Detection of KRAS, NRAS and BRAF by mass spectrometry - a sensitive, reliable, fast and cost-effective technique

Background: According to current clinical guidelines mutational analysis for KRAS and NRAS is recommended prior to EGFR-directed therapy of colorectal cancer (CRC) in the metastatic setting. Therefore, reliable, fast, sensitive and cost-effective methods for routine tissue based molecular diagnostics are required that allow the assessment of the CRC mutational status in a high throughput fashion. Methods: We have developed a custom designed assay for routine mass-spectrometric (MS) (MassARRAY®, Agena Bioscience) analysis to test the presence/absence of 18 KRAS, 14 NRAS and 4 BRAF mutations. We have applied this assay to 93 samples from patients with CRC and have compared the results with Sanger sequencing and a chip hybridization assay (KRAS LCD-array Kit, Chipron). In cases with discordant results, next-generation sequencing (NGS) was performed. Results: MS detected a KRAS mutation in 46/93 (49 %), a NRAS mutation in 2/93 (2 %) and a BRAF mutation in 1/93 (1 %) of the cases. MS results were in agreement with results obtained by combination of the two other methods in 92 (99 %) of 93 cases. In 1/93 (1 %) of the cases a G12V mutation has been detected by Sanger sequencing and MS, but not by the chip assay. In this case, NGS has confirmed the G12V mutation in KRAS. Conclusions: Mutational analysis by MS is a reliable method for routine diagnostic use, which can be easily extended for testing of additional mutations

Integration of the 3D Environment for UAV Onboard Visual Object Tracking

Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D structure of the observed scene into a detection-by-tracking algorithm. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking. By representing the position of the target in 3D space rather than in image space, we stabilize the tracking during ego-motion and improve the handling of occlusions, background clutter, and small-scale objects. We evaluated our approach on prototypical image sequences, captured from a UAV with low-altitude oblique views. For this purpose, we adapted an existing dataset for visual object tracking and reconstructed the observed scene in 3D. The experimental results demonstrate that the proposed approach outperforms methods using plain visual cues as well as approaches leveraging image-space-based state estimations. We believe that our approach can be beneficial for traffic monitoring, video surveillance, and navigation.Comment: Accepted in MDPI Journal of Applied Science

READMem: Robust Embedding Association for a Diverse Memory in Unconstrained Video Object Segmentation

We present READMem (Robust Embedding Association for a Diverse Memory), a modular framework for semi-automatic video object segmentation (sVOS) methods designed to handle unconstrained videos. Contemporary sVOS works typically aggregate video frames in an ever-expanding memory, demanding high hardware resources for long-term applications. To mitigate memory requirements and prevent near object duplicates (caused by information of adjacent frames), previous methods introduce a hyper-parameter that controls the frequency of frames eligible to be stored. This parameter has to be adjusted according to concrete video properties (such as rapidity of appearance changes and video length) and does not generalize well. Instead, we integrate the embedding of a new frame into the memory only if it increases the diversity of the memory content. Furthermore, we propose a robust association of the embeddings stored in the memory with query embeddings during the update process. Our approach avoids the accumulation of redundant data, allowing us in return, to restrict the memory size and prevent extreme memory demands in long videos. We extend popular sVOS baselines with READMem, which previously showed limited performance on long videos. Our approach achieves competitive results on the Long-time Video dataset (LV1) while not hindering performance on short sequences. Our code is publicly available.Comment: Accepted to BMVC 2023. Code @ https://github.com/Vujas-Eteph/READMe