development of AKI. Furthermore, [TIMP-2]*[IGFBP7] remained stable in patients with AKI ≤1, but its levels increased significantly as early as 24 h after TAVI in patients who developed AKI 2/3 in the further course (4.77 ± 3.21 vs. 0.48 ± 0.68, p = 0.022). Mean patients age was 81.2 ± 5.6 years, 16 patients were male (40.0 %). 35 patients underwent transapical and five patients transaortic TAVI. 15 patients (37.5 %) developed any kind of AKI; eight patients (20 %) met the primary endpoint and seven patients required renal replacement therapy (RRT) within 72 h after surgery. Early elevation of urinary cell cycle arrest biomarkers after TAVI is associated with the development of postoperative AKI. [TIMP-2]*[IGFBP7] provides an excellent diagnostic accuracy in the prediction of AKI that is superior to that of serum creatinine.
An accurate and uncertainty-aware 3D human body pose estimation is key to enabling truly safe but efficient human-robot interactions. Current uncertainty-aware methods in 3D human pose estimation are limited to predicting the uncertainty of the body posture, while effectively neglecting the body shape and root pose. In this work, we present GloPro, which to the best of our knowledge the first framework to predict an uncertainty distribution of a 3D body mesh including its shape, pose, and root pose, by efficiently fusing visual clues with a learned motion model. We demonstrate that it vastly outperforms state-of-the-art methods in terms of human trajectory accuracy in a world coordinate system (even in the presence of severe occlusions), yields consistent uncertainty distributions, and can run in real-time.
Forestry constitutes a key element for a sustainable future, while it is supremely challenging to introduce digital processes to improve efficiency. The main limitation is the difficulty of obtaining accurate maps at high temporal and spatial resolution as a basis for informed forestry decision-making, due to the vast area forests extend over and the sheer number of trees. To address this challenge, we present an autonomous Micro Aerial Vehicle (MAV) system which purely relies on cost-effective and light-weight passive visual and inertial sensors to perform under-canopy autonomous navigation. We leverage visual-inertial simultaneous localization and mapping (VI-SLAM) for accurate MAV state estimates and couple it with a volumetric occupancy submapping system to achieve a scalable mapping framework which can be directly used for path planning. As opposed to a monolithic map, submaps inherently deal with inevitable drift and corrections from VI-SLAM, since they move with pose estimates as they are updated. To ensure the safety of the MAV during navigation, we also propose a novel reference trajectory anchoring scheme that moves and deforms the reference trajectory the MAV is tracking upon state updates from the VI-SLAM system in a consistent way, even upon large changes in state estimates due to loop-closures. We thoroughly validate our system in both real and simulated forest environments with high tree densities in excess of 400 trees per hectare and at speeds up to 3 m/s - while not encountering a single collision or system failure. To the best of our knowledge this is the first system which achieves this level of performance in such unstructured environment using low-cost passive visual sensors and fully on-board computation including VI-SLAM.
Immune surveillance depends in part on the recognition of peptide variants by T cell antigen receptors. Given that both normal B cells and malignant B cells accumulate mutations we chose a murine model of multiple myeloma to test conditions to induce cell-mediated immunity targeting malignant plasma cell (PC) clones but sparing of normal PCs. Revealing a previously unknown function for intracellular C3d, we found that C3d engaged T cell responses against malignant PC in the bone marrow of mice that had developed multiple myeloma spontaneously. Our results show that C3d internalized by cells augments immune surveillance by several mechanisms. In one, C3d induces a master transcription regulator, E2f1, to increase the expression of long noncoding (lnc) RNAs, to generate peptides for MHC-I presentation, and increase MHC-I expression. In another, C3d increases expression of RNAs encoding ribosomal proteins linked to processing of defective ribosomal products that arise from noncanonical translation and known to promote immunosurveillance. Cancer cells are uniquely susceptible to increased expression and presentation of mutant peptides given the extent of protein misfolding and accumulation of somatic mutations. Accordingly, although C3d can be internalized by any cell, C3d preferentially targets malignant clones by evoking specific T cell–mediated immunity and sparing most nontransformed polyclonal B cells and PC with lower mutation loads. Malignant PC deletion was blocked by cyclosporin or by CD8 depletion confirming that endogenous T cells mediated malignant clone clearance. Besides the potential for therapeutic application our results highlight how intracellular C3d modifies cellular metabolism to augment immune surveillance.
Robotic applications are continuously striving towards higher levels of autonomy. To achieve that goal, a highly robust and accurate state estimation is indispensable. Combining visual and inertial sensor modalities has proven to yield accurate and locally consistent results in short-term applications. Unfortunately, visual-inertial state estimators suffer from the accumulation of drift for long-term trajectories. To eliminate this drift, global measurements can be fused into the state estimation pipeline. The most known and widely available source of global measurements is the Global Positioning System (GPS). In this paper, we propose a novel approach that fully combines stereo Visual-Inertial Simultaneous Localisation and Mapping (SLAM), including visual loop closures, with the fusion of global sensor modalities in a tightly-coupled and optimisation-based framework. Incorporating measurement uncertainties, we provide a robust criterion to solve the global reference frame initialisation problem. Furthermore, we propose a loop-closure-like optimisation scheme to compensate drift accumulated during outages in receiving GPS signals. Experimental validation on datasets and in a real-world experiment demonstrates the robustness of our approach to GPS dropouts as well as its capability to estimate highly accurate and globally consistent trajectories compared to existing state-of-the-art methods.
