Smart Cities and Digitized Urban Management

By 2050, two-thirds of the world’s population is expected to reside in cities and urban agglomerations (UN Department of Economic and Social Affairs 2014). Cities have always showcased the best and the worst aspects of humanity – gleaming skyscrapers, art, and inventiveness on the one hand; slums, crime, and abject poverty on the other. Such challenges, which cities already face, will be further amplified by increasing urbanization, and it will be coastal cities in particular that bear the brunt of the global threat of climate change. Not without reason is the quest for “Sustainable Cities and Communities” one of the UN’s explicit sustainable development goals (United Nations 2015)

Prognostic genetic markers in malignant gliomas

Glioblastomas are the most frequent and malignant brain tumors in adults. Surgical cure is virtually impossible and despite of radiation and chemotherapy the clinical course is very poor. Epigenetic silencing of MGMT has been associated with a better response to temozolomide-chemotherapy. We previously showed that temozolomide increases the median survival time of patients with tumors harbouring deletions on 9p within the region for p15(INK4b), p16(INK4a), and 10q (MGMT). The aim of this study was to investigate the methylation status of p15, p16, 14ARF and MGMT in glioblastomas and to correlate the results with the clinical data.Only patients with KPS > 70, radical tumor resection, radiation and temozolomide-chemotherapy after recurrence were included. We observed promoter methylation of MGMT in 56% (15/27) and of p15 in 37% (10/27) of the tumors, whereas methylation of p16 and p14ARF were rare. Interestingly, methylation of p15 emerged as a significant predictor of shorter overall survival (16.9 vs. 23.8 months, p=0.025), whereas MGMT promoter methylation had no significant effect on median overall survival under this treatment regimen (22.5 vs. 22.1 months, p=0.49). In the presence of other clinically relevant factors, p15 methylation remains the only significant predictor (p=0.021; Cox regression).Although these results need to be confirmed in larger series and under different treatment conditions, our retrospective study shows clear evidence that p15 methylation can act as an additional prognostic factor for survival and underlines that this tumor suppressor, involved in cell cycle control, can act as an attractive candidate for therapeutic approaches in glioblastomas

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

Collaboration and Delegation Between Humans and AI: An Experimental Investigation of the Future of Work

A defining question of our age is how AI will influence the workplace of the future and, thereby, the human condition. The dominant perspective is that the competition between AI and humans will be won by either humans or machines. We argue that the future workplace may not belong exclusively to humans or machines. Instead, it is better to use AI together with humans by combining their unique characteristics and abilities. In three experimental studies, we let humans and a state of the art AI classify images alone and together. As expected, the AI outperforms humans. Humans could improve by delegating to the AI, but this combined effort still does not outperform AI itself. The most effective scenario was inversion, where the AI delegated to a human when it was uncertain. Humans could in theory outperform all other configurations if they delegated effectively to the AI, but they did not. Human delegation suffered from wrong self-assessment and lack of strategy. We show that humans are even bad at delegating if they put effort in delegating well; the reason being that despite their best intentions, their perception of task difficulty is often not aligned with the real task difficulty if the image is hard. Humans did not know what they did not know. Because of this, they do not delegate the right images to the AI. This result is novel and important for human-AI collaboration at the workplace. We believe it has broad implications for the future of work, the design of decision support systems, and management education in the age of AI