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With the increasing prevalence of video recordings there is a growing need for tools that can maintain the privacy of those recorded. In this paper, we define an approach for redacting personally identifiable text from videos using a combination of optical character recognition (OCR) and natural language processing (NLP) techniques. We examine the relative performance of this approach when used with different OCR models, specifically Tesseract and the OCR system from Google Cloud Vision (GCV). For the proposed approach the performance of GCV, in both accuracy and speed, is significantly higher than Tesseract. Finally, we explore the advantages and disadvantages of both models in real-world applications.

BackgroundNext-generation cancer immunotherapies are designed to broaden the therapeutic repertoire by targeting new immune checkpoints including lymphocyte-activation gene 3 (LAG-3) and T cell immunoglobulin and mucin-domain containing-3 (TIM-3). Yet, the molecular and cellular mechanisms by which either receptor functions to mediate its inhibitory effects are still poorly understood. Similarly, little is known on the differential effects of dual, compared with single, checkpoint inhibition.MethodsWe here performed in-depth characterization, including multicolor flow cytometry, single cell RNA sequencing and multiplex supernatant analysis, using tumor single cell suspensions from patients with cancer treated ex vivo with novel bispecific antibodies targeting programmed cell death protein 1 (PD-1) and TIM-3 (PD1-TIM3), PD-1 and LAG-3 (PD1-LAG3), or with anti-PD-1.ResultsWe identified patient samples which were responsive to PD1-TIM3, PD1-LAG3 or anti-PD-1 using an in vitro approach, validated by the analysis of 659 soluble proteins and enrichment for an anti-PD-1 responder signature. We found increased abundance of an activated (HLA-DR+CD25+GranzymeB+) CD8+ T cell subset and of proliferating CD8+ T cells, in response to bispecific antibody or anti-PD-1 treatment. Bispecific antibodies, but not anti-PD-1, significantly increased the abundance of a proliferating natural killer cell subset, which exhibited enrichment for a tissue-residency signature. Key phenotypic and transcriptional changes occurred in a PD-1+CXCL13+CD4+ T cell subset, in response to all treatments, including increased interleukin-17 secretion and signaling toward plasma cells. Interestingly, LAG-3 protein upregulation was detected as a unique pharmacodynamic effect mediated by PD1-LAG3, but not by PD1-TIM3 or anti-PD-1.ConclusionsOur in vitro system reliably assessed responses to bispecific antibodies co-targeting PD-1 together with LAG-3 or TIM-3 using patients’ tumor infiltrating immune cells and revealed transcriptional and phenotypic imprinting by bispecific antibody formats currently tested in early clinical trials.

Natural Killer (NK) cells utilize effector functions, including antibody-dependent cellular cytotoxicity (ADCC), for the clearance of viral infection and cellular malignancies. NK cell ADCC is mediated by Fc RIIIa (CD16a) binding to the fragment crystallizable (Fc) region of immunoglobulin G (IgG) within immune complexes on a target cell surface. While antibody-induced clustering of CD16a is thought to drive ADCC, the molecular basis for this activity has not been fully described. Here we use MINFLUX nanoscopy to map the spatial distribution of stoichiometrically labeled CD16a across the NK cell membrane, revealing the presence of pairs of CD16a molecules with intra-doublet distance of approximately 17 nm. NK cells activated on supported lipid bilayers by Trastuzumab results in an increase of synaptic regions with greater CD16a density. Our results provide the highest spatial resolution yet described for CD16a imaging, offering new insight into how CD16a organization within the immune synapse could influence ADCC activity. MINFLUX holds great promise to further unravel the molecular details driving CD16a-based activation of NK cells.

In the future, mobility will be strongly shaped by the increasing use of digitalization. Not only will individual road users be highly interconnected, but also the road and associated infrastructure. At that point, a Digital Twin becomes particularly appealing because, unlike a basic simulation, it offers a continuous, bilateral connection linking the real and virtual environments. This paper describes the digital reconstruction used to develop the Digital Twin of the Test Area Autonomous Driving-Baden-Württemberg (TAF-BW), Germany. The TAF-BW offers a variety of different road sections, from high-traffic urban intersections and tunnels to multilane motorways. The test area is equipped with a comprehensive Vehicle-to-Everything (V2X) communication infrastructure and multiple intelligent intersections equipped with camera sensors to facilitate real-time traffic flow monitoring. The generation of authentic data as input for the Digital Twin was achieved by extracting object lists at the intersections. This process was facilitated by the combined utilization of camera images from the intelligent infrastructure and LiDAR sensors mounted on a test vehicle. Using a unified interface, recordings from real-world detections of traffic participants can be resimulated. Additionally, the simulation framework's design and the reconstruction process is discussed. The resulting framework is made publicly available for download and utilization at: https://digit4taf-bw.fzi.de The demonstration uses two case studies to illustrate the application of the digital twin and its interfaces: the analysis of traffic signal systems to optimize traffic flow and the simulation of security-related scenarios in the communications sector.