Crea.Blender: A Neural Network-Based Image Generation Game to Assess Creativity

We present a pilot study on crea.blender, a novel co-creative game designed for large-scale, systematic assessment of distinct constructs of human creativity. Co-creative systems are systems in which humans and computers (often with Machine Learning) collaborate on a creative task. This human-computer collaboration raises questions about the relevance and level of human creativity and involvement in the process. We expand on, and explore aspects of these questions in this pilot study. We observe participants play through three different play modes in crea.blender, each aligned with established creativity assessment methods. In these modes, players "blend" existing images into new images under varying constraints. Our study indicates that crea.blender provides a playful experience, affords players a sense of control over the interface, and elicits different types of player behavior, supporting further study of the tool for use in a scalable, playful, creativity assessment.Comment: 4 page, 6 figures, CHI Pla

A Phase II Trial of a Personalized, Dose-Intense Administration Schedule of (177)Lutetium-DOTATATE in Children With Primary Refractory or Relapsed High-Risk Neuroblastoma-LuDO-N

Background:& nbsp;Half the children with high-risk neuroblastoma die with widespread metastases. Molecular radiotherapy is an attractive systemic treatment for this relatively radiosensitive tumor. I-131-mIBG is the most widely used form in current use, but is not universally effective. Clinical trials of (177)Lutetium DOTATATE have so far had disappointing results, possibly because the administered activity was too low, and the courses were spread over too long a period of time, for a rapidly proliferating tumor. We have devised an alternative administration schedule to overcome these limitations. This involves two high-activity administrations of single agent Lu-177-DOTATATE given 2 weeks apart, prescribed as a personalized whole body radiation absorbed dose, rather than a fixed administered activity. "A phase II trial of (177)Lutetium-DOTATATE in children with primary refractory or relapsed high-risk neuroblastoma - LuDO-N " (EudraCT No: 2020-004445-36, Identifier: NCT04903899) evaluates this new dosing schedule.& nbsp;Methods:& nbsp;The LuDO-N trial is a phase II, open label, multi-center, single arm, two stage design clinical trial. Children aged 18 months to 18 years are eligible. The trial is conducted by the Nordic Society for Pediatric Hematology and Oncology (NOPHO) and it has been endorsed by SIOPEN (). The Karolinska University Hospital, is the sponsor of the LuDO-N trial, which is conducted in collaboration with Advanced Accelerator Applications, a Novartis company. All Scandinavian countries, Lithuania and the Netherlands participate in the trial and the UK has voiced an interest in joining in 2022.& nbsp;Results:& nbsp;The pediatric use of the Investigational Medicinal Product (IMP) Lu-177-DOTATATE, as well as non-IMPs SomaKit TOC (R) (Ga-68-DOTATOC) and LysaKare (R) amino acid solution for renal protection, have been approved for pediatric use, within the LuDO-N Trial by the European Medicines Agency (EMA). The trial is currently recruiting. Recruitment is estimated to be finalized within 3-5 years.& nbsp;Discussion:& nbsp;In this paper we present the protocol of the LuDO-N Trial. The rationale and design of the trial are discussed in relation to other ongoing, or planned trials with similar objectives. Further, we discuss the rapid development of targeted radiopharmaceutical therapy and the future perspectives for developing novel therapies for high-risk neuroblastoma and other pediatric solid tumors.Peer reviewe

Deskilling, Upskilling, and Reskilling: a Case for Hybrid Intelligence

International audienceAdvances in AI technology affect knowledge work in diverse fields, including healthcare, engineering, and management. Although automation and machine support can increase efficiency and lower costs, it can also, as an unintended consequence, deskill workers, who lose valuable skills that would otherwise be maintained as part of their daily work. Such deskilling has a wide range of negative effects on multiple stakeholders –– employees, organizations, and society at large. This essay discusses deskilling in the age of AI on three levels - individual, organizational and societal. Deskilling is furthermore analyzed through the lens of four different levels of human-AI configurations and we argue that one of them, Hybrid Intelligence, could be particularly suitable to help manage the risk of deskilling human experts. Hybrid Intelligence system design and implementation can explicitly take such risks into account and instead foster upskilling of workers. Hybrid Intelligence may thus, in the long run, lower costs and improve performance and job satisfaction, as well as prevent management from creating unintended organization-wide deskilling

Automated analysis of neuronal morphology, synapse number and synaptic recruitment

The shape, structure and connectivity of nerve cells are important aspects of neuronal function. Genetic and epigenetic factors that alter neuronal morphology or synaptic localization of pre- and post-synaptic proteins contribute significantly to neuronal output and may underlie clinical states. To assess the impact of individual genes and disease-causing mutations on neuronal morphology, reliable methods are needed. Unfortunately, manual analysis of immuno-fluorescence images of neurons to quantify neuronal shape and synapse number, size and distribution is labor-intensive, time-consuming and subject to human bias and error. We have developed an automated image analysis routine using steerable filters and deconvolutions to automatically analyze dendrite and synapse characteristics in immuno-fluorescence images. Our approach reports dendrite morphology, synapse size and number but also synaptic vesicle density and synaptic accumulation of proteins as a function of distance from the soma as consistent as expert observers while reducing analysis time considerably. In addition, the routine can be used to detect and quantify a wide range of neuronal organelles and is capable of batch analysis of a large number of images enabling high-throughput analysis