Clinical outcomes in ROS1-fusion positive (ROS1+) non-small cell lung cancer (NSCLC) by fusion partner and resistance mechanisms are limited. This cohort study included 56 ROS1+ patients (FISH or NGS confirmed); fusion partners were identified in 27 cases, including CD74 (n = 10), EZR (n = 7), and SDC4 (n = 7). Clinical data were available for 50 patients (median age 62; 51% female; 32% never-smokers). Forty patients received tyrosine kinase inhibitors (TKIs), mostly crizotinib (n = 38). Crizotinib showed a 55% objective response rate (ORR) and a median progression-free survival (mPFS) of 5.3 months. Brain metastases (HR 2.65, 95% CI 1.06–6.60, P = 0.037) and prior chemotherapy (HR 3.17, 95% CI 1.35–7.45, P = 0.008) had a higher risk of progression. Sixteen patients received subsequent lorlatinib, with an ORR of 28% and mPFS of 3.7 months. G2032R and L2026M resistance mutations were identified in four lorlatinib non-responders, and in vitro studies confirmed resistance to lorlatinib. Fusion partners did not affect crizotinib outcomes. Lorlatinib was ineffective against on-target resistance. Real-world data showed lower TKI efficacy than clinical trials, highlighting the role of clinical and molecular factors in treatment response.
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This paper presents three qualitative models that were developed for the Stargazing Live! program. This program consists of a mobile planetarium that aims to inspire and motivate learners using real telescope data during the experience. To further consolidate the learning experience three lessons are available that teachers can use as follow up activities with their learners. The lessons implement a pedagogical approach that focuses on learning by creating qualitative models with the aim to have learners learn subject specific concepts as well as generic systems thinking skills. The three lessons form an ordered set with increasing complexity and were developed in close collaboration with domain experts.
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In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
The traffic safety of cyclists is under pressure. The number of fatalities and injuries is increasing, and the number of single-bicycle accidents is on the rise. However, from a traffic safety perspective, the most concerning trend is the growing number of incidents between motorized vehicles and cyclists. In addition to infrastructural solutions, such as more segregated and wider bike lanes, both industry and government are exploring technological developments to better safeguard cyclist safety. One of the technological solutions being considered is the use of C-V2X communication. C-V2X, Cellular Vehicle-to-X, is a technology that enables short-range signal exchanges between road users, informing them of each other's presence. C-V2X can be used, for example, to alert drivers via dedicated in-car information systems about the presence of cyclists on the road (e.g. at crossings). Although the technology and chipsets have been developed, the application of C-V2X to improve cyclist safety has not yet been thoroughly investigated. Therefore, HAN, Gazelle, and ARK Infomotives are researching the impact of C-V2X (on cyclist safety). Using advanced simulations with a digital twin in an urban environment and rural environment, the study will analyze how drivers respond to cyclist presence signals and determine the maximum penetration rate of ‘connected’ cyclists. Based on this, a pilot study will be conducted in a controlled environment on HAN terrain to validate the direction of the simulation results. The project aligns with the Missiegedreven Innovatiebeleid and the KIA Sleuteltechnologieën, specifically within application of digital and information technologies. This proposal aligns with the innovation domain of Semiconductor Technologies by applying advanced sensor and digital connectivity solutions to enhance cyclist safety. The project fits within the theme of Sleuteltechnologieën en Duurzame Materialen of the strategic research agenda of the VH by utilizing digital connectivity, sensor fusion, and data-driven decision-making for safer mobility solutions.
Traditioneel worden robots voornamelijk ingezet in gestructureerde, afgeschermde en voorspelbare omgevingen zoals fabrieken en magazijnen. Door technologische ontwikkelingen kunnen robots ook steeds beter in ongestructureerde en complexere omgevingen opereren, soms zelfs tussen mensen en dieren. Inspectierobots, verkenningsrobots, voederrobots of fruitplukrobots doen steeds vaker repeterend, vermoeiend of gevaarlijk werk. Ze kunnen bijvoorbeeld dag en nacht inspecties uitvoeren of onvermoeibaar op de akker werken. Ook kunnen ze worden ingezet voor zoek- en reddingsoperaties in gevaarlijke gebieden, bijvoorbeeld in conflictsituaties of na een ramp. Ondanks dat er afgelopen jaren grote stappen zijn gezet op het gebied van sensoren en kunstmatige intelligentie, blijft het een uitdaging om een robot volledig autonoom, dus zonder menselijke operator, te laten werken in een complexe omgeving. Eén uitdaging zit in het slim combineren van de verschillende sensoren om een goed beeld van de omgeving en van zijn eigen positie in die omgeving te creëren. Als dit niet goed lukt, dan moet de robot alsnog worden geholpen door een menselijke operator. Een robot gebruikt sensoren om te bepalen waar hij is. Huidige sensoren hebben echter tekortkomingen en maken meetfouten. Sensorfusie is het combineren van data uit verschillende sensoren om daarmee een betere schatting te doen. Het consortium heeft ervaring met het ontwikkelen van autonome robots en heeft daarbij geconstateerd dat het ontwikkelen van sensorfusie niet alleen essentieel is, maar dat het tevens uitdagend is om gestelde doelen te halen. De wens is daarom om te onderzoeken hoe we sensorfusie naar een hoger niveau kunnen brengen. In dit project analyseren en optimaliseren we de meest gebruikte methode voor het fuseren van encoders, IMUs, kompas en GNSS en vergelijken de huidige aanpak met recent ontwikkelde methodes. Met deze kennis kunnen Nederlandse technologiebedrijven voorop blijven lopen bij de ontwikkeling van autonome robots voor agrifood, inspectie, defensie en security-toepassingen.
